AN ABSTRACT of the DISSERTATION of Lyle T. Wallace for the Degree of Doctor of Philosophy in Horticulture Presented on March

AN ABSTRACT OF THE DISSERTATION OF

Lyle T. Wallace for the degree of Doctor of Philosophy in Horticulture presented on March 15, 2018.

Title: Sensory Analysis and Genetic Mapping of Green Bean Flavor.

Abstract approved:

______James R. Myers

Green bean flavor has been studied since the 1960’s to better understand Blue Lake flavor in processed green beans in Oregon. Research by Stevens and colleagues showed that Blue Lake flavor could be reconstituted in a bland bean by adding 0.4ppm of 1-octen-3-ol and 1.6ppm of 3-hexen-1-ol. These two volatile compounds are derived from fatty acid metabolism with the key enzymes being a lipoxygenase, a hydroperoxide lyase, and an alcohol dehydrogenase. They also showed that another important class of green beans, the Tendercrop green beans, could be reconstituted with the addition of 0.2ppm of linalool. This volatile compound is derived from the terpenoid pathway, and the key enzymes are geranyl pyrophosphate synthase and linalool synthase. Early work into the genetics of flavor in green beans seemed to show that a relatively small number of genes were involved in the production of linalool and 1-octen-3-ol, but no attempt was made to map these genes. With the advent of the common bean genome (Phaseolus vulgaris L.) and associated molecular tools, such as the microarray single nucleotide polymorphism (SNP) chip, it has become possible to map the genes involved in flavor production in green beans. To better understand the relationship of these compounds to sensory descriptors, a sensory analysis of 205 green bean varieties by eleven panelists was conducted in which the measured levels of linalool and 1-octen-3-ol were correlated to eight sensory descriptors. Measurements of volatile levels were taken using head space analysis on a gas chromatography – mass spectrometry (GC-MS) instrument. The results showed a correlation between linalool and the floral descriptor and 1-octen-3-ol and the nutty descriptor. The results also showed a negative correlation between linalool and 1-octen-3-ol. Linkage mapping with QTL analysis was then conducted. Linkage mapping utilized a biparental cross between a bland bean, ‘Serin’, and a more flavorful bean, ‘OR5630’. One hundred forty progeny were maintained through single seed descent to the F7 generation. The progeny were genotyped with an Illumina Infinium Genechip BARCBEAN6K_3 SNP microarray. A linkage map was generated and Multiple QTL Models (MQM) analysis was conducted to identify Quantitative Trait Loci (QTL). Six QTL were identified for linalool, three for 1-octen-3-ol, and three for 3-hexen-1-ol. In addition to linkage mapping and QTL analysis, a Genome Wide Association Study (GWAS) was conducted utilizing the Fixed and random model Circulating Probability Unification (FarmCPU) method. GWAS provides higher resolution than linkage mapping but cannot map rare alleles. GWAS analysis was conducted on a diverse panel of 201 green bean varieties, including numerous landraces, commercial pure lines, and heirloom beans. A correction for population structure was made by the addition of one principal component to the model. This principal component reflected the divide in all common bean lines between the Mesoamerican and Andean lineages. GWAS analysis identified 27 significant associations above a Bonferroni threshold for eight volatile compounds, including linalool, 1- octen-3-ol, 3-hexen-1-ol, hexanal, 1-hexanol, 1-penten-3-ol, 1-penten-3-one, and β-ionone. A cluster of alcohol dehydrogenase genes is located within 0.58Mb to significantly associated SNPs for hexanal and 1-hexanol and is located within 1.75Mb to a significantly associated SNP for 3-hexen-1-ol. This cluster of genes may be candidate genes for these SNPs because alcohol dehydrogenase genes are involved in the biosynthetic production of these compounds.

Sensory Analysis and Genetic Mapping of Green Bean Flavor

by Lyle T. Wallace

A DISSERTATION

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Presented March 15, 2018 Commencement June 2018

Doctor of Philosophy dissertation of Lyle T. Wallace presented on March 15, 2018

APPROVED:

Major Professor, representing Horticulture

Head of the Department of Horticulture

Dean of the Graduate School

I understand that my dissertation will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my dissertation to any reader upon request.

Lyle T. Wallace, Author

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. Jim Myers, for the opportunity to do this research. It has been a grand adventure and I will be forever in Jim’s debt. Jim has both nudged me further when necessary but also allowed me a great deal of freedom to pursue ideas. I would particularly like to thank Dr. Elizabeth Tomasino whose assistance with the chemical analysis and human sensory aspects of this research were invaluable. I would like to thank Dr. Pat Hayes for his sponsoring of Topics Courses that allow students to get out and meet plant breeders in the field, and for his instruction in Plant Genetics. I would like to thank Dr. Shaun Townsend for interesting conversations on flavor in hops and BLUP analysis. I would like to thank Dr. Samira Mafi Moghaddam for training me in GWAS mapping and mentoring me in other aspects of molecular analysis. I would like to thank my fellow lab mate, Haidar Arkwazee, who has done parallel research to mine and from whom I have learned much through discussions of our research. Conversations with my lab mate, Abigail Huster, have been similarly enlightening. I would like to acknowledge Dr. Jennifer Kling who has engaged me in numerous discussions of statistics and quantitative genetics that have helped me understand these subjects better. I would like to also acknowledge Don Caine of Del Monte Foods Inc. whose support and friendliness have been important to my research. Last but not least, I would like to thank my parents, spouse, and children for their unending patience and support in this endeavor.

CONTRIBUTION OF AUTHORS

Dr. James R. Myers assisted with the design, analysis and writing of Chapters 1,3, 4, and 5. Dr. James R. Myers conceived of the idea for the overall project and a wrote a proposal to obtain funding. Dr. Elizabeth Tomasino determined the concentrations of volatiles and assisted with the design, analysis, and writing of chapter 2, and she performed all chemical measurements used in chapters 2 and 4. She also assisted with the design and execution of human sensory experiments in chapter 2. Dr. Stuart Mangini performed all chemical measurements used in chapter 3. Dr. Samira Mafi Moghaddam performed the imputation of the SNP data set used in the GWAS study. Dr. Cliff Pereira performed the randomization used for the sensory evaluation in chapter 2.

TABLE OF CONTENTS

Page

CHAPTER 1. Introduction..………………………………………………………………………………………..……..1 References…………………………………………………………………………………………………………17 Figures…………………..………………………………………………………………………………….……….25

CHAPTER 2. A Descriptive Sensory Evaluation of a Diverse Snap Bean Panel Containing Commercial Pure Lines, Landraces, and Heirloom Types..……….……….28 Introduction……………………………………………………………………………………………………...28 Materials & Methods…………………………………………………………………………………………33 Results……………………………………………………………………………………………………………….38 Discussion………………………………………………………………………………………………………….40 References…………………………………………………………………………………………………………43 Figures……………………………………………………………………………………………………………….47 Tables………………………………………………………………………………………………………..………49

CHAPTER 3. Linkage Mapping of Three Volatile Compounds in Snap Beans Using a Recombinant Inbred Population ……………………….………………………………....59 Introduction………………………………………………………………………………………………….…..59 Materials & Methods……………………………………………………………………….………………..63 Results…………………………………………………………………………………………………….…………67 Discussion………………………………………………………………………………………………….………68 References……………………………………………………………………………………………….………..72 Figures…………………………………………………………………………………………………….…………77 Tables………………………………………………………………………………………………………….…….83

CHAPTER 4. Association Mapping of Volatile Flavor Compounds in Snap Beans……….……87 Introduction……………………………………………………………………………………………..…..…..87 Materials & Methods……………………………………………………………………………….………..91 Results……………………………………………………………………………………………………….……...97 Discussion……………………………………………………………………………………….……………….100 References……………………………………………………………………………………………….……...105 Figures……………………………………………………………………………………………………………..110 Tables………………………………………………………………………………………………………..…….117

CHAPTER 5. Conclusion………………………………….…………………………………………………….……..132 References…………………………………………………………………………………………………...…137 Figures……………………………………………………………………………………………………..….….139

Bibliography………………………………………………………………………………………………………………..141

LIST OF FIGURES

Figure Page

1.1 Biochemical pathways leading to the volatiles in fruits and vegetables ………….….……25

1.2 Biochemical pathways leading to the fatty acid derived volatiles...... 26

1.3 Biochemical pathways leading to the terpenoid derived volatiles ……………………….....27

2.1 Correlation coefficients (ρ) and P values for green beans………………………………..………47

2.2 Correlation coefficients (ρ) and P values for wax beans ..………………..……………………...48

3.1 Histogram of linalool…………………………………………………..……………………………..….….…….77

3.2 Ordered scatterplot of linalool……………………………………………………………………..…………77

3.3 Histogram of 1-octen-3-ol………………………………………………………………………………………..77

3.4 Ordered scatterplot of 1-octen-3-ol…………………………………………………………………..…….77

3.5 Histogram of 3-hexen-1-ol…………………………………………………………………………………..…..77

3.6 Ordered scatterplot of 3-hexen-1-ol………………………………………………………………………..77

3.7 Charts of linkage maps and associated QTL…………………………………………………………..…78

4.1 Biplot of the first and second axis of the principal component analysis…………………110

4.2 A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-octen-3-ol………………..111

4.3 A Manhattan plot and QQ plot for the FarmCPU GWAS of linalool……………………..…111

4.4 A Manhattan plot and QQ plot for the FarmCPU GWAS of hexanal.……………..……….112

4.5 A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-hexanol…………….……..112

4.6 A Manhattan plot and QQ plot for the FarmCPU GWAS of 2-hexenal…………………….113

4.7 A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-penten-3-ol……………...113

4.8 A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-penten-3-one…….…….114

LIST OF FIGURES (Continued)

4.9 A Manhattan plot and QQ plot for the FarmCPU GWAS of β-ionone………………….….114

4.10 Histograms of 3-hexen-1-ol peak area data…………………………………………………………115

4.11 Manhattan plots of SNPs associated with 3-hexen-1-ol…………………………………….…115

4.12 QQ plots of 3-hexen-1-ol…………………………………………………………………………………..…116

5.1 Manhattan plot and QQ plot of SNPs associated with 3-hexen-1-ol……………………...139

5.2 Manhattan plot and QQ plot of SNPs associated with 1-octen-3-ol…………..…………..139

5.3 Plot of the LOD trace for linkage group 2 (Pv2)………………………………………….…………..140

LIST OF TABLES

Table Page

2.1 Germplasm utilized in the study……………………………………………………………………….……..49

2.2 Linear mixed-effects models analysis of green beans………………………………………………54

2.3 Linear mixed-effects models analysis of wax beans…………………………………………………54

2.4 Concentrations of linalool and 1-octen-3-ol (µg/L) in the green and wax beans………54

3.1 Kendall’s ranked correlation between linalool, 1-octen-3-ol, and 3-hexen-1-ol……….83

3.2 Comparative linkage groups…………………………………………………………………………………….83

3.3 QTL generated by Interval Mapping (IM)…………………………………………………………………84

3.4 QTL generated by Kruskal-Wallis Mapping (KW)……………………………………………………..84

3.5 QTL generated by Multiple-QTL Mapping (MQM)……………………………………………………85

3.6 QTL relative to physical position of candidate genes……………………………………………….86

4.1 Comparisons of the mean values for nine volatiles………………………………………..……..117

4.2 SNPs that were significantly associated with one or more volatiles……………………….118

4.3 The SNPs that were significantly associated with 3-hexen-1-ol………………………..……121

4.4 Lines used in the study and their relative peak area……………………………………………...122

4.5 Candidate regulatory genes within 50Kb of either side of the associated SNP……...131

INTRODUCTION

An overview of green beans and their domestication Green beans are the vegetable form of common bean (Phaseolus vulgaris L.), a warm season annual of the Fabaceae (legume) family (Rubatzky & Yamaguchi, 1996). The common names for the vegetable form varies greatly, and includes “green bean”, “snap bean”, “string bean”, and “French bean”. The vegetable form of common bean can be distinguished from the dry bean form by the reduction of fiber in the pods, the reduction of fiber in the suture string of the pods, and a thickening of the pods to make them more succulent. The flowers of green beans, as with all common beans, are cleistogamous and self-pollinating with extremely low out-crossing rates. Green beans are grown on either short, upright, determinate plants called bush types or on indeterminate long vines that require support called pole types. Green beans are a recent development in the natural history of domesticated common bean whose ancestral form is a dry bean. Green beans were primarily developed in Europe after the Columbian Exchange through mass selection by farmers for the pod traits characteristic of green beans (Myers and Baggett, 1999). It appears that a small number of green beans were independently developed in China from dry beans that were brought with the European traders after the Columbian Exchange (Singh et al., 1991). The ‘Trail of Tears’ bean variety of the Cherokee people also possesses green bean pod traits, and may represent a true Native American development of the green bean, even if the vast majority of beans eaten by Native Americans were dry beans (Wallace & Myers, 2017; Orin, 2008). Common beans were domesticated approximately 4,000 to 8,000 years ago concurrently in two distinct centers located in Mesoamerica and the Andes respectively (Chacon et al., 2005; Mamidi et al. 2011). These contrasting gene pools have important practical consequences, both positive and negative, to green bean breeding and the mapping of traits. Genetic incompatibilities are known to exist between beans originating from these two centers of domestication. For example, intercrossing these two gene pools can lead to dwarf lethals that can kill some progeny before they flower (Kelly, 1988). Aesthetic changes to the leaf

2 morphology from intercrossing have also been observed that are sometimes mistaken for viral infection (Singh & Molina, 1996). In addition to these problematic effects, each gene pool has characteristic traits that can be useful to breeding and genetic mapping. For example, Mesoamerican bean lines tend to have smaller seeds, better tolerance to poor soil conditions, and higher dry seed yields than the Andean bean lines (Singh et al., 1991). In addition, the two separate centers of origin are the source of distinctive market classes that come from each of these gene pools, such as pinto (Mesoamerican) or kidney (Andean) beans, and form the basis of seven bean races (Singh et al., 1991; Gepts, 1998). These market classes and bean races can be a source of distinctive traits for mapping or breeding in both green beans and dry beans. Recent sequencing efforts have shed light on the domestication of common bean and shown that its path to domestication is more complicated than originally assumed. In 2014, the common bean genome was sequenced using the International Center for Tropical Agriculture (CIAT) accession G19833 (a Peruvian landrace called ‘Chaucha Chuga’) and limited resequencing was done of 160 additional bean lines (Schmutz et al., 2014). Since this genome was representative of the Andean center of domestication, a further paper was published in 2016 of a bean genome of Mesoamerican derivation using a bean line called ‘BAT93’ (Vlasova et al., 2016). Beans from both gene pools contain 2n=2x=22 chromosomes and possess a physical genome size of ~587 Mb (Schmutz et al., 2014), but important differences in the genomes of the two gene pools are also present. Of 2,583 candidate loci identified as likely involved in domestication in whole genome sequencing and limited resequencing, only 59 were shared between Mesoamerican and Andean gene pools (Gaut, 2014; Schmutz et al., 2014). The ‘BAT93’ genome contained two gene families involved in seed size that were expanded through gene duplications not found in the Andean genome, which suggests that the increase in seed size seen in both gene pools after domestication may be taking place through changes to different gene components (Vlasova et al., 2016). It was also found that the Andean gene pool had 4.5- fold less sequence diversity than the Mesoamerican gene pool (Gaut, 2014). These interesting findings were underscored by their phylogenetic work on the genomic sequence showing the pre-domestication split between Mesoamerican and Andean gene pools took place 146,000–

184,000 years before present and further support locating the ultimate origin of common beans in Mesoamerica before splitting into Mesoamerican and Andean populations (Gaut 2014; Schmutz et al. 2014).

The history of Blue Lake green beans and flavor research at Oregon State University On a worldwide scale, green beans are far less commonly grown than dry beans, and the green bean market is primarily focused on more developed economies. Within the United States, the dollar value for the green bean crop for the whole country in 2016 was $441 million, and the dollar value of the green bean crop in Oregon in 2016 was 10th in the nation at $17 million (USDA NASS 2017a; USDA NASS 2017b). There are many vegetable crops in Oregon that equal or exceed green beans in dollar value today and the production of green beans in Oregon is less than half of the contemporary production of Wisconsin, but green beans were once central to the processing economy of Oregon, and Oregon was once the largest producer of green beans in the United States (USDA NASS 2017a; Baggett & Lucas, 2005). This great economic activity surrounding green beans in Oregon in the past funded research in flavor and other quality characteristic as well as yield and disease resistance. Research and economics were intertwined in such a way as to drive the research questions and provide the research materials for a long history of green bean science at Oregon State University spanning many decades. But to see the relevance of the green bean research, especially research into flavor, it is necessary to form a picture of all the shaping economic factors. The history of research into green beans in Oregon centers around a variety of bean that was brought to Oregon from the Blue Lakes region of California in 1923 by J.O. Holt of the Eugene Fruit Growers Association (Baggett & Lucas, 2005). The seeds he carried with him were simply labeled ‘Blue Lake’ because they represented the distinctive bean variety that had been grown in that region of California since the late 1800’s, although they almost certainly derived from a selection out of a much older variety called ‘White Creaseback’ originating in the Missouri River Valley (Baggett & Lucas, 2005). This variety grew in importance, and had become the number one bean variety by many Oregon processors by 1933 (Baggett & Lucas, 2005). The

‘Blue Lake’ variety had a cluster of quality traits that made it highly desirable and allowed farmers to make a profit shipping the canned beans to Eastern markets. Among these traits were excellent flavor and color as well as the ability to maintain a firm texture, and to not disintegrate through the canning process or in steam tables at restaurants (Baggett & Lucas, 2005). By 1940, seed companies, such as Rogers Brothers and Ferry Morse, had released new varieties of the ‘Blue Lake’ pole bean, and ‘Blue Lake’ had changed from being a single variety to a recognizable class of beans (Baggett & Lucas, 2005). The early breeding improvements by the seed companies focused on reducing the stringiness of the pods, which was the one salient defect of the Blue Lake type. Later breeding improvements by Ferry Morse seed included bean common mosaic virus resistance and earliness, such as the highly successful cultivar ‘FM-1 Pole Blue Lake’, which was introduced in 1953 and continued to be highly used for more than a decade (Baggett & Lucas, 2005). The burgeoning green bean industry powered by the success of the Blue Lake type helped to fund research by professor W.A. ‘Tex’ Frazier at Oregon State College (not yet a University) starting in 1948 (Baggett & Lucas, 2005). Tex Frazier worked on breeding pole beans for the industry, but he also foresaw the need to develop a bush bean, which could be harvested by a machine instead of through manual labor (Baggett & Lucas, 2005). Tex Frazier attempted to introgress the bush habit into Blue Lake beans by crossing to a bush bean and then backcrossing back to the Blue Lake pole bean many times, but this ultimately failed to produce an acceptable bush bean because the pole Blue Lake genetic background led to a floppy and weak plant (Baggett & Lucas, 2005). A machine harvester of bush green beans would eventually be developed in 1955, and Tex Frazier was sharing his bush Blue Lake lines with the industry in the late 1950’s (Baggett & Lucas, 2005). Despite the lack of a really good bush Blue Lake bean variety, the green bean harvesters became more prevalent and Tex Frazier’s varieties started to be produced commercially on a small scale, although pole beans continued to dominate the industry until 1968 (Baggett & Lucas, 2005). In 1970, Tex Frazier officially released ‘Oregon 58’, which was the first bush ‘Blue Lake’ bean to be reasonably close in flavor to the original Blue Lake pole bean and it was upright enough in plant architecture to keep field

5 debris out of the harvester. From 1970 until 1972, ‘Oregon 58’ was grown in significant acreage and the pole bean industry began a rapid decline from which it never recovered (Baggett & Lucas, 2005). In 1972, the ‘Oregon 1604’ bush Blue Lake cultivar was released and it immediately took hold in the green bean industry in Oregon (Baggett & Lucas, 2005). Tex Frazier’s eventual successor in vegetable breeding at Oregon State University, Jim Baggett, was already assisting at this point, and Tex Frazier would retire in 1973. In 1980, ‘Oregon 91G’ was introduced, which had excellent color, good flavor, high yields, and a more upright plant architecture (Baggett & Lucas, 2005). Within a few years, ‘Oregon 91G’ had eliminated ‘Oregon 1604’ from commercial production entirely and was grown on 85% of the green bean acreage in Oregon (Baggett & Lucas, 2005). Eventually, ‘Oregon 91G’ would be eclipsed by ‘Oregon 5630’. The last recorded commercial field of pole Blue Lake green beans was harvested in 1978, although the few remaining processors who packed pole beans were already struggling to find enough beans to fill their orders by 1972 (Baggett & Lucas, 2005). From the first introduction of bush Blue Lake beans until today, there still remains some question as to what may have been lost in the conversion of pole Blue Lake beans to bush Blue Lake beans. The basis of the green bean industry in Oregon, at some level, was the high quality of Blue Lake beans because there was stiff competition from green beans that were being grown in significant quantities in the Midwest and East coast and much of the bean pack in Oregon was shipped to the East coast. Even as Tex Frazier developed bush Blue Lake varieties, there was a strong sense that some of the quality traits of Blue Lake beans were suffering through the introgression of the bush habit because simply back crossing to pole Blue Lake had failed and a more complicated breeding scheme had to be developed involving multiple crosses to many different bean lines. The crossing scheme appeared to dilute and change the Blue Lake characteristics, especially flavor. Tex Frazier stated in the introduction to his 1967 paper that, “This study was undertaken as an adjunct to the effort to develop a bush Blue Lake snap bean” (Stevens et al., 1967a). The problem with flavor in the bush Blue Lake can be seen in the sensory panel results from an Oregon Processed Vegetable Commission funded study that showed that the average score of flavor of the best tasting bush Blue Lake type, ‘Oregon 91G’,

6 was almost 2 points below the average score of pole Blue Lake on a 9 point scale (Baggett et al. 1991). This sensory study shows what was concerning Tex Frazer when he conducted his own research into Blue Lake flavor twenty years earlier. Driven by these concerns, Tex Frazier found funding with the Campbell Soup Company to conduct chemical and genetic analysis of flavor in canned Blue Lake beans. His findings were published in two papers in 1967 entitled, “Volatile components of canned snap beans”, and “Inheritance of oct-1-en-3-ol and linalool in canned snap beans” (Stevens et al., 1967a; Stevens et al., 1967b). The paper on volatile components of canned green beans did not directly address the issue of loss of flavor in bush Blue Lake beans, but rather attempted to address flavor in green bean in a more general sense. It looked at four green bean varieties that were as far apart as possible in terms of flavor in order to detect varietal differences: ‘FM-1L Pole Blue Lake’, ‘Gallatin-50’, ‘Romano’, and ‘OSU-9025’. Chemical analysis was done on canned beans using a liquid-liquid extraction method which entailed first saturating the beans with sodium chloride and then extracting with peroxide-free ethyl ether. This extract was passed through a gas chromatography column followed by a mass spectrometry instrument (i.e. GC-MS), which identified 21 volatile compounds in green beans. The researchers also engaged in a sniff test of gas-chromatography effluents as they exited the instrument to determine if, in the opinion of the researcher, it smelled like green beans, Blue Lake beans, or something else. This sniff test identified 1-octen-3-ol, linalool, and 3-hexen-1-ol as likely involved in green bean and Blue Lake flavor. These three compounds could be described in organoleptic terms as “mushroom”, “floral”, and “green” respectively. Chromatograms from the GC-MS showed ‘Gallatin-50’ to have relatively higher quantities of linalool based on gas chromatography retention time peak area but lower quantities of 1-octen-3-ol, whereas ‘FM-1L Pole Blue Lake’ was found to have relatively higher amounts of 1-octen-3-ol but lower amounts of linalool; ‘Romano’ had high amounts of both compounds and ‘OSU-9025’ had low amounts of both compounds. Finally, they added specific compounds identified in their sniff test to an extremely bland bean, ‘OSU- 9025’, to try to recreate the flavor of ‘Gallatin-50’ or ‘FM-1L Pole Blue Lake’. They found that adding 0.4ppm of 1-octen-3-ol and 1.6ppm of 3-hexen-1-ol to ‘OSU-9025’ resulted in the flavor

7 approximating an ‘FM-1L Pole Blue Lake’ green bean. They also found that 0.2ppm of linalool added to ‘OSU-9025’ resulted in its flavor approximating ‘Gallatin-50’. The second paper from Tex Frazier’s laboratory on the inheritance of 1-octen-3-ol and linalool in canned beans (Stevens et al., 1967b) was more directly focused on the issue of developing new bush Blue Lake bean varieties: “The present study was undertaken as an adjunct to the efforts to develop a bush Blue Lake snap bean. One of the unknowns in efforts to develop new varieties is the effect of selection pressures on the flavor of new lines….” In this study, they focused on analyzing the progeny of crosses between ‘FM-1L Pole Blue Lake,’ ‘Romano’, and ‘Gallatin-50’ with GC-MS. They compared the parental levels of 1-octen-3-ol and linalool to the F1 progeny and to the F2 progeny. They determined that the 160ppm mean concentration with a 45.5 standard deviation (SD) of 1-octen-3-ol in ‘FM-1L Pole Blue Lake’ was most similar to the F1 generation mean of crosses to both ‘Romano’ (µ = 121ppm, SD = 28.4) and ‘Gallatin-50’ (µ = 145ppm, SD = 52). In comparison, the ‘Gallatin-50’ parental mean was

15ppm (SD = 7.7) and the ‘Romano’ parental mean was 240ppm (SD = 78.2). In addition, 232 F2 progeny were also evaluated and broadly categorized as being most similar to one of the parents. By making these comparisons and placing the F2 progeny into one of these categories, the authors came up with a 3:1 ratio that suggested a single dominant gene. Their data for linalool showed an intermediate value for the F1 generation and a 1:2:1 ratio in their F2 generation, thus suggesting a single gene with additive effects. It should be noted regarding these ratios that the data were continuous and different bins and ratios could have been applied. Three years after Tex Frazier retired, his successor, Jim Baggett, continued the research into the inheritance of 1-octen-3-ol in green beans with the publication of “Inheritance of 1- octen-3-ol concentration in frozen pods of bush snap beans, Phaseolus vulgaris L.” (Toya et al., 1976). The motivation for this research is plainly stated in the introduction: “Flavor differences among green bean cultivars have become increasingly important to breeders. In areas where the canning industry was formerly based on the ‘Blue Lake’ pole cultivar, the flavor of ‘Blue Lake’ is preferred or required in bush cultivars….” Using similar chemical analysis techniques to

Tex Frazier’s work, the concentration of 1-octen-3-ol was measured in ‘OSU58-110’ and ‘Bush Romano FM-14’. The former bean line was considered representative of a Blue Lake type and the latter was considered representative of a Romano type, such as the ‘Romano’ cultivar that Tex Frazier had previously used in his research. An analysis of these parental lines had found a mean value of 1-octen-3-ol of 191ppb (SD=17.4) in ‘OSU58-110’ and a mean of 163ppb

(SD=19.7) in ‘FM-14’. The F2 population was skewed to the lower end of the spectrum at 161ppb (SD=78.9), but showed a continuous distribution with no bimodal pattern and numerous transgressive segregants. The authors concluded that the inheritance of 1-octen-3-ol was multigenic and quantitative, which was very different from the conclusions of Stevens et al. in 1967. Tex Frazier in his earlier research on the inheritance of 1-octen-3-ol and linalool had observed a large standard deviation in his measurements of parental lines, which he attributed to differences in the volatile content of pods depending on pod maturity, and Jim Baggett would later have similar problems with the volatile contents of progeny due to maturity effects (Stevens et al., 1967b; Toya et al., 1976). These issues with pod maturity and volatile content lead to a study by Jim Baggett to measure pod maturity effects in ‘Bush Romano FM-14’, ‘Gallatin-50’, and ‘OSU58-110’ (Toya et al., 1974). GC-MS measurements of volatile contents were made in a similar fashion to the previous work by Tex Frazier. Measurements were made in fresh, frozen, and canned bean pods at 11, 16, 21, and 28 days after anthesis. The mean 1- octen-3-ol level dropped from ~300ppb to ~100ppb from 11 days to 28 days post anthesis in frozen beans. The researchers also excised seeds from the pods and measured 1-octen-3-ol levels in seeds and pod wall tissue separately, which led to the discovery that seeds contribute ~1% of the 1-octen-3-ol to the overall 1-octen-3-ol content of the bean. They hypothesized that the increase in seed size relative to pod tissue as the pod matures tends to dilute the concentration of 1-octen-3-ol, which would explain the maturity effect. As for a maturity effect on a volatile beyond the production of 1-octen-3-ol, it was not addressed in this study, but earlier work by Tex Frazier had found a similar decrease in linalool as a pod matures (Stevens et al., 1967b).

The loss of flavor quality in green beans and why it matters Green beans are an excellent source of pro-vitamin A, vitamin C, vitamin K, folate, and manganese as well as being a reasonably good source of niacin, thiamin, riboflavin, vitamin B6, iron, calcium, copper, magnesium, phosphorus, and potassium (USDA ARS NDL, 2016). They are also high in fiber and protein. There is abundant evidence that the consumption of vegetables, such as green beans, results in measurable reductions in heart disease, stroke, and many types of cancer (Van Duyn & Pivonka, 2000). Increasing the consumption of fruits and vegetables has been a major goal of numerous government and private programs, such as “5 A Day For Better Health” and “Healthy People 2020” due to the low rates of fruit and vegetable consumption and their importance to health (Baxter et al., 2002). Clearly it would be desirable for vegetables, such as green beans, to be consumed more frequently, and one of the key determinants of vegetable consumption is taste (Kader, 2008; Drewnowski, 1997). In fact, taste has been found to be more important than cost, appearance, convenience, diet concerns, and nutrition in predicting consumption (Glanz et al., 1998). The importance of taste can be seen in studies of vegetable consumption in public school children. For example, a study of 4th graders in Georgia public schools found that liking (presumably conditioned by taste) was a strong predictor of actual consumption of vegetables in school lunches (Baxter et al., 2002). In particular, they found that children who reported liking a food item not at all, tended to only taste the food item at best, but half a serving of food items that were liked a little were eaten on average whereas four-fifths of a food item liked a lot were eaten on average. This study also found that green beans were liked a little or a lot by 83% of the children who were surveyed about green beans and green beans ranked fourth out of thirteen vegetable choices on the menu for consumption by the children. This can be compared to broccoli or cole slaw that were both liked a little or a lot by only 38% of students who were surveyed about these vegetables, and these vegetables ranked near the bottom for consumption by the students. A later study in Norwegian public schools found that school children preference correlated most strongly to intake of vegetables over six other variables studied, including accessibility at home, awareness

10 of the importance of vegetables, modelling of others, intentions, and self-efficacy (Bere & Klepp, 2005). This study also involved a longitudinal study of the same individuals to see how intake of vegetables changed in adulthood. This longitudinal study found that preference and accessibility at home in childhood contributed the most among these seven variables to determining vegetable intake in adulthood. Clearly, flavor matters to consumption of vegetables and the consumption of vegetables is a worthy goal, but selection in plant breeding has run almost completely counter to the improvement of flavor for more than 50 years (Klee, 2010). We have already seen how converting the pole Blue Lake bean to bush Blue Lake appears to have diminished its flavor profile based on a taste testing done in 1990 by Jim Baggett for the Oregon Processed Vegetable Commission, but there is evidence that tomatoes and many other vegetables have seen a similar decline in sugars, acids, and flavor volatiles (Klee, 2010). This happened for a number of different reasons. For one, the true customer of the seed company that employs the plant breeder is the grower, not the end consumer, and the grower is most concerned with the factors that will bring the greatest economic gain, such as yield and appearance (Klee, 2010; Klee & Tieman, 2013). Growers are generally not paid for great tasting produce, so the financial incentives are mostly lacking. In fact, many of the financial incentives have run counter to good flavor because of the emphasis on storability, which often means breeding for genetics that delay ripening and decrease the flavor profile (Klee, 2010). In addition to these negative financial incentives, it is not easy to breed for flavor. Many breeding programs do not have the capacity to screen for flavor because it involves large scale taste tasting that is scientifically conducted and corroborated with chemical analysis (Klee, 2010; Klee & Tieman, 2013). The lack of rigorous screening means that flavor may be lost through genetic drift if nothing else. Finally, breeders who choose to accept the challenge of breeding for flavor must contend with complicated, multi-genic traits. Overall flavor can be affected by dozens of chemical compounds produced by distinct biochemical pathways. Flavor traits are typically quantitatively inherited with numerous Quantitative Trait Loci (QTL) (Klee & Tieman, 2013).

Green beans have been no exception to these challenges. As with so many other vegetable crops, the post-WWII period was a time of intense breeding activity, which brought many benefits, including large increases in yield, disease resistance, and reduced costs to farmers to produce that yield, but there have been losses in quality traits, such as flavor. The conversion to a bush plant habit almost certainly compromised flavor traits, but there have been other issues as well that have parallels to virtually every other vegetable crop. Like other crops, the emphasis on yield and the conversion to bush habit has had financial incentives that have generally emphasized the delivery of tonnage per acre to fulfill processor contracts with no concern for high end quality traits, such as flavor. There has been a demand for green bean plants that will set pods in a narrow time frame for harvesting, even though this narrow window likely results in reduced sugars and soluble solids as the available photosynthate is spread and diluted among a large number of pods. Green bean breeders have also had to contend with an inability to effectively screen for flavor and a minimal understanding of the flavor molecules and genetics involved. To deliver on the promise of better tasting green beans that will attract more consumers and increase intake, new screening methods need to be developed and molecular tools need to be applied to clarify the genetics and allow marker assisted breeding.

The detection of flavor volatiles through sensory studies and analytical chemistry A way to quantify and judge flavor is needed if plant breeding for flavor is to progress. The tools are currently available but require a significant investment in instrumentation for GC- MS analysis and time and labor costs for the scientific evaluation of sensory responses. To effectively utilize our senses to assess flavor quality in a consistent and objective manner, one must turn to the discipline of sensory evaluation. Sensory evaluation is defined by the Sensory Evaluation Division of the Institute of Food Technologies as, “a scientific discipline used to evoke, measure, analyze, and interpret reactions to those characteristics of foods and materials as they are perceived by the senses of sight, smell, taste, touch, and hearing” (Stone & Sidel, 2004). This science began in embryonic form during WWII and blossomed into a fully formed

12 discipline in the late 1950’s when classes and academic degrees were offered at a number of universities on the West and East coasts of the United States (Stone & Sidel, 2004). To measure and analyze panelists responses in sensory evaluation, aspects of psychology, physiology, and statistics are applied to a systematic testing process in which validity and reliability are of great importance (Stone & Sidel, 2004). Sensory evaluation is focused on (1) discrimination testing, (2) descriptive analysis, and (3) affective testing (Stone & Sidel, 2004). In the case of green beans, little has been done in terms of sensory evaluation science. Instead, informal “cuttings” in the processed vegetable industry have predominated as a source of sensory data. These are tasting events that allow industry professionals to evaluate the latest varieties and products. A good cutting can have standardized ambient conditions, uniform sample preparation, numerous panelists (i.e. a good sized n), blind samples assigned a three digit number, and good record keeping (Stone & Sidel, 2004). A considerable amount of sensory evaluation data has been accumulated in this manner over many decades, although the data is not readily available and may require a trip to the archives of various industrial groups. Moreover, the quality of data found in industry cuttings is variable. The testing conditions are not always ideal, samples are often identified with their real names, panelists openly discuss samples, and responses are not always systematically recorded. Flavor can also be characterized through instrumentation. Instrumental analysis, such as GC-MS, allows a sensitivity and quantification of flavor volatiles not possible with the human senses alone. This approach attempts to identify (1) all the volatile compounds present in a food and then attempts to identify (2) the relevant compound(s) important to flavor (Van Ruth et al., 1995b). Although techniques have changed over time, the identification of volatile compounds present through modern GC-MS often entails a solid phase micro extraction (SPME) fiber consisting of a silica fiber coated with various stationary phases that is introduced into the headspace of a vial containing the food sample (Barra et al., 2007). After absorption of volatiles from the food sample, this fiber is then introduced into the injection port of the gas chromatography instrument where it is heated to release the volatiles into the instrument for analysis. The identification of retention peaks generated by the gas chromatography instrument

13 involves multiple steps, such as matching the ensuing ionization fragments from a mass spectrometry instrument to a set of standard fragments found in the NIST/EPA/NIH Mass Spectral Database generated by the National Institute of Standards and Technology of the United States Department of Commerce. In addition, one can attempt to identify retention peaks by comparing volatile peaks to published literature on the peaks present in a food, running an n-alkane series on the column, comparing the retention times of peaks to known standards, and yet other methods that are less commonly used (Quirós et al., 2000; Barra et al., 2007). In the most recent GC-MS analysis of green beans, headspace SPME analysis and peak identification through comparisons to the literature and to NIST ionized fragment databases as well as through an n-alkane series resulted in the detection of 104 volatile compounds in green beans (Barra et al., 2007). This was preceded by several papers that detected varying numbers of volatiles in green beans using GC-MS. The work of Stevens et al. (1967a) found 21 volatiles and a paper by MacLeod & MacLeod (1970) found 26 volatile compounds. Much later, three papers were published identifying volatiles in green beans. The first was by Van Ruth et al. (1995a) that identified 30 volatiles. This was followed quickly by Hinterholzer et al. (1998) that found 25 compounds, and by Quirós et al. (2000) that found 27 compounds. By comparison, the 26 volatiles detected in barley are roughly similar in number to green beans, but upwards of 400 volatiles compounds have been identified in tomatoes, and some evidence suggests that hops might contain more than 1000 volatile compounds (Cramer et al., 2005; Tieman et al., 2012; Almaguer et al., 2014). The second part of instrumental analysis is the determination of relevance, and each of these papers on the chemical analysis of green beans has attempted to tackle this issue of relevance in its own way. The earliest GC-MS paper, published by Tex Frazer’s lab, used the gas chromatography effluent sniff test approach to match volatiles with sensory descriptors (Stevens et al., 1967a). Work by Van Ruth and colleagues in the 1990’s directly analyzed the oral vapor of 12 subjects who masticated green beans in a controlled manner, and the vapor of three mouth model systems that mimicked mastication, and then used known sensory characteristics found in the literature to show relevance (Van Ruth et al., 1995a). The work of Hinterholzer and colleagues focused first on an

14 effluent sniff test approach followed by dilutions of an extract of green beans to determine potency at different levels of dilution (Hinterholzer et al., 1998). Quirós et al. concentrated on comparing different methods of green bean preparation and cooking, and attempted to match compounds to flavor through literature searches (Quirós et al., 2000). Finally, Barra et al. (2007) attempted to conduct the most comprehensive survey of green bean volatiles to date, but left it to other researchers to determine relevance. These approaches to relevance should not be considered exhaustive. Another important approach is the reduction of volatile complexity with multivariate analysis, such as principal component analysis, followed by univariate correlation to sensory evaluation data, such as has been done in tomato (Van Ruth et al., 1995b; Tieman et al., 2012). A further extension of the human senses that is on the horizon is the electronic nose. Electronic noses are defined as, “an instrument that comprises an array of heterogeneous electrochemical gas sensors with partial specificity and a pattern recognition system.” (Loutfi et al., 2015). Sensor materials in electronic noses typically consist of metal oxides or conductive polymers (Loutfi et al., 2015). Electronic noses have been successfully used to distinguish tea varieties and to test for tea quality (Loutfi et al., 2015). However, comparisons of human sensory panels for wine and electronic noses showed that electronic noses cannot identify some aromas as well as human olfaction. Moreover, there remains issues with associating the rich descriptive language of human panelists with the responses of the electronic nose (Loutfi et al., 2015). Once this this technology matures, it seems likely that electronic noses will eventually become an important tool to further our understanding of vegetable flavor quality.

The biochemistry and genetics of flavor volatiles in green beans The majority of aroma volatiles in fruits and vegetables are the product of four biochemical pathways: fatty acid derived, terpenoid derived, phenylalanine derived, and branched chain amino acid derived (Fig. 1.1, 1.2, 1.3) (Rambla et al., 2014; Dudareva et al., 2013; Klee, 2010; Lewinsohn et al., 2001). Among these four pathways, the most important to green bean flavor is the fatty acid derived pathway shown in Figure 1.2 (Quirós et al., 2000;

Lumen et al., 1978). This pathway begins with the oxidation of pyruvate from glycolysis into Acetyl coenzyme A (Acetyl CoA). Acetyl CoA is then converted through several steps to linolenic acid and linoleic acid. These acids are oxygenated with a lipoxygenase enzyme and then split with a hydroperoxide lyase enzyme (Baysal & Demirdöven, 2007). This cleavage, along with one or two additional enzymatic steps, results in numerous volatile compounds, such as 1-octen-3- ol, 3-hexen-1-ol, hexanal, 1-hexanol, and 1-penten-3-ol (Lumen et al., 1978). Some of the organoleptic descriptors for these compounds are “mushroom” (1-octen-3-ol) and “green” (3- hexen-1-ol, hexanal, 1-hexanol) (Klee, 2010; Stevens et al. 1967a). The second major source of green bean volatiles is the terpenoid pathway (Figure 1.3). This pathway begins with isopentenyldiphosphate (IPP) and dimethylallyldiphosphate (DMAPP) from the methylerythritol phosphate (MEP) pathway (Dudareva et al., 2013). IPP and DMAPP are then used as substrates for geranyldiphosphate (GPP) synthase, which results in GPP. Linalool synthase then acts upon GPP to produce linalool (Lewinsohn et al., 2001). IPP and DMAPP can also be substrates for geranylgeranyldiphosphate (GGPP) synthase, which results in GGPP. GGPP is then modified through several more steps to produce all the carotenoid compounds (Lewinsohn et al., 2001). β-ionone is the product of the degradation of beta-carotene (Zhang et al, 2015). Linalool and β- ionone add strong perfumy or floral notes to green beans (Hinterholzer et al., 1998; Stevens et al., 1967a). Finally, the phenylalanine and branched chain amino acid derived biosynthetic pathways result in green bean volatiles such as, 2-methylbutanal, 3-methylbutanal, and phenylacetaldehyde, which may add malty or honey-like notes (Klee, 2010; Hinterholzer et al., 1998). There are many more volatiles of lesser importance in green beans that may be produced by pathways other than the four described here, and some volatiles have an unknown origin (Klee, 2010; Barra et al., 2008; Hinterholzer et al., 1998). The biochemistry of volatiles naturally leads to questions regarding the structural genes and regulatory genes that control the biochemical network. Little work has been published in this, and the two studies by Tex Frazier in 1967 and Jim Baggett in 1976 constitute the entire literature on the genetics of flavor in green beans (Stevens et al., 1967b; Toya et al., 1976). To reiterate the research, Tex Frazier presented evidence for a single additive gene for linalool

16 production and a single dominant gene for 1-octen-3-ol production, but Jim Baggett found a continuous distribution of 1-octen-3-ol values and transgressive segregation in the F2 generation that led him to conclude inheritance was quantitative for 1-octen-3-ol. The contradictory results for 1-octen-3-ol production should not be entirely surprising because different genotypes were used in the two studies, pod maturity confused some of the results, and the data in the earlier study appeared far more continuous in nature than was acknowledged by the authors. Neither of these studies attempted to map the genes, and the technology for mapping was very primitive in the late 1960’s and early 1970’s in any case. Since that time, an integrated linkage map of common bean based on a BAT93 x Jalo EEP558 recombinant inbred population was published in 1998 and a physical genome was published in 2014 (Freyre et al. 1998; Schmutz et al., 2014). There have also been significant advances in gene mapping methods and technology as well. Advances first came in linkage mapping, which required relatively fewer markers than association mapping. Linkage mapping utilizes recombination rates in a family or pedigree to identify loci linked to a phenotypic trait of interest (Laird & Lange, 2011). Linkage mapping can cover the entire genome of an organism sufficiently to detect a Quantitative Trait Locus (QTL) with as little as 400 markers placed five to ten centiMorgans (cM) apart across the genome (Laird & Lange, 2011). Potential QTL generated from linkage mapping are assessed based on a Logarithm of the Odds (LOD) score with a LOD score of three indicating a 1/1000 chance that the apparent linkage is a chance event, which is roughly equivalent to a P-value of 0.05 (Morton, 1955; Nyholt, 2000). The equation for LOD scores is: (1 − θ)n−rx θr 퐿푂퐷 = 푙표푔10 [ ] 0.5n In this equation, n is the total number of offspring, r is the number of recombinant offspring, and θ is the recombination fraction equal to r/n (Laird & Lange, 2011; Morton, 1955). With the advent of a Single Nucleotide Polymorphism (SNP) microarray chip prototype in 1998 and the development of a commercial chip shortly thereafter by Affymetrix, Inc. (Santa Clara, CA), the tools were in place for higher resolution mapping. Coupled with the publication of the genome

17 in 2014, this allowed for genome wide association studies (GWAS) to be a practical method for gene mapping (Myles et al., 2009; Zhu et al., 2008). GWAS can be applied to a pedigree like a linkage study, but it is not limited to family data (Fardo et al., 2014). Typically, GWAS are done on large, diverse populations of unrelated or distantly related individuals and the population structure that would create false positives (type I statistical errors) is corrected with a principal component analysis or similar method (Myles et al., 2009). GWAS utilizes linkage disequilibrium (LD) to generate correlations between marker alleles and phenotypes (Myles et al., 2009). Altogether, these advances in maps and gene mapping indicate that an update is due on the genetics of volatiles in green beans. Such an update has already been completed in tomato with a substantial body of recent literature having grown up around flavor in tomato. This work in tomato has utilized the complete tomato genome and modern association mapping techniques to identify 125 significant associations for 28 flavor volatiles in one study in 2015 and 251 significant associations for 15 flavor volatiles and 4 non-volatile sugars and acids in a second study in 2016 (Tieman et al., 2017; Zhang et al., 2015). Still other metabolite profiling and GWAS studies of sensory traits and volatiles have been conducted in tomato (Bauchet et al., 2017; Baldina et al., 2016). Modern GWAS studies have also been conducted in common bean for traits other than flavor volatiles, such as for Potyvirus resistance, biomass, yield, symbiotic nitrogen fixation, anthracnose resistance, angular leaf spot resistance, cooking time, lodging, seed weight, canopy height, days to maturity, growth habit, and days to maturity (Moghaddam et al., 2016; Perseguini et al., 2016; Cichy et al., 2015; Kamfwa et al., 2015a; Kamfwa et al., 2015b; Hart & Griffiths, 2015). It seems clear that the tools are available and the time is ripe for a major new initiative to analyze flavor volatiles in green beans.

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Figures

Figure 1.1. Biochemical pathways leading to the volatiles in fruits and vegetables. The numerous steps in glycolysis and the Krebs cycle are symbolized by a rectangular arrow and circle, respectively. Similarly, there are several steps involved in the production of each class of volatiles that is summarized in each case by a series of three arrows. PEP is an abbreviation for phosphoenolpyruvate. PEP is shown inside the arrow of glycolysis because it is the penultimate product of this pathway. The pathways depicted are adapted from Rambla et al., 2014; Dudareva et al., 2013; Klee, 2010; Lewinsohn et al., 2001. The branching of phenylalanine derived volatiles from PEP and branched chain amino acid derived aromas from pyruvate is taken specifically from Rambla, and other references show differing starting points for these compounds.

Figure 1.2. Biochemical pathways leading to the fatty acid derived volatiles. Parenthetical numbers indicate the number of carbon atoms followed by the number of unsaturated bonds. The 13-hydroperoxy intermediate is 13-hydroperoxy-cis-9,trans-11-octadecadienoic acid. The FAS complex is the fatty acid synthase complex. It is assumed that 1-octen-3-ol is derived from linoleic acid and the 13-HPOX intermediate based on Lumen and colleagues (1978) and known pathways in fungal systems (Combet et al., 2006), but the pathway remains relatively uncertain in plants and there may be an alternative intermediate to 13-HPOX for this compound. The derivation of 1-penten-3-ol and 1-penten-3-one from linolenic acid is based on Lumen and colleagues (1978). The derivation of 1-hexanal and 1-hexanol from linoleic acid is taken from Lumen and colleagues (1978) and Klee (2010), but other references show a derivation from linolenic acid. Adapted from Rambla et al., 2014; Dudareva et al., 2013; Klee, 2010; Clemente & Cahoon, 2009; Combet et al, 2006; Aghoram et al., 2006.

Figure 1.3. Biochemical pathways leading to the terpenoid derived volatiles. The three arrows below pyruvate indicate the numerous steps in the methylerythritol phosphate (MEP) pathway leading to IPP and DMAPP. The three arrows following GGPP indicate the several steps leading to β-carotene, including phytoene, ζ-carotene, lycopene, and γ-carotene. IPP is isopentenyl diphosphate and DMAPP is dimethylallyl diphosphate. GGPP is geranylgeranyl pyrophosphate and GPP is geranyl pyrophosphate. It should be noted that diphosphate is commonly substituted for pyrophosphate in the nomenclature for GGPP and GPP. Adapted from Rambla et al., 2014; Dudareva et al., 2013; Lewinsohn et al., 2001; Rodriguez-Concepcion & Stange, 2013.

A DESCRIPTIVE SENSORY EVALUATION OF A DIVERSE SNAP BEAN PANEL CONTAINING COMMERCIAL PURE LINES, LANDRACES, AND HEIRLOOM TYPES

Introduction

Food aromas are perceived via two pathways: through the front of the nose and through the back of the nasal passages via the back of the mouth while drinking or eating (Bojanowski & Hummel, 2012; Ruijschop et al., 2009). The former is referred to as orthonasal olfaction and the latter is referred to as retronasal olfaction. This can be seen in Figure 1 showing the differing routes of air flow through these two distinct olfactory regions. Although both olfaction pathways can perceive aromas, they serve different purposes. Orthonasal olfaction provides sensations of smell of environmental aromas whereas retronasal olfaction provides sensations of food flavor. Not included in orthonasal or retronasal olfaction is taste, which is perceived through receptors within taste buds on the tongue, throat, and between the hard and soft palate (Comeau et al., 2001; Bellisle, 1999). The taste buds are localized within fungiform, foliate, and circumvallate papillae. The taste buds have receptors for sweet, sour, salty, bitter, and umami. A sixth taste receptor is now recognized as oleogustus for fatty substances in addition to the traditional five tastes (Running et al., 2015). Numerous complex interactions can take place between olfactory aromas that can change perception. It is well known that the presence of one aroma (sometimes imperceptible in itself) may enhance or depress the perception of another aroma or aromas (Ferreira, 2010). For example, the presence of β-damascenone in wine has been shown to enhance the perceived fruity intensity of ethyl cinnamate and caproate but mask the herbaceous aroma of 2-isobutyl-3-methoxypyrazine (Pineau et al., 2007). Similarly, the addition of geraniol adds nothing to the odor characteristics related to geraniol but will rather greatly increase the perceived odor characteristics of linalool (Ferreira, 2010). Sometimes characteristic aroma notes can depend on the concerted action of several compounds present in a complex mixture, such as several branched chain ethyl esters with norisoprenoids and dimethyl sulfide forming a

29 berry fruit note in wine or the concerted action of furaneol, homofuraneol, methional, maltol, sotolon, and norisoprenoids forming a cherry or chocolate note in wine (Ferreira, 2010; Escudero et al., 2007). Still another example of these interactions between aromas can be found in artificial root beer flavoring, which contains the separate components of wintergreen, anise, vanillin, and camphor in a specific ratio that when mixed, “bind the same four olfactory receptors as safrole” (Baldwin et al., 2000). To add further to the complexity of aroma interactions, aromas often blend indistinguishably into a complex mixture of aroma compounds and only a few aromas form distinctly recognizable flavor nuances (Ferreira, 2010). In addition to interactions among and within aromas, there are also interactions with taste as well. These interactions with taste are based on congruency, that is to say, the familiar pairing of a taste and volatile, such as sugar and citrus aroma or salt and soy sauce aroma (Lim et al., 2014; Pfeiffer et al., 2006). Numerous instances of congruency have been catalogued in the literatures, such as sugar (sweet) and strawberry aroma, sour and lemon aroma, citric acid (sour) and citral volatile, and sugar (sweet) and citral volatile (Lim et al., 2014; Pfeiffer et al., 2006). In all these cases, retronasal olfaction was enhanced by taste through congruency, although there is weak evidence that the reverse may sometimes be true as well, i.e. that retronasal odors may accentuate tastes. Congruency and enhancement of retronasal olfaction can take place even when the taste is below the perceptible threshold, such as the enhancement of almond odor when sugar is given to a sensory panelist at a concentration below the detectable threshold of sweetness (Pfeiffer et al., 2006). Congruency also appears to be additive, such that sweet and sour tastes simultaneously presented to a sensory panelist will result in an even greater enhancement of the strawberry aroma than either taste alone (Pfeiffer et al., 2006). These cross-modal effects between retronasal olfaction and taste are likely associated and integrated in the orbitofrontal cortex of the brain as brain imaging studies show that signals from taste and aromas are integrated there (Pfeiffer et al., 2006). The complexities and interactions involved in flavor perception, such as the enhancement of one aroma by presence of another or congruency with taste, are a stark reminder of the importance of coupling sensory evaluation with chemical analysis. One might

30 be tempted to substitute a measured concentration of e.g. linalool relating to a published sensory threshold for linalool in place of testing the sensory perception of linalool in that particular food or drink, but this would problematic. Simply knowing olfactory aroma detection thresholds may miss the importance of the mouth liquid matrix to volatile release, or the congruency of sweetness with the floral characteristics generated by linalool, or the interaction of linalool and geraniol among other things. The volatiles present in a food are clearly related to the sensory evaluation, but the context and the meaning of that volatile requires the sensory experience of the end user. Nevertheless, it would be possible with more data on human thresholds and the sensory correlates to instrumentation, to use chemical analysis as a substitute for sensory. There are abundant examples of chemical analysis of green beans in the literature (Barra et al., 2007; Quirós et al., 2000; Hinterholzer et al., 1998; Van Ruth et al., 1995b; Lumen et al., 1978; Stevens et al., 1967a). The vast catalogue of compounds contained in these chemical analysis studies have not been balanced by an equal number of sensory evaluations to give context and meaning to their results. One formal sensory has been reported in the literature that used 21 panelists to do a descriptive sensory evaluation of a single freeze dried green bean product supplied by Top Foods of the Netherlands (Van Ruth et al., 1995b). This sensory evaluation likely involved only a single commercial cultivar or a small number of closely related cultivars that were freeze dried and packaged for their commercial sample. Based on this sample, they determined that, “no volatile compounds identified in rehydrated French beans possessed a particular French Bean flavor”, although they did identify more than a dozen descriptors that applied to green beans, including chocolate, grassy, chemical, mushroom, fatty, caramel, rancid, and sweet. In 2012, a more comprehensive descriptive analysis of 147 genotypes comprising the Bean CAP (Coordinated Agriculture Project) Snap Bean Diversity Panel (http://www.beancap.org/) scored beans on the descriptors of beany, floral, sweet, and sour, where a wide diversity of scores for flavor and taste were found (James R. Myers, pers. comm., 2017). In this study, the identity of the beans was not hidden, and scoring was done by a single expert bean taster. Probably the most important study of the sensory value of volatiles

31 in green beans continues to be the work of Stevens et al. (1967a). Stevens identified aromas from a gas chromatography sniff port that are characteristic of green beans, esp. Blue Lake green beans. Stevens identified linalool, 1-octen-3-ol, and 3-hexen-1-ol as being of paramount importance to green bean flavor, and he applied floral, mushroom, and green descriptors to each of these compounds respectively. He found that 1-octen-3-ol was generally not concentrated enough to be detectable in itself but mixed with other volatiles to form an earthy green aroma that was characteristic of green beans. He also performed sensory experiments in which he added linalool, 1-octen-3-ol, and 3-hexen-1-ol to a very bland bean that was very low in all the compounds of interest. When Stevens added a precise ratio of 0.4ppm 1-octen-3-ol to 1.6ppm 3-hexen-1-ol to this bland bean variety, he was able to recreate a close approximation to the flavor of Blue Lake green beans. When Stevens added 0.2ppm of linalool to this bland bean variety, the resulting flavor was very close to ‘Gallatin-50’. Some of the limiting factors to Stevens work were the small number of genotypes involved (four in total) and the small group of people who did the sensory evaluations and assigned the volatile descriptors (limited to his laboratory coworkers only). Numerous sensory evaluations of green beans (cuttings) have also been done by industry groups to test their latest materials for processing and canning, but these cuttings have not led to any real advances in our sensory understanding of green beans other than to highlight the flavor diversity that exists in green beans. To date, much work still needs to be done to address all the sensory aspects of green beans. The work of Stevens et al. (1967a) and Van Ruth et al. (1995b) were limited to less than half a dozen commercial pure lines and even the unpublished descriptive study of the BeanCAP was based on mainly commercial pure lines albeit with a broader genetic base. No study has looked at landraces and heirlooms that lie outside of commercial production and represent an even broader genetic base. Yet the evidence from the BeanCAP and from the work of Stevens et al. (1967a) shows important flavor differences between genetically different green beans that may be even greater with the examination of larger and more diverse green bean panels. There is also a dearth of studies using rigorous methods, such as large panels of judges to evaluate blind samples that are randomized and replicated. The one exception is the study by

Van Ruth et al. (1995b) that used 21 panelist judges, although only one sample was evaluated. There is no published result of a correlation between a flavor volatile and sensory data either, even though this is commonly done in other arenas, such as has been done with tomato flavor (Tieman et al., 2012; Mayer et al., 2008). Van Ruth et al. (1995b) did report performing correlations in green beans but correlation results were not published, and no descriptor was identified as being correlated with a volatile compound, although they did align descriptors with volatile compounds on a table implying, perhaps, that they were statistically correlated but this is not indicated in the table header or legend. There are also other types of sensory evaluation that have not been performed but that may be useful, such as acceptance tests using a hedonic scale or texture and appearance evaluations. Acceptance testing could give important data about liking. The importance of this type of testing can be seen in the analysis of tomato flavor by Tieman et al. (2012). They found seemingly minor volatiles were very important to consumer preference but several prominent volatiles with a highly identifiable odor that varied between tomato varieties were found nevertheless to be unimportant to preference. Sensory evaluation of appearance may also provide important information that supplements a narrow focus on aroma since, “In food products, especially meats, fruits, and vegetables, the consumer often assesses the initial quality of the product by its color and appearance…” (Lawless & Heymann, 1998). Lastly, texture sensory evaluation is also lacking in current research into green beans, despite the fact that consumers often rate texture as more important than flavor (Lawless & Heymann, 1998). Indeed, important differences in green bean texture have long been recognized with Blue Lake green beans being rated more highly for maintaining acceptable texture after cooking (Baggett & Lucas, 2005). The purpose of this study is to elucidate the connection between volatiles and sensory traits and the complex interconnections inherent in the total sensory experience of consumer end use. This will be pursued using a large panel of judges on a large and diverse set of green bean samples, which it is hoped will give a broader and deeper picture of flavor in green beans that addresses some of the deficiencies in earlier studies of green bean flavor.

Materials & methods

Bean varieties Two hundred and five bean varieties were selected for sensory evaluation (Table 2.1). These included the BeanCAP snap bean diversity panel consisting of 147 green beans mostly from commercial bean lines in North America and Europe. Also included in the BeanCAP were a small number of old heirloom types such as ‘Trail of Tears’, ‘Blue Peter Pole’, and ‘Kentucky Wonder’. Dry beans of the BeanCAP, such as ‘Olathe’, ‘Montcalm’, and ‘Seafarer’, were excluded from this study due to the high fiber content of their pods. In addition to the BeanCAP, 58 landraces from China from an uncatalogued set of accessions collected by Michael Dickson (Emeritus, Cornell Univ., Ithaca, NY) were included in the sensory evaluation. These Chinese landraces are true green beans with low fiber pods, but were selected and adapted to a different environment and set of cultural preferences than most green beans, thus providing greater green bean diversity than has been previously evaluated. For correlations to linalool and 1-octen-3-ol, only 171 bean varieties were used (133 BeanCAP varieties and 38 Chinese landraces) due to samples lost during preparation and analysis, but all other analysis, not involving measurements of linalool and 1-octen-3-ol, were done on the full 205 genotypes. In 2013, plants were grown in unreplicated plots at the Oregon State University Vegetable Research Farm in Corvallis, Oregon. This farm is on fine-silty Chehalis soil and is located approximately 68 meters above sea level at latitude 44°34’25.93”N and longitude 123°14’12.37”W. Overhead irrigation provided 2.5 to 5.0 cm (one to two inches) of water weekly as needed. Pelleted fertilizer was banded beneath the row just prior to planting at the rate of 22.7kg (50lbs) of nitrogen per acre. Plots were planted on July 2, 2013 using a manual belt-planter; seeds were treated with Captan fungicide and planted to a depth of approximately five centimeters (two inches) in three meter (ten foot) plots at a rate of 60 seeds per plot. Rows were 77 cm (30 inches) apart for bush types and 154 cm (60 inches) apart for pole types. Pole

34 types were trellised on a metal wire approximately two meters (six feet) above the ground; bush types were unsupported. The picking time was varied to match the differing maturity dates of plots, which was judged by slicing the pod open lengthwise and observing seed development. Several representative pods from across the plot were picked and transported in a cooler for further processing or freezing.

Samples Samples for GC-MS analysis were placed in a cooler and rapidly brought to freezers for freezing at-20°C for later analysis. The frozen bean pods were ground into a fine powder with liquid nitrogen inside a specially modified steel Waring blender. The slurry was allowed to boil off most of the liquid nitrogen within a plastic bag and then it was poured or tapped into a 40 ml amber vial with a PTFE liner (Supelco/Sigma-Aldrich, Bellefonte, PA, U.S.A.). This was frozen at -20°C until GC-MS analysis could be performed. Samples for sensory evaluation were immediately taken to the Oregon State University Pilot Processing Plant housed within Food Science & Technology for processing. Green beans were cut into approximately one-inch lengths and blanched for 100 seconds in 93°C water, and then immediately placed in cold water to stop the blanching process. The green beans were drained of water, packed in quart sized freezer bags, and frozen at -20°C for later sensory evaluation.

Panelists Panelists were recruited from the students and staff of Oregon State University. Exclusion criteria included smoking and oral deficits. Panelists needed to be regular green bean consumers to be selected, which has been shown to be sufficient for sensory analysis (Arvisenet et al., 2016). Twelve experienced green bean eaters were selected for the study (6 Male, 6 Female). Other than selecting for experienced green bean eaters, no training was conducted. One panelist dropped out shortly before the study began leaving eleven panelists for the study. Ethics approval was received from the Oregon State University Internal Review Board.

Experimental design Eleven of the green bean varieties included in the study were yellow colored wax beans. This obvious color difference could be a source of bias, so they were evaluated separately from the green colored beans. The wax beans were evaluated in a single session in April of 2014 with bean sample order randomized and a three-digit number assigned to each bean variety. The wax bean randomization was generated using XLSTAT (Addinsoft, NY, New York, version 2013.2.01). The remaining 194 green colored bean varieties were evaluated over 16 sessions during April 2014. Tasting session occurred 2 days of each week with 2 sessions per day. All panelists participated in each session with a fifteen-minute break between sessions in the same day. Thirteen bean varieties were tasted in each session, except for the last session where only twelve were tasted. Sample order was determined using a resolvable incomplete block design generated using PROC OPTEX in SAS that maximized the number of single pairwise occurrences in each session (SAS 9.4 (M0)) (Pereira & Tobias, 2015). This type of design reduces carry-over effects and order effects (Ball, 1997). All 205 bean varieties were evaluated once by each panelist and all attributes were assessed in every instance. Bean varieties were assigned three- digit random numbers. To clean the palate and allow restoration from sensory fatigue, water and unsalted plain crackers were consumed by panelists between sessions within a given day.

Sensory evaluation Green bean samples (three to six segments of green bean) were placed in numbered plastic 103.5 ml (3.25oz) soufflé cups with lid. Beans were then allowed to thaw to room temperature (20°C) for approximately 30 minutes before sensory evaluation. Sensory evaluations were done at the Arbuthnot Dairy Center at Oregon State University. The room was illuminated by a mixture of both natural and artificial lighting and held at a constant temperature of 20°C. Panelists were distributed across the tables in the room

36 so as to maximize the space between each panelist. No talking was allowed. Each session generally took about fifteen minutes to complete, although panelists were given as much time as they wished to complete each session. The ballots consisted of a single sheet of eight and one-half by eleven inch paper with the descriptors and scales therein for the panelist to fill out. The ballots assessed eight descriptors: beany, floral, fruity, sweet, bitter, sour, nutty, and green, determined in preliminary tests (data not shown). The ballots contained a 100mm horizontal visual analogue scale for each descriptor. Panelists were instructed to mark any point between two-word anchors that represented the minimum and maximum extreme values. The word anchors used on the ballot were “none” and “extreme”, which were indented on the line.

GC-MS conditions GC-MS was performed on a Shimadzu GCMS-QP2010 Ultra instruments with an attached Shimadzu AOC-5000 Plus auto sampler and chiller (Shimadzu Corporation, Kyoto, Japan). Helium was the carrier gas. The GC-MS column consisted of a 30-meter Stabilwax column with a 0.25mm internal diameter (Restek, Bellefonte, PA, U.S.A.). The solid-phase microextraction (SPME) fiber consisted of a 50/30 μm Divinylbenzene/Carboxen/ Polydimethylsiloxane fiber with a 24-gauge needle size (Supelco/Sigma-Aldrich, Bellefonte, PA, U.S.U.). Autosampler vials consisted of 20ml amber SPME vials with an 18mm orifice and magnetic screw-thread caps (Restek, Bellefonte, PA, U.S.A.) Temperature, pressure and flow parameters included a column oven temperature of 35°C, an injection temperature of 250°C, a pressure of 40 kpa, a total flow of 1.9 mL/min, a column flow of 0.45ml/min, a linear velocity of 121 cm/sec, and a purge flow of 1.0 mL/min. A split injection mode was used with a ratio of one and the flow control mode was pressure. The column oven temperature was initially set to 35°C with a hold time of 10 minutes followed by a 4°C/min increase to a final temperature of 200°C with a hold time of 2 minutes, then an additional ramp of 10°C/min to a final temperature of 250°C for 5 minutes. The MS parameters consisted of an ion surface temperature of 200°C, an interface temperature of 250°C, an absolute detector voltage of 1k V, a solvent cut time of

37 three minutes, a microscan width of zero, a microscan threshold of 200 u, and a GC program time of 61.25 minutes. The scan mode parameters consisted of a start time of 3 minutes, an end time of 60 minutes with an event time of 0.22, a scan speed of 1428, and a starting and ending m/z of 33 to 330. The Combi Pal method included pre-incubation for 10 minutes at 35° C with agitation, vial penetration to 51mm, extraction for 40 minutes at 35° with no agitation, injection penetration to 54mm with desorption for 10 minutes. The agitation was on for 5 seconds and off for 2 seconds. Post-fiber-condition time was 10 minutes. Thirty green bean samples were randomly selected and thawed per GC-MS run to fill the chilled autosampler. 1g of material was weighed and added to a SPME vial with 1µg/L of deuterated linalool as an internal standard. The vial was capped and placed in the auto sampler stack cooler. Samples were run continuously from November 5, 2014 to November 18, 2014.

GC-MS analysis GC-MS data was analyzed using Shimadzu GCMSsolutions Postrun Analysis software. Mass spectrometry fragment patterns were identified with a NIST/EPA/NIH Mass Spectral Database (NIST 11). All compounds identified and quantified were previously found in green beans in the published literature and peak identification with the NIST11 library was in most cases a better than 95% match (Stevens et al., 1967b; Toya et al., 1976; De Lumen et al., 1978; De Quirós et al., 2000; Barra et al., 2007). The concentrations of linalool and 1-octen-3-ol were determined by the standard addition method on a calibration curve (Bader, 1980). Linalool and 1-octen-3-ol were chosen for analysis because of their importance to green flavor in the published literature.

Data analysis The data was analyzed in R using functions of the lme4 package, lmerTest package, GGally package, pwr package, and the base functions of cor.test, hist, plot, t.test, and prcomp (Bates et al., 2015; Kuznetsova et al., 2017; R Core Team, 2013). Linear mixed-effect models were used to determine panel consistency and differences between sensory attributes. The

38 data was normalized on an individual panelist basis by subtracting the mean from an observation and dividing by the standard deviation. This removed any scaling effect. Residual plots were checked for normality. Bean variety, tasting session, and panelist were main effects and all possible two-way and three-way interactions were examined. Bean variety was a fixed effect and panelist and session were random effects. P values were determined using a likelihood ratio test to compare a full model to a reduced model. Wax beans were separately analyzed using linear mixed-effects models, and the main effect of session and its interactions were removed because the wax beans were tasted in a single session. For both wax beans and green beans, correlations between sensory descriptors and linalool and 1-octen-3-ol were performed using the Spearman’s rank correlation coefficient.

Results

Panelist performance A significant three-way interaction in the ANOVA indicates inconsistent panel performance (Tomasino et al., 2013). No significant three-way interactions were found for the sensory descriptors for green beans (Table 2.2 & Table 2.3). Other interactions observed, such as session by panelist, are common in sensory studies and do not indicate issues that would require rejecting the data. Principle component analysis and multidimensional scaling were conducted to determine if any beans clustered together (data not shown). No clear clustering or groupings occurred based on sensory descriptors.

Chemical composition The concentrations of linalool and 1-octen-3-ol are shown in Table 2.4. The arithmetic mean concentration of linalool was 6091.3 µg/L, and the arithmetic mean for 1-octen-3-ol was 127.2 µg/L. The standard deviations were 15,731.4 and 127.7, respectively. The range of values for linalool was 1.5 µg/L to 125,770.6 µg/L and the range for 1-octen-3-ol was 0.1 µg/L to 686.1 µg/L. A threshold for human detection of linalool in water is 5.3µg/L (Ahmed et al., 1978), and

98% of samples had a concentration higher than 5.3µg of linalool. The threshold for 1-octen-3- ol in water is 2x10-3 µg/L, and 100% of samples exceeded this threshold (Callejón et al., 2016).

Correlations Significant correlations (α = 0.05) were found between multiple descriptors (Figures 2.1 & 2.2). Floral, fruity and sweet descriptors were correlated. The floral descriptor was moderately correlated to fruity and sweet with a correlation coefficient (ρ) of 0.42 and 0.39 respectively, but fruity and sweet were strongly correlated at a ρ of 0.77. Bitter was negatively correlated to floral, fruity and sweet with ρ values of -0.14, -0.42, and -0.52 respectively, but positively correlated to beany with a ρ of 0.19. Sour and bitter were positively correlated, although only weakly with a ρ of 0.32. The nutty descriptor was positively correlated with fruity and sweet, ρ of 0.35 and 0.37, respectively. The green descriptor was negatively correlated to fruity (ρ of-0.25) and sweet (ρ of -0.22). Significant correlations (α = 0.05) were found among volatiles and descriptors as well. Linalool was moderately negatively correlated to 1-octen-3-ol with a ρ of -0.39. Linalool was weakly correlated to the floral descriptor with a ρ of 0.21. Furthermore, 1-octen-3-ol was weakly correlated to the nutty descriptor with a ρ of 0.16. Correlations in the wax bean data set resulted in fewer correlations between descriptors with statistical significance (Figure 2.2). The fruity descriptor was very strongly correlated to the sweet descriptor with a ρ of 0.91. The fruity descriptor was also strongly negatively correlated to the sour and nutty descriptors with a ρ of -0.62 and -0.65, respectively. The sweet descriptor, in addition to being strongly correlated to the fruity descriptor, was also strongly negatively correlated to the sour and nutty descriptors with a ρ of -0.75 and -0.67, respectively. The floral descriptor was also negatively correlated to the nutty descriptor with a ρ of -0.63, but the sour descriptor was strongly correlated to the nutty descriptor with a ρ of 0.72. No correlation with statistical significance resulted between a volatile and a sensory descriptor in the wax bean data set.

Discussion

These correlations calculated in this work directly address questions posed by Stevens et al. (1967a) regarding the connection between volatiles, such as linalool and 1-octen-3-ol, and flavor descriptors of green beans. Research by Stevens et al. (1967a), had found differences in both flavor and volatile content in ‘FM-1L Blue Lake’, ‘Gallatin-50’, ‘Romano’, and ‘OSU-9025’. They described the ‘Gallatin-50’ bean, which they found to be high in linalool but low in 1- octen-3-ol, as floral in flavor. Whereas, they found the ‘FM1-L Blue Lake’ bean to be high in 1- octen-3-ol but low in linalool and to have what they described as a beany flavor. No formal sensory evaluation was ever conducted to test these connections between linalool and 1-octen- 3-ol with floral and beany beans. With this study, there is evidence that linalool is indeed associated with beans that are described as having floral flavor. A significant correlation between 1-octen-3-ol and the nutty flavor also occurred. This is not entirely unexpected as 1- octen-3-ol is an aroma compound present in chestnuts (Krist et al. 2004), hazelnuts (Alasalvar et al 2003) and almonds (Vazquez-Araujo et al. 2008). This correlation in green beans requires future study as it has been reported nowhere else in the literature. No evidence was found, on the other hand, for a significant correlation between 1-octen-3-ol and beany flavor. Therefore, beany flavor is perhaps due to a combination of compounds of which 1-octen-3-ol is only a small component. A moderately negative correlation occurred between linalool and 1-octen-3-ol. When a bean contained high levels of linalool it had low levels of 1-octen-3-ol and vice versa. These compounds are derived from the isoprenoid pathway (linalool) and the fatty acid pathway (1- octen-3-ol), and these two pathways are not known to interact or interfere with each other and do not share gene regulatory mechanisms except those regulatory mechanisms that have a very high degree of pleiotropy across many diverse pathways. An example of a transcription factor affecting numerous disparate biochemical pathways (although not necessarily linalool and 1-octen-3-ol) through a high level of pleiotropy would be the LEAFY transcription factor, which binds genes for floral organs, meristem development, gibberellic acid production, and

41 auxin production (Moyroud et al., 2011; Kalisz & Kramer, 2008). This type of pleiotropy is formally possible, but the negative correlation is also likely linked to genetic differences resulting from differing selective pressures or genetic drift in distinct lineages of beans. It has been suggested, particularly in the research of Stevens et al. (1967a), that Blue Lake beans in general (not just ‘FM-1L Blue Lake’) may form such a distinct lineage and are believed to be high in 1-octen-3-ol and low in linalool, and the high linalool content but low 1-octen-3-ol content of ‘Gallatin-50’ measured by Stevens et al. (1967a) may be found in a broader class of beans since ‘Gallatin-50’ is closely related to many commercially produced green beans that are sometimes termed Tendercrop types. In short, Blue Lake and Tendercrop beans may in fact represent distinct lineages of beans with distinct volatile profiles. This supposition that the volatile profiles are different is not proven, but the negative correlation found in this data may lend tentative evidence to it as numerous examples of Blue Lake and Tendercrop type beans are present in this study. The highly correlated nature of the floral, fruity, and sweet descriptors suggests that they may not be distinguished clearly in sensory evaluation and may overlap. The correlation of sweet with fruity and floral is a clear example of congruency between commonly associated tastes and flavors as fruits and flowers are often sweet and would naturally create this mental association (Lim et al., 2014; Pfeiffer et al., 2006). This congruency of sweet, fruity, and floral may be linked to bitter and green through an association to ripeness because bitter and green are both negatively correlated to the floral, fruity, and sweet descriptors, which is suggestive of a flavor dichotomy between floral-fruity-sweet and bitter-green. However, green beans do not ripen in the classic sense from sweet to bitter as is observed in most fruits, even if sweet and bitter traits are present. Surprisingly, green and bitter are not significantly correlated to each other (P value = 0.054), even if they are separately negatively correlated to sweet, fruity, and floral. The correlation between bitter and sour is a weak to moderate correlation with a correlation coefficient of 0.32, but with a highly significant P-value. This correlation is not surprising and has been identified in numerous other sensory studies (Rubico et al., 1992).

There is apparently a confusion in the minds of many people as to the difference between sour and bitter as both tastes can be aversive and garner a strong reaction. The wax bean correlations give a somewhat different picture of the relationship between sensory descriptors and volatiles. The two significant correlations to linalool and the one significant correlation to 1-octen-3-ol disappear in the wax bean data set. Moreover, the four correlations to bitter and the three correlations to floral also disappear. Furthermore, correlations between nutty and fruity and sweet become negative correlations, and a new correlation between nutty and sour appears and a new negative correlation between nutty and floral appears. The correlations to green also disappear in the wax bean data set, and a correlation between bitter and sour occurs. The only correlation with a highly significant P value is that between sweet and fruity, which is also highly significant in the green bean data set. It is notable that all the correlations in the wax bean data set are barely below the α=0.05 and the sample size is only eleven, which is approximately eighteen-fold smaller than the green bean sample size. The green bean data set, on the other hand, contains eleven correlations with P value smaller than 0.001. A power analysis of the sample size in the wax bean data set using the pwr.r.test function of the pwr package in R shows that the power to detect a correlation coefficient of 0.5 is only 0.365, but the power to detect the same correlation coefficient in the larger green bean data set is 0.999. Despite this difference in sample size that greatly affects statistical power, there are still good reasons to think that the differences reflect something biologically important about wax beans. Wax beans are yellow in color because they are highly deficient in carotenoids (Myers et al., 2018). Carotenoids are ultimately derived from the isoprenoid/terpenoid biochemical pathway that also leads to monoterpenes, such as linalool (Dudareva et al., 2013). It may be that whatever genetic change that takes place in wax beans leading to a deficiency in this carotenoid pathway also includes the part of the isoprenoid/terpenoid pathway that leads to linalool. There is a large difference in the arithmetic mean value of linalool in the green bean population (6320 µg/L) in comparison to the wax bean population (2791 µg/L), although a t-test fails to show a significant difference between the means (P value = 0.079). This difference is not as dramatic in the mean values for

1-octen-3-ol, which are 130 µg/L in green beans in comparison to 91 µg/L in wax beans, respectively. Assuming this hypothesis is true that linalool is deficient in wax beans (and perhaps other important flavor compounds in the same pathway such as β-ionone), it is likely that the reduction of these compounds would affect all sensory traits because of the perceptual interactions between volatiles (Ferreira, 2010). Our results indicate exciting possibilities for research that increases our understanding of the sensory experience of green beans. Linalool has been shown to correlate to the floral descriptor as expected, but 1-octen-3-ol did not correlate significantly to the beany descriptor. On the other hand, 1-octen-3-ol did correlate to the nutty descriptor, which should be explored in future research. This correlation between 1-octen-3-ol and nutty brings up the need to further survey snap bean professionals who regularly attend cutting events to derive more of a lexicon of flavor components in snap beans, or to more widely survey consumers on descriptors that match their gustatory experiences. The correlations that have been shown here between linalool and floral and 1-octen-3-ol and nutty will assist breeders in selecting for flavor quality in snap beans. It also verifies that important flavor differences do exist in green beans, which will help industry to better assess flavor quality in their products.

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Figures

Figure 2.1. Correlation coefficients (ρ) and P values for green beans. Red shading indicates a positive correlation and blue shading indicates a negative correlation according to the shading scale shown to the right of the figure. (*; p<0.05, **; p<0.01, ***; p<0.001)

Figure 2.2. Correlation coefficients (ρ) and P values for wax beans. Red shading indicates a positive correlation and blue shading indicates a negative correlation according to the shading scale shown to the right of the figure. (*; p<0.05, **; p<0.01, ***; p<0.001)

Tables

Table 2.1. Germplasm utilized in the study. Bean Variety Germplasm Source: Pod Color: 91-1009 Dickson Collection Green Bean 91-1028 Dickson Collection Green Bean 91-1033 Dickson Collection Green Bean 91-1073 Dickson Collection Green Bean 91-1096 Dickson Collection Green Bean 91-1098 Dickson Collection Green Bean 91-1104 Dickson Collection Green Bean 91-1145 Dickson Collection Green Bean 91-1215 Dickson Collection Green Bean 91-1285 Dickson Collection Green Bean 91-1309 Dickson Collection Green Bean 91-1443 Dickson Collection Green Bean 91-1542 Dickson Collection Green Bean 91-1555 Dickson Collection Green Bean 91-1574 Dickson Collection Green Bean 91-1613 Dickson Collection Green Bean 91-1643 Dickson Collection Green Bean 91-1664 Dickson Collection Green Bean 91-1672 Dickson Collection Green Bean 91-1728 Dickson Collection Green Bean 91-1738 Dickson Collection Green Bean 91-1748 Dickson Collection Green Bean 91-1750 Dickson Collection Green Bean 91-1755 Dickson Collection Green Bean 91-1759 Dickson Collection Green Bean 91-1768 Dickson Collection Green Bean 91-1772 Dickson Collection Green Bean 91-1892 Dickson Collection Green Bean 91-1940 Dickson Collection Green Bean 91-1976 Dickson Collection Green Bean 91-1989 Dickson Collection Green Bean 91-2093 Dickson Collection Green Bean 91-2094 Dickson Collection Green Bean 91-2095 Dickson Collection Green Bean 91-2096 Dickson Collection Green Bean 91-2097 Dickson Collection Green Bean 91-2099 Dickson Collection Green Bean 91-2100 Dickson Collection Green Bean 91-2101 Dickson Collection Green Bean

91-2102 Dickson Collection Green Bean 91-3008 Dickson Collection Green Bean 91-3013 Dickson Collection Green Bean 91-3110 Dickson Collection Green Bean 91-3225 Dickson Collection Green Bean 91-3255 Dickson Collection Green Bean 91-3346 Dickson Collection Green Bean 91-3389 Dickson Collection Green Bean 91-3405 Dickson Collection Green Bean 91-3436 Dickson Collection Green Bean 91-3588 Dickson Collection Green Bean 91-3594 Dickson Collection Green Bean 91-3709 Dickson Collection Green Bean 91-3736 Dickson Collection Green Bean 91-3857 Dickson Collection Green Bean 91-3915 Dickson Collection Green Bean 91-3918 Dickson Collection Green Bean 91-3921 Dickson Collection Green Bean 91-3982 Dickson Collection Green Bean Acclaim BeanCAP Green Bean Angers BeanCAP Green Bean Astun BeanCAP Green Bean Balsas BeanCAP Green Bean Banga BeanCAP Green Bean BBL156 BeanCAP Green Bean BBL274 BeanCAP Green Bean Benchmark BeanCAP Green Bean Benton BeanCAP Green Bean Black Valentine BeanCAP Green Bean Blue Peter Pole BeanCAP Green Bean Bogota BeanCAP Green Bean Booster BeanCAP Green Bean Brio BeanCAP Green Bean Brittle Wax BeanCAP Wax Bean Bronco BeanCAP Green Bean Cadillac BeanCAP Green Bean Calgreen BeanCAP Green Bean Carlo BeanCAP Green Bean Carson BeanCAP Wax Bean Castano BeanCAP Green Bean Catania BeanCAP Green Bean Celtic BeanCAP Green Bean Charon BeanCAP Green Bean

Cherokee BeanCAP Wax Bean Coloma BeanCAP Green Bean Contender BeanCAP Green Bean Corbette Refugee BeanCAP Green Bean Cyclone BeanCAP Green Bean Dandy BeanCAP Green Bean Derby BeanCAP Green Bean Doral BeanCAP Green Bean Dusky BeanCAP Green Bean Dutch Double White BeanCAP Green Bean Eagle BeanCAP Green Bean Ebro BeanCAP Green Bean Embassy BeanCAP Green Bean Envy BeanCAP Green Bean Espada BeanCAP Green Bean Esquire BeanCAP Green Bean EZ Pick BeanCAP Green Bean Ferrari BeanCAP Green Bean Festina BeanCAP Green Bean Flavio BeanCAP Green Bean Flavor Sweet BeanCAP Green Bean Flo BeanCAP Green Bean Fortex BeanCAP Green Bean FR 266 BeanCAP Green Bean Fury BeanCAP Green Bean Gallatin 50 BeanCAP Green Bean Galveston BeanCAP Green Bean Gina BeanCAP Green Bean Gold Mine BeanCAP Wax Bean Goldrush BeanCAP Wax Bean Green Arrow BeanCAP Green Bean Grenoble BeanCAP Green Bean Hayden BeanCAP Green Bean Hercules BeanCAP Green Bean Hialeah BeanCAP Green Bean Hystyle BeanCAP Green Bean Idaho Refugee BeanCAP Green Bean Igloo BeanCAP Green Bean Impact BeanCAP Wax Bean Kentucky Wonder BeanCAP Green Bean Koala BeanCAP Green Bean Kylian BeanCAP Green Bean Labrador BeanCAP Green Bean

Landmark BeanCAP Green Bean Landreths Stringless Green BeanCAP Green Bean Magnum BeanCAP Green Bean Masai BeanCAP Green Bean Matador BeanCAP Green Bean McCaslan no. 42 BeanCAP Green Bean Medinah BeanCAP Green Bean Mercury BeanCAP Green Bean Minuette BeanCAP Green Bean Navarro BeanCAP Green Bean Nicelo BeanCAP Green Bean Nomad BeanCAP Green Bean Normandie BeanCAP Green Bean NY6020_5 BeanCAP Green Bean Opus BeanCAP Green Bean Oregon 1604M BeanCAP Green Bean Oregon 2065 BeanCAP Green Bean Oregon 5402 BeanCAP Green Bean Oregon 5630 BeanCAP Green Bean Oregon 91G BeanCAP Green Bean Oregon Giant Pole BeanCAP Green Bean Palati BeanCAP Green Bean Paloma BeanCAP Green Bean Panama BeanCAP Green Bean Paulista BeanCAP Green Bean Pix BeanCAP Green Bean Polder BeanCAP Green Bean Pole Blue Lake BeanCAP Green Bean Pole Blue Lake S7 BeanCAP Green Bean Pretoria BeanCAP Green Bean Profit BeanCAP Green Bean Prosperity BeanCAP Green Bean Provider BeanCAP Green Bean Redon BeanCAP Green Bean Renegade BeanCAP Green Bean Rocdor BeanCAP Wax Bean Rockport BeanCAP Green Bean Roller BeanCAP Green Bean Roma II BeanCAP Green Bean Romano 118 BeanCAP Green Bean Romano Gold BeanCAP Wax Bean Royal Burgundy BeanCAP Green Bean Saporro BeanCAP Green Bean

Scorpio BeanCAP Green Bean Seabiscuit BeanCAP Green Bean Secretariat BeanCAP Green Bean Selecta BeanCAP Green Bean Serengeti BeanCAP Green Bean Serin BeanCAP Wax Bean Seville BeanCAP Green Bean Shade BeanCAP Green Bean Sirio BeanCAP Green Bean Slenderella BeanCAP Green Bean Slenderpack BeanCAP Green Bean Sonesta BeanCAP Wax Bean Spartacus BeanCAP Green Bean Speedy BeanCAP Green Bean Stallion BeanCAP Green Bean Stayton BeanCAP Green Bean Storm BeanCAP Green Bean Strike BeanCAP Green Bean Stringless French Fillet BeanCAP Green Bean Summit BeanCAP Green Bean Tapia BeanCAP Green Bean Tendercrop BeanCAP Green Bean Tendergreen BeanCAP Green Bean Teseo BeanCAP Green Bean Thoroughbred BeanCAP Green Bean Titan BeanCAP Green Bean Top Crop BeanCAP Green Bean Trail of Tears BeanCAP Green Bean True Blue BeanCAP Green Bean Unidor BeanCAP Wax Bean US Refugee no. 5 BeanCAP Green Bean Valentino BeanCAP Green Bean Venture BeanCAP Green Bean Warrior BeanCAP Green Bean Widusa BeanCAP Green Bean Zeus BeanCAP Green Bean Zodiac BeanCAP Green Bean

Table 2.2. Linear mixed-effects models analysis of green beans. Statistical significance shown with asterisks. (*; p<0.05, **; p<0.01, ***; p<0.001)

Source of variation Beany Floral Fruity Sweet Sour Bitter Nutty Green sample *** *** *** ** *** *** panelist session *** *** sample x panelist *** *** sample x session *** *** panelist x session *** *** *** *** *** *** *** sample x panelist x session

Table 2.3. Linear mixed-effects models analysis of wax beans. Statistical significance shown with asterisks. (*; p<0.05, **; p<0.01, ***; p<0.001)

Beany Floral Fruity Sweet Sour Bitter Nutty Green sample (fixed) panelist ** ** * (random) sample x panelist

Table 2.4. Concentrations of linalool and 1-octen-3-ol (µg/L) in the green and wax beans.

Bean Variety Linalool 1-octen-3-ol 91-1028 1,328.7 200.7 91-1073 17,293.4 10.7 91-1145 487.6 400.5 91-1215 2,025.6 241.0 91-1285 2,678.1 2.6 91-1309 30,055.9 6.4 91-1443 12,152.6 7.4 91-1555 54,825.4 12.7 91-1643 146.6 461.8 91-1672 19,860.4 13.5 91-1728 13,542.6 0.4 91-1738 186.8 231.0 91-1748 66,817.8 8.0 91-1750 203.0 502.7

91-1755 130.4 491.8 91-1759 237.7 530.0 91-1768 51,611.3 0.8 91-1892 535.9 211.5 91-1940 23,798.7 6.5 91-1976 2,121.0 638.2 91-1989 155.5 325.1 91-2093 13,427.5 5.0 91-2094 23,094.3 0.2 91-2097 1,636.7 234.7 91-2099 76.6 198.5 91-2101 66,805.6 1.5 91-2102 125,770.6 10.9 91-3013 717.5 686.1 91-3255 128.4 162.0 91-3346 76,236.3 1.2 91-3405 44,061.6 1.8 91-3594 7,559.2 0.5 91-3709 25,413.5 9.4 91-3736 1,718.8 207.2 91-3857 217.2 163.0 91-3918 117.7 249.9 91-3921 521.8 390.4 91-3982 48,805.1 0.5 Acclaim 70.4 245.3 Angers 7,206.7 76.1 Astun 27,794.9 0.6 Balsas 12,461.0 25.2 BBL 156 37,570.7 10.9 BBL 274 637.2 172.4 Benton 59.4 144.7 Black Valentine 2,193.7 28.0 Bogota 862.6 86.4 Booster 2,194.0 107.0 Brittle Wax 11,173.9 4.7 Bronco 13,981.9 0.4 Carson 13,592.1 5.2 Castano 267.1 65.0 Catania 11,282.3 0.1 Charon 103.7 130.9 Cherokee 1,214.3 68.8 Contender 11,074.1 0.6 Corbette Refugee 3,858.4 215.0

Cyclone 809.3 140.0 Dandy 9,184.8 2.9 Derby 644.4 58.2 Doral 42.6 82.0 Dusky 11,008.7 0.3 Dutch Double White 7,399.2 5.5 Eagle 59.2 323.4 Ebro 2,235.8 227.1 Embassy 66.2 157.8 Envy 561.3 26.9 Espada 161.1 85.9 Esquire 139.2 62.4 EZ Pick 4,294.8 93.2 Ferrari 1,647.1 20.1 Festina 5.0 72.6 Flavio 14.1 35.3 Flavor Sweet 14.0 34.3 Flo 162.1 65.5 Fortex 2,127.0 61.4 FR 266 35.9 73.7 Fury 683.3 74.9 Gallatin 50 2.7 61.2 Galveston 6,383.4 31.7 Gina 1,586.1 53.6 Gold Mine 20.6 71.0 Goldrush 6.8 340.5 Green Arrow 116.3 226.6 Grenoble 384.8 18.6 Hayden 192.2 10.5 Hercules 39.4 28.8 Hialeah 40.4 54.2 Hystyle 1,075.1 116.5 Idaho Refugee 1,312.2 91.9 Igloo 76.2 145.6 Impact 269.4 32.7 Kentucky Wonder 2,131.3 184.3 Koala 1,637.5 11.7 Kylian 2,197.2 34.7 Labrador 85.8 309.5 Landmark 4,315.4 324.1 Landreth's Stringless 1,043.1 47.4 Magnum 1,239.0 64.5 Matador 60.3 210.4

McCaslan no. 42 499.9 355.2 Medinah 1,112.4 123.3 Mercury 1,186.8 46.4 Minuette 1.5 31.5 Navarro 89.4 64.0 Nicelo 9.2 179.8 Nomad 1,610.7 143.7 Normandie 132.0 221.9 Opus 66.7 154.7 Oregon 1604M 3,884.1 204.9 Oregon 2065 531.3 241.4 Oregon 5402 138.2 87.4 Oregon 5630 248.0 112.8 Oregon 91G 246.5 97.7 Oregon Giant Pole 17.7 254.9 Palati 77.2 76.7 Paloma 2,478.2 423.8 Panama 10,926.5 96.6 Paulista 1,212.6 72.6 Pix 721.5 28.2 Polder 3,707.2 141.7 Pole Blue Lake 7,492.8 291.5 Pole Blue Lake S7 1,964.3 145.8 Pretoria 39.3 66.8 Profit 19.0 123.2 Prosperity 1,266.6 115.2 Provider 48.3 101.1 Redon 1,121.3 59.2 Renegade 97.3 74.6 Rocdor 418.8 47.0 Rockport 5,062.2 68.4 Roller 1,146.0 93.6 Roma II 2,290.7 39.3 Romano Gold 357.6 59.8 Royal Burgundy 351.9 160.4 Saporro 76.4 246.5 Scorpio 14.3 195.6 Seabiscuit 104.7 135.0 Secretariat 43.2 166.0 Selecta 2,126.3 64.6 Serengeti 767.0 141.1 Serin 2,934.8 140.0 Seville 1,377.7 77.4

Shade 23.3 134.0 Slenderella 1,348.8 85.7 Slenderpack 357.6 120.5 Sonesta 40.3 138.5 Spartacus 57.6 40.0 Speedy 103.8 42.2 Stallion 1,906.8 214.5 Stayton 1,643.4 101.9 Storm 14.2 58.0 Strike 1.6 58.5 Stringless French Fillet 1,058.6 57.2 Tapia 1,676.7 102.9 Tendercrop 147.8 287.7 Tendergreen 1,674.8 244.8 Teseo 580.3 142.2 Thoroughbred 44.4 103.3 Titan NA 63.7 Top Crop 122.1 291.0 Trail of Tears 128.1 370.1 True Blue 26.2 18.4 Unidor 667.2 96.9 US Refugee no.5 248.4 177.1 Valentino 19.2 64.2 Venture 4,283.8 195.4 Warrior 46.2 110.6 Widusa 911.3 158.0 Zeus 12.9 51.5 Zodiac 743.4 262.5

LINKAGE MAPPING OF THREE VOLATILE COMPOUNDS IN SNAP BEANS USING A RECOMBINANT INBRED POPULATION

Introduction

Snap beans produce an array of secondary metabolites – volatile and otherwise. The number of volatile secondary metabolites that may be involved in flavor number at least 104 (Barra et al., 2007). This complexity represents a daunting task to geneticists and plant breeders due not only to the number of potential volatile compounds that may be affecting flavor but also due to the complexity of the biochemical and regulatory pathways that underlie these volatiles compounds. Until recently, flavor in vegetables has been largely ignored due to this complexity (Klee, 2010). There is now a growing awareness among both consumers and plant breeders of the loss of flavor quality that has happened over the last fifty years as a result of an intense focus on yield and disease resistance (Klee, 2010). There are also a growing number of molecular tools that allows the molecular, biochemical, and genetic pathways to be more finely dissected than ever before. This is particularly the case for molecular markers, statistical mapping, and improved GC-MS instrumentation, which have all seen major advances in the last ten years. In short, both consumer awareness about the loss of flavor quality, and the tools necessary to remedy the problem are peaking simultaneously. This convergence of tools and awareness make it an especially good time to apply these new tools to the problem and attempt to advance our knowledge and breeding success in flavor quality. An essential part of advancing our knowledge of the underlying genetics of flavor quality is genetic mapping using potentially either linkage mapping and Quantitative Trait Loci (QTL) analysis, and/or genome-wide association studies (GWAS). Linkage mapping and QTL analysis, in particular, has been and continues to be a critical tool for identifying candidate genes and understanding the underlying genetic structure to traits. Linkage mapping has been done at least since 1913 in fruit flies and QTL analysis with molecular markers dates to a flurry of papers published in the late 1980’s on their use. In comparison, GWAS was first used in human genetic mapping shortly after the human genome project was completed in 2003, although much more limited association studies of portions of the genome were done as far back as the 1950’s

(Altshuler et al., 2008; Jansen & Stam, 1994). A linkage map in common bean was first published by Lamprecht in 1961 with major revisions in 1991 by Bassett and another major revision by Freyre in 1998 (Bassett, 1991; Freyre et al, 1998). QTL analysis in common beans began to produce results beginning in the 1990’s as Random Amplified Polymorphic DNA (RAPD) markers become readily available with the mapping of QTL for Common Bacterial Blight, day length sensitivity, Ashy Stem Blight, Bean Common Mosaic Virus, Bean Golden Mosaic Virus, and Halo Blight among others (Jung et al., 1999; Miklas et al., 1998; Koinange et al., 1996; Miklas et al., 1996; Aryarathne et al., 1999). This continued apace until the publication of the common bean genome in 2014 with QTL mapping studies of seed size, seed shape, root architecture, and multiple papers on White Mold among others (Schmutz et al., 2014; Beebe et al., 2006; Ender & Kelly, 2005; Kolkman & Kelly, 2003; Park et al., 2001; Park et al., 2000). After the publication of the common bean genome, new molecular markers became available, such as Single Nucleotide Polymorphism (SNP) markers and genotype-by-sequencing (GBS) technologies that have provided faster results on larger datasets at less cost with far higher resolution than was previously possible. Despite the availability of GWAS at this juncture, QTL analysis research continued identifying QTL for Common Bacterial Blight, drought tolerance, Angular Leaf Spot, seed color, pod length, pod wall thickness, and processing quality traits (Hagerty et al., 2016; Viteri et al., 2015; Keller et al., 2015; Cichy et al., 2014; Mukeshimana et al., 2014). Even within the last year as GWAS papers have become commonplace in the literature, QTL analysis has identified QTL for nitrogen fixation, time-to-flowering, seed color, and Angular Leaf Spot (Diaz et al., 2017; Bhakta et al., 2017; Zhu et al., 2017; Bassi et al., 2017). Many recent QTL analyses have been incorporating genotype by environment interactions into their models and QTL analysis has evolved with new statistical methods developed in the mid- 1990’s that utilize cofactors and multiple regression analysis to more precisely pin down QTL intervals (Bhakta et al., 2017; Zeng, 1994; Jansen & Stam, 1994; Jansen, 1993). With the advent of the genomic era, GWAS became a viable and commonly utilized technique for genetic mapping, yet, clearly, it has not supplanted linkage mapping and QTL analysis because the strengths and weaknesses of each mapping technique are complementary and mutually useful. In some cases, the continued use of linkage mapping and QTL analysis is

due to the fact that a complete genome does not yet exist for a particular crop, but this is not the case for common bean, whose sequence was published in 2014 (Schmutz et al., 2014). Linkage mapping and QTL analysis offer the possibility of analyzing a rare allelic variant, which is not possible in GWAS because rare alleles are screened from analysis, usually at the minor allele frequency (MAF) of 5% or less (Cadic et al., 2013). A common byproduct of linkage mapping studies includes recombinant inbred lines that can provide isolated QTL for a trait, and that can be propagated and maintained indefinitely and readily shared with other researchers. Linkage mapping and QTL analysis also offers the flexibility to adapt to whatever genotyping technology is available from SSR markers to RFLP markers and GBS. Association mapping, on the other hand, relies heavily on more advanced high-resolution genotyping techniques that may not be readily available at all research institutions. Finally, association mapping, despite the best efforts to control for population structure, can still be subject to it, and linkage mapping can be more robust than association mapping in these instances (Cadic et al., 2013). For these reasons, linkage mapping remains a relevant and important alternative to association mapping. Flavor quality in green beans appears to be critically impacted by three volatile compounds, which would be of particular interest in mapping and QTL analysis. These three volatile compounds were identified by Stevens as linalool, 1-octen-3-ol, and 3-hexen-1-ol (Stevens et al., 1967a). Of these three, the two that have garnered the greatest focus and resources have been linalool and 1-octen-3-ol, but Stevens had shown that 3-hexen-1-ol is also critically important to creating the flavor unique to premium Blue Lake green beans. The biochemistry of these three compounds has been well studied. Linalool derives from the terpenoid pathway in which geranyl diphosphate is converted to linalool through the action of linalool synthase (Rambla et al., 2014; Lewinsohn et al., 2001). Both 1-octen-3-ol and 3-hexen- 1-ol come out of the fatty acid pathway in which linoleic acid and linolenic acid are converted to various fatty acid derivatives through the action of a lipoxygenase followed by a hydroperoxide lyase and then an alcohol oxidoreductase and possibly an isomerase in the case of 1-octen-3-ol (Rambla et al., 2014; Lumen et al., 1978). These structural genes of the biochemical pathway provide obvious targets for a QTL analysis, although regulatory genes, such as transcription

factors, are also likely to be captured as well. More exotic regulatory elements might also be reflected in a QTL analysis, such as a microRNA, although identifying a candidate for such elements would be extremely challenging as the genomic annotations are currently insufficient. In addition to this biochemistry, early research into the genetic control of linalool and 1-octen- 3-ol production suggest that only a few genes are involved in each (Stevens et al., 1967b; Toya et al., 1976). Altogether, these biochemical underpinnings and research into the genetics of linalool and 1-octen-3-ol give a clear path forward for fruitful QTL mapping. There exists extensive variation for flavor volatiles, which can provide genetic resources for both QTL mapping and breeding for flavor. This can particularly be seen in the USDA-NIFA funded Common Bean Coordinated Agricultural Project (BeanCAP) diversity panel, and in the uncatalogued landrace accessions collected by Michael Dickson (Emeritus, Cornell Univ., Ithaca, NY) in China in the early 1990’s. Analysis of the BeanCAP population and Dickson collection show that the linalool concentration ranges in value from 1.5 µg/L to 125,770.6 µg/L and 1- octen-3-ol ranges from 0.1 µg/L to 686.1 µg/L (Chapter 2). The research of Stevens also showed a high degree of variation for linalool and 1-octen-3-ol in the four genotypes that they examined, but they did not see variation for 3-hexen-1-ol, which is why 3-hexen-1-ol was largely ignored thereafter (Stevens et al., 1967a). A survey of the BeanCAP population and the Dickson collection shows a far different result in terms of the variability of 3-hexen-1-ol production. This greater variability seen in the larger BeanCAP population and Dickson collection suggests that the four genotypes examined by Stevens may not have contained the genetic diversity necessary to capture this 3-hexen-1-ol variation. With the more diverse population now available, QTL mapping and breeding efforts should be greatly facilitated. The objective of this research is to use recombinant inbred lines (RIL) from a ‘Serin’ by ‘OR5630’ cross to identify QTL for the production of linalool, 1-octen-3-ol, and 3-hexen-1-ol in snap beans. It is hoped that this will elucidate the underlying genetic structure for these volatiles and inform plant breeding by better gauging the number of genes involved, the associated markers for marker assisted selection, and in identifying genetic resources for these QTL.

Materials & methods

Mapping population and sample collection Preliminary taste tests indicated that the extra fine wax bean, ‘Serin’ was especially flavorless (data not shown). A discriminate analysis on the SNP data generated from the Illumina Infinium Genechip BARCBEAN6K_3 platform indicated that this bean line was of Andean origin (Wallace & Myers, 2017). In contradistinction to this flavorless variety, ‘OR5630’ is known as a medium sieve green bean with particularly good flavor. This Blue Lake cultivar is known to have Mesoamerican ancestry (Baggett & Lucas, 2005). ‘OR5630’ was crossed as the male with the ‘Serin’ as the female to generate a biparental mapping population of RIL. ‘Serin’ was chosen as the female because the recessive gene for wax (y) would make accidental self- pollination immediately apparent if the progeny were yellow podded, which is not possible until the F2 generation if a successful cross has taken place. One hundred forty progeny were maintained to the F7 generation by single seed descent. Seventeen lines were lost from the population due to segregation for the dwarf lethal syndrome, which is a common problem when Andean and Mesoamerican gene pools are intercrossed (Kelly, 1988). There may have been more lost because young seedlings that had recently emerged were quickly replaced if they died, and this was not recorded. This was possible because residual seed from a single harvested pod was retained for each plant. The surviving generations were alternated between field growth at the Oregon State University Vegetable Research Farm (Corvallis, OR) during summer months and greenhouse growth at the Oregon State University research greenhouses (Corvallis, OR) during winter months. In the summer of 2016, young trifoliate leaves were collected from each line (F7 generation) in 1.5ml Eppendorf tubes and processed that day for genomic DNA. Also in the summer of 2016, several pods were collected across each plot for each RIL and placed in a Ziploc bag within a cooler and rapidly brought to a -20°C freezer for later processing. This 2016 field planting was originally based on an augmented design with seven check genotypes (‘OR5630‘, ‘Serin’, ‘OR91G’, ‘Selecta’, ‘Ebro’, ‘DMC04-88’, ‘Gallatin-50’) repeated in three randomized blocks with blocks parallel to presumed Willamette river water and soil deposition gradients. The RILs could not be replicated because seed was in short supply

and phenotyping with GC-MS instrumentation is extremely costly and time intensive. Therefore, an augmented design was used with replication of standard checks across the blocks for measurements of variance and heritability. However, the controls were lost in a freezer failure, which resulted in a completely unreplicated design.

DNA isolation and genotyping Genotypes were extracted using a modified CTAB method. Approximately 0.5g of material from young trifoliate leaves were ground in 500µl of CTAB buffer and then incubated for 1 hour at 65°. This was extracted with 500µl of chloroform. The supernatant was precipitated with 400µl of 76% ethanol and 10% ammonium acetate. The pellets were dried and resuspended in 200µl of TE buffer. The DNA was then treated with 8µg of RNase A for 1 to 2 hours at 37°. This was extracted with 300µl of chloroform. The supernatant was precipitated with 15µl of 3M sodium acetate (pH 5.2) and 300µl of 95% ethanol. The resulting pellet was washed with 400µl of 70% ethanol. The pellet was air dried and resuspended in 50µl of TE buffer. The quality of the DNA was checked by running 1µg of each sample on an agarose gel. Concentrations were determined by nanodrop (ND-1000 UV-Vis Spectrophotometer). DNA samples were shipped to the USDA Soybean Genomics and Improvement Laboratory (Beltsville, MD) where they were analyzed using the Illumina Infinium Genechip BARCBEAN6K_3 platform. This Genechip contains 5,398 SNP positions. The raw data was manually processed on GenomeStudio software (Illumina). Non-polymorphic SNP loci were removed leaving only 1,523 SNP that were polymorphic.

GC-MS conditions The frozen bean pods were ground into a fine powder with liquid nitrogen, as previously described in chapter 2, and stored in 40ml PTFE lined amber vials. Samples were run at the Del Monte Research Center laboratory of Del Monte Foods, Inc. (Walnut Creek, CA) on a Varian Saturn 2000 GC/MS and Varian Cp-3800 Gas Chromatograph with a water-jacketed cooling rack held at 4C for samples awaiting injection. The GC-MS method and column are as previously described in Chapter 2 with the exception that 1ml of deionized water was added to each

sample. No satisfactory internal standard with adequate peak separation could be found using this Varian instrumentation. Samples were run in duplicate from March of 2017 to May of the same year.

GC-MS analysis GC-MS data was analyzed using OpenChrom community edition software (version 1.1.0.201607311225). Retention time peaks were identified through the relative timing of the peak in comparison to previous GC-MS work in green beans (chapters 2 & 4), and through manual comparison of the mass spectrometry fragment patterns to a NIST/EPA/NIH Mass Spectral Database (NIST 11). The volatiles analyzed for this study were linalool, 1-octen-3-ol, and 3-hexen-1-ol (The corresponding International Union of Pure and Applied Chemistry (IUPAC) nomenclature is 3,7-Dimethylocta-1,6-dien-3-ol, Oct-1-en-3-ol, and (Z)-hex-3-en-1-ol, respectively). All volatile compounds were previously identified in the literature as present in green beans (Stevens et al., 1967b; Toya et al., 1976; De Lumen et al., 1978; De Quirós et al., 2000; Barra et al., 2007). Peak area for phenotyping the bean cultivars was determined using the peak identification and peak integration tools of the OpenChrom software. Specifically, peaks were detected using the first derivative peak detector, and peak area was determined using the trapezoid peak integrator. In a few cases, manual peak detection was necessary, but the trapezoid peak integrator was still used. Analysis of phenotypes was conducted using the functions of hist, plot, and cor.test in R (R Core Team, 2013).

Linkage mapping Linkage mapping was implemented in JoinMap software (version 4.1). A total of 140 RIL

(F7 generation) genotyped with the Illumina BARCBEAN6K_3 Genechip were utilized for mapping. Default settings were used to calculate locus genotype frequencies, similarity of loci, similarity of individuals, and groupings. The default grouping LOD threshold was two. Selection against distorted markers was relaxed because a more stringent cutoff of 0.05 alpha resulted in the loss of chromosome 6 and most of chromosome 3. A sudden drop in quality of the genotype ratios was present at a chi square critical value of 14.33 (1df), which was used as a

cutoff for acceptable segregation ratios. Similar loci were excluded with a similarity ≥ 1.0. No similar individuals were identified with the similarity of individuals calculation. Eight SNP loci that were not part of their linkage group based on the physical map location were removed. Three hundred eighty-eight SNP loci remained for mapping and a total of 1,134 SNP loci were removed. The remaining groupings were used to calculate linkage maps using default settings. The default settings included Maximum Likelihood mapping for marker order and Haldane’s mapping function for map distance.

QTL analysis QTL analysis was performed using MapQTL software (version 6). The linkage maps generated from JoinMap were loaded into MapQTL. The SNP genotypes for all 140 RIL were also loaded into MapQTL. Due to samples lost to a freezer failure and the quality of retention time peak separation in some samples, only sixty phenotypes were used for analysis and the remainder were entered as missing data. Interval mapping (IM), Kruskal-Wallis mapping (KW), and Multiple-QTL mapping (MQM) were done using default parameters. A 1,000 iteration permutation analysis was run on each trait to generate both genome-wide and linkage group specific significance thresholds at α = 0.05 and α = 0.01 for the LOD scores in IM and MQM. Only genome-wide thresholds were used to identify QTLs. The KW QTL significance threshold was α = 0.0001 based on Bonferroni correction for multiple testing. QTL identified in IM mapping and KW mapping were used as cofactors in an initial MQM mapping. Further QTL were identified in this MQM mapping that were not reflected in IM and KW. These additional QTL were incorporated and utilized as cofactors for Automatic Cofactor Selection (ACS), which is a MapQTL tool that uses backward elimination to select the best cofactors for MQM (Ooijen, 2009). The ACS tool was run on default parameters including a P value threshold for elimination of cofactors of 0.02 (Ooijen, 2009). The cofactors identified in ACS were then used in a final round of MQM mapping. The cofactors in MQM mapping reduce the residual variance and thereby increase the power of the test to identify other QTL that are a lesser proportion of the total variance (Ooijen, 2009).

Results

GC-MS phenotypes The parental types in this study (‘Serin’ & ‘OR5630’) show a twelve-fold difference in the amount of linalool produced but were roughly equivalent in the amount of 1-octen-3-ol produced based on the previous analysis (chapter 2). Relative peak area measurements from the same data set show that 3-hexen-1-ol was seven-fold different between the parental types. Direct comparisons of the ‘Serin’ and ‘OR5630’ parental lines to the progeny are not possible because parental controls in the linkage mapping population were lost in a freezer failure and measurements taken on the linkage mapping population lacked an internal standard to enable broader comparisons. Histograms of linalool, 1-octen-3-ol, and 3-hexen-1-ol indicated significant right skewing, although 1-octen-3-ol was nearly normal when a single outlier was removed (Figures 3.1 – 3.6). A correlation analysis showed linalool to be weakly correlated to 1- octen-3-ol (Table 3.1).

Linkage map Fourteen linkage groups containing 388 SNP markers were generated through JoinMap analysis. The physical map coordinates of these 388 SNP markers were used to identify the chromosome of each linkage group and their orientation relative to the physical map. Three of these linkage groups (1b, 3b, and 10b) represented a second fragment of a chromosome. When these fragments were aligned properly, eleven linkage groups were generated representing the eleven chromosomes of the physical map (Figures 3.7 – 3.17). The total length of all the linkage groups combined was 740.3cM, which is ~60% of the 1230.0cM total found by Freyre’s research group (Freyre et al., 1998) (Table 3.2). Marker density averaged 1.9 markers per cM, but actual density varied across the genome.

QTL analysis Four and six QTL were identified in IM mapping and KW mapping respectively (Tables 3.3 & 3.4). All four QTL of IM mapping were overlapping with QTL identified in KW mapping.

MQM mapping identified twelve QTL of which six were overlapping with KW mapping and four were overlapping with IM mapping (Table 3.5). KW mapping identified one QTL (LIN8.3KW) on Pv8 at mapping position 55.75-59.13cM that was not identified by IM or MQM, although a weak signal below the threshold was observed in these other mapping methods. MQM mapping identified six unique QTL not found by IM or KW mapping: LIN1.1, LIN9.1, OCT3.1, OCT11.1, HEX4.1, and HEX7.1. Among the QTL identified by MQM, four had positive additive effects and eight had negative additive effects. Percent variance explained by a QTL ranged from 5% to 22%.

Discussion

The IM method maps QTLs singly without regard for the presence of other QTLs (Jansen & Stam, 1994). Yet there is accumulating evidence that mapping QTL singly results in a loss of power and efficiency that could be gained from mapping multiple QTL simultaneously because “QTLs located elsewhere on the genome can have an interfering effect”. (Jansen & Stam, 1994; Jansen, 1994; Jansen, 1993). The use of cofactors in MQM obtained through backward selection (such as in the Automatic Cofactor Selection utility in MapQTL) allows one to reduce the genetic background of large QTL to allow more powerful and accurate mapping (Jansen, 1994). This gain in power and accuracy is apparent in the division of a very large QTL interval identified in KW mapping on chromosome eight into two smaller and more precisely defined QTL through MQM mapping (LIN8.1 & LIN8.2). MQM was also able to identify eight more QTL than IM due to its increased power. The QTL interval lengths were generally much smaller and more precisely defined through MQM mapping in comparison to IM mapping or to KW mapping as can be seen in the intervals of LIN8.1 or LIN8.2. MQM can also be more robust against a number of problems in the data, such as adjacent QTL that cancel each other out through repulsion, ghost QTL generated through the conflation of two neighboring QTL, incidental associations caused by the skewing of segregation ratios, and the shifting of peaks from low information content regions (low marker density) to high information content regions (Jansen, 2001).

An important issue in examining the results of IM and MQM is determining a threshold of significance. It is possible to use a LOD of three (an odds ratio of 1/1000) as a generic threshold. A permutation test with 1000 iterations provides a more precise threshold. The permutation test executed in MapQTL is, “a resampling method to obtain empirical significance threshold values” in which individuals are sampled without replacement while maintaining the same markers to validate the null hypothesis (Ooijen, 2009). A genome-wide threshold at α = 0.05 was used to identify QTL, but the linkage group specific thresholds were also examined. The linkage group specific thresholds were generally much lower than the genome-wide thresholds. If these less restrictive thresholds had been used instead of the genome-wide thresholds, an additional seven QTL would be identified but at the cost of an increased type I error rate. Unlike the IM and MQM mapping methods, the KW method is not based on a linear model and marker-trait associations are individually scored (Wang et al., 2012; Holmans, 2001). The KW method has the advantage of being non-parametric. Since the distributions for linalool and 3-hexen-1-ol are highly right skewed, it is important to make this comparison. Since it is based in marker-trait associations that are singly analyzed, it gives a different picture of the data with one QTL appearing on Pv8 for linalool at position 55.75-59.13cM (LIN8.3KW) where no other method identifies a QTL. It may be that this QTL resulted from the greater robustness of KW mapping to non-normally distributed data. An important consideration in KW mapping is the significance threshold of the P value because multiple testing is occurring and an appropriate adjustment to the P value must be made, such as a Bonferroni correction. Although KW can give an interesting perspective on the data and provide robustness against non-normal distributions, it lacks power (Holmans, 2001). This lack of power is apparent in the relatively low number of QTL detected and in the disappearance of MQM QTL at LIN1.1, LIN9.1, OCT3.1, OCT11.1, HEX4.1, and HEX7.1. It is also lacks robustness towards skewed segregation ratios because skewed ratios can generate coincidental trait-marker associations whereas MQM is robust towards skewed ratios (Jansen, 2001). The presence of QTLs implies modulations to the underlying biochemical pathways due to genetic variations detected by the IM, MQM, and KW mapping. There are two biochemical

pathways involved in the production of linalool, 1-octen-3-ol, and 3-hexen-1-ol: the terpenoid pathway for linalool and the fatty acid pathway for 1-octen-3-ol and 3-hexen-1-ol. A keyword search for structural genes in Phytozome (Phytozome12.1, Phaseolus vulgaris, 2.1) produced numerous hits across the genome to annotated gene models. Thirty-five matches were found to lipoxygenase on every chromosome except Pv03, Pv04, and Pv08. A similar search for alcohol dehydrogenase yielded twenty-nine hits on every chromosome except Pv10. Hydroperoxide lyase yielded only a single hit on Pv05. Linalool synthase and geranyl diphosphate synthase had two hits and three hits respectively on Pv02, Pv03, and Pv06. Since the QTL interval consists of both pseudomarkers and physically mapped SNP markers, it is not always possible to match up QTL to the locations of these structural genes. There are also uncertainties generated by the relatively low resolution of linkage mapping and the non-uniform coverage by SNP markers. Nevertheless, using the available SNP markers, comparisons between approximate QTL locations on the physical map and the locations of structural genes were made. These comparisons are summarized in Table 3.6 and show close physical proximity between several QTLs and structural genes. It is notable that two QTL for different pathways partially overlap: OCT6.1 and LIN6.1. The overlap at LIN6.1 and OCT6.1 may be explained by two different genes being very close to one another on the same chromosome. Indeed, there is a lipoxygenase gene of the fatty acid pathway very near to a linalool synthase gene and near to both QTL. It seems unlikely that they share an enzyme because the pathways are so different and have different origins. It is nevertheless possible that a regulatory element could be affecting disparate biochemical pathways through a high degree of pleiotropy. There are also numerous regulatory elements near or within all the QTLs, such as bHLH DNA-binding proteins, zinc fingers, AP2 transcriptional factor family protein, homeobox protein, F-box, Myb transcription factor, and RING finger proteins. There is a Myb transcription factor, for instance, that is located on chromosome 8 at position 4.3 Mb, which is very near to the LIN8.2 QTL at position 3.1 Mb. In short, these regulatory genes may also underlie some QTLs. Based on the limited number of structural gene overlaps, it seems very likely that many of these QTL are representing a regulatory element, not a structural gene.

There are two issues with the data that may affect QTL positions. One issue is the skewing of the segregation ratios of the loci, which can affect IM and KW mapping. In particular, KW mapping is susceptible to spurious QTL identification due to this skewing (Jansen, 2001). On the other hand, MQM mapping is relatively robust to this skewing (Jansen, 2001). The skewing also affects the linkage map because removal of all the markers deviating from a 1:1 ratio would result in the loss of chromosomes six and three. The source of this skewing is likely the death of bean plants due to dwarf lethal interactions between the Andean and Mesoamerican gene pools represented by the ‘Serin’ and ‘OR5630’ parents. Some dwarf lethal interactions may be caused by two recessive genes – one from each gene pool – that interact to kill plants starting in the F2 generation (Kelly, 1988). One would expect a fourth of plants to have died by the end of the process as each allele was fixed in its homozygous state: ¼ AA/BB, ¼ AA/bb, ¼ aa/BB, ¼ aa/bb. The seventeen recorded deaths do not match this ratio (chi square test not shown), but young seedling deaths were not recorded and were quickly replaced by residual seed. The genomic location of the dwarf lethal genes is currently unknown, but segregation distortion for Pv03 and Pv06 suggests that these genes may reside on these chromosomes. The other issue that may be affecting QTL positions is the non-normality of data distributions. KW mapping is robust to non-normal data and provides a check on the results of IM and MQM mapping. Five of the six QTL generated by KW mapping directly overlap with MQM, thus providing some verification of the results of MQM mapping. The sixth QTL generated by KW mapping is ostensibly unique to KW mapping. However, a weak signal is present in the same mapping location as this sixth QTL for both IM and MQM mapping, but this signal is not sufficient to surpass the genome-wide permutation threshold. The reason for this skewing of the distribution may be similar to the dwarf lethal phenomenon: the interaction of two recessive genes for the production of the volatile. For example, the bulk of the data points in Figure 3.1 for linalool are to the left of the histogram with a much smaller number of data points to the right that may represent double recessives. The argument against this interpretation is the very small number of data points in the tail that does not seem to match the ¼ of expected double recessives.

Thirteen non-overlapping, unique QTL positions were identified in this study. Although highly skewed, the data distributions also appear to support quantitative inheritance for these traits (Figures 3.1, 3.3, and 3.5). This represents significant complexity for marker-assisted selection and plant breeding because there are so many targets spread out across the genome. There are also significant uncertainties involved in identifying a marker from a large QTL interval without knowing how tightly linked it may be to the causal genetic variation. There are also many uncertainties in the way in which these volatiles will interact at the sensory level and what constitutes consumer preference in flavor. There may also be different QTL present in different bean lineages that are not represented in this biparental cross. Due to these uncertainties, more research needs to be conducted to validate results in other mapping populations, refine the resolution of mapping, and test their effects in human sensory studies. With these further validations and tests, the effectiveness of plant breeding for flavor in green beans will be greatly accelerated and magnified to the benefit of both producers and consumers.

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Figures

Figure 3.1. Histogram of linalool showing relative peak area measurements on the x-axis and their frequency (number of Figure 3.2. Ordered scatterplot of linalool RILs) on the y-axis showing individual bean lines on the x-axis and relative peak area measurements on the y- axis.

Figure 3.3. Histogram of 1-octen-3-ol showing relative peak area measurements on the x-axis and their frequency (number Figure 3.4. Ordered scatterplot of 1-octen-3-ol of RILs) on the y-axis showing individual bean lines on the x-axis and relative peak area measurements on the y- axis.

Figure 3.5. Histogram of 3-hexen-1-ol showing relative peak Figure 3.6. Ordered scatterplot of 3-hexen- area measurements on the x-axis and their frequency (number of 1-ol showing individual bean lines the x-axis RILs) on the y-axis and relative peak area measurements on the y-axis.

Pv01 Pv02

Pv03 Pv04

Pv05 Pv06

Pv07 Pv08

Pv09 Pv1 0

Pv11

Figures 3.7. Charts of linkage maps and associated QTL. Chromosomes are indicated with Pv01 through Pv11. Each linkage map is shown as a bar with cM positions shown to the left and marker names shown to the right. A dashed blue line indicates a presumed connection between linkage groups based on inferences from the physical map. On the far right of each linkage map are QTL positions generated by MQM mapping. (LIN8.3KW generated from Kruskal-Wallis mapping was not included.) Boxes indicate markers or pseudo-markers with a LOD score whose associated P value is below the α = 0.01 threshold and stems indicate markers or pseudo-markers with a LOD score whose associated P value is below the α = 0.05 threshold. The P values are determined by a permutation test and cutoffs are determined by genome-wide thresholds. Figures generated using MapChart (2.3).

Tables

Table 3.1. Kendall’s ranked correlation between linalool, 1-octen-3-ol, and 3-hexen-1-ol. Correlation coeff. P value Linalool to 1-octen-3-ol 0.200 0.026 Linalool to 3-hexen-1-ol -0.024 0.793 1-octen-3-ol to 3-hexen-1-ol -0.136 0.126

Table 3.2. Comparative linkage groups. Linkage groups designated with an “a” and “b” with a plus sign indicates the combined values for both linkage groups that together represent one chromosome. Length cM shows the linkage map length in centiMorgans and the length bp shows the length in base pairs of the interval between the outer most two markers of a linkage group using the Phytozome genome (Phytozome 12.0, Phaseolus vulgaris, version 2.1). The Freyre map indicates the published values for the standard linkage map found in Freyre et al, 1998. Linkage group Chrom. Length (cM) Freyre map (cM) Length (bp) group 1a + 1b 1 78.7 107.0 10,922,247 group 2 2 90.3 175.0 47,829,726 group 3a + 3b 3 49.6 132.0 40,911,185 group 4 4 75.3 95.0 46,472,060 group 5 5 50.7 72.0 36,242,393 group 6 6 33.4 113.0 6,403,609 group 7 7 82.6 109.0 38,132,427 group 8 8 101.5 133.0 62,717,059 group 9 9 64.8 105.0 26,134,826 group 10a + 10b 10 35.5 89.0 39,808,753 group 11 11 78.1 100.0 53,170,406 all linkage groups All 740.3 1,230.0 408,744,691

Table 3.3. QTL generated by Interval Mapping (IM). Trait indicates the type of volatile measurements used as a phenotype. QTL name indicates a name assigned to a QTL. Chrom. indicates the chromosome to which a QTL and linkage group belong based on marker comparisons to the physical map found on Phytozome (Phytozome version 12, Phaseolus vulgaris, 2.1). Group indicates the linkage groups generated in this study. QTL interval specifies the consecutive markers or pseudomarkers with a LOD score P value below the genome-wide 0.05 α value as generated in a permutation test. Genome-wide LOD thresholds as generated in a permutation test are 2.5, 2.5, and 2.7 for linalool, 1-octen-3-ol, and 3-hexen-1-ol, respectively. LOD, additive effect, and percent variation explained are the values for the marker with the highest single LOD in the entire interval. Trait QTL name Chrom. Group QTL interval (cM) LOD Additive effect % Expl. Linalool LIN8.1 8 8 9.86 - 13.91 2.94 -14,601.0 20.2 Linalool LIN8.2 8 8 20.75 - 33.10 3.01 -14,575.8 20.6 1-octen-3-ol OCT6.1 6 6 10.42 - 20.57 3.06 -13,739.4 20.9 3-hexen-1-ol HEX10.1 10 10b 9.88 2.85 -55,324.3 19.7

Table 3.4. QTL generated by Kruskal-Wallis Mapping (KW). Trait indicates the type of volatile measurements used as a phenotype. QTL name indicates a name assigned to a QTL. Chrom. indicates the chromosome to which a QTL and linkage group belong based on marker comparisons to the physical map found on Phytozome (Phytozome version 12, Phaseolus vulgaris, 2.1). Group indicates the linkage groups generated in this study. QTL interval specifies the consecutive markers or pseudomarkers with a P value below a 0.001 α value. QTL Interval Trait QTL Name Chrom. Group K Value (cM) Linalool LIN5.1 5 5 0.00 - 17.10 19.44 Linalool LIN6.1 6 6 22.32 9.53 Linalool LIN8.1 & LIN8.2 8 8 9.91 - 35.87 15.65 Linalool LIN8.3KW 8 8 55.75 - 59.13 8.82 1-octen-3-ol OCT6.1 6 6 7.68 - 23.44 13.58 3-hexen-1-ol HEX10.1 10 10b 3.03 - 9.88 11.05

Table 3.5. QTL generated by Multiple-QTL Mapping (MQM). Trait indicates the type of volatile measurements used as a phenotype. QTL name indicates a name assigned to a QTL. Chrom. indicates the chromosome to which a QTL and linkage group belong based on marker comparisons to the physical map found on Phytozome (Phytozome version 12, Phaseolus vulgaris, 2.1). Group indicates the linkage groups generated in this study. QTL interval specifies the consecutive markers or pseudomarkers with a LOD score P value below the genome-wide 0.05 α value as generated in a permutation test. Genome-wide LOD thresholds as generated in a permutation test are 2.5, 2.5, and 2.7 for linalool, 1-octen-3-ol, and 3-hexen-1-ol, respectively. LOD, additive effect, and percent variation explained are the values for the marker with the highest single LOD in the entire interval.

Trait QTL name Chrom. Group QTL interval (cM) LOD Additive effect % Expl. Linalool LIN1.1 1 1b 31.05 - 33.15 8.19 57,079.2 21.2 Linalool LIN5.1 5 5 2.02 - 5.39 3.97 -9,641.3 8.6 Linalool LIN6.1 6 6 19.57 - 23.32 4.37 -9,876.4 9.7 Linalool LIN8.1 8 8 9.86 - 11.91 2.83 -11,526.0 4.7 Linalool LIN8.2 8 8 21.57 - 25.94 7.13 -12,981.8 17.3 Linalool LIN9.1 9 9 10.12 - 10.31 3.37 9,031.0 7.2 1-octen-3-ol OCT3.1 3 3b 9.52 - 21.80 3.02 10,139.1 10.5 1-octen-3-ol OCT6.1 6 6 10.42 - 21.57 4.40 -11,395.9 16.3 1-octen-3-ol OCT11.1 11 11 55.64 - 56.82 3.07 -8,960.4 10.8 3-hexen-1-ol HEX4.1 4 4 16.25 - 17.25 2.70 47,018.9 11.4 3-hexen-1-ol HEX7.1 7 7 28.46 2.82 -44,712.4 12.0 3-hexen-1-ol HEX10.1 10 10b 9.51 - 9.88 4.72 -59,287.9 22.0

Table 3.6. QTL relative to physical position of candidate genes. Shown is a comparison between the approximate physical positions of the QTL in this study and the physical positions of annotated gene models in Phytozome 12.1 (Phaseolus vulgaris, 2.1). The first three columns identify the QTL and its chromosomal location and rough physical map coordinates in mega base pairs (Mb). These coordinates were generated by identifying the SNP markers within or flanking very near to a QTL that came closest to spanning the interval of the QTL. If only one SNP marker was present within the QTL interval and no closely flanking SNP markers could be identified, then the physical map position of this one marker was given. The gene models for lipoxygenase, alcohol dehydrogenase, and linalool synthase are shown as the identifier found in Phytozome 12.1. Position indicates the chromosome first followed by the mega base pair (Mb) position after the colon for each gene model. Hydroperoxide lyase was not included because no QTL near to the hydroperoxide lyase gene was found. Approximate QTL Alcohol Chrom. physical Lipoxygenase Position Position Linalool synthase Position name dehydrogenase location (Mb) LIN1.1 1 49.30-49.51 LIN5.1 5 7.22-7.63 LIN6.1 6 28.27-29.05 Phvul.006G195700 6:29.4 LIN8.1 8 1.13 LIN8.2 8 3.09 - 3.14 LIN9.1 9 18.16-18.52 OCT3.1 3 32.92-41.44 Phvul.006G156300, 6:26.1, OCT6.1 6 26.60-28.27 Phvul.006G200600 6:29.7 Phvul.006G185300 6:28.6 OCT11.1 11 48.46-48.70 Phvul.011G215000 11:53.4 HEX4.1 4 3.48-4.46 HEX7.1 7 26.19 Phvul.007G179600 7:30.0 Phvul.007G154200 7:26.1 HEX10.1 10 44.01-44.09 Phvul.010G135900 10:41.9

ASSOCIATION MAPPING OF VOLATILE FLAVOR COMPOUNDS IN SNAP BEANS

Introduction

Plants produce a myriad of volatile secondary metabolites, and snap beans (Phaseolus vulgaris L.), in particular, produce at least a hundred volatile compounds in their pods (Barra et al., 2007). Although the functions of these compounds are not always known, it has been shown that plant volatiles have important roles in attracting beneficial insects, repelling pest insects, directly killing pathological microorganisms, priming defensive responses, attracting pollinators or attracting seed dispersers (Noordermeer et al., 2001; Matsui, 2006; Takabayashi and Dicke, 1996; Kunishima et al., 2016; Frost et al., 2008). Volatiles also serve as the primary source of flavor in most fruits and vegetables. To sense these flavors, volatilized compounds from our foods pass through the back of the mouth into the nasal passages for sensing through retronasal olfaction. This retronasal olfaction allows humans to experience thousands of different compounds or distinct mixtures of compounds, thus allowing for much of the variety and enjoyment of food (Bojanowski & Hummel, 2012). Flavor volatiles are derived primarily from four biosynthetic pathways in plants (Dudareva et al., 2013). These four pathways include the pathways for fatty acids, terpenoids, phenylpropanoids (phenylalanine derived), and branched chain amino acids. The fatty acid pathway begins with acetyl CoA and proceeds through malonyl CoA, the FAS complex, palmitic acid, stearic acid, and oleic acid (Clemente & Cahoon, 2009; Aghoram et al., 2006). Oleic acid is converted in the plastids to linoleic acid and linolenic acid through the action of desaturases (Clemente & Cahoon, 2009). Linoleic and linolenic acids are, in turn, converted to volatile compounds important to flavor through the action of a lipoxygenase followed by a hydroperoxide lyase (Noordermeer et al., 2001). The last steps depend on which volatile is being produced, but it typically involves an isomerase and/or alcohol dehydrogenase (Noordermeer et al., 2001; Lumen et al, 1978). The fatty acid pathway leads to the majority of flavor volatiles in snap beans, such as 1-octen-3-ol, 1-penten-3-one, 1-penten-3-ol, hexanal, 1- hexanol, 2-hexenal, and 3-hexen-1-ol (Lumen et al., 1978). The terpenoid pathway leading to

monoterpenes and carotenoids begins with pyruvate and glyceraldehyde-3-P and progresses through several steps leading to isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) (Dudareva et al., 2013; Dubey et al., 2003). IPP and DMAPP are believed to be interchangeable through an isomerase and are the substrates for geranyl diphosphate synthase to produce geranyl diphosphate (GPP). GPP can, in turn, be used to produce linalool through the action of linalool synthase. Alternatively, the IPP and DMAPP can be substrates for a synthase of geranylgeranyl diphosphate (GGPP). Several more enzymatic steps lead from GGPP to carotenoids and β-carotene. The break-down of β-carotene by the carotenoid cleavage dioxygenase I (CCD1) enzyme results in β-ionone (Wei et al., 2011). Both linalool and β-ionone are known to be present in snap bean pods (Barra et al., 2007). Finally, the phenylpropanoid pathway and the pathway for branched chain amino acids, although of apparently lesser significance in green bean flavor, nevertheless generates numerous volatile compounds in addition to array of other compounds, such as flavonoids, lignans, esters, coumarins, and stilbenes (Dudareva et al, 2013; Vogt, 2010). Chemical analysis of snap beans using gas chromatography – mass spectrometry (GC- MS) has found many tens of volatile compounds in snap bean pods (MacLeod & MacLeod, 1970; Van Ruth et al., 1995a; De Quirós, 2000; Barra et al., 2007). In particular, the study by Barra and colleagues identified 104 volatiles in green beans, although it is not known how many of these compounds were at a sufficient concentration to be detectable by olfaction or how they may interact with other volatile compounds in olfaction. They also did not attempt to analyze variation within bean lines for these compounds, or the genetic and biochemical basis of variation. Research into the chemistry of volatiles in green beans leaves untouched the broader context and underlying mechanisms. Research into the relevance of volatiles to green bean flavor has been analyzed using several commercial snap beans that can be categorized under the rubric of Blue Lake, which share a common ancestry and are considered to be superior snap beans in both flavor and texture, and were the basis of the pole snap bean processing industry in Oregon in the 1960’s and 1970’s as well as the contemporary bush snap bean industry in Oregon (Baggett & Lucas, 2005). This research into the relevance of volatile compounds identified 1-octen-3-ol, linalool,

and 3-hexen-1-ol as being likely important to green bean flavor in general based on a sniff test of gas chromatography effluent and based on reconstituted flavor in a bland bean (Stevens et al., 1967a; Toya et al., 1976). These researchers attempted to recreate the characteristic flavor of Blue Lake beans by adding pure compounds to the canned pods of an especially bland bean variety (Stevens et al., 1967b). They found that it was necessary to add both 1-octen-3-ol and 3- hexen-1-ol (0.4ppm and 1.6ppm respectively) to the bland bean in order to closely approximate the flavor of Blue Lake beans. After investigating the relevance of volatile compounds, these researchers further analyzed the underlying genetic mechanisms. They focused on linalool and 1-octen-3-ol because the small number of genotypes utilized in their research did not vary for 3-hexen-1-ol concentrations. Their research showed that linalool and 1-octen-3-ol levels are heritable and that these variable levels appeared to be attributable to only a small number of genetic loci, although they did not attempt to map the loci and these papers disagreed as to how many loci were present (Stevens et al., 1967b; Toya et al., 1976). The research into the genetics underlying the production of volatiles in green beans was stymied by the lack of molecular markers and insufficient tools available to researchers at that time. This changed dramatically when the genome sequence of common bean (Phaseolus vulgaris L.) was published in 2014 by Schmutz et al. With the advent of the genomic era in beans, new molecular markers became available for all types of research, and genome wide association study (GWAS) mapping was possible for the first time. GWAS, in particular, has many advantages in furthering progress in the understanding of the genetic basis of any trait in common bean. Previous to this, linkage mapping and QTL mapping had been the standard starting in the late 1980’s, but GWAS mapping allows much higher resolution mapping than linkage mapping and QTL analysis because it relies upon recombination events that have taken place over many generations and long periods of time whereas linkage mapping and QTL analysis relies upon the recombination events generated in a limited biparental population (Korte & Farlow, 2013; Huang & Han, 2014). This higher resolution can assist in mapping more discrete loci and help refine the candidate gene search to a smaller interval. This advantage comes at the cost of spurious associations generated by population structure and kinship. To compensate for the inherent population structure and kinship, a principal component analysis,

STRUCTURE analysis (Porras-Hurtado et al., 2013), and/or kinship analysis are done in some GWAS models. One such model that addresses all these issues is the Mixed Linear Model (MLM), which can control for both kinship and population structure simultaneously. The MLM reduces type 1 statistical errors but it tends to inflate type 2 statistical errors (Liu et al., 2016). Work on the methodology and modeling of GWAS has resulted in The Fixed and Random Model Circulating Probability Unification (FarmCPU) method in which a fixed model controls false positives (type 1 statistical errors) but a random model containing kinship is run to control confounding for complex traits, and these two models run iteratively until the number of estimated pseudo Quantitative Trait Nucleotides (QTNs) stabilizes (Liu et al., 2016). FarmCPU was tested on published data sets with known genes from Arabidopsis, maize, pig, mouse, and human, and FarmCPU had superior power to detect real associations over other commonly used models, and it did so without inflating type 1 errors or type 2 errors (Liu et al., 2016). On both real and simulated data, FarmCPU performed better at detecting QTNs for complex traits or traits with low heritability (Liu et al, 2016). Using GWAS, it is possible to identify loci and candidate genes in a way that was not possible before. The results of GWAS can readily be converted to inexpensive molecular markers, such as sequence specific PCR (Welsh & Bunce, 1999), and then be utilized in marker- assisted selection (MAS) to identify bean lines containing preferable alleles. It would also be useful to know how many loci are present to determine how many progeny to examine and gauge the complexity of the breeding task at hand. Breeding design could also be helped by knowledge of a candidate gene and its mechanism of action. As exciting as these possibilities are, the flavor of green beans, or any vegetable, involves complicated interactions between different volatiles, which makes the task of breeding challenging. Understandably, plant breeders have focused on other traits with simpler genetics and a more immediate impact on the bottom line. But a new set of tools and a deeper understanding of the genetics could allow plant breeders to give higher priority to flavor and other quality characteristics in relation to yield, appearance, and post-harvest issues (Kader, 2008). The objective of this research is to use GWAS analysis of a diverse snap bean population to identify SNPs associated with the production of linalool, 1-octen-3-ol, 3-hexen-1-ol, hexanal,

2-hexenal, 1-hexanol, 1-penten-3-ol, 1-penten-3-one and β ionone in snap beans. It is hoped that this will help clarify the genetic structure that underlies these volatiles and thus assist plant breeding through a better understanding of the genes involved. It is hoped that marker-assisted selection and the identification of genetic resources will be facilitated through the identification of associated SNPs.

Materials and methods

Plant material and growth conditions Most of the genotypes used in this study were from the Common Bean Coordinated Agricultural Project (BeanCAP), which was a USDA-NIFA funded CAP to genotype and phenotype dry and snap bean diversity panels. The BeanCAP population consists of 150 genotypes of which 142 were used in this study. Three genotypes from the BeanCAP dry bean Mesoamerican and Andean diversity panels were added: ‘Montcalm’, ‘Olathe’, and ‘Seafarer’. In addition, 56 genotypes from an uncatalogued set of accessions collected by Michael Dickson (Emeritus, Cornell Univ., Ithaca, NY) in China in 1991 were included in the study. In total, 201 genotypes were utilized (Table 4.4). In 2013, Plants were grown in unreplicated plots at the Oregon State University Vegetable Research Farm in Corvallis, Oregon. This farm is on fine-silty Chehalis soil and is located approximately 68 meters above sea level at latitude 44°34’25.93”N and longitude 123°14’12.37”W. Overhead irrigation delivered 2.5cm to 5.0cm (one to two inches) of water weekly as needed. Pelleted fertilizer was banded beneath the row just prior to planting at the rate of 22.7 kg (50lbs) of nitrogen per acre. Planting was done on July 2 using a manual belt- planter; seeds were treated with Captan fungicide and planted to a depth of approximately 5.0cm (two inches) in three meter (ten foot) plots at a rate of 60 seeds per plot. Rows were 30 inches apart for bush types and 60 inches apart for pole types. Pole types were trellised on a metal wire approximately 6 feet above the ground; bush types were unsupported. The picking time was varied to match the differing maturity dates of plots. Several representative pods

from across the plot were picked and transported in a cooler to a freezer where they were frozen at -20°C.

Sample preparation The frozen bean pods for GC-MS were ground into a fine powder with liquid nitrogen inside a specially modified steel Warring blender. The top of the blender had a long metal tube welded to the top to allow gases from the liquid nitrogen to vent while still maintaining most of the liquid inside the blender. The slurry was allowed to boil off most of the liquid nitrogen within a plastic bag and then it was poured or tapped into a 40 ml amber vial with a PTFE liner (Supelco/Sigma-Aldrich, Bellefonte, PA, U.S.A.). This was frozen at -20°C until GC-MS analysis could be performed. Genomic DNA was extracted in two groups using a modified CTAB procedure. Approximately 0.5g of material from young trifoliate leaves taken early in the season were ground in 500µl of CTAB buffer and then incubated for 1 hour at 65°. This was extracted with 500µl of chloroform. The supernatant was precipitated with 400µl of 76% ethanol and 10% ammonium acetate. The pellets were dried and resuspended in 200µl of TE buffer. The DNA was then treated with 8µg of RNase A for 1 to 2 hours at 37°. This was extracted with 300µl of chloroform. The supernatant was precipitated with 15µl of 3M sodium acetate (pH 5.2) and 300µl of 95% ethanol. The resulting pellet was washed with 400µl of 70% ethanol. The pellet was air dried and resuspended in 50µl of TE buffer. The quality of the DNA was checked by running 1µg of each sample on an agarose gel. Concentrations were determined by nanodrop (ND-1000 UV-Vis Spectrophotometer). Genomic DNA for the first group including ‘Acclaim’, ‘BBL274’, ‘Benchmark’, ‘Booster’, ‘Calgreen’, ‘Castano’, ‘Coloma’, ‘Cyclone’, ‘Flavor Sweet’, ‘Fortex’, ‘Kentucky Wonder’, and ‘Mercury’ plus the Dickson collection genotypes was extracted in 2013. All other BeanCAP lines were extracted previously in 2009 using the same procedure.

Genotyping DNA samples were shipped to the USDA Soybean Genomics and Improvement Laboratory (Beltsville, MD) where they were analyzed using the Illumina Infinium Genechip

BARCBEAN6K_3 platform. This Genechip contains 5,398 SNP positions. The raw data was manually processed on GenomeStudio software (Illumina). Missing data was imputed using FastPhase software. Heterozygous positions were treated as missing data and imputed. SNPs with greater than 50% missing data were filtered and removed from the study, which resulted in two SNPs being removed. SNPs not assigned to a chromosome position were also removed. The final number of SNPs actually used in the study was 5,317. After MAF filtering, 5,317 SNPs was further reduced to 4,540 SNPs.

GC-MS conditions GC-MS was conducted on a Shimadzu GCMS-QP2010 Ultra instruments with an attached Shimadzu AOC-5000 Plus auto sampler and chiller (Shimadzu Corp., Kyoto, Japan). The carrier gas was helium. The column was a 30 meter Stabilwax column with a 0.25mm internal diameter (Restek, Bellefonte, PA, U.S.A.). The solid-phase microextraction (SPME) fiber was a 50/30 μm Divinylbenzene/Carboxen/ Polydimethylsiloxane with a 24 gauge needle size (Supelco/Sigma- Aldrich, Bellefonte, PA, U.S.U.). Vials for the autosampler consisted of Restek 20ml amber SPME vials with an 18mm orifice and magnetic screw-thread caps (Bellefonte, PA, U.S.A.) The GC parameters included a column oven temperature of 35°C, an injection temperature of 250°C, a pressure of 40 kpa, a total flow of 1.9 mL/min, a column flow of 0.45ml/min, a linear velocity of 121 cm/sec, and a purge flow of 1.0 mL/min. The injection mode was split with a ratio of 1 and the flow control mode was pressure. The column oven temperature was set to 35°C with a hold time of 10 minutes followed by a 4°C/min increase to a final temperature of 200°C with a hold time of 2 minutes, then an additional ramp of 10°C/min to a final temperature of 250°C for 5 minutes. The MS parameters were set to an ion surface temperature of 200°C, an interface temperature of 250°C, an absolute detector voltage of 1k V, a solvent cut time of 3 minutes, a microscan width of 0, a microscan threshold of 200 u, and a GC program time of 61.25 minutes. The scan mode parameters were set to a start time of 3 minutes, and an end time of 60 minutes with an event time of 0.22, a scan speed of 1,428, and a starting and ending m/z of 33 to 330. The Combi Pal method consisted of pre-incubation for 10 minutes at 35° C with agitation, vial penetration to 51mm, extraction for 40 minutes at 35° with no agitation,

injection penetration to 54mm with desorption for 10 minutes. The Combi Pal agitation was on for 5 seconds and off for 2 seconds. There was a post-fiber-condition time of 10 minutes. The green bean samples were randomly selected and thawed in groups of 30 to fill the chilled autosampler. One gram of material was weighed into a SPME vial and 1µg of deuterated linalool was added as an internal standard. The vial was capped and placed in the auto sampler. Samples were run continuously in a dedicated fashion from November 5, 2014 to November 18, 2014.

GC-MS analysis GC-MS data was analyzed using Shimadzu GCMS Postrun Analysis software and OpenChrom community edition software version 1.1.0.201607311225. A NIST/EPA/NIH Mass Spectral Database (NIST 11) was integrated into the Shimadzu software, which allowed for the identification of mass spectrometry fragment patterns. All compounds mapped had been positively identified in green beans in previously published research and peak identification with the NIST11 library was in most cases a better than 95% match (Stevens et al., 1967b; Toya et al., 1976; De Lumen et al., 1978; De Quirós et al., 2000; Barra et al., 2007). Nine volatiles were chosen for analysis based on (1) their importance to past research in flavor research in beans or (2) their presence in the biochemical pathway in snap beans proposed by de Lumen et al. (1978) or (3) their novel organoleptic quality. These volatiles were linalool, 1-octen-3-ol, hexanal, 1-hexanol, 2-hexenal, 3-hexen-1-ol, 1-penten-3-ol, 1-penten-3-one, and β-ionone. (The corresponding International Union of Pure and Applied Chemistry (IUPAC) nomenclature is 3,7- Dimethylocta-1,6-dien-3-ol, Oct-1-en-3-ol, hexanal, hexan-1-ol, (E)-hex-2-enal, (Z)-hex-3-en-1- ol, pent-1-en-3-ol, pent-1-en-3-one, and (E)-4-(2,6,6-trimethylcyclohexen-1-yl)but-3-en-2-one, respectively). Peak area for phenotyping the bean cultivars was determined using OpenChrom software (version 1.1.0.201607311225). Peaks were detected using the first derivative peak detector, and peak area was determined using the trapezoid peak integrator. In a few cases, manual peak detection was necessary, but the trapezoid peak integrator was still used. For hexanal, 1-hexanol, and 2-hexenal, the peak areas were very large, and OpenChrom would not

calculate a peak area. To correct this, peak areas for these three compounds were calculated in Shimadzu GCMS Postrun Analysis software version 4.20 using the Peak Integrate TIC option for all groups.

GWAS and statistical analysis Most statistical analysis and GWAS analysis were performed in R statistical software, version 3.3.2, (https://www.r-project.org/) using base R functions or R packages, with data formatted in Microsoft Excel 2016 ( https://office.microsoft.com/excel/) or TextPad version 7.5.1 (www.textpad.com). The visualization of the principal component analysis was done in XLSTAT (Addinsoft, NY, New York, version 19.7.48771). The discriminate principal component analysis with two clusters was done using the adegenet R package (Jombart, 2008). Mean values were calculated for these pools for each compound and the data was visualized as histograms. Homogeneity of variances (Fligner-Killeen test) and normality (Shapiro-Wilk test) were tested in addition to the histograms to determine the need for transforming the data or for non-parametric tests. In some cases, the data met all assumptions of a t-test, and a 2-tailed t-test was performed comparing the mean values of Mesoamerican vs. Andean lines. In other cases, data was log transformed and if necessary, a non-parametric Mann-Whitney test was conducted. All visualizations, transformations, and analysis were performed using base R functions. To adjust for population structure, principal component analysis was performed on the unfiltered SNP data using the adegenet R package (Jombart, 2008). The first axis accounted for 35.7% of the variation and the first three axes together accounted for nearly half the variation. Four models were tested: no principal component (PC), 1 PC, 2 PC, and 3 PC. GWAS was performed in both Tassel 5.2.24 (Bradbury et al., 2007) using MLM and in FarmCPU using the iterative fixed and random model (Liu et al., 2016). In both Tassel and FarmCPU, 1 PC usually resulted in the best QQ plot. A 1 PC model also closely corresponds to the split between the centers of domestication. In no case did a 2 PC or 3 PC produce a tighter fit on the QQ plots, although no PC axis did sometimes appear to be slightly better than 1 PC. Out of an abundance of caution and due to the generally superior QQ plots and the biological basis of two centers of

domestication, 1 PC was chosen as the method of population structure adjustment for the remainder of the study. Association tests assume normality in the data distribution and transforming data to improve normality can improve the sensitivity of GWAS (Goh and Yap, 2010). To this end, histogram visualizations of the data and Shapiro-Wilke normality tests were done. With the exception of hexanal, all histograms were right skewed to some degree. Log transformation significantly improved normality for 1-octen-3-ol and 3-hexen-1-ol as measured by a Shapiro- Wilke normality test, and slightly improved normality for 1-penten-3-one and linalool. For these volatiles, GWAS was performed on both untransformed and transformed data sets. All log transformations, visualizations, and tests were performed using base R functions. Manhattan plot cutoffs were generated using both α = 0.05 Bonferroni cutoff and α = 0.05 Bonferroni cutoff based on effective marker numbers. Unmodified Bonferroni cutoffs are not justified in GWAS studies because tests (i.e. SNPs) are not truly independent (Gao et al., 2010). To correct for these inherent correlations between tests, effective marker numbers can be calculated. The SimpleM method for deriving effective marker numbers has been shown to be highly comparable to a permutation test in terms of the cutoff generated, and permutation tests are considered to be the best method available for deriving a cutoff in human GWAS studies (Gao et al., 2010). To this end, the SimpleM method was used to calculate effective marker numbers. This changed the marker number from 5,317 total markers to 1,363 effective markers. Two lines were generated for all Manhattan plots showing these two cutoffs. The SNP positions and chromosomes used for generating the Manhattan plots were derived from version 1.0 of the Phaseolus vulgaris genome, but all other analysis were done in version 2.1. Due to the aforementioned virtues of FarmCPU, it was chosen as the primary model for GWAS with an added covariate of 1 PC. Analysis was performed in R using the FarmCPU source code provided by Liu et al. (2016). A minor allele frequency (MAF) of 0.05 was used, which reduced the SNP number to 4,540. An additional line of code not shown in the manual was used to generate a complete list of SNPs in the results document: threshold.output=1. A Bonferroni cutoff at α = 0.05 using all markers and a Bonferroni cutoff at α = 0.05 using the effective marker number were generated using the following code: cutoff=c(0.05,

0.05*4,540/1,363). The negative log value cutoffs on the Manhattan plots were 4.958 and 4.435 respectively. Some models were also tested using a MLM in Tassel, version 5.2.24. These Tassel analyses used one or more PC and included a centered Identity By State (IBS) kinship generated by Tassel and 0.05 MAF filtering.

Candidate gene search Using the BLAST and Genome Browser tools of Phytozome12 (Phaseolus vulgaris, version 2.1), the proximity of local genes was determined. A 50Kbp flanking sequence was examined on either side of each significantly associated SNP (i.e. a 100Kbp window). Structural genes relating to the fatty acid pathway and terpenoid pathway were identified using a keyword search and their proximity to significantly associated SNPs was gauged. A keyword search in Phytozome12 for “lipoxygenase”, “hydroperoxide lyase”, and “alcohol dehydrogenase” resulted in 65 matches across the genome for “lipoxygenase” and “alcohol dehydrogenase”, although there was only a single match on chromosome 5 for “hydroperoxide lyase”. A similar search for “carotenoid cleavage dioxygenase”, “linalool synthase”, “geranyl diphosphate synthase”, and “geranyl pyrophosphate synthase” in the terpenoid pathway resulted in 8 matches across Pv2, Pv3, Pv6, Pv9, and Pv11.

Results

Population structure Using a discriminate principal component analysis with two clusters in the adegenet R package (Jombart, 2008), it is possible to divide the 201 genotypes into 62 predominantly Mesoamerican genotypes and 139 predominantly Andean genotypes as shown in Table 4.4. A major component of the Mesoamerican gene pool in our GWAS population was composed of the Dickson collection with 77% (48 genotypes) of all Mesoamerican types in our population coming from this collection. Considering that the first principal component was essentially a division between Mesoamerican and Andean types (Figure 4.1), we chose to more carefully examine the differences between these two pools. Shown in Table 4.1 are comparisons of the

means between Mesoamerican and Andean types for each volatile. Tests of homogeneity of variances and normality were performed to determine which test to perform and whether or not to transform the data. The results were highly significant for most volatiles. The Mesoamerican pool has statistically significant higher mean values for 1-octen-3-ol, 1-hexanol, 1-penten-3-ol, and 1- penten-3-one but statistically significant lower mean values for 3-hexen-1-ol, and β-ionone. The mean values for linalool, hexanal, and 2-hexenal were not significantly different between centers of domestication.

GC-MS The resulting GC-MS peak area measurements are shown in Table 4.4. The fold changes calculated by dividing the largest peak area value by the smallest peak area value for linalool, 1- octen-3-ol, hexanal, 1-hexanol, 2-hexenal, 3-hexen-1-ol, 1-penten-3-one, and β-ionone were, respectively: 47-fold, 129-fold, 244-fold, 22-fold, 12-fold, 24-fold, 59-fold, and 21-fold. Excluding zero values, a similar calculation for 1-penten-3-ol was 8-fold.

GWAS GWAS analysis using FarmCPU generated 24 significant associations spread out over eight chromosomes for linalool, 1-octen-3-ol, 1-hexanol, 1-penten-3-ol, 1-penten-3-one, and β- ionone (Table 4.2). 1-hexanol had three associations on Pv11, Pv8, and Pv5. 1-octen-3-ol had three associations on Pv2 (position 19.7Mb), Pv7, and Pv2 (position 47.4Mb). 1-penten-3-ol had two associations on Pv3 (position 44.2Mb), and Pv3 (position 32.9Mb). 1-penten-3-one had eight associations on Pv7 (position 24.6Mb), Pv7 (position 1.0Mb), Pv7 (position 37.3Mb), Pv10 (position 40.8Mb), Pv10 (position 34.0Mb), Pv2, Pv5, and Pv9. Β-ionone had 2 associations on Pv2 and Pv7. Linalool had 6 associations on Pv2, Pv5, Pv6 (position 25.5Mb), Pv6 (position 25.2Mb), Pv7 (position 32.6Mb), and Pv7 (position 9.9Mb). The Manhattan plots and Quantile- Quantile (QQ) plots are shown in Figures 4.2 to 4.9 and Figures 4.11 to 4.12. The GWAS of 3- hexen-1-ol, hexanal and 2-hexenal did not generate any significant associations, although one

SNP for hexanal was included in Table 4.2 because it is the only shared SNP between volatiles (1-hexanol) and it was very close to the Bonferroni threshold of 4.435 negative log. GWAS was also performed on log transformed data where appropriate. Histograms were generated for all data sets and tests of normality were performed to determine if transformation might be beneficial. The normality of the data was improved for 1-octen-3-ol and 3-hexen-1-ol. A small, improvement was discernable for 1-penten-3-one and linalool but this did not alter conclusions obtained from untransformed data. The QQ plots for 1-octen-3-ol, 1-penten-3-one, and linalool demonstrated a poor model fit when they were transformed as defined by the tightness of fit of the points on the QQ plot around the line of equality. Only 3- hexen-1-ol had both improved normality and an improved QQ plot when the data was log transformed. FarmCPU GWAS of the log transformed 3-hexen-1-ol data resulted in 3 significant associations shown in Table 4.3. Comparisons of the untransformed and transformed data for 3-hexen-1-ol are shown in Figures 4.10 to 4.12. An MLM model GWAS with 1 PC and 0.05 MAF filtering using an IBS kinship performed in Tassel did not generate a single SNP that was above the effective marker number Bonferroni threshold for any volatile. Nevertheless, SNP ss715649798 for 1-octen-3-ol came very close to the Bonferroni threshold, and would be significant by less stringent standards, such as the threshold described by Vuong and colleagues (2015). This SNP was the best association as well for 1-octen-3-ol using FarmCPU.

Candidate gene search By comparing the locations of structural genes with the locations of the associated SNPs, a cluster of three duplicated alcohol dehydrogenase genes (Phvul.008G207700, Phvul.008G207800, and Phvul.008G207900) that were separated by only 4,103bp and 8,629bp, respectively, were consistently near to significantly associated SNPs for 3-hexen-1-ol, 1- hexanol, and hexanal. The location of these dehydrogenase genes is from 55.55Mb to 55.57Mb on chromosome 8. The only shared SNP between any two volatiles was between hexanal and 1-hexanol, namely, position 54,970,429bp on chromosome 8 for SNP ss715639302. The distance between

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the alcohol dehydrogenase gene cluster and this SNP is 0.58Mb. Similarly, the best SNP for 3- hexen-1-ol was ss715645122 at position 53.77Mb on chromosome 8, which is 1.75Mbp away from the alcohol dehydrogenase cluster. The 1-penten-3-one SNP ss715645462 at position 38.97Mb is within 0.38Mb of a cluster of lipoxygenase gene models: Phvul.005G157000, Phvul.005G156700, Phvul.005G156800, and Phvul.005G156900. There is also a 1-penten-3-one SNP ss715646324 at position 40.77Mb that is within 0.198Mb of a chloroplastic-related lipoxygenase, Phvul.010G128800.

Discussion

Organoleptic qualities The organoleptic qualities of the 9 volatiles analyzed here are complex with numerous descriptors that sometimes appear contradictory. For example, the odor of 1-penten-3-ol is described as both horseradish-like and with tropical fruity nuances in addition to being green vegetable-like (Mosciano, 1991a). 1-penten-3-one, on the other hand, is more consistently spicy with descriptors such as pungent, peppery, garlic, onion, and mustard (Mosciano, 1998). Linalool is overwhelmingly described as floral, but also has descriptors such as aldehydic, rosy, orange finish, and sweet (Mosciano, 2007). Hexanal and 1-hexanol have similar organoleptic qualities and garner descriptors such as green and fruit-like (Mosciano, 1997; Mosciano, 1993a). 2-hexenal has not been formally evaluated for organoleptic qualities but is described elsewhere as “green leaf odor” (Kunishima et al., 2016). 3-hexen-1-ol is similarly described as “a very green odor” (Stevens et al., 1967b). Finally, β-ionone is described as woody, berry-like, fruit-like, green, and floral (Mosciano, 1991b). This complexity should not be surprising considering the importance of the interactions of that volatile with other volatiles and with the food matrix. Moreover, the sensory detection thresholds may or may not have been reached for these green bean volatiles. The organoleptic quality of 1-octen-3-ol is both important and especially complicated. Its typical organoleptic description is mushroom-like, and it has a high potency with a low odor threshold (Mosciano, 1993b). There are two isomers of 1-octen-3-ol with slightly different

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odors, although the clear sensation of either isomer, especially if unmixed with other compounds, is of mushrooms and fungi. Yet this organoleptic quality is greatly changed in green beans when mixed with other compounds, and pure compounds added in large amounts to green beans creates unacceptable off flavors. In particular, 1-octen-3-ol is considered to be one of the key volatiles constituting ‘Blue Lake’ flavor, but it does not impart this characteristic flavor unless it is first mixed in a 1:4 ratio with 3-hexen-1-ol (Stevens et al. 1967b). In the relatively low concentrations found in green beans, 1-octen-3-ol mixes with 3-hexen-1-ol to form an earthy-green aroma: “It is difficult to detect this compound in snap bean aroma, because at low concentrations it has an ‘earthy green’ odor that blends into the overall aroma” (Stevens et al., 1967b). Interestingly, despite the obvious importance of 3-hexen-1-ol in mixtures with 1-octen- 3-ol to create the characteristic ‘Blue Lake’ flavor, Stevens et al. (1967b) and subsequent researchers did not pursue any further study of 3-hexen-1-ol because it did not appear to vary among cultivars based on the limited number of cultivars available for research at that time. It was assumed that only 1-octen-3-ol was a heritable trait that gave identity to the flavor of ‘Blue Lake’ beans. However, in this study, bean cultivars varied 24-fold for 3-hexen-1-ol concentration and three SNP were identified that are significantly associated with the peak area of this compound.

Population structure & plant breeding When examining gene pools for purposes of breeding improved flavor in green beans, it is clear that the Mesoamerican gene pool is a potential source for gene variants related to 1- octen-3-ol production. The mean value of 1-octen-3-ol in the Mesoamerican gene pool was more than double the value of the Andean gene pool and the range of values was also greater. Considering the high proportion of Chinese genotypes from the Dickson collection in the Mesoamerican gene pool, this germplasm collection should be considered a resource for plant breeders. The compound 3-hexen-1-ol, on the other hand, had a slightly higher mean value in the Andean gene pool, although both gene pools show high levels of the compound. Rather than looking to a gene pool, it is more relevant to examine individual cultivars that are high in

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3-hexen-1-ol, such as (in order from highest to lowest), ‘Scorpio’, ‘Brio’, ‘Labrador’, ‘Nomad’, ‘Pole Blue Lake’, ‘Koala’, ‘US Refugee No. 5’, ‘Green Arrow’, ‘Castano’, ‘Normandie’, and ’91- 3110’. A similar look at individual cultivars with the highest levels of 1-octen-3-ol includes only Dickson collection genotypes for the top 12: ’91-3110’, ’91-3008’, ’91-1664’, ’91-1759’, ’91- 2101’, ’91-3389’, ’91-2100’, ’91-2102’, ’91-3013’, ’91-1613’, ’91-3982’, and ’91-1772’. These cultivars, coupled with SNPs associated with the production of these compounds as markers for selection, should form a good foundation for breeding improved flavor in green beans.

GWAS The 27 SNPs that were significantly associated with the peak area of volatiles may be an overly conservative list. The cutoffs on the Manhattan plots can be calculated in different ways that represent a different trade-off between type I statistical errors and type 2 statistical errors. The Bonferroni cutoff is clearly too conservative with a high type 2 statistical error because it assumes that all SNPs are independent, and it fails to account for the correlations among SNPs. The SimpleM method of determining effective marker numbers allows for a Bonferroni test with a more realistic estimate of the number of independent tests. Gao et al. (2010) reported that the SimpleM method closely approximates the cutoff generated by a permutation test. It would appear that the effective marker number generated by SimpleM for a Bonferroni cutoff is the best compromise between avoiding false positives while also avoiding too many false negatives. Nevertheless, there are a number of SNPs that did not make this cutoff, but they were significantly higher in their observed negative log p-value than the majority of SNPs on the Manhattan plot and still probably represent real associations. These associations have a higher chance of type 1 (false positive) statistical errors but are worthy of consideration. It is particularly noteworthy that two SNPs for hexanal, which otherwise had no significant associations, came close to the cutoff and had the appearance of true associations on the Manhattan plot. Indeed, using an alternative bootstrap cutoff at α = 0.05 (Mamidi et al, 2014) results in three significant associations for SNPs ss715639302, ss715647186, and ss715645206 on Pv8, Pv9, and Pv6 respectively for hexanal that are above this bootstrap cutoff. There are other cutoffs that are less restrictive than Bonferroni and that would also result in many more

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significant associations (Vuong et al., 2015; Johnson et al., 2010). Only the GWAS analysis of 2- hexenal appears to have no significant associations using the methods and the population in this study.

Candidate gene search Published measurements of linkage disequilibrium (LD) decay for Phaseolus vulgaris in a diverse population of 188 bean lines has yielded estimates that vary between ~2055Kb for Andean lines and ~312Kb for Mesoamerican lines when LD decayed to half at a threshold of r2=0.23 using the Hill and Weir method (Valdisser et al., 2017). Corrections for population structure and relatedness further reduced these numbers to ~395Kb and ~130Kb, respectively,

2 for the r SV value (Valdisser et al., 2017). In line with these decay rates, a 100Kb window was examined around each significantly associated SNP for potential gene candidates. Many of the gene models within this 100Kb window appeared to have no relationship to volatile production, although a SNP for 1-hexanol was associated with a Hydroxymethylglutaryl-CoA lyase involved in breaking down acetyl-CoA, and two SNPs for 1-octen-3-ol and 1-penten-3-ol are associated with hydrolase family proteins that may be lipase enzymes upon further scrutiny. These structural genes do not have any obvious importance in the pathways immediately preceding the production of volatiles and are only tentatively involved in fatty acid metabolism. In addition to these structural genes, a large number of regulatory genes, such as zinc fingers and MYB transcription factors, are in close association with multiple SNPs as summarized in Table 4.5. These transcription factors may represent better targets for further analysis as candidate genes, although knowing the pathways where these regulatory elements are active is challenging. One of the most promising SNPs is ss715639302 located at 54.97Mb on Pv8. This SNP is associated to both 1-hexanol and hexanal. Five genes are located within a 100Kb window around this SNP: lysosomal carboxypeptidase, hydroxymethylglutaryl-CoA lyase, CLAVATA3/ESR(CLE)-related protein, a protein of unknown function, and a proteasome component. None of these five genes appears to be relevant to volatile production, but a cluster of alcohol dehydrogenase genes exists 0.58Mb downstream. The alcohol

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dehydrogenase gene cluster is located at 55.55Mb to 55.57Mb on the physical map (Phvul.008G207700, Phvul.008G207800, and Phvul.008G207900). This distance appears to be roughly in line with the average LD decay rate shown by Valdisser and colleagues (2017), although local LD decay rates vary across the genome. Interestingly, the SNP effect on 1- hexanol is a -30,205,476 peak area but the SNP effect on hexanal is in the opposite direction at a 41,379,346 peak area. An examination of the biochemical pathway suggests that an alcohol dehydrogenase should convert hexanal to 1-hexanol. If such an alcohol dehydrogenase enzyme failed to catalyze the conversion of hexanal to 1-hexanol due to a mutation that caused a truncated or non-functioning protein, then one would expect a decrease in the amount of 1- hexanol and possibility an accumulation of excess hexanal. This circumstantial evidence supports the notion that the ss715639302 SNP is correlated to a mutation in an alcohol dehydrogenase gene that is preventing the production of 1-hexanol. There is also a SNP (ss715645122) for 3-hexen-1-ol that is located at 53.8Mb on Pv8, which is 1.75Mb upstream to this cluster of alcohol dehydrogenase genes. It too requires an alcohol dehydrogenase gene to convert 3-hexenal to 3-hexen-1-ol in the final step, although it is branching from linolenic acid, not linoleic acid (Chapt. 1). The SNP effect for ss715645122 is a -21,648,793 peak area, which appears to be similar to the negative value for 1-hexanol, although no measurement was taken of the 3-hexenal precursor. These parallels to 1-hexanol and the placement of the SNP near the same alcohol dehydrogenase genes suggests the possibility of a connection between these two pathways assuming that the alcohol dehydrogenase gene cluster really is the causative factor in both. If such a connection exists, it may reflect one alcohol dehydrogenase that is acting in both pathways or two alcohol dehydrogenase enzymes that are acting separately. An alignment of amino acid sequences for the three alcohol dehydrogenase genes shows numerous amino acid differences between them (alignment not shown). As such, the three alcohol dehydrogenase genes may have different functions in different pathways. Two SNPs for 1-penten-3-one are near to lipoxygenase genes. The first SNP, ss715645462, is located at 38.96Mb on Pv5, which is very close to a cluster of four lipoxygenase genes (Phvul.005G156700, Phvul.005G156800, Phvul.005G156900, Phvul.005G157000) located

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at 38.55Mb to 38.58Mb. The distance between the SNP and the lipoxygenase cluster is 0.38Mb. Incidentally, this SNP is also within 3.5Mb to a hydroperoxide lyase (Phvul.005G116800). The second SNP, ss715646324, is located at 40.77Mb on Pv10, which is close to a chloroplastic- related lipoxygenase, Phvul.010G128800, located at 40.97Mb on Pv10. The distance in this case is only 0.2Mb. These findings are consistent with the biosynthetic pathway for fatty acid volatiles in which a lipoxygenase and hydroperoxide lyse generate volatiles directly or precursors to volatiles (Chapter 1). The chloroplastic location of the latter lipoxygenase is consistent with findings in tomato that show a chloroplast specific lipoxygenase as generating important flavor volatiles (Chen et al., 2004). Twenty-seven QTN positions were identified in this study. These SNP positions are potentially valuable as markers for marker-assisted selection because the resolution of GWAS is much higher than linkage mapping and QTL analysis. This higher resolution implies tight linkage to a functional SNP position. Nevertheless, the value of these SNPs depends on the thresholds of detectability by human olfaction and on preference, which could both be addressed by further sensory research. The uncertainties are particularly acute for volatiles that have been little studied in green beans, such as β-ionone.

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Figures

Principal Component Analysis

0 -30 -25 -20 -15 -10 -5 0 5 10 15 20

PC2 (7.7% of variance) of (7.7% PC2 -5

-10

-15 PC1 (35.7% of variance)

Andean Mesoamerican

Figure 4.1. Biplot of first and second axis of the principal component analysis of the SNP data. Shown as red circles are the genotypes identified in discriminate principal component analysis as being of predominantly Mesoamerican origin; shown in blue triangles are genotypes identified as being predominantly Andean in origin.

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Figure 4.2. A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-octen-3-ol. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Bonferroni cutoffs for all markers and effective markers are shown as lines across the Manhattan plot. Shown on the x-axis of the QQ plot (right) are expected negative log p-values and on the y-axis are observed negative log p-values.

Figure 4.3. A Manhattan plot and QQ plot for the FarmCPU GWAS of linalool. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Bonferroni cutoffs for all markers and effective markers are shown as lines across the Manhattan plot. Shown on the x-axis of the QQ plot (right) are expected negative log p-values and on the y-axis are observed negative log p-values. This Manhattan plot contains physical map positions from version 1 of the Phaseolus vulgaris genome, but version 2.1 moves the SNP that was significantly associated with linalool from chromosome 11 to chromosome 6. These changes are shown in Table 4.2 of the results.

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Figure 4.4. A Manhattan plot and QQ plot for the FarmCPU GWAS of hexanal. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Bonferroni cutoffs are not shown because no SNP surpassed either cutoff. Shown on the x-axis of the QQ plot (right) are expected negative log p- values and on the y-axis are observed negative log p-values.

Figure 4.5. A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-hexanol. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Bonferroni cutoffs for all markers and effective markers are shown as lines across the Manhattan plot. Shown on the x-axis of the QQ plot (right) are expected negative log p-values and on the y-axis are observed negative log p-values.

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Figure 4.6. A Manhattan plot and QQ plot for the FarmCPU GWAS of 2-hexenal. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Shown on the x-axis of the QQ plot (right) are expected negative log p-values and on the y-axis are observed negative log p-values. Bonferroni cutoffs are not shown because no SNP surpassed either cutoff.

Figure 4.7. A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-penten-3-ol. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Bonferroni cutoffs for all markers and effective markers are shown as lines across the Manhattan plot. Shown on the x-axis of the QQ plot (right) are expected negative log p-values and on the y-axis are observed negative log p- values.

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Figure 4.8. A Manhattan plot and QQ plot for the FarmCPU GWAS of 1-penten-3-one. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Bonferroni cutoffs for all markers and effective markers are shown as lines across the Manhattan plot. Shown on the x-axis of the QQ plot (right) are expected negative log p-values and on the y-axis are observed negative log p- values. This Manhattan plot contains physical map positions from version 1 of the Phaseolus vulgaris genome, but version 2.1 moves the SNP that was significantly associated with 1- penten-3-one from chromosome 8 to chromosome 9. These changes are shown in Table 4.2 of the results.

Figure 4.9. A Manhattan plot and QQ plot for the FarmCPU GWAS of β-ionone. 1PC was used and data was not transformed. Chromosomes are shown on the x-axis of the Manhattan plot (left) and negative log p-values on the y-axis. Bonferroni cutoffs for all markers and effective markers are shown as lines across the Manhattan plot. Shown on the x-axis of the QQ plot (right) are expected negative log p-values and on the y-axis are observed negative log p-values.

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A. B.

Figure 4.10. Histograms of 3-hexen-1-ol peak area data (x axis) where y-axis shows the number of individuals. Shown left (A.) is log transformed and shown right (B.) is untransformed. In the case of the log transformed data, the units on the x-axis are log transformed.

A. B.

Figure 4.11. Manhattan plots of SNPs associated with 3-hexen-1-ol. Shown are (A.) transformed 3-hexen-1-ol peak area data and (B.) untransformed data. On the x-axis are chromosomes, which are also indicated by the color of the SNPs graphically depicted above the chromosome number. On the y-axis are the negative log p-values. The two thresholds of significance, based on a Bonferroni cutoff using all markers (-log = 4.958) and a Bonferroni cutoff using only effective markers (-log = 4.435), are shown as two lines across the middle of the plot. In the case of the untransformed data, the p-values are too large to cross any Bonferroni threshold and the y-axis is adjusted accordingly with no Bonferroni cutoff lines shown.

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A. B.

Figure 4.12. QQ plots of 3-hexen-1-ol. Shown left (A.) is log transformed and shown right (B.) is untransformed. The x-axis is expected negative log p-values and the y-axis is observed negative log p-values. The model fit of the transformed data set is slightly better indicated by the SNPs on the x=y line, and only the transformed data has significant associations as seen in the SNP data points above the x=y line on the tail end of the graphic.

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Tables

Table 4.1. Comparisons of the mean values for nine volatiles for bean lines of predominantly Andean or Mesoamerican origin.

1-octen-3-ol Linalool 3-hexen-1-ol Hexanal 1-hexanol 2-hexenal 1-penten-3- 1-penten-3- β-Ionone ol one Peak area Andean 247,249,962 292,837,707 106,071,241 306,939,296 136,140,210 292,516,774 61,211,715 67,755,320 18,780,038 mean Mesoameric 563,821,099 252,787,925 80,659,097 290,183,186 157,106,680 301,111,169 93,132,325 176,778,512 13,426,529 an mean Probability Shapiro-Wilk ****z **** **** 0.1526 **** ** ** **** *y test Shapiro-Wilk 0.4286 **** * **** **** *** **** ** **** test x

Fligner- **** 0.06879 ** 0.06146 * 0.6616 0.2112 **** 0.2097 Killeen test Fligner- 0.5026 0.05823 0.05151 0.1144 *** 0.8269 * 0.2099 0.1148 Killeen test x

Test data log untransform untransform untransform untransform untransform untransform untransform untransform setv transformed ed ed ed ed ed ed ed ed Test utilizedu 2-tailed t- Mann- Mann- 2-tailed t- Mann- Mann- Mann- Mann- 2-tailed t- test Whitney test Whitney test test Whitney test Whitney test Whitney test Whitney test test

Test results 6.45E-11 0.9362 0.004482 0.37 0.02572 0.5124 3.43E-14 < 2.2e-16 1.30E-05 zCutoffs at or below 0.05, 0.01, 0.001, and 0.0001 are indicated with *, **, ***, **** yAlthough this is technically below the 0.05 cutoff, it was, nevertheless, very close at 0.04227 and the histogram appeared normal. xThis was performed on log transformed data. vBased on the results of the Shapiro-Wilke test and Fligner-Killeen test, it was determined whether or not to proceed with log transformed data. uData that was normally distributed with homogeneous variances was analyzed using a 2-tailed t-test. If normality or homogeneity of variances could not be achieved, a non-parametric Mann-Whitney test was performed on untransformed data.

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Table 4.2. SNPs that were significantly associated with one or more volatiles.

Compound SNP name P- -Log P- Effectv MAFu Flanking sequencet BLAST result Phytozome12 P. vulgaris valuex valuew v2.1s 1-hexanol ss715640836 2.46x 6.61 28,679,039 0.11 ATTTGTCAAAGAGACAATAGTGTAAAGTTCCGGAGTAGGAG Chromosome:11 10-07 AGAAATTTTGGAAAATTAG[A/G]AATTTGCACCATAAATTTG Position:51964647..51964767 (- strand) GCAAAACGTGGATTAAGGTTTTTGTGAGAAACAAATAATGG

1-hexanol ss715639302 3.65x 6.44 -30,205,476 0.47 TTGTCGATGTGAGATTTTCAATACATCCGCTTACGTTGAGAT Chromosome:8 10-07 TCTCTAACTCGTGCGTAC[G/A]ACTATATATTTATGAGTGGT Position:54970369..54970489 (- strand) CCGATAATAAACCCAACAAACTCTCATTATGATAGATTCT

1-hexanol ss715648240 2.53x 4.60 18,869,014 0.19 CTCAACATGTTGTAGCCTCAATGAGATCTCTTGTTTGATTTGT Chromosome:5 10-05 CCATCTTCAAGGGAAAT[T/G]ATACCATCTTTAAGAAGACTG Position:2642270..2642390 (+ strand) TTGTCACCACTTGCTATCCATTTTCCAACTTCTGTACTC

1-octen-3-ol ss715649798 3.54x 8.45 158,309,655 0.16 ATTTCATCAACTTCACATCACCGATTCTCAAATTCTCTTTATTC Chromosome:2 10-09 TCATGTGTTGCAAAAT[T/G]ACATAGTTGCTTAACGATCTAT Position:19725336..19725456 (+ strand) GTTCAGAATTCTGATTCGTTGCTAATTGTTAGTGATTA

1-octen-3-ol ss715645225 4.53x 7.34 119,135,660 0.24 TTCTTCCTTCTACTTTGTATCACAGGCAGTTCCTTCTGACACA Chromosome:7 10-08 TTACAAGATTATATTTT[A/C]TGTTGTCTTAAAAGGAAATAG Position:39538152..39538272 (+ strand) ATAAAGATCAAGAAAAATAGGGgAAGGTAAACCATATGA

1-octen-3-ol ss715646922 7.37x 5.13 -129,960,442 0.09 TGTGTACAGAATGCGGGGTGAAAGAAAATGAAGAATGGTG Chromosome:2 10-06 GTGGCAGATTTATTTTGGAA[A/G]AAGAATCCACTTATGCTC Position:47396281..47396401 (- strand) TGTAAATAGTGTATTCTGAAGGTATGGGTGAAGAGTGAAGA A

1-penten-3- ss715639252 7.82x 5.11 -13,335,595 0.33 GGTCAAAGAAGCAATTAAAGATAAAAAAAaTAGACAAGGG Chromosome:3 ol 10-06 TGAAATCTGAATGTGATCTG[T/C]GCTCAAGAATAACATAGT Position:44170059..44170179 (- strand) CTTAGAAAACATCTTCATTTTGAACAAAATCTTAAAGGGAGA

1-penten-3- ss715648169 1.37x 4.86 7,258,652 0.23 TTCATCATTTCCTCCAACATAAACCATACTTCTTATTACTCACT Chromosome:3 ol 10-05 GCTCACTTCAGCTACT[A/G]CTTCTGCTTGATTGCATTTCGAT Position:32905959..32906079 (- strand) TAATCCGCTTCTTAATACTTCACAAATCTCAATACCC

1-penten-3- ss715643726 2.44x 8.61 -37,401,950 0.12 ATAAAGAACATTTGTATTAAATTAGATGTAATACATAAATAT Chromosome:7 one 10-09 AAATTAATATAATGCATT[C/A]TTGTCATATCTCAGTCGTCAT Position:24621925..24622045 (- strand) TTCTACTTTGGTCTCTGAGGTGGGGgAGGAGGATGAATC

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1-penten-3- ss715646324 2.09x 6.68 17,349,336 0.29 CCAAGAGCTGCTGGGTAGATCTGCTGAAGAATATGGATTTA Chromosome:10 one 10-07 GGAATCAAGGGATTCTCAG[A/G]ATCCAATTTGAAGCTCAA Position:40769823..40769943 (+ strand) GATTTTGAACAATGGTTGATTACAACATCCAACTCCAATCTC

1-penten-3- ss715645695 4.88x 6.31 -26,298,708 0.22 TATATCAGTTGATTGAATTTGATCCTTCTTTCCAAATTCCAAA Chromosome:7 one 10-07 GCACAAAACATTACTTC[A/C]TCCTTAGCAAGTGTCATAGAA Position:962228..962348 (+ strand) ACAGTTGAAGAAGATGAGATTGGCAATTCGCTTTTCCCC

1-penten-3- ss715646016 9.84x 6.01 19,595,789 0.17 CACCAGATTGAGAAACATAAATCAAATACACTTTCCTTTACT Chromosome:7 one 10-07 GCAATCCAAGAACAAAAT[T/C]ATCAACCTTACCCTGCCAAG Position:37362202..37362322 (- strand) TAGAAAAAACCAAGAAAACCTGCAGAAACACACCATACCT

1-penten-3- ss715651042 3.26x 5.49 17,893,667 0.44 CAACAAAAGATTTTCCCTCTCATTCCATGCCAATCAACCAATG Chromosome:9 one 10-06 CTGCCCCTATTTACACA[T/C]ACGTAAGCCTTTCATTATTCTCT Position:11805338..11805458 (+ strand) TCCTATTTtCATTTTTTtGTTtGTTTTACAGCTAACA

1-penten-3- ss715649686 5.04x 5.30 18,424,180 0.13 AAAAGGGATTAAATAAAAAAaTTTCACTTACCATTTAAAGTA Chromosome:2 one 10-06 GTTACAAAGGTTCCATTC[A/G]GCAATGACAACTTTCAATTT Position:38378608..38378728 (- strand) CAATTGAAAGTTGACTTCACCGTTGACAAGGCAACTTTGG

1-penten-3- ss715645462 2.26x 4.65 19,698,823 0.12 TGGTGGTGTGCACTTGACCCACTTCGACTTATTACAAGATAC Chromosome:5 one 10-05 CAATCATCAACATCAAAG[A/G]ACTAGATCTCCATGTTGGCT Position:38965641..38965761 (+ strand) TCGATAATAAAGAAGGGCTATTGATAATGTGACACTTCCT

1-penten-3- ss715642058 2.62x 4.58 20,884,624 0.11 AATTCATCAGTAATTGTGGTGTGACTATTCGTAGATCTAAAA Chromosome:10 one 10-05 TTCGCTCATAAACCGCAC[G/A]CGTAGATTCCAAAGAACCTA Position:33962295..33962415 (- strand) AGCTTTCCTCCAAATCAACATAAAAATTCCACAACCTTAA

β-Ionone ss715642582 4.14x 5.38 2,608,526 0.21 TTCCAAATTGTGCATCTACTAACCATATTCCTTCCTGCAGCAA Chromosome:7 10-06 CATGGATAGTACCCCAA[G/T]CTAAGACAAGGAGAATTTAA Position:18092122..18092242 (- strand) CCAGACCACAACACAATGAGCATACCAGACCCTAGAGGAA

β-Ionone ss715639371 2.97x 4.53 -2,443,140 0.20 ATATTTCATGCATCTCCATGTTTTCAAGTGGCCACATATAGA Chromosome:2 10-05 ATATCATCTGCATCTATT[C/A]TTGGATCCTTAAAAGTTATAT Position:729555..729675 (+ strand) TATTTGTATGATTTCATATTCTCCTTACTATATCAATTA hexanal ss715639302 6.04x 4.22 41,379,346 0.47 TTGTCGATGTGAGATTTTCAATACATCCGCTTACGTTGAGAT Chromosome:8 10-05 TCTCTAACTCGTGCGTAC[G/A]ACTATATATTTATGAGTGGT Position:54970369..54970489 (- strand) CCGATAATAAACCCAACAAACTCTCATTATGATAGATTCT

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linalool ss715648287 2.72x 7.57 118,205,608 0.16 TTTATTCTAGATTTGGCTGCTTGAAATTTATAGGTTTTGATGA Chromosome:7 10-08 AAATATGCTTATTGATG[T/G]TCTTGTGCCTAGCAGAGGTTC Position:32623418..32623538 (- strand) TCTCATTAGCACACAATACAAACATGAACTACGTATTGA

linalool ss715645775 2.35x 6.63 -131,859,791 0.31 ATACATACAGCAGCAAGTTATTATTATGATTTGATCTAGACC Chromosome:6 10-07 TTACTATTTCAACCTGGT[A/G]TAGTGGTATTTGGCCCGGAA Position:25480693..25480813 (- strand) ATTCATTGGCACCTTGTGGTGGCCGTTTtACAACATCATA

linalool ss715644427 2.86x 5.54 -78,794,762 0.29 CATAAAATGACCTAACTCGGAACAAATACAGTTACTCCGATG Chromosome:5 10-06 ACCCGCAACCAAAAAaTG[A/G]CCAAACTCGGAACAATTACA Position:13887655..13887775 (- strand) GTTGCTCTGATTAGGCACACCTATAAGATGAACCACTCCC

linalool ss715645789 5.88x 5.23 92,908,013 0.45 TTCACTATATACACAAAATTTGATCCTCTTACTGACTTAAGCT Chromosome:6 10-06 TCGGAGTATTTCCTGTA[A/G]GGGGCCCCCTCATTCCTTGGA Position:25176697..25176817 (- strand) GGTTGAATCACTAGAGCTTGAAGGGATACCATTAGGCGA

linalool ss715647672 1.01x 4.99 -75,221,156 0.47 ACCACTCACACTCTCAACTTTCTTCTTTCTTCTTTCTTCTCCCA Chromosome:7 10-05 GTTAACTTTTACAATC[T/C]CATTTGGACATTCATGTCAATGT Position:9931037..9931157 (+ strand) AGATATTCTTATAATAAATCATAATCCACTCCTTTTC

linalool ss715647231 2.09x 4.68 55,650,845 0.38 ATCTAAAGTCTGCAAAATTGAAGACACATTTTTTCTTCTCTCT Chromosome:2 10-05 CTAACAGAAAATATCTC[T/C]CCATTCGAGCTTACATTGGTGT Position:2144022..2144142 (- strand) GGCTATTCAAAAGGGTCTCAAACCACCCACTACCTGAT xThe p-value from the FarmCPU GWAS analysis. wThe negative log value of the p-value, which is used to construct Manhattan plots. vThe effect from the FarmCPU GWAS analysis in units of peak area. uThe minor allele frequency of the SNP. tThe sequence immediately flanking either side of the SNP position. The SNP is indicated with brackets. The variant forms of the SNP are indicated with a slash. sThe BLAST results from Phytozome12 for Phaseolus vulgaris, version 2.1. The strand in Phytozome12 is indicated in parenthesis with either a + or -. All matches are 121 base pairs in length indicating the SNP and 120bp of flanking sequence.

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Table 4.3. The SNPs that were significantly associated with 3-hexen-1-ol after log transformation. Compound SNP name P-valuex -Log P- Effectv MAFu Flanking sequencet BLAST result Phytozome12 P.vulgaris valuew v2.1s 3-hexen-1-ol ss715645122 5.20x10-09 8.28 -18,480,315.93 0.40 CAAAATTTGCTGACTGCTTAGGTTTTGCATATAATTGGTGAA Chromosome:8 TACAGATTACCAATTTTA[T/C]TGAATAAATAAAACTTATTTG Position:53768323..53768443 (+ strand) AATATGATTACCTGCTGAGAAACACGAACTGCCTCTGTC

3-hexen-1-ol ss715645089 2.52x10-06 5.60 30,407,323.46 0.23 AGTTTGGTTCCTCTGAATTTATATTTATTTTATTACATGAGTTT Chromosome:1 TTTTTttAtAATAATT[A/C]ATGCACATGAATCTTAATATTCAA Position:2939630..2939750 (+ strand) AGTAGAAACTAATCTTGATACCACATATTTAAAGTG

3-hexen-1-ol ss715645954 3.12x10-06 5.51 -16,651,418.16 0.37 GAAGAACTCAATATTTATGTCAAAAGAAAACGATAGTAAGA Chromosome:6 TGACCTTCTGAAGGAACTG[A/G]AAATTGTTGAATAAAACTT Position:14800612..14800732 (+ strand) TTCAAGTCTGGACCACTGGTTTTACCTGCATAGAGATTGTA xThe p-value from the FarmCPU GWAS analysis. wThe negative log value of the p-value, which is used to construct Manhattan plots. vThe back transformed SNP effect from the FarmCPU GWAS analysis. Back transformation was conducted by first taking the arithmetic mean of the log transformed phenotypic data. This average was then added to the SNP effect, which was then back- transformed by raising ten to the power of this number. The average of the log transformed phenotypic data was also back- transformed and subtracted from the back-transformed average with the SNP effect added. The difference was inserted into the table as a SNP effect. uThe minor allele frequency of the SNP. tThe sequence immediately flanking either side of the SNP position. The SNP is indicated with brackets. The variant forms of the SNP are indicated with a slash. sThe BLAST results from Phytozome12 for Phaseolus vulgaris, version 2.1. The strand in Phytozome12 is indicated in parenthesis with either a + or -. All matches are 121 base pairs in length indicating the SNP and 120bp of flanking sequence.

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Table 4.4. Lines used in the study and their relative peak area in GC-MS chromatograms.

Source Origin Cultivar/line Hexanal 2-hexenal 1-hexanol 1-penten-3- 1-penten-3- 3-hexen-1- 1-octen-3-ol Linalool β-ionone one ol ol Dickson Mesoamerican 91-1009 323,322,57 298,494,07 177,754,36 223,828,79 94,595,574 99,134,522 890,191,561 110,238,626 6,443,028 Collection 5 1 3 6 Dickson Mesoamerican 91-1028 189,517,12 475,952,28 358,993,50 84,443,930 71,887,489 76,581,812 145,021,277 112,466,860 5,475,741 Collection 6 1 2 Dickson Mesoamerican 91-1073 547,569,68 554,819,16 50,114,727 344,220,45 51,490,427 56,642,347 148,007,925 275,448,561 10,296,94 Collection 5 7 0 7 Dickson Mesoamerican 91-1096 509,207,74 638,520,77 161,973,94 137,806,44 0 90,683,803 204,847,192 410,898,396 20,810,52 Collection 2 5 6 4 2 Dickson Mesoamerican 91-1098 263,261,08 331,320,70 181,840,11 203,920,75 93,735,077 64,295,736 170,942,375 314,027,058 11,344,99 Collection 5 7 9 2 3 Dickson Mesoamerican 91-1104 333,424,89 480,740,54 165,216,75 103,591,63 92,669,173 34,832,725 495,785,527 539,667,125 19,616,60 Collection 1 1 0 5 9 Dickson Mesoamerican 91-1145 337,123,67 305,551,44 209,688,88 261,879,33 98,479,271 63,963,162 474,865,510 71,585,139 6,229,918 Collection 6 4 9 2 Dickson Andean 91-1215 177,623,97 312,729,76 178,593,78 105,772,80 105,570,07 157,006,02 295,787,077 345,262,277 13,060,27 Collection 3 7 4 0 3 4 8 Dickson Mesoamerican 91-1285 238,161,13 397,388,22 159,490,51 139,027,43 71,630,317 55,334,354 61,717,516 142,149,894 13,308,52 Collection 5 2 5 3 2 Dickson Andean 91-1309 427,706,74 499,069,93 129,191,25 92,691,904 54,636,770 57,430,224 351,819,892 181,344,110 18,706,40 Collection 2 4 1 8 Dickson Andean 91-1443 327,662,46 511,373,32 100,140,47 102,963,23 80,909,373 31,080,667 97,751,146 171,678,114 12,362,43 Collection 6 6 7 3 0 Dickson Mesoamerican 91-1542 110,548,06 291,693,54 199,775,56 198,738,72 95,612,155 100,164,95 711,648,733 92,313,824 22,435,21 Collection 2 4 6 6 7 8 Dickson Mesoamerican 91-1555 397,714,33 337,820,10 186,909,90 222,207,73 105,419,66 97,021,914 639,885,929 357,125,067 14,193,98 Collection 3 4 0 8 1 8 Dickson Andean 91-1574 402,118,81 484,107,73 42,507,166 89,544,193 105,179,54 111,841,48 866,072,944 287,894,207 17,941,43 Collection 0 7 2 6 0 Dickson Mesoamerican 91-1613 228,314,23 325,837,52 164,169,42 220,994,68 101,597,58 94,127,107 947,835,363 371,381,574 21,700,25 Collection 2 2 8 1 3 4 Dickson Mesoamerican 91-1643 294,985,62 342,864,71 202,893,87 292,473,14 116,830,88 102,344,44 608,812,792 100,797,448 14,704,25 Collection 7 0 1 4 2 6 4 Dickson Mesoamerican 91-1664 159,428,30 186,708,68 232,312,57 147,582,35 121,361,26 57,391,506 1,521,084,79 116,965,477 6,509,293 Collection 1 1 5 1 1 4 Dickson Mesoamerican 91-1672 414,057,64 367,742,40 109,429,47 86,524,861 77,977,666 47,554,984 199,244,264 373,445,532 9,695,438 Collection 9 2 2 Dickson Mesoamerican 91-1728 319,824,70 162,666,27 79,653,650 279,895,77 65,898,178 46,298,579 295,836,346 118,876,754 6,074,993 Collection 2 7 6 Dickson Andean 91-1738 204,141,12 259,271,07 111,942,03 156,774,71 91,739,911 48,494,381 341,220,133 125,774,473 13,646,33 Collection 4 4 5 3 6 Dickson Mesoamerican 91-1748 2,401,437 124,183,24 136,762,63 17,837,659 133,266,42 102,388,04 806,894,460 266,433,409 15,834,71 Collection 6 0 2 8 2

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Dickson Mesoamerican 91-1750 210,004,05 168,696,14 101,603,70 181,345,07 129,962,70 85,202,528 799,111,771 104,360,692 16,282,84 Collection 6 4 4 6 6 4 Dickson Mesoamerican 91-1755 317,392,12 248,565,71 179,567,92 285,661,38 102,173,27 113,540,93 679,815,523 84,263,375 6,884,267 Collection 7 1 2 2 8 6 Dickson Mesoamerican 91-1759 361,513,33 420,519,00 142,894,10 210,089,16 74,809,963 63,711,322 1,483,947,89 175,295,423 21,504,95 Collection 2 8 3 6 4 4 Dickson Mesoamerican 91-1768 347,562,57 338,035,70 97,773,644 266,327,22 99,756,187 96,214,127 546,258,370 71,177,537 10,433,03 Collection 8 8 3 2 Dickson Mesoamerican 91-1772 242,978,58 264,393,37 96,418,913 207,189,52 103,200,51 86,963,782 932,339,689 255,386,149 18,736,16 Collection 6 1 5 9 1 Dickson Mesoamerican 91-1892 281,813,29 537,919,46 131,189,87 196,897,31 88,508,886 69,786,308 283,063,386 135,570,509 17,030,68 Collection 2 0 5 3 1 Dickson Mesoamerican 91-1940 420,347,37 418,247,12 156,915,42 131,791,73 69,306,632 67,757,111 566,529,071 424,020,786 11,695,80 Collection 1 4 2 1 9 Dickson Mesoamerican 91-1976 325,416,47 326,700,97 111,013,33 120,704,43 90,107,736 34,293,066 582,069,369 276,446,812 6,938,269 Collection 3 9 5 8 Dickson Mesoamerican 91-1989 249,117,00 244,395,33 104,071,01 230,155,37 146,399,74 64,173,444 463,220,566 75,740,750 13,603,03 Collection 8 3 0 1 4 5 Dickson Mesoamerican 91-2093 297,798,97 275,845,60 110,262,52 237,187,76 77,405,359 64,377,508 328,724,195 375,092,916 20,891,17 Collection 1 4 2 7 8 Dickson Mesoamerican 91-2094 293,182,02 288,482,17 71,193,072 154,783,34 79,003,491 64,265,159 502,756,195 102,173,836 6,342,260 Collection 8 1 1 Dickson Mesoamerican 91-2095 390,341,07 213,409,00 106,075,74 270,012,83 93,100,085 127,111,44 517,012,941 385,366,783 23,897,94 Collection 6 8 7 3 3 8 Dickson Mesoamerican 91-2096 227,096,61 369,694,80 281,924,07 287,464,11 111,907,03 137,904,39 146,392,470 101,693,668 10,355,89 Collection 0 1 0 6 9 7 8 Dickson Mesoamerican 91-2097 214,679,90 318,565,87 124,442,73 236,182,12 80,507,586 43,664,224 306,677,337 340,630,123 15,879,13 Collection 9 2 8 6 5 Dickson Mesoamerican 91-2099 257,762,47 216,351,76 108,627,27 227,092,46 139,290,83 62,702,803 318,863,385 93,104,515 8,001,399 Collection 0 4 2 8 5 Dickson Mesoamerican 91-2100 307,051,48 273,493,67 145,981,48 245,946,53 123,571,64 153,730,23 1,385,207,13 97,777,458 20,168,57 Collection 5 0 4 0 4 2 2 0 Dickson Mesoamerican 91-2101 39,141,184 55,305,112 261,464,57 5,801,444 116,741,34 72,549,932 1,476,570,69 79,752,211 5,814,211 Collection 0 2 6 Dickson Andean 91-2102 403,298,86 228,030,53 37,127,353 144,161,23 89,452,071 142,911,53 1,357,823,79 279,582,470 11,961,21 Collection 8 5 4 5 2 6 Dickson Mesoamerican 91-3008 288,496,23 266,567,28 131,029,80 139,898,37 122,675,77 111,102,88 1,682,782,44 807,149,597 21,522,43 Collection 5 7 3 6 3 0 0 1 Dickson Mesoamerican 91-3013 257,789,90 141,133,30 73,029,233 99,627,502 72,855,213 73,360,639 1,006,754,38 103,566,533 6,006,074 Collection 5 1 4 Dickson Andean 91-3110 345,229,20 277,332,16 51,427,127 134,429,47 89,522,000 224,576,33 1,755,936,63 454,642,937 25,271,82 Collection 3 0 5 0 7 5 Dickson Mesoamerican 91-3225 325,874,09 361,108,84 168,434,81 199,638,26 105,586,60 122,355,65 152,749,141 135,313,829 4,194,123 Collection 1 0 1 1 6 4 Dickson Mesoamerican 91-3255 264,529,87 244,248,37 192,972,83 184,550,34 99,924,479 49,030,725 399,879,749 102,416,374 4,877,714 Collection 8 5 7 5

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Dickson Mesoamerican 91-3346 443,158,18 435,903,73 101,480,77 233,928,95 70,866,402 56,114,137 541,225,733 81,590,477 12,317,49 Collection 8 5 5 2 7 Dickson Mesoamerican 91-3389 306,914,82 319,564,01 203,389,80 169,204,77 143,974,89 77,254,116 1,433,083,97 190,726,141 19,286,77 Collection 2 7 7 9 4 9 1 Dickson Mesoamerican 91-3405 470,444,41 426,606,83 77,569,021 219,752,95 75,675,907 72,377,940 714,874,139 60,714,992 15,454,32 Collection 4 0 5 4 Dickson Mesoamerican 91-3436 449,777,88 325,563,81 129,792,33 128,539,74 73,000,845 55,403,152 436,116,048 327,775,907 12,008,45 Collection 1 6 4 3 8 Dickson Andean 91-3588 357,766,79 433,704,50 144,513,93 94,500,699 71,307,791 86,098,525 804,677,209 665,468,968 15,849,95 Collection 9 7 2 5 Dickson Mesoamerican 91-3594 285,491,51 433,240,71 129,153,18 171,508,49 80,153,117 59,065,992 102,486,613 80,516,692 9,563,744 Collection 8 3 0 2 Dickson Mesoamerican 91-3709 326,933,65 157,835,70 65,843,802 153,643,91 85,734,439 83,136,952 887,426,067 810,491,172 19,334,77 Collection 6 5 2 8 Dickson Mesoamerican 91-3857 247,150,27 278,594,21 141,226,86 234,314,25 128,464,89 61,369,221 234,249,053 107,158,176 12,036,16 Collection 5 0 0 7 7 4 Dickson Mesoamerican 91-3915 350,192,99 284,076,34 170,333,97 261,727,09 122,190,57 84,619,857 624,188,428 100,347,652 10,712,74 Collection 0 3 3 3 8 4 Dickson Mesoamerican 91-3918 232,109,67 317,789,04 155,908,27 201,123,42 107,028,99 37,322,465 358,261,903 99,103,248 11,598,78 Collection 4 7 2 0 9 6 Dickson Mesoamerican 91-3921 266,449,27 305,359,43 179,503,44 84,159,605 108,971,42 21,664,341 448,701,633 63,914,981 10,773,11 Collection 8 0 6 2 8 Dickson Mesoamerican 91-3982 373,751,51 424,630,54 121,609,72 210,603,01 88,859,261 109,451,61 935,197,730 92,237,136 13,117,98 Collection 5 7 3 9 4 5 BeanCAP Andean Acclaim 380,884,01 272,563,09 83,908,730 59,155,551 37,361,875 115,320,33 292,728,444 74,417,263 10,889,65 9 0 1 6 BeanCAP Andean Angers 343,786,44 312,728,02 126,153,78 214,021,19 96,064,085 65,458,071 68,228,933 818,615,585 20,089,65 5 9 7 1 8 BeanCAP Andean Astun 288,387,10 106,391,61 72,676,749 41,035,257 50,736,315 103,093,95 528,613,542 101,476,650 17,923,39 2 2 4 3 BeanCAP Andean Balsas 211,054,83 284,805,52 187,396,67 164,154,09 84,188,115 81,813,653 111,803,274 564,828,849 17,580,03 3 7 8 2 0 BeanCAP Andean Banga 237,178,67 382,252,64 169,548,90 132,195,87 75,581,584 91,731,817 117,703,398 374,848,434 27,063,58 0 3 0 8 3 BeanCAP Andean BBL156 225,538,47 249,824,66 94,550,613 47,776,872 64,473,358 43,469,776 799,562,297 544,724,177 13,055,02 2 9 8 BeanCAP Andean BBL274 254,555,96 93,579,110 183,299,11 20,831,527 55,038,676 57,182,455 462,570,587 290,368,948 11,127,10 7 7 9 BeanCAP Andean Benchmark 151,869,70 258,029,52 219,044,94 103,607,42 70,437,875 118,018,11 156,630,644 107,531,280 16,860,91 5 5 0 3 1 3 BeanCAP Andean Benton 320,045,61 392,009,82 159,957,99 66,840,438 44,863,292 119,565,65 223,784,504 107,107,052 21,099,43 4 5 8 7 0 BeanCAP Andean Black Valentine 395,719,34 277,749,62 76,783,317 111,283,60 75,126,917 68,143,278 40,194,117 437,805,571 15,941,95 5 1 7 0 BeanCAP Mesoamerican Blue Peter Pole 313,083,31 267,858,12 166,311,52 254,515,55 93,875,243 69,517,213 737,642,527 154,004,747 15,664,76 6 7 5 7 3

125

BeanCAP Andean Bogota 145,146,33 149,803,87 163,801,37 73,337,284 90,496,534 77,790,247 129,200,871 213,009,180 7,891,221 7 9 1 BeanCAP Andean Booster 165,544,69 280,003,36 217,475,91 33,219,385 101,821,50 53,146,018 101,936,952 308,600,382 27,682,30 3 2 7 0 3 BeanCAP Andean Brio 494,926,58 273,575,10 63,512,107 31,105,413 82,286,608 338,797,21 647,708,944 43,864,153 23,973,18 0 8 5 7 BeanCAP Andean Brittle Wax 558,124,96 303,832,34 27,927,889 37,500,498 38,422,761 36,217,396 111,747,495 120,906,021 1,962,413 4 8 BeanCAP Andean Bronco 259,880,10 269,423,47 66,017,804 29,124,056 37,129,297 217,005,03 461,223,690 160,971,046 26,112,86 8 5 5 3 BeanCAP Andean Cadillac 318,217,40 305,264,09 166,880,71 134,921,00 59,826,828 56,269,058 81,563,136 1,215,866,17 29,842,38 8 4 5 2 7 3 BeanCAP Andean Calgreen 137,818,28 303,400,02 195,614,67 62,552,414 60,281,227 69,280,881 29,107,916 263,041,844 20,959,11 1 4 6 4 BeanCAP Andean Carlo 419,517,52 432,741,21 106,771,09 129,750,34 76,764,009 78,739,312 118,704,382 1,155,401,33 23,811,26 3 8 2 8 7 1 BeanCAP Andean Carson 93,526,585 378,303,33 135,947,95 24,826,788 40,055,469 128,818,59 322,586,834 369,569,023 3,158,621 7 8 3 BeanCAP Andean Castano 319,370,81 537,209,46 174,743,48 45,720,393 50,906,213 231,514,55 106,605,897 57,037,054 18,704,77 8 2 8 5 4 BeanCAP Andean Catania 119,906,98 267,643,51 139,398,99 48,915,387 55,120,919 99,115,995 230,028,445 95,908,832 20,688,57 3 3 9 5 BeanCAP Andean Celtic 400,385,02 164,497,95 212,880,25 33,167,604 79,474,369 39,043,528 381,910,846 1,073,045,23 24,248,84 6 4 5 1 7 BeanCAP Andean Charon 428,415,19 333,717,65 64,873,207 80,640,895 44,699,853 172,429,62 168,478,124 92,990,616 16,951,39 6 2 8 1 BeanCAP Andean Cherokee 383,804,46 385,770,64 156,006,08 81,507,753 52,106,416 102,552,56 59,741,512 536,716,281 3,627,040 5 0 7 0 BeanCAP Andean Coloma 357,107,78 151,606,39 116,906,90 60,498,236 86,023,850 15,052,219 88,333,563 38,824,482 8,918,280 8 5 3 BeanCAP Andean Contender 203,963,06 283,616,66 107,333,50 143,177,17 76,234,075 43,769,683 153,237,199 80,460,672 9,826,675 9 1 6 7 BeanCAP Andean Corbette Refugee 118,456,42 284,517,31 172,342,89 106,883,99 101,806,81 82,974,793 258,910,789 608,196,532 10,232,41 4 9 9 2 7 2 BeanCAP Andean Cyclone 346,621,66 244,952,12 127,906,72 96,846,440 64,965,410 65,428,957 213,486,968 302,772,994 13,986,30 5 1 5 2 BeanCAP Andean Dandy 388,862,29 293,312,61 101,283,56 89,479,556 51,467,090 57,315,457 283,737,288 111,734,958 20,210,61 8 8 6 0 BeanCAP Mesoamerican Double Dutch White 83,427,935 210,935,79 227,188,17 51,271,219 81,109,177 24,473,486 156,420,039 345,685,309 10,676,27 3 8 7 BeanCAP Andean Derby 334,199,36 114,590,28 136,030,37 30,627,720 65,449,839 18,767,449 86,513,442 194,370,035 11,492,80 3 4 4 7 BeanCAP Andean Doral 434,002,12 393,855,42 114,884,87 73,366,595 55,251,451 171,246,76 98,611,723 67,948,825 23,034,38 5 4 4 9 9 BeanCAP Andean Dusky 326,367,63 134,186,39 64,061,433 17,886,050 25,946,773 128,821,52 235,410,226 109,042,706 8,889,889 6 4 5

126

BeanCAP Andean Eagle 499,681,49 271,077,19 37,503,472 20,931,023 36,813,705 114,020,74 479,725,788 87,536,443 15,203,85 6 8 3 8 BeanCAP Andean Ebro 133,652,22 113,516,98 186,975,21 49,497,598 91,958,068 57,920,323 397,553,251 531,685,025 14,656,92 5 8 5 9 BeanCAP Andean Embassy 341,837,43 447,345,28 126,713,40 32,768,779 32,970,159 170,893,63 277,036,300 54,151,486 22,971,48 8 1 4 6 6 BeanCAP Andean Envy 402,648,70 298,826,28 158,458,78 59,124,515 56,639,197 91,045,007 26,275,401 374,743,770 3,469,589 0 5 0 BeanCAP Andean Espada 279,682,58 218,752,10 211,701,71 56,392,872 54,095,777 144,521,79 222,249,306 79,888,681 28,991,14 7 2 9 6 0 BeanCAP Andean Esquire 292,404,40 409,009,21 161,866,71 83,347,328 73,220,532 50,839,154 112,167,398 128,897,304 17,087,77 7 5 3 2 BeanCAP Mesoamerican EZ Pick 286,131,33 387,261,74 126,235,03 78,585,470 67,505,561 38,205,518 123,070,060 746,264,022 24,492,49 1 4 8 3 BeanCAP Andean Ferrari 300,513,77 196,891,14 96,761,527 65,241,551 46,062,783 151,858,57 36,342,916 424,334,123 34,081,41 9 8 1 4 BeanCAP Andean Festina 314,839,28 393,572,92 164,903,76 94,889,376 64,874,394 141,562,05 92,479,838 41,049,756 25,693,15 6 1 2 7 2 BeanCAP Andean Flavio 274,760,06 329,826,55 222,805,21 80,844,098 47,167,594 90,500,310 75,622,057 71,273,173 30,912,89 0 6 4 6 BeanCAP Andean Flavor Sweet 300,108,43 346,926,92 102,335,58 55,232,805 50,765,500 135,194,05 58,157,613 42,248,040 33,031,60 0 0 8 9 5 BeanCAP Andean Flo 401,867,55 258,709,23 28,696,987 56,926,144 49,197,678 79,574,801 279,885,788 114,654,548 25,550,72 5 1 6 BeanCAP Mesoamerican Fortex 332,153,39 343,681,79 128,959,21 70,284,912 55,006,198 52,910,409 101,359,205 466,191,529 27,046,37 3 2 1 5 BeanCAP Andean FR 266 86,211,894 188,267,69 228,274,01 106,781,06 0 145,641,01 207,590,689 78,162,956 22,339,69 3 9 4 1 0 BeanCAP Andean Fury 405,555,90 229,394,55 114,960,92 76,568,685 34,652,562 39,349,118 85,341,024 254,569,538 12,247,28 5 7 3 7 BeanCAP Andean Gallatin 50 499,832,05 341,060,21 60,132,249 59,084,638 56,918,989 115,214,23 219,772,061 110,468,293 3,128,986 7 4 7 BeanCAP Andean Galveston 404,602,92 241,427,69 115,995,10 127,319,30 69,598,613 108,894,07 51,197,657 1,331,747,74 17,136,79 0 5 5 1 0 1 1 BeanCAP Andean Gina 178,580,86 179,682,69 240,932,89 37,759,587 61,061,292 93,732,587 141,454,286 575,403,906 28,389,08 1 6 3 4 BeanCAP Andean Gold Mine 587,108,92 489,464,45 32,322,891 55,913,202 41,954,601 70,490,182 216,249,043 93,064,462 2,465,874 6 3 BeanCAP Andean Goldrush 453,190,33 375,596,88 17,907,553 30,090,095 28,227,426 113,612,66 550,324,081 49,880,553 5,276,491 9 5 6 BeanCAP Andean Green Arrow 397,280,34 269,454,08 59,338,155 46,710,455 44,670,965 236,245,54 348,518,891 119,348,892 35,004,75 5 9 7 2 BeanCAP Andean Grenoble 291,676,75 339,420,80 104,025,01 55,530,211 27,910,997 82,585,870 13,642,965 293,707,336 23,599,15 3 5 1 9 BeanCAP Andean Hayden 300,693,81 316,996,15 131,722,47 83,457,603 74,993,178 74,837,155 34,134,840 118,826,250 15,824,98 9 2 9 5

127

BeanCAP Andean Hercules 254,958,41 429,267,50 182,181,10 38,768,191 49,623,035 104,185,02 79,542,044 101,376,732 25,711,04 9 1 8 1 4 BeanCAP Andean Hialeah 232,979,47 252,210,05 140,735,39 58,690,358 41,279,613 96,344,346 118,735,940 117,235,626 22,904,95 5 7 2 1 BeanCAP Andean Hystyle 408,422,23 228,610,94 112,457,70 82,307,712 62,144,301 87,722,388 185,031,771 199,021,649 19,954,80 0 9 2 9 BeanCAP Andean Idaho Refugee 413,642,17 423,042,08 67,648,422 147,550,86 63,532,158 67,648,460 358,098,319 700,828,441 8,306,718 0 7 9 BeanCAP Andean Igloo 89,871,599 344,493,73 247,566,65 52,897,308 53,009,777 212,670,46 308,310,220 115,578,308 34,417,02 0 8 9 4 BeanCAP Andean Impact 404,216,07 340,250,91 84,786,266 26,813,138 20,214,711 63,727,621 44,810,113 85,170,653 3,871,448 0 5 BeanCAP Mesoamerican Kentucky Wonder 142,774,75 189,243,83 271,636,25 127,599,86 86,642,748 137,248,13 402,961,053 632,660,632 8,207,546 2 8 4 4 1 BeanCAP Andean Koala 343,177,93 212,352,35 193,398,68 87,098,059 57,785,821 249,264,26 60,353,261 1,151,253,56 24,553,03 4 6 4 7 3 9 BeanCAP Andean Kylian 401,582,44 245,891,53 42,719,454 38,940,838 59,772,896 63,393,387 99,216,289 800,334,598 28,373,84 4 0 8 BeanCAP Andean Labrador 384,975,86 199,036,04 114,873,63 18,579,202 84,550,661 305,344,91 538,979,538 86,714,439 40,301,01 4 9 4 9 1 BeanCAP Andean Landmark 120,723,56 241,543,68 166,613,65 57,368,794 92,670,911 72,467,294 477,542,684 714,120,208 10,808,19 5 6 0 6 BeanCAP Andean Landreths Stringless 199,747,96 212,910,98 109,278,29 60,806,115 49,082,348 105,414,67 69,094,726 299,760,670 13,873,19 Green 6 8 9 3 9 BeanCAP Andean Magnum 361,858,46 432,862,11 133,189,95 34,777,658 48,906,485 78,326,363 95,322,531 264,325,441 14,118,71 2 2 0 9 BeanCAP Andean Matador 505,045,28 214,514,70 86,982,904 37,780,165 44,199,225 179,786,35 309,593,883 77,822,827 16,660,83 4 3 3 1 BeanCAP Mesoamerican McCaslan No 42 306,758,42 216,997,29 162,114,78 110,609,20 75,504,512 71,405,108 928,222,911 318,270,788 3,022,206 1 1 8 1 BeanCAP Andean Medinah 199,905,14 170,247,48 341,984,06 14,958,351 84,692,745 27,747,122 151,619,981 226,770,433 12,081,51 0 0 2 8 BeanCAP Andean Mercury 404,504,44 220,637,62 67,098,659 62,127,881 51,332,460 34,488,969 76,821,123 279,004,914 13,092,05 9 6 8 BeanCAP Andean Minuette 293,761,27 433,383,21 84,427,868 37,647,576 40,594,089 97,653,507 84,847,188 80,711,670 35,766,51 6 7 7 BeanCAP Andean Montcalm 317,093,62 505,536,01 242,567,35 105,252,88 145,933,08 100,703,41 209,917,120 241,468,614 8,795,135 9 5 6 4 6 6 BeanCAP Andean Navarro 398,992,72 575,545,95 149,230,03 34,291,730 32,472,679 163,784,81 126,992,996 113,526,114 16,488,98 5 3 6 6 2 BeanCAP Andean Nicelo 406,996,05 162,955,56 61,663,771 16,174,100 48,747,529 152,621,42 400,085,296 69,046,928 19,055,47 7 2 2 9 BeanCAP Andean Nomad 496,405,89 221,497,12 74,243,309 80,072,874 42,096,812 255,252,05 286,450,343 459,519,555 23,761,56 3 5 9 1 BeanCAP Andean Normandie 267,068,79 192,049,84 141,909,14 18,653,596 81,363,408 224,837,70 416,317,708 59,369,975 26,499,59 1 5 5 1 1

128

BeanCAP Mesoamerican Olathe 131,433,64 215,552,11 199,151,60 189,450,36 122,926,22 114,222,39 100,243,204 39,002,640 6,591,294 8 8 3 0 3 6 BeanCAP Andean Opus 365,857,39 195,714,22 52,594,602 49,184,795 40,199,324 124,821,55 242,375,041 43,760,511 13,630,81 6 1 7 1 BeanCAP Andean Oregon 1604M 254,280,46 236,314,85 157,026,53 44,771,852 64,314,320 17,232,210 278,849,959 628,557,771 20,421,79 9 3 0 8 BeanCAP Mesoamerican Oregon 2065 278,876,92 222,341,13 146,475,84 67,395,330 61,939,766 32,147,357 320,760,608 184,483,139 2,279,177 8 8 8 BeanCAP Andean Oregon 5402 104,756,47 172,634,60 213,249,67 64,140,788 51,745,208 101,106,04 243,430,578 168,883,735 15,214,11 0 5 2 8 0 BeanCAP Andean Oregon 5630 152,882,56 221,191,68 393,268,74 10,589,689 137,878,67 139,088,84 393,714,198 231,172,835 20,734,07 3 2 8 9 9 2 BeanCAP Andean Oregon 91G 226,218,18 683,931,83 381,666,73 43,457,389 58,753,806 161,022,40 129,787,069 114,570,915 13,054,75 4 4 2 9 6 BeanCAP Mesoamerican Oregon Giant Pole 389,602,84 385,249,79 163,732,64 65,320,244 42,677,172 70,017,622 545,826,856 68,974,844 7,341,433 5 1 7 BeanCAP Andean Palati 460,755,37 248,749,47 80,738,638 83,111,825 54,753,075 119,449,60 194,926,901 110,923,091 28,090,38 3 4 6 7 BeanCAP Andean Paloma 186,141,60 227,589,72 176,305,64 54,887,241 85,607,207 93,625,301 915,900,676 643,915,520 29,393,28 4 2 1 5 BeanCAP Andean Panama 122,704,10 320,128,18 245,989,74 71,561,008 81,718,938 70,018,477 126,701,406 1,665,043,13 28,147,52 5 3 1 5 0 BeanCAP Andean Paulista 77,628,864 279,950,72 152,726,47 33,125,742 52,734,611 86,271,044 84,810,197 233,138,861 27,614,32 8 5 7 BeanCAP Andean Pix 223,577,08 282,674,77 169,301,91 100,387,74 69,498,983 104,661,20 62,313,520 419,309,877 20,177,45 3 1 5 7 7 5 BeanCAP Andean Polder 433,032,75 155,993,11 36,402,821 64,672,759 52,084,829 108,609,75 240,557,088 916,744,446 19,723,39 7 2 9 1 BeanCAP Mesoamerican Pole Blue Lake 530,240,38 261,005,80 88,226,415 116,002,89 65,289,577 254,639,03 492,919,906 1,457,865,67 29,249,03 6 9 0 1 7 1 BeanCAP Mesoamerican Pole Blue Lake S7 94,153,512 68,603,577 340,901,19 8,563,906 90,796,457 154,969,99 246,008,769 426,485,560 29,296,54 8 0 7 BeanCAP Andean Pretoria 180,182,62 312,928,19 234,062,43 83,060,195 61,012,672 144,961,89 260,873,050 155,046,875 28,038,83 0 8 2 3 7 BeanCAP Andean Profit 215,102,44 358,292,19 232,919,94 43,100,607 78,468,500 126,683,96 333,134,042 85,596,755 23,784,15 9 1 8 8 1 BeanCAP Andean Prosperity 181,625,35 163,623,61 147,148,49 29,265,273 54,944,220 17,813,213 246,286,651 390,308,351 19,647,26 3 6 9 7 BeanCAP Andean Provider 42,683,002 253,819,19 191,050,31 27,524,264 68,194,785 175,856,68 133,074,322 68,778,380 17,456,77 6 8 5 9 BeanCAP Andean Redon 68,444,235 211,374,02 199,631,01 95,717,357 57,271,648 83,189,907 127,406,532 484,608,833 16,629,53 5 9 7 BeanCAP Andean Renegade 360,843,07 273,833,35 95,802,305 71,211,657 77,840,219 157,018,82 129,039,318 96,769,120 33,238,04 0 9 1 8 BeanCAP Andean Rocdor 222,263,13 452,665,09 207,091,54 67,275,022 50,922,060 58,380,013 188,178,593 209,232,652 3,276,317 7 3 1

129

BeanCAP Andean Rockport 147,694,99 238,040,84 193,069,03 60,319,376 77,825,990 115,866,28 97,720,075 860,654,244 21,094,20 0 0 7 8 7 BeanCAP Andean Roller 375,481,36 277,648,29 113,424,63 103,253,10 64,528,528 91,965,149 140,137,187 263,646,035 21,521,16 8 3 0 0 0 BeanCAP Andean Roma II 438,605,66 302,390,51 122,297,44 134,866,96 81,539,122 82,224,123 92,074,043 762,416,304 19,665,83 0 8 6 8 2 BeanCAP Andean Romano Gold 431,698,41 354,984,56 63,755,752 48,903,183 37,099,437 31,809,761 27,914,366 143,930,081 2,731,405 9 9 BeanCAP Andean Royal Burgundy 242,949,27 327,368,59 175,604,13 75,962,543 44,870,053 39,991,234 247,505,821 113,256,578 12,496,06 5 7 8 6 BeanCAP Andean Saporro 109,834,82 193,769,88 172,174,55 48,883,713 113,012,61 97,929,122 331,912,210 49,349,642 26,396,47 4 9 6 0 7 BeanCAP Andean Scorpio 57,962,688 175,959,22 266,483,46 8,685,682 60,849,001 362,263,08 453,724,091 69,316,681 28,954,64 9 6 1 9 BeanCAP Andean Seabiscuit 312,734,05 258,773,96 86,316,422 30,193,565 43,317,871 96,707,928 163,415,667 65,184,269 31,749,07 2 6 1 BeanCAP Mesoamerican Seafarer 134,408,96 162,116,39 225,615,11 183,448,73 122,031,66 137,895,00 55,582,937 195,091,077 7,624,739 4 9 8 4 1 5 BeanCAP Andean Secretariat 346,728,15 147,233,63 44,888,891 47,786,831 28,030,449 83,867,995 254,071,725 38,940,786 23,764,06 9 4 9 BeanCAP Andean Selecta 199,156,57 355,156,74 288,782,51 179,125,43 105,046,56 48,970,990 56,998,205 285,009,692 19,182,90 5 4 2 1 5 2 BeanCAP Andean Serengeti 303,092,55 318,897,39 167,314,43 57,121,875 53,719,095 66,003,720 398,697,483 329,839,654 27,252,91 3 1 0 2 BeanCAP Andean Serin 123,192,60 139,734,43 253,787,37 16,674,630 81,398,194 19,443,101 300,419,508 781,310,417 3,052,452 9 2 3 BeanCAP Andean Seville 466,823,82 414,331,98 61,455,455 24,592,110 57,418,364 72,430,793 96,377,849 277,208,805 20,049,15 1 4 9 BeanCAP Andean Shade 516,095,35 413,873,79 26,158,212 46,098,899 41,720,041 116,452,32 174,618,808 46,685,042 23,954,77 0 3 7 9 BeanCAP Andean Slenderella 284,502,17 142,478,44 137,507,87 23,383,009 63,246,560 45,115,062 143,847,509 320,708,489 16,818,94 1 0 7 8 BeanCAP Andean Slenderpack 260,058,96 317,789,76 102,613,04 46,415,210 34,237,339 145,865,14 176,228,576 96,572,715 20,686,15 9 4 1 6 1 BeanCAP Andean Sonesta 403,431,99 250,522,52 45,122,975 52,080,421 50,648,869 172,531,09 203,518,193 35,492,599 3,016,767 6 7 6 BeanCAP Andean Spartacus 219,308,62 415,869,77 161,186,26 52,826,041 49,921,879 157,292,00 65,774,370 89,194,808 33,034,83 8 3 4 3 7 BeanCAP Andean Speedy 384,162,10 242,393,53 81,189,104 25,703,495 60,897,984 151,912,48 50,810,098 76,571,301 15,728,66 1 3 2 9 BeanCAP Andean Stallion 408,688,36 467,622,95 95,558,420 65,785,369 0 171,825,64 392,042,937 464,773,829 19,705,30 5 2 1 0 BeanCAP Andean Stayton 172,542,08 200,427,48 259,038,24 30,425,131 99,860,743 17,245,760 172,403,190 378,388,668 24,053,33 2 4 0 4 BeanCAP Andean Storm 363,189,66 417,518,24 94,061,727 37,872,180 19,265,495 113,139,50 81,732,295 48,431,994 14,454,24 2 7 5 7

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BeanCAP Andean Strike 476,902,94 321,831,17 44,948,309 33,030,700 42,735,947 101,230,58 81,426,812 108,137,589 19,653,40 0 3 7 4 BeanCAP Andean Stringless French 133,533,01 237,628,41 260,462,30 126,472,35 99,322,305 63,284,338 84,927,384 270,692,463 15,236,86 Fillet 1 2 5 1 9 BeanCAP Andean Tapia 294,923,20 149,208,25 148,640,24 73,121,688 64,001,901 93,340,495 456,380,293 962,506,994 19,635,33 1 5 2 3 BeanCAP Andean Tendercrop 100,202,91 115,434,12 245,512,58 109,906,67 48,098,371 57,930,311 629,670,245 128,138,511 9,584,802 0 1 1 7 BeanCAP Andean Tendergreen 206,543,80 309,184,38 203,310,64 113,128,25 69,298,607 87,735,312 486,270,610 436,465,923 27,526,50 7 4 8 5 4 BeanCAP Andean Teseo 552,172,38 362,931,60 37,030,451 89,691,632 90,140,679 50,505,878 183,773,633 90,460,312 20,913,83 6 3 0 BeanCAP Andean Thoroughbred 314,931,75 218,193,73 78,444,776 41,896,302 41,343,827 146,886,76 148,058,804 41,393,901 14,378,81 7 6 8 8 BeanCAP Andean Titan 316,742,58 359,585,75 178,235,27 62,469,494 50,867,394 62,749,076 120,280,383 60,242,761 18,552,97 7 4 5 6 BeanCAP Andean Top Crop 418,427,89 274,514,14 131,825,68 92,873,596 57,291,419 84,240,831 503,785,903 63,933,919 14,693,71 6 8 8 0 BeanCAP Mesoamerican Trail of Tears 373,819,35 252,548,33 142,757,25 313,859,70 105,938,32 93,541,817 697,844,336 126,017,890 25,390,02 0 2 0 3 1 4 BeanCAP Andean True Blue 318,000,75 424,571,20 81,633,576 47,862,961 52,972,268 66,556,729 40,450,977 89,082,339 26,760,82 5 1 5 BeanCAP Andean Unidor 526,735,40 360,091,76 78,366,315 81,420,125 49,829,509 57,815,283 252,218,523 366,395,357 4,932,245 9 2 BeanCAP Andean US refugee no5 83,082,551 211,921,13 299,455,50 155,668,25 83,596,735 237,289,76 483,862,530 144,792,274 12,733,63 7 7 0 5 6 BeanCAP Andean Valentino 462,595,79 408,381,27 70,573,268 64,337,909 53,893,336 102,892,95 149,542,390 68,282,721 27,320,03 5 4 7 2 BeanCAP Andean Venture 467,627,32 162,148,75 35,202,429 32,441,174 57,252,096 56,745,962 358,765,225 939,173,665 16,352,50 0 3 8 BeanCAP Andean Warrior 449,276,19 254,804,09 68,296,755 117,016,36 49,364,126 82,412,022 119,996,192 67,487,227 19,198,00 9 9 7 0 BeanCAP Mesoamerican Widusa 253,367,62 162,350,01 201,631,88 171,534,10 98,794,533 79,332,942 448,887,126 397,156,302 13,624,10 5 2 8 6 7 BeanCAP Andean Zeus 357,797,89 296,426,57 96,873,350 85,777,054 58,274,015 96,125,227 59,699,217 74,256,423 23,895,61 6 7 5 BeanCAP Andean Zodiac 476,760,01 333,345,12 34,654,386 44,107,626 37,262,880 46,246,996 290,233,147 157,501,195 18,324,58 8 5 8

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Table 4.5. Candidate regulatory genes within 50Kb of either side of the associated SNP. SNP is the significantly associated SNP and volatile is the volatile it is associated with. Gene identifier is the identifier assigned to an annotated gene model sequence in Phytozome 12.1, Phaseolus vulgaris 2.1., and gene model description is the annotated description provided by Phytozome 12.1 for the gene model.

SNP Volatile Gene identifier Gene model description ss715648240 1-hexanol Phvul.005G028900 AP2/B3-like transcriptional factor ss715640836 1-hexanol Phvul.011G203400 transcription factor TGA2 ss715646922 1-octen-3-ol Phvul.002G306000 MYB domain protein 68 ss715645225 1-octen-3-ol Phvul.007G275200 RNA binding protein ss715648169 1-penten-3-ol Phvul.003G132400 ELF-like 4 transcription factor ss715639252 1-penten-3-ol Phvul.003G214300 Myb domain 85a ss715649686 1-penten-3-one Phvul.002G215500 MADS-box transcription factor ANR1 ss715645462 1-penten-3-one Phvul.005G161900 basic helix-loop-helix DNA-binding protein ss715645695 1-penten-3-one Phvul.007G014100 zinc finger protein ss715646016 1-penten-3-one Phvul.007G251900 heat shock transcription factor ss715651042 1-penten-3-one Phvul.009G066960 WUSCHEL homeobox4 ss715651042 1-penten-3-one Phvul.009G066980 ethylene-responsive transcription factor ss715646324 1-penten-3-one Phvul.010G126400 MYB-like transription factor domain ss715639371 β-ionone Phvul.002G006900 AT-hook DNA binding protein ss715639371 β-ionone Phvul.002G007100 C2H2/C2HC zinc finger ss715642582 β-ionone Phvul.007G112900 RNA recognition motif ss715647361 linalool Phvul.004G171200 MYB-like transcription factor ss715645789 linalool Phvul.006G145800 KNOX/ELK homeobox transcription factor aThis gene model is located 50.5Kb away from its SNP.

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CONCLUSION

Why is flavor in green beans or any vegetable important and why breed for it? Flavor has always been important to some degree but changes in economics and consumer expectations are causing this quality trait to be increasingly valuable. One could argue that there is an overall shift in perspective from quantitative results to qualitative results in many spheres of human activity due to cultural and social shifts. Just as there is a turn towards a happiness index in economic formulations, there is also the beginnings of a turn away from a singular focus on yields and disease resistance in plant breeding and towards quality of life issues such flavor and micronutrient levels. Related to these shifting cultural trends and concerns with qualitative results is a consumer demand for quality and flavor in food as can be seen in the current interest in heirloom and organic vegetables. Consumers are expecting more from the quality of food and breeders and food processors need to respond. Plant breeders need to incorporate this value-added trait to products to fulfill the interests and needs of modern consumers and to bring higher prices and greater rewards to farmers. Yet it is important to maintain balance in any breeding endeavor. Breeding for flavor treads a fine line between incorporating an important quality trait that has sometimes been lost in green beans, and the loss of equally important agronomic traits. It may be necessary to conduct an index selection for many traits simultaneously or to use alternating cycles of selection for flavor in one generation and selection for agronomic traits in the next generation. Some breeding programs have addressed this issue by selecting for agronomic traits early in the breeding process and then by selecting for flavor traits only in the last rounds of selection. Flavor cannot be reduced merely to volatiles and future research into flavor in green beans must be cognizant of this fact, even if it may be possible to substitute concentrations of critical volatile compounds for flavor once more research is conducted on the sensory characteristics of green beans. Future research on flavor volatiles in green beans must include a sensory evaluation component because it cannot be deciphered beforehand as to how a given volatile will relate to other volatiles and to the food matrix. Not understanding the broader context of a volatile within a food assumes that a given volatile will alter the flavor of a food in

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a particular way, but this is unproven until it is tested. The first step that must be taken is to develop a set of thresholds of detection that are specific to green beans. Preferably, these thresholds should be developed in a macerated green bean matrix and should be specific to retronasal detection. Simply sniffing for the presence of a volatile through orthonasal olfaction is inadequate to understanding the true nature of the volatile in the presence of the food matrix. Research has shown that retronasal olfaction can be remarkably different from orthonasal olfaction and inferring one from the other is incorrect (Bojanowski & Hummel, 2012; Ruijschop et al., 2009). Once thresholds are established, attempts should be made to reconstitute green bean flavor in a very bland bean with volatiles that are suspected of contributing to green bean flavor, such as was done by Stevens and colleagues (1967a). A bland bean could be identified through descriptive sensory work on a diverse panel of beans. In addition to thresholds and reconstitution experiments, much work needs to be done to understand how volatiles correlate with descriptors. The work presented here correlating linalool and 1-octen-3-ol to eight descriptors needs to be expanded to more volatile compounds, such as 3-hexen-1-ol. Preference also needs to be factored in as a trait that is correlated to all the volatiles. The value of preference analysis can be seen in the work of Tieman and colleagues (2012) in which compounds that were both highly prevalent and very noticeable in the flavor of tomatoes had very little impact on preference. Another tool that will likely be needed is multivariate analysis, such as principal component analysis, to reduce complexity and identify connections between instrumental measurements and sensory measurements (Dijksterhuis, 1995; Noble & Ebeler, 2002). Of critical importance to future genetic mapping research into green bean flavor is the replication of results. Repeating an experiment for validation is an important part of any scientific endeavor, and it is especially important to the mapping of a trait that has never been mapped before, such as flavor in green beans. The overall project was designed to concurrently map flavor in snap beans by both GWAS and linkage mapping in different populations in the hopes of finding mutually validating results. This has come to pass to some degree, but the results are complicated. Four SNPs are shared between the results of GWAS and linkage mapping with QTL analysis. The first of these SNPs is ss715648121. This SNP is found in the

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GWAS study as associated with 3-hexen-1-ol with a negative log value of 4.32, which is just slightly below the Bonferroni cutoff of 4.435 as shown in Figure 5.1. This SNP is within the interval of the HEX4.1 QTL identified in the linkage mapping and QTL analysis. Defining the interval for HEX4.1 is somewhat challenging because the interval as defined by the genome- wide LOD threshold of the permutation test is restricted to pseudo-markers, which do not have a physical map position. But this region is immediately flanked by two SNP positions that are within an interval defined by the linkage group specific LOD threshold (LOD = 1.6). This interval is 3.48Mb to 4.46Mb on Pv4, which includes the 4.42Mb position of ss715648121. The second SNP to overlap between association and linkage mapping is ss715639422. This SNP is associated with 1-octen-3-ol and is located at 40.97Mb on Pv3. Similar to the previous SNP, the negative log P value (4.09) is just below the Bonferroni threshold on the Manhattan plot (Figure 5.2). This SNP is within the interval of the OCT3.1 QTL identified in linkage mapping and QTL analysis. Again, as with the previous SNP, a physical map interval is not possible because most of the QTL is based on pseudo-markers, but two real markers can be identified within the interval defined by the linkage group specific LOD threshold (LOD=1.3). This interval is 32.92Mb to 41.44Mb, which includes the 40.97Mb SNP position. The next overlap is found with linalool SNP ss715647231 at position 2.14Mb on Pv2. This SNP has a negative log value of 4.68 in GWAS, which is above the Bonferroni threshold. In this case, the SNP for linkage mapping is identical, ss715647231, although the LOD score is just below the genome-wide threshold utilized in chapter 2 as a cutoff, but above the linkage group specific LOD threshold (LOD = 1.7) (Figure 5.3.). The last SNP is shared between the Kruskal-Wallis non-parametric mapping of linalool and GWAS mapping. This SNP is ss715644427 at 13.89Mb on Pv5. Kruskal-Wallis mapping identified this SNP as being associated to linalool at a better than 1x10-7. The Kruskal-Wallis threshold for significance is 3.28x10-5 based on a Bonferroni calculation. This SNP was not included in the results of the linkage mapping and QTL analysis because it is not incorporated into any linkage group (i.e. an unmapped SNP). GWAS mapping also identified this exact SNP as being associated to linalool with a negative log value of 5.54, which is well above the Bonferroni threshold. In addition to these four SNPs shared between GWAS and linkage mapping, is a SNP that is internally validated within GWAS. GWAS analysis for both hexanal and 1-hexanol identifies

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ss715639302 as significantly associated, although the negative log P value for hexanal is just below the Bonferroni threshold at 4.22. These compounds are biochemically related to one another as hexanal is converted to 1-hexanol. If the conversion process through an alcohol dehydrogenase is in some way altered by a single gene, then it could result in the identical SNP being identified by GWAS analysis for each compound. Although there are four marginal overlaps between mapping methods and one internal overlap in GWAS, most QTL and associated SNPs are unreplicated and unverified. The lack of validation for most QTL in the linkage mapping and GWAS mapping is expected for several reasons. These two populations were grown in different years (2013 & 2016) in different fields of the OSU Vegetable Research Farm, which should engender some environmental variation. There may be SNP effects that are highly sensitive to environmental changes and others that are consistent across environments. There may be some variability in the maturity of the pods that also plays into these environmental effects. It is also important to realize that the two populations are genetically very different. The GWAS population contains a high degree of genetic diversity and many of the genetic variations that underlie QTL may not be present in the more limited biparental population. Indeed, it seems probable that many of the associated SNPs in the GWAS population are derived from the Dickson landrace collection because the measurements of volatiles in this collection are often at the extreme end of the range whereas the biparental population is a cross between two relatively generic commercial lines of North American or European descent with more modest measurements of volatiles. Another possible reason why the GWAS mapping may not be validated in the linkage mapping and vice versa is the continuing issue of population structure in the GWAS study. It may be that 1 PCA is still not adequate to capture all the population structure. Conversely, it may be that the 1 PCA was a slight over correction despite the improved appearance of the QQ plots. Whether there was an over correction or under correction, it may be that linkage mapping is less biased. There are also differences in threshold that may account for the lack of validation of some SNPs. The permutation test of LOD scores may give a very different threshold than the very strict Bonferroni applied to the GWAS study. It may be that adjustments to the GWAS threshold might yield more overlaps with the linkage mapping. To overcome these issues with

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environmental variation, population diversity, population structure, and thresholds, it seems clear that more work needs to be done to both replicate previous QTL as well as identify new ones. There needs to be more populations tested in more environments across at least two years. An augmented design needs to be used to measure variances and heritability. A higher resolution marker system, such as Genotyping-By-Sequencing, might also be beneficial. An important incidental result of this research into the sensory and genetic aspects of flavor in green beans has been the characterization of the Dickson bean collection. This collection was nearly lost due to a mis-cataloguing of the materials. Luckily, a portion of the seeds packets was sent to our Oregon State program, and now this portion is the only remaining portion of the collection. It was added to the GWAS population to create a larger and more diverse population for study, not because of any previous knowledge of its potential for flavor. An informal tasting of pods in the field was highly surprising due to the novel flavors that were present – flavors that are not typical even of dry beans or heirloom types. Later GC-MS analysis bore out this apparent novelty. The Dickson collection has 2.7-fold greater 1-octen-3-ol levels on average than the BeanCAP collection and 2.6-fold greater 1-penten-3-one, but 1.3-fold less linalool. The top ten bean lines for 1-octen-3-ol are all from the Dickson collection. There are other peculiar traits in this collection, such as two lines that possess pods that are very flat and wide to an extreme degree such that the appear to be caricatures of a snow pea pod. This research has been a first foray into the mapping of flavor in green beans and an initial attempt to dissect flavor at the sensory level in support of this mapping. It is hoped that this research will have some meaningful impact on both plant breeding and fundamental research. In looking ahead as to how this sensory and genetic research may continue to unfold, it is helpful to review research into tomato flavor because it has been more extensively studied than green bean flavor and it presents models of what can be achieved and how it can be achieved (Tieman et al., 2017; Zhao et al., 2016; Zhang et al., 2015; Tieman et al., 2012; Baldwin et al., 2008; Baldwin et al., 2000; Buttery et al., 1987). Other paths of research into flavor and plant volatiles may open in the future as well. For example, more research is needed into the possible health benefits of these volatiles, as can be seen with linalool, which preliminary research shows to be potentially anti-inflammatory and protective of DNA integrity (Lee et al.,

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2018; Gunaseelan et al., 2017). There are also evolutionary questions implied in volatile research as to why common bean produces these secondary metabolites. This is particularly the case for 1-octen-3-ol, which has been recorded in a handful of other plants, such as oregano (Milos et al., 2000), but which does not appear to have a meaningful role in the ecology of the plant that is apparent at this time. This is in contradistinction to linalool, which has a body of research in other plant species showing meaningful roles in beneficial insect attraction and repelling pest insects (Halbert et al., 2009; Degenhardt et al., 2003). Altogether, there is much work that needs to be done to tease out the secrets of this plant and the benefits it can bring through its volatiles and through other means.

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Figures

A. B.

Figure 5.1. Manhattan plot and QQ plot of SNPs associated with 3-hexen-1-ol (log transformed data). On the x-axis of the Manhattan plot (A.) are chromosomes, which are also indicated by the color of the SNPs graphically depicted above the chromosome number. On the x-axis of the QQ plot (B.) are expected negative log p-values. On the y-axis of A. are the negative log p- values. The two thresholds of significance, based on a Bonferroni cutoff using all markers (-log = 4.958) and a Bonferroni cutoff using only effective markers (-log = 4.435), are shown as two lines across the middle of the plot. On the y-axis of B. are observed negative log p-values. The red arrows points to ss715648121 in both A. and B. A. B.

Figure 5.2. Manhattan plot and QQ plot of SNPs associated with 1-octen-3-ol. On the x-axis of the Manhattan plot (A.) are chromosomes, which are also indicated by the color of the SNPs graphically depicted above the chromosome number. On the x-axis of the QQ plot (B.) are expected negative log p-values. On the y-axis of A. are the negative log p-values. The two thresholds of significance, based on a Bonferroni cutoff using all markers (-log = 4.958) and a Bonferroni cutoff using only effective markers (-log = 4.435), are shown as two lines across the middle of the plot. On the y-axis of B. are observed negative log p-values. The red arrows points to ss715639422 in both A. and B.

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Figure 5.3. Plot of the LOD trace for linkage group 2 (Pv2). Shown on the x-axis are centiMorgans on the linkage map shown in chapter 2. On the y-axis is the LOD. The dashed line shows the genome-wide threshold and the dotted line shows a linkage group specific threshold as shown by a permutation test. The red arrow points to the QTL containing ss715647231.

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