Genetic Study of Compositional and Physical Kernel Quality Traits in Diverse Maize

(Zea mays L.) Germplasm

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Si Hwan Ryu, M.S. Graduate Program in Horticulture and Crop Science

The Ohio State University 2010

Dissertation Committee: Dr. Richard C. Pratt, Advisor Dr. Joseph C. Scheerens Dr. Peter R. Thomison Dr. Margaret G. Redinbaugh

Si Hwan Ryu

2010

Abstract

Grain quality traits of maize such as protein, oil, starch, and kernel size and density are essential for various end-uses; feed for animals, food for humans, and raw materials for industry. Kernel pigments like anthocyanins and carotenoids have numerous nutritional functions in animals and human beings. Increasing the levels of these compositional traits and pigments in kernels should increase the nutritional quality of maize.

An investigation of protein content and its relationship with kernel physical traits and identification of quantitative trait loci (QTL) underlying these traits was conducted in a population arising from a cross between a low protein temperate dent inbred (B73) and a high protein tropical flint breeding line (H140). The QTL associated with these traits were determined by selective genotyping and the correlations among kernel traits were calculated. A preliminarily examination of QTL associated with oil and starch was also undertaken. Kernel pigment content of representative Arido-American land race maize accessions was evaluated and relationships between pigments, protein, and oil contents were determined. Reciprocal effects when high and low pigment containing progenies were crossed also were examined.

Multiple regression analysis detected QTL for protein, density, and 100-kernel weight that explained 38 to 71%, 15 to 42%, 27 to 64% of phenotypic variation,

respectively. Initial findings for oil and starch suggested that the QTL explained 13 to

64% and 38 to 80% of phenotypic variation, respectively. Starch content was negatively correlated with protein and oil, and these correlations were consistent across environments. Starch content was negatively correlated with kernel density and protein content was also negatively correlated with 100-kernel weight. Protein content and kernel density were positively correlated. Our results indicate that certain desirable trait combinations may be easily selected, whereas other combinations will be much more difficult due to negative associations between them.

Orange and yellow colored kernels contained large amounts of carotenoids and blue and purple colored kernels contained high anthocyanin concentrations. It was concluded that the Arido-American germplasm generally does not possess high carotenoid content, but does display high anthocyanin content which may provide health and nutritional benefits to those who consume it. Phenotypic correlations between carotenoid and oil content, and between carotenoid and protein content, were not consistent across populations, suggesting that simultaneous enhancement of these traits may be difficult in some populations. Carotenoids in reciprocal crosses were intermediate between the parents, or were more affected by the female parent, suggesting a dosage effect. Anthocyanin content of reciprocal crosses was not significantly different from that of their female parents, indicating a strong maternal effect. The selected high anthocyanin progenies from Arido-American grermplasm may serve as a germplasm resource for development of high anthocyanin temperate maize.

iii

Dedication

This document is dedicated to my parents, wife, and sons.

Acknowledgments

It is with pleasure that I thank my advisor, Dr. Richard C. Pratt who provided, guided, and supported me during my graduate study. His advice as a mentor guided my family‟s life in USA and as a professor allowed me to pursue my research.

I am extremely grateful to all my committee members, Dr. Joe Scheerens, Dr. Peter

Thomison, and Dr. Margaret Redinbaugh for their valuable suggestions to my project, and for their positive criticism to my dissertation.

I am thankful to Dr. Godfrey Asea for his guidance at the beginning on my QTL study, Mark Casey for his support in field research, Mark Jones for his help in DNA analysis, Dr. Edward Souza for his support of machine, Dr. Ann Chanon for her help in pigments analysis, Suzanne Nelson for collaboration and gift of seed stocks from collection in Native Seeds/SEARCH (NSS), Lindsay Werth, Benito Gutierrez, and Chris

Lowen for field support in Arizona, Andrew Burt in Univ. of Guelph for advice of carotenoid analyses.

I am also grateful to Dr. Hwang-Kee Min for his extraordinary support of my study abroad.

I deeply thank my family. To my parents, for believing in me and unconditional support in my whole life. To my wife, Jaesook Noh, for her great support, love, and dedication. To my dearly beloved sons, Jongha and Wonha, for their strong friendship v

and cheerful life making me happy. To my brother, sister, and their families for their assistance.

Lastly, very special thanks to my mother-in-law who passed away during my study.

Vita

February 14, 1969 ...... Born – Chungju, S. Korea

1987...... Chungju High School, S. Korea

1994...... B.S. Agriculture, Chung Buk National

University, S. Korea

1999...... M.S. Agriculture, Chung Buk National

University, S. Korea

1996 to 2006 ...... Agricultural Researcher, Gangwondo

Agricultural Research and Extension

Services, S. Korea

2007 to present ...... Graduate Research Associate, Department

of Horticulture and Crop Science, The Ohio

State University

Fields of Study

Major Field: Horticulture and Crop Science

vii

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments...... v

Vita ...... vii

List of Tables ...... xi

List of Figures ...... xv

Chapter 1: Background and Introduction ...... 1

Chapter 2: Quantitative Trait Loci study of Maize Grain Quality Traits in a Temperate by

Tropical Population Using Selective Genotyping ...... 6

2.1. Abstract ...... 6

2.2. Introduction ...... 7

2.3. Materials and Methods ...... 10

2.3.1. B73  H140 (BH) population development and field evaluation ...... 10

2.3.2. Phenotypic trait analysis ...... 11

2.3.3. Genotypic analysis ...... 12

viii

2.3.4. Statistical analysis...... 12

2.4. Results and Discussion ...... 13

2.4.1. QTL analysis...... 13

2.4.2. Kernel trait variation and correlations ...... 18

Chapter 3: Variation of Kernel Anthocyanin and Carotenoid Content in Arido-American

Land Races of Maize ...... 28

3.1. Abstract ...... 28

3.2. Introduction ...... 29

3.3. Materials and Methods ...... 32

3.3.1. Selection of accessions and field evaluation ...... 32

3.3.2. Harvest, milling and kernel oil and protein composition ...... 32

3.3.3. Total carotenoid pigment analysis ...... 33

3.3.4. Total anthocyanin pigment analysis ...... 34

3.3.5. Statistical analysis...... 35

3.4. Results ...... 36

3.5. Discussion ...... 38

Chapter 4: Improving Maize Kernel Anthocyanin and Carotenoid Content Using

Population (Germplasm) of Diverse Origin ...... 53

4.1. Abstract ...... 53

4.2. Introduction ...... 54

4.3. Materials and Methods ...... 57

4.3.1. Entry selection and field evaluation for carotenoids ...... 57

4.3.2. Entry selection and field evaluation for anthocyanins ...... 59

4.3.3. Milling and compositional analysis ...... 60

4.3.4. Total carotenoid pigment analysis ...... 61

4.3.5. Total anthocyanin pigment analysis ...... 62

4.3.6. Statistical analysis...... 63

4.4. Results and Discussion ...... 63

4.4.1. Carotenoid contents in self- and cross-pollinated seeds ...... 63

4.4.2. Anthocyanin contents in self- and cross-pollinated seeds ...... 67

List of References ...... 82

List of Tables

Table 2.1. Regression models with QTL for kernel protein content among 45 selected F2:3

and F2:4 progenies from B73  H140 population at King farm (K) in 1999 and

Shaffter farm (S) in 1999, 2002, 2006, and 2008...... 20

Table 2.2. Regression models with QTL for kernel density among 45 selected F2:3 and

F2:4 progenies from B73  H140 population at King farm (K) in 1999 and

Shaffter farm (S) in 1999, 2002, 2006, and 2008...... 21

Table 2.3. Regression models with QTL for 100-kernel weight among 45 selected F2:3

and F2:4 progenies from B73  H140 population at King farm (K) in 1999 and

Shaffter farm (S) in 1999, 2002, 2006, and 2008...... 22

Table 2.4. Regression models with QTL for kernel oil content among 45 selected F2:3 and

F2:4 progenies from B73  H140 population at King farm (K) in 1999 and

Shaffter farm (S) in 1999, 2002, 2006, and 2008...... 23

Table 2.5. Regression models with QTL for kernel starch content among 45 selected F2:3

and F2:4 progenies from B73  H140 population at King farm (K) in 1999 and

Shaffter farm (S) in 1999, 2002, 2006, and 2008 ...... 24

Table 2.6. ANOVA mean square value for kernel protein, oil, starch, density, and 100-

kernel weight of 45 selected progenies of BH population obtained by crossing

between B73 and H140...... 25

Table 2.7. Means for kernel protein, oil, starch, density, and 100-kernel weight of 45

selected progenies of BH population obtained by crossing between B73 and

H140...... 26

Table 2.8. Correlation of kernel protein, oil, starch, density, and 100-kernel weight of 45

selected progenies from a population obtained by crossing between B73 and

H140 at King farm in 1999 and Shaffter farm in 1999, 2002, 2006, and 2008.

...... 27

Table 3.1. Agronomic traits of Arido-American maize accessions in Ohio, 2008...... 43

Table 3.2. Agronomic traits of Arido-American maize accessions in Arizona, 2008...... 44

Table 3.3. Relationship among Arido-American accessions of maize across Arizona and

Ohio locations and range of accession means within locations for agronomic

traits...... 45

Table 3.4. Mean values and ANOVA for 100-kernel weight, kernel protein, oil,

carotenoids, and anthocyanins of 48 accessions from Native Seed/SEARCH

stocks, grown in Ohio in 2008...... 46

xii

Table 3.5. Mean values and ANOVA mean square value for 100-kernel weight, kernel

protein, oil, carotenoids, and anthocyanins of 48 accessions from Native

Seed/SEARCH stocks, grown in Arizona in 2008...... 47

Table 3.6. Waller-Duncan grouping of carotenoid and anthocyanin contents by kernel

colors...... 50

Table 3.7. Waller-Duncan grouping of carotenoid and anthocyanin contents by kernel

types. Corn Belt controls were excluded to identify color variation in NSS

germplasms...... 51

Table 4.1. Kernel compositional traits of selected partially inbred progenies in two

population, (Oh43  Oh608)  Oh43 BC1F5 and B73  H140 F4, in 2008 and

2009 grown in Ohio ...... 72

Table 4.2. Agronomic characteristics of two populations, (Oh43  Oh608)  Oh43 and

B73  H140 in Ohio 2009 ...... 73

Table 4.3. Correlation of kernel protein, oil, and carotenoid of selected progenies from

two populations in 2009 ...... 74

Table 4.4. Mean value of kernel carotenoid content in hybrid seeds from crosses between

high and low carotenoid progenies in 2009 ...... 76

Table 4.5. Agronomic traits of parents and hybrids in carotenoid development progenies

in 2010 ...... 77

Table 4.6. Total anthocyanin contents of self-pollinated progenies in 2008 and 2009 .... 78 xiii

Table 4.7. Agronomic characteristics of selected high anthocyanin progenies and „Ohio

Blue‟ in Ohio in 2009 ...... 80

Table 4.8. Agronomic traits of parents and hybrids in antocyanin development progenies

in Ohio in 2010 ...... 81

xiv

List of Figures

Figure 3.1. Frequency distributions of samples by kernel color (A) and kernel type (B)

evaluated in Wooster, Ohio and Patagonia, Arizona in 2008...... 48

Figure 3.2. Frequency ranges by samples of carotenoids (A) and anthocyanins (B) content

in Wooster, Ohio and Patagonia, Arizona in 2008...... 49

Figure 3.3. Comparison of average high temperature and average precipitation in two

environments, Patagonia, Arizona and Wooster, Ohio...... 52

Figure 4.1. Mean value of total kernel carotenoid content by self-pollinated and cross-

pollinated group in two populations (Oh43  Oh608)  Oh43 (BC) and B73 

H140 (BH) in 2009 ...... 75

Figure 4.2. Mean value of total kernel anthocyanin content of self-pollinated and cross-

pollinated groups derived from crosses between selected high anthocyanin

progenies and „Ohio Blue‟ (blue or white) in 2009 ...... 79

Chapter 1: Background and Introduction

Maize (Zea mays L.) is one of the most important crops in the world. It provides feed for animals, food for humans and raw materials for various industrial uses. Maize is an important food in underdeveloped countries, but the majority of maize products are used directly as animal feed in developed countries. Due to continued success of maize breeding, yields have increased remarkably. However, the crop production for humans and also animals is not enough and there are over 900 million people suffering from hunger in the world (FAO, 2010). In some areas such as sub-Saharan Africa, maize is a staple food and many people suffer from vitamin A deficiency. An estimated 190 million pre-school children and 19 million pregnant women are exposed to a vitamin A deficiency which can cause visual impairment and blindness, or weaken the immune system (UNICEF, 2009). To overcome this food problem, we need to increase not only crop yield but also nutritional value of the diet.

Maize kernel types are generally classified by characteristics of kernel endosperm such as flint, flour, dent, pop, sweet, and waxy. Flint maize has a hard outer layer and can protect against insects or diseases, but it needs more energy for processing. Floury maize can be easily milled into flour and cooks quickly, but it can be easily damaged by insects or diseases. Breeding for various types of specialty maize is an important part of maize

development. Sweet and waxy maize are used for fresh consumption. High-lysine maize which has higher amounts of lysine and lower amounts of α-zein was developed by scientists from Purdue University and CIMMYT. High oil maize is an excellent energy source for humans and live-stock. Animals can grow faster when fed high oil content varieties. NutriDense® maize was developed by seed companies and offers nutritionally enhanced grain containing more protein and oil (Dickerson, 2003; Thomison and Geyer,

2000).

The protein content of maize is roughly 8 to 11% (Bletsos and Goulas, 1999).

Four major protein fractions of protein in maize are defined by their solubility: water soluble albumin, salt soluble globulin, alkali soluble glutelin, and alcohol soluble zein.

Glutelin and zein account for about 40% of the grain protein, respectively (Shukla and

Cheryan, 2001). Although 70% of the protein supply of human consumption comes from plants, most food crops have some nutritional deficiency: among the amino acids, rice lacks lysine and threonine; soybeans lack methionine and cystine; and maize is deficient in lysine and tryptophan (Acquaah, 2007). Maize grain protein is used for animal feed, edible film, and industrial polymers. Grain starch is an important carbohydrate for the human diet and is now increasingly used for producing ethanol. Grain oil is used for making cooking oil and biodiesel.

Following the development of inexpensive DNA markers and statistical methodology, QTL linkage analysis has been used for various genetic and breeding programs. Quantitative traits such as yield, grain quality, and some diseases are

controlled by many genes. Trait phenotype, polymorphic markers, and genetic population structure are essential factors in QTL studies. A mapping population is generated by crossing lines with extreme phenotypic traits and segregated to make structures such as

F2, backcrosses (BC), recombinant inbred line (RIL), and doubled haploid (DH)

(Acquaah, 2007). By establishing associations between trait (phenotype) values and marker (genotype) data, informative markers can be identified in QTL mapping studies and these candidate markers can then confirmed in independent populations (Collard at al., 2005). Confirmed, or validated, QTL can then be targeted for breeding of the trait.

Multifunctional antioxidant pigments help protect plant tissues against internal and environmentally-induced oxidative stress and these pigments may be of considerable benefit to human health as well. The two major pigment classes determining maize kernel color are carotenoids and anthocyanins. Carotenoids are associated with yellow or orange colors and anthocyanins are associated with red, purple, or blue colors. Yellow maize contains carotenoids as precursors for vitamin A including β-carotene, β-cryptoxanthin, and α-carotene (Wong et al., 2004). Lutein and zeaxanthin are non-provitamin A and major carotenoids in maize kernels may protect against cataracts and age-related blindness (Chitchumroonchokchai et al., 2004; Krinsky et al., 2003; Kurilich and Juvik,

1999; Moros et al., 2002). Anthocyanin pigments have various physiological activities; high antioxidant capacity or free radical scavenging ability (Cevallos-Casals and

Cisneros-Zevallos, 2003; Oki et al., 2002), and inhibition of cancer cell growth

(Hagiwara et al., 2001; Jing et al., 2008).

Pigmented maize with various colors is still conserved in Central- and South-

America and also the southwestern USA. The latter germplasm sources have not been studied or subjected to improvement (Betran et al., 2001); therefore, this diverse germplasm may serve as a potential breeding resource for development of high pigment maize lines. When taken from its region of adaptation, exotic germplasm may display high lodging, susceptibility to disease inciting pathogens and insects, photoperiodicity, and late maturity. We should consider these characteristics in the selection process when we introduce exotic germplasm into temperate areas.

The specific objectives of the research were;

1) to investigate QTL associated with kernel protein, oil, starch, density, and 100- weight by selective genotyping and to determine correlations between the traits. For this study, we produced a population by crossing a temperate inbred line and a tropical recombinant inbred line, and selecting 45 lines from the F2:3 population based on protein content and evaluated several kernel traits in different environments.

2) to discover accessions with the highest concentrations of kernel anthocyanin and carotenoid pigments and to investigate the morphological traits from the native maize germplasm originated from Arido-America in cooperation with Native Seeds/SEARCH

(NSS). The germplasm resources are still cultivated in the southwestern USA and northwestern Mexico (Arido-America) and show considerable diversity for many kernel traits including coloration.

3) to measure the female or male effect when high and low pigments lines are crossed, evaluate relationships among carotenoids, protein, and oil contents, and to introduce the high anthocyanin trait from the germplasm of southwestern USA and northwestern Mexico (Arido-America) into the „Ohio Blue‟ population. Increasing levels of anthocyanins and carotenoids in maize kernels should increase their nutritional quality for animal and human consumption. It is also necessary to know how the kernel traits of male and female parentage affect the pigment levels of maize kernels in maize breeding and seed production.

This dissertation deals with kernel nutritional quality related to macronutrients such as protein, oil, and starch and micronutrients such as carotenoids and anthocyanins.

It also deals with kernel physical traits such as density and 100-kernel weight instead of test weight because test weight is a “packing value” mainly affected by kernel density and size. Chapter 2 presents a QTL study for maize kernel compositional traits, chapter 3 identifies variation of kernel pigments and documents the selection of high anthocyanin lines, and chapter 4 describes expression of pigments in hybrid (F1) kernels.

Chapter 2: Quantitative Trait Loci Study of Maize Grain Quality Traits in a

Temperate by Tropical Population Using Selective Genotyping

2.1 Abstract

Both compositional and physical kernel traits of maize are essential for various end-uses; feed for animals, food for humans, and raw materials for industry. Important compositional traits include protein, oil, and starch content and physical traits include density and size. We developed a partially inbred population from a cross between a low protein temperate maize inbred (B73) and a high protein recombinant inbred line (H140) of tropical origin and selected 45 partially inbred lines representing the extreme and intermediate classes for protein content from 169 segregating F2:3 progenies grown at two locations in 1999. These selected progenies were self-pollinated and F2:4 were tested over three years near Wooster, Ohio. We then used selective genotyping to examine relationships among kernel traits and to identify QTL underlying these traits in the population. Multiple regression analysis was used to detect QTL for protein, density, and

100-kernel weight that explained 38 to 71%, 15 to 42%, 27 to 64% of phenotypic variation at test sites, respectively. Oil and starch also were preliminarily examined and explained 13 to 64% and 38 to 80% of phenotypic variation, respectively. Protein content was negatively correlated with 100-kernel weight, and protein content and kernel density

were positively correlated. Starch content was negatively correlated with protein and oil content, and these correlations were consistent across environments. Starch content was also negatively correlated with kernel density. Our results indicate that certain desirable trait combinations, such as high protein and oil, may be amenable to selection, but negative associations between these traits and other traits (e.g. protein and 100-kernel weight) may result in unwanted changes to the kernel phenotype.

Abbreviations: BH, a population derived from the cross between a low protein temperate inbred line B73 and a high protein recombinant inbred line H140; CTAB, cetyl trimethylammonium bromide; NIR, near-infrared reflectance; NIT, near-infrared transmittance; OARDC, Ohio Agricultural Research and Development Center; PCR, polymerase chain reaction; SSR, simple sequence repeat; QTL, quantitative trait loci.

2.2 Introduction

Maize (Zea mays L.) is an extremely versatile crop. It provides feed for animals, food for humans and raw materials for various industrial uses. Maize kernel traits such as protein, oil, starch, density, and size are essential factors for those end-uses. The protein content of maize kernels varies between 80 to 110 g kg-1 (Bletsos and Goulas, 1999).

Although some essential amino acids are lacking in maize, high protein maize provides adequate feed for ruminant animals (Dudley et al., 2007). Maize grain protein is used in various applications: gluten meal is an important animal feed; maize protein fraction is

used for industrial polymers such as biodegradable plastics and films; zein is used to make coated food, edible film, fish feed, and adhesives (Shukla and Cheryan, 2001); and maize protein as gluten meal is also used as a natural pre-emergence herbicide (Liu et al.,

1994). Maize oil is a good source of energy for poultry production, cooking oil, and biodiesel production (Thomison et al., 2003). Starch is an important source for copy paper, baking powder, and ethanol production (Gutierrez et al., 2002). Kernel hardness, determined by density measurements, is related to the amount of void space and the portion of vitreous endosperm (Ngonyamo-Majee et al., 2008). Dry-milling industries prefer hard kernels because they produce large flakes, but wet-milling industries prefer soft kernels because they need less steep time (White and Johnson, 2003). These kernel traits are related to each other; therefore, increasing one trait may result in a concomitant increase or decrease in other traits.

Quantitative traits such as yield, grain quality, and resistance to infection by some pathogens are controlled by polygenes, each contributing a small effect to the overall phenotypic expression of a trait. Affected by environment, the genes lack dominance and their action is additive. Studies of compositional trait QTL have identified several chromosomal locations of the genes governing these traits. The accuracy of QTL analysis depends on the density of the markers, the size of the population, and the accuracy of trait scoring. Increasing the size of the population is difficult when several traits are being measured or if individual traits are costly or laborious to measure.

Increasing marker density is not always useful because accuracy arises primarily from the number of recombination events (Vreugdenhil et al., 2007).

Large populations generally yield more information in QTL studies, but they require more time and expense for phenotypic evaluation in multiple locations with adequate replication. In order to reduce time and costs, researchers have introduced selective genotyping to test the phenotypically extreme individuals in a population. These individuals contain the largest amount of linkage information (Foolad and Jones, 1993;

Lander and Botstein, 1989). Tanksley (1993) pointed out that individuals in the extreme high and low tails of the frequency distribution tend to have more positive and negative alleles. Lander and Botstein (1989) reported that about 33% of the progeny in a population have phenotypes that are more than one standard deviation from the population mean, and these progenies contribute about 81% of the total linkage information. Selective genotyping was useful for detecting QTL affecting salt tolerance in tomato, and bidirectional selection was more effective than unidirectional selection for

QTL detection (Foolad and Jones, 1993). Zhang et al. (2003) used selective genotyping to identify QTL for early blight resistance of tomatoes. Among 820 BC1 plants derived from a cross between early blight susceptible and resistant parents, the most susceptible 30 plants and the most resistant 46 plants were selected and analyzed. They showed that seven resistance QTLs could be detected on several chromosomes. Gordon et al. (2004) conducted selective genotyping using 46 high and low gray leaf spot resistance lines in a segregating maize population and detected markers linked to resistance on chromosomes

2 and 4. In our study, we produced a population by crossing between B73 and H140 and selected 45 progenies from the F2:3 population for selective genotyping, and evaluated their F2:4 progenies in field tests over several years. The main objectives of this study were to discover QTL associated with kernel protein, density, and 100-weight by selective genotyping and to determine the correlations between these traits. We also preliminarily examined kernel oil and starch contents and their relationships with other traits.

2.3 Materials & Methods

2.3.1 B73  H140 (BH) population development and field evaluation

A low protein Corn Belt yellow dent inbred (B73, female parent) and a single plant selection from tropical high protein line (H140, male parent) were crossed in the greenhouse during 1997/1998. B73 was selected from Iowa Stiff Stalk Synthetic (BSSS)

(Russell, 1972) and H140 is a recombinant inbred line (RIL) created from a cross between Hi34 and Tzi4 (Moon et al., 1999). F1 kernels from a single ear were planted at the Schaffter Farm in summer 1998 at the Ohio Agricultural Research and Development

Center (OARDC), Wooster, OH and several plants were self-pollinated to produce F2 seed. F2 kernels from one ear were planted in the winter nursery 1998/1999 and 369 F2:3 ears were produced by controlled self-pollination. Two replications of 169 F2:3 progenies which had sufficient seed were planted at the King and Schaffter Farms at OARDC in

1999. These were self-pollinated to produce F2:4 progenies. F2:4 progenies were planted in

the 2002, and in subsequent experiments, full-sib mated progenies were utilized to minimize further inbreeding depression and supply adequate seed for future studies.

The population structure used for selective genotyping consisted of 15 intermediate protein progenies in addition to the 15 highest and 15 lowest progenies (tails) for total protein content. This selection was based on data from the 1999 experiments at both King and Schaffter farms. These 45 F3 and F4 progenies subsequently will be referred to as the BH population. The selected BH progenies, with parents, were planted at Schaffter farm in 2002, 2006, and 2008. Each experimental plot was a single 5m row with 0.75m row spacing. The field experiments were arranged in randomized block design with two replications. Nitrogen was applied at a rate of 112 kg/ha (Barker et al.,

2005), weeds were controlled by pre- and post-plant herbicides (Loux et al., 2007), and plots were irrigated using overhead sprinklers when drought conditions appeared imminent.

2.3.2 Phenotypic trait analysis

Ears were individually hand-harvested and dried in forced air drying ovens at

30°C. Kernels were hand-shelled from the middle part of each ear and stored at 4°C prior to analysis. Compositional traits were assessed with a Tecator 1225 near-infrared transmittance (NIT) whole-grain analyzer equipped with the ISU System One Calibration

(Foss North America, Silver Spring, MD). Foreign material and broken kernels were thoroughly removed prior to analysis. Multiple samples of at least 45 g from each

replicate were scanned with five to ten subsamples per sample, and the mean value was calculated. Moisture content of samples ranged from 10 to 14%.

2.3.3 Genotypic analysis

Leaf tissue samples were collected from ten plants per progeny and from each parent grown in the field in 2002, and bulked for DNA extraction to conduct simple sequence repeat (SSR) marker analysis. DNA was extracted from samples by a modified cetyl trimethylammonium bromide (CTAB) method (Asea, 2005).

PCR was carried out in 20-µl reaction mixtures, and products were resolved on a

4.5% (w/v) high-resolution agarose gels (Asea, 2005). Over 700 SSR primers available from the Maize Genetics and Genomics Database (www.maizegdb.org) were screened for polymorphisms between the two parents, and over 200 of these were polymorphic. One hundred nineteen easily scorable, polymorphic SSR markers were used to genotype all 45 progenies for QTL analysis.

2.3.4 Statistical analysis

Statistical analysis of measured traits was conducted with SAS V9.1 (SAS

Institute, Inc., 2002). Analysis of variance (ANOVA) using PROC GLM was performed on phenotypic data for each environment. PROC CORR was used to determine Pearson and Spearman‟s rank correlation coefficients between traits for each environment. The

data sets had some missing data; therefore, least square means values were used to confirm general correlations for the combined environments.

Associations between individual marker loci and kernel traits were identified with single-factor ANOVA using PROC GLM with a threshold significance level of p = 0.05 for preliminary detection of QTL. Total phenotypic variation for each marker was calculated by variance component estimation by restricted maximum likelihood (REML) using PROC VARCOMP (SAS Institute, Inc., 2002). Multiple regression analysis was used to determine the best multi-locus model between phenotypic trait and molecular markers selected in single factor ANOVA, and the percentage of the phenotypic variance was explained by the model (Berte and Rocheford, 1995). Multiple regression analysis was also carried out using PROC REG and p values < 0.05 were considered significant.

2.4 Results and Discussion

2.4.1 QTL analysis

The range in number of significant markers identified by single factor ANOVA was 12 to 17 for protein content, 3 to 14 for density, and 5 to 17 for 100-kernel weight, with considerable variation observed across test environments. Using these significant markers, we were also able to detect several QTL depending on traits and environments through multiple regression analysis. Four to nine markers were significantly linked to protein content in each environment, and seven markers were detected in two or more environments. They were detected on chromosomes 1, 2, 3, 5, 6, 7, and 8 and explained

38 to 71% of phenotypic variation (Table 2.1). Two to four markers were significantly linked to density at each environment and five markers were detected in two or more environments. They were detected on chromosomes 3, 4, 5, 6, 7, 9, and 10 and explained

15 to 42% of phenotypic variation (Table 2.2). Two to seven markers were significantly linked to 100-kernel weight at each environment and five markers were detected in two or more environments. They were detected on chromosomes 1, 2, 3, 4, 5, 6, 7, and 10 and explained 27 to 64% of phenotypic variation (Table 2.3).

To examine the effectiveness of detected QTL markers from BH population, we compared our findings with those from previous studies. Some bin locations detected

QTL from BH population were also reported in the previous studies, but some other bin locations were not confirmed. Protein QTL markers on bins 1.03 (umc1073), 1.09

(umc1431), 6.05 (umc2320), 6.07 (umc1063), and 7.04 (umc1944) were detected in at least two environments and these bin locations were also reported in previous studies

(Goldman et al., 1994; Melchinger et al., 1998; Schon et al., 1994; Wassom et al., 2008;

Zhang et al., 2008). A protein QTL was detected in bin 5.08 (bnlg1208) in four of five environments and was the strongest protein content QTL in two environments, but this

QTL has not been previously reported.

Kernel weight QTL markers on bins 2.02 (umc1261), 2.04 (bnlg108), 4.08

(umc1559), and 5.06 (umc1941) were detected in at least two environments, and QTL in these bin locations have been previously reported (Austin and Lee, 1998; Berke and

Rocheford, 1995; Messmer et al., 2009; Sene et al., 2001). Kernel density QTL markers

on bin 3.05 (bnlg420), 4.06 (umc2291), 5.05 (umc2164) were detected in at least two environments, and QTL at these bin locations have been previously reported (Gutierrez-

Rojas et al., 2010; Garcia-Lara, 2009). Kernel weight QTL on bin 5.08 (bnlg1208), and kernel density QTL on bins 6.01 (umc2313), and 10.04 (umc1930) were significantly detected in at least two environments, but these chromosomal locations were not reported in previous studies. Detection of QTL across several environments in our population coincident with other studies constitutes evidence these QTL may have broad utility for breeding purposes. This evidence is supported by the use of different populations in different environments.

QTL markers for oil and starch content were preliminarily checked and compared with other studies. The range in number of significant markers identified by single factor

ANOVA was 7 to 14 for oil content, 7 to 18 for starch content with considerable variation observed across trials. From the multiple regression analysis, one to six markers were significantly linked to oil content in each environment and six markers were detected in two or more environments. They were detected on chromosomes 1, 2, 4, 5, 6,

7, 8, and 10 and explained 13 to 64% of phenotypic variation (Table 2.4). Three to eight markers were significantly linked to starch content at each environment and eight markers were detected in two or more environments. They were detected on chromosomes 1, 2, 3, 5, 6, 7, 8, and 9 and explained 38 to 80% of phenotypic variation

(Table 2.5).

QTL markers in bins 7.02 (umc1983) and 10.04 (umc2350) for oil content, 1.03

(umc1073), 2.06 (umc1875), 3.02 (bnlg1144), and 6.05 (umc2320) for starch content were detected in at least two environments, and QTL in these bin locations have been previously reported (Berke and Rocheford, 1995; Dudley et al., 2004; Goldman et al.,

1994; Lubberstedt et al., 1997; Sene et al., 2001; Wang et al., 2010; Wassom et al., 2008;

Zhang et al., 2008). Bins 1.03 (bnlg1458 and umc1073), 5.02 (bnlg1879) for oil QTL, bin

2.09 (bnlg1893), 2.10 (umc2214), 5.08 (bnlg1208), and 6.07 (umc1063) for starch QTL were significantly detected in at least two environments, but these locations were not reported in previous studies. QTL positions are detected by phenotypic variation; therefore, QTL detection in a population may be different from those found in another population as reported by Wassom et al. (2008), and QTL may also vary in different environments as reported by Zhang et al. (2008).

A few QTL were associated with several kernel traits. Marker bnlg1208 on bin

5.08 was detected for protein, starch content, and 100-kernel weight. Umc1552 (bin 2.01), umc1961 (bin 2.02), umc1875 (bin 2.06), bnlg1144 (bin 3.02), umc2320 (bin 6.05), and bnlg1176 (bin 8.05) were detected for protein and starch content and umc1431 (bin 1.09) and bnlg1188 (bin 6.01) were detected for protein and 100-kernel weight. Protein content was negatively correlated with starch and 100-kernel weight, and starch content was positively correlated with 100-kernel weight by the least square means in our population.

These relationships suggest that a single pleiotropic gene might affect these QTL. It could also be considered that two or more QTL are closely linked. Therefore, additional studies

are needed to determine whether one QTL with pleiotropic effects, or two or more tightly linked QTL underlie trait expression.

The negative correlation between protein and 100-kernel weight implies that selection of high protein content might result in indirect selection for decreased kernel weight which may also result in reduced grain yield. This is a disadvantage for high protein selection because farmers will not accept low yielding varieties and some end- users may find small kernel size objectionable. Umc1073 (bin 1.03) and umc1063 (bin

6.07) were detected for protein, oil, and starch content, but 100-kernel weight was not detected at these locations. It suggests that these QTL might be useful for selection of high protein content while holding the 100-kernel weight stable. Umc1552 (bin 2.01), umc1961 (bin 2.02), umc1875 (bin 2.06), bnlg1144 (bin 3.02), and bnlg1176 (bin 8.05) detected for protein and starch content were not detected for 100-kernel weight and these

QTL are also possible candidates for selection of high protein without reducing kernel weight. In some cases, a single QTL marker associated with several kernel traits is valuable, because pleiotropic gene or linked genes may be useful for selection of multiple traits.

Selection of the 45 tested progenies from our population was based on protein contents; therefore, variation in oil, starch, density, and 100-kernel weight were assumed to be smaller relative to protein. This limitation makes the population less powerful for detection of QTL associated with these traits, but sufficient variation may enable relationships between these traits to be examined.

2.4.2 Kernel trait variation and correlations

The content of protein, oil, starch, density, and 100-kernel weight traits of the BH population were significantly affected by both progeny and environment main effects

(Table 2.6). There was a significant progenies x environment interaction for protein, oil, density, and 100-kernel weight, indicating that BH progeny means could not be pooled over environments for QTL analysis. The range for protein in BH population progenies did not exceed the value of H140 and the range for density exceeded the low parent B73

(Table 2.7). Starch and oil contents in the BH progenies were intermediate between the parents. Protein, oil, starch, density, and 100-kernel weight had high positive Spearman rank correlations among environments with the exception of the density correlation between 1999 and 2006 indicating that progenies show stable relative ranks across environments.

Phenotypic correlation coefficients at each environment are shown in Table 2.8

(below the diagonal). Negative correlations between starch and protein, and starch and oil were consistent in every trial. These had higher correlation values than other trait correlations indicating that these correlations are stable across environments. Correlations between protein and oil content, and protein and 100-kernel weight were also consistent except in 2008 Shaffter and 1999 King farm, respectively, but other correlations were highly affected by environments. Correlations analyzed by least square means showed that starch and protein, starch and oil, starch and density, 100-kernel weight and density,

and 100-kernel weight and protein were negatively correlated. Protein and density, and

100-kernel weight and starch were positively correlated (above the diagonal in Table

2.8). The correlation of protein and oil was not significant by least square means, but the p value (0.051) was close to the significance level and the correlation was positive in four out of five environments. The strongest correlation was between starch and protein (r = -

0.90**) suggesting that expression of starch and protein levels are closely associated as concluded by Dudley et al. (2007) and Wassom et al. (2008). The positive correlation between protein content and density, and the negative correlation between protein content and 100-kernel weight were not surprising, because smaller, denser kernels are considered to have higher protein levels. Siska and Hurburgh (1995) also reported that protein was positively correlated with density and protein content was negatively correlated with starch.

The positive correlation between 100-kernel weight and starch content was expected, because kernel endosperm is comprised primarily of starch (Boyer and Hannah.

2001). High levels of oil are usually associated with increased kernel weight as reported by Dudley and Lambert (1992), but our study and that of Goldman et al. (1994) did not confirm the correlation between oil content and kernel weight. This correlation difference between some kernel traits among studies indicates that the relationships of kernel traits are population dependent.

Table 2.1. Regression models with QTL for kernel protein content among 45 selected F2:3 and F2:4 progenies from B73  H140 population at King farm (K) in 1999 and Shaffter farm (S) in 1999, 2002, 2006, and 2008. Protein was analyzed by near-infrared transmittance (NIT). The QTL were included in regression models if pr>f values were lower than 0.05.

F2:3 progenies F2:4 progenies Bin† Marker 1999K 1999S 2002S 2006S 2008S R2 R2 R2 R2 R2 1.03 umc1073 0.16*** 1.09 umc1431 0.07* 0.14*** 0.02* 2.01 umc1552 0.21*** 2.02 umc1961 0.05* 2.06 umc1875 0.13*** 3.02 bnlg1144 0.06* 5.08 umc1225 0.07** 5.08 bnlg1208 0.04* 0.15*** 0.15*** 0.25*** 5.09 umc1829 0.07* 6.01 umc2313 0.05** 6.01 bnlg1188 0.04* 6.05 umc2320 0.03* 0.05* 6.07 umc1653 0.10** 6.07 umc1063 0.11** 0.08** 0.06** 0.03* 6.08 phi089 0.19*** 0.08** 7.04 umc1944 0.05* 0.04** 0.05* 8.06 mmc0181 0.04* Cumulative R2 0.51 0.38 0.42 0.71 0.56 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

†Bins are estimated based on the proximity of the QTL to flanking markers and the bin location of markers in the MaizeGDB. 20

Table 2.2. Regression models with QTL for kernel density among 45 selected F2:3 and

F2:4 progenies from B73  H140 population at King farm (K) in 1999 and Shaffter farm

(S) in 1999, 2002, 2006, and 2008. Density was analyzed by near-infrared transmittance

(NIT). The QTL were included in regression models if pr>f values were lower than 0.05.

F2:3 progenies F2:4 progenies Bin† marker 1999K 1999S 2002S 2006S 2008S R2 R2 R2 R2 R2 3.05 bnlg420 0.13*** 0.15 ** 4.06 umc2291 0.14*** 0.04* 5.05 umc2164 0.04* 0.06* 5.08 umc1225 0.13** 6.01 umc2313 0.04* 0.07* 7.05 phi082 0.08* 9.07 umc1804 0.09** 10.00 phi117 0.06* 10.04 umc1930 0.05* 0.07*** 0.13** Cumulative R2 0.19 0.15 0.24 0.30 0.42 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

†Bins are estimated based on the proximity of the QTL to flanking markers and the bin location of markers in the MaizeGDB.

Table 2.3. Regression models with QTL for 100-kernel weight among 45 selected F2:3 and F2:4 progenies from B73  H140 population at King farm (K) in 1999 and Shaffter farm (S) in 1999, 2002, 2006, and 2008. The QTL were included in regression models if pr>f values were lower than 0.05.

F2:3 progenies F2:4 progenies Bin† marker 1999K 1999S 2002S 2006S 2008S R2 R2 R2 R2 R2 1.09 umc1431 0.03* 2.02 umc1261 0.15*** 0.12*** 2.04 bnlg108 0.13** 0.16*** 3.03 bnlg1523 0.15*** 3.04 umc1223 0.09* 3.08 umc1844 0.08** 4.06 umc2291 0.20*** 4.08 umc1559 0.05* 0.05** 5.03 umc1355 0.05* 5.06 umc1941 0.05** 0.06** 5.08 umc1225 0.09*** 5.08 bnlg1208 0.19*** 0.05* 6.01 bnlg1188 0.04* 7.05 umc1760 0.09** 10.03 umc2016 0.06* Cumulative R2 0.27 0.27 0.64 0.28 0.44 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

†Bins are estimated based on the proximity of the QTL to flanking markers and the bin location of markers in the MaizeGDB.

Table 2.4. Regression models with QTL for kernel oil content among 45 selected F2:3 and

F2:4 progenies from B73  H140 population at King farm (K) in 1999 and Shaffter farm

(S) in 1999, 2002, 2006, and 2008. Oil was analyzed by near-infrared transmittance

(NIT). The QTL were included in regression models if pr>f values were lower than 0.05.

F2:3 progenies F2:4 progenies Bin† marker 1999K 1999S 2002S 2006S 2008S R2 R2 R2 R2 R2 1.03 bnlg1458 0.04* 0.20*** 1.03 umc1073 0.23*** 0.11*** 0.09** 1.04 umc1917 0.18*** 1.10 bnlg1347 0.09** 2.09 bnlg1520 0.15*** 2.10 umc2214 0.13** 4.01 umc1276 0.23*** 5.02 bnlg1879 0.03* 0.06** 5.05 umc2164 0.03* 5.06 umc1941 0.09*** 6.01 umc2313 0.30*** 7.02 umc1983 0.03* 0.06* 10.04 umc1930 0.04* 10.04 umc2350 0.02* 0.05* Cumulative R2 0.42 0.13 0.52 0.46 0.64 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

†Bins are estimated based on the proximity of the QTL to flanking markers and the bin location of markers in the MaizeGDB.

Table 2.5. Regression models with QTL for kernel starch content among 45 selected F2:3 and F2:4 progenies from B73  H140 population at King farm (K) in 1999 and Shaffter farm (S) in 1999, 2002, 2006, and 2008. Starch was analyzed by near-infrared transmittance (NIT). The QTL were included in regression models if pr>f values were lower than 0.05.

F2:3 progenies F2:4 progenies Bin† marker 1999K 1999S 2002S 2006S 2008S R2 R2 R2 R2 R2 1.03 umc1073 0.05* 0.04** 2.01 umc1552 0.24*** 2.02 umc1961 0.21*** 2.06 umc1875 0.08*** 0.04** 2.09 bnlg1893 0.04* 0.06** 2.09 bnlg1520 0.13*** 2.10 umc2214 0.20*** 0.27*** 0.37*** 3.02 bnlg1144 0.05* 0.04** 0.02* 5.08 bnlg1208 0.04* 0.04** 6.05 umc2320 0.04* 0.10*** 0.06** 6.07 umc1063 0.07* 0.14*** 0.11*** 0.06** 7.00 mmc0171 0.13*** 8.05 bnlg1176 0.15*** 9.04 umc1492 0.12** Cumulative R2 0.55 0.38 0.62 0.52 0.80 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

†Bins are estimated based on the proximity of the QTL to flanking markers and the bin location of markers in the MaizeGDB.

Table 2.6. ANOVA mean square value for kernel protein, oil, starch, density, and 100- kernel weight of 45 selected progenies of BH population obtained by crossing between

B73 and H140. Kernel traits were determined from observations at King farm in 1999 and Shaffter farm in 1999, 2002, 2006, and 2008 when grown in the field at Wooster,

OH.

Source of Protein Oil Starch Density 100 wt.† Variation ------g kg-1 ------g cm-3 g Progenies 1416** 102** 1578** 0.0008** 79.8** Environments 8590** 200** 82260** 0.0326** 229.3** Progeny x 89** 10** 89 0.0002** 11.1** environment Error 54 4 71 0.0001 7.9 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

†100 wt., 100-kernel weight

Table 2.7. Means for kernel protein, oil, starch, density, and 100-kernel weight of 45 selected progenies of BH population obtained by crossing between B73 and H140.

Kernel traits were determined from observations at King farm in 1999 and Shaffter farm in 1999, 2002, 2006, and 2008 when grown in the field at Wooster, OH.

Protein Oil Starch Density 100 wt.† Means ------g kg-1 ------g cm-3 g BH progenies 126 41 615 1.329 22.9 Range among 104 - 145 34 - 47 589 - 638 1.307 - 1.352 17.5 - 28.8 BH B73 110 38 639 1.290 23.6 H140 157 45 590 1.347 17.8

LSD0.05 6 2 7 0.009 2.4

Means of selected groups by protein contents High (15) 139 42 602 1.332 21.9 Intermediate(15) 127 43 612 1.331 22.7 Low(15) 113 39 629 1.325 24.1

LSD0.05 2 1 2 0.003 0.9 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

†100 wt., 100-kernel weight

Table 2.8. Correlation of kernel protein, oil, starch, density, and 100-kernel weight of 45 selected progenies from a population obtained by crossing between B73 and H140 at

King farm in 1999 and Shaffter farm in 1999, 2002, 2006, and 2008. Correlation levels at each environment are below the diagonal and correlation values using least square means are above the diagonal.

Protein Density 100 wt.† Oil Starch

Protein 0.43** -0.48** 0.29 -0.90**

Density ** ns ** ns **‡ -0.32* -0.04 -0.31*

100 wt. ns * ** ** ** ns ns ns ns ** -0.09 0.33*

Oil ** ** ** * ns ns * ns ns ns ns ns ns ns ns -0.51**

Starch ** ** ** ** ** ** ns ** ns ** ns ns ns ** ** ** ** ** ** ** *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

†100 wt., 100-kernel weight

‡Order of significance at below the diagonal: 1999K(King farm), 1999S(Shaffter farm),

2002S, 2006S, 2008S

Chapter 3: Variation of Kernel Anthocyanin and Carotenoid Content in Arido-

American Land Races of Maize

3.1. Abstract

Traditional agricultural systems in Arido-America still utilize numerous land races of maize that display considerable variation for kernel color, but their pigment content has not been studied. Our objective was to examine the kernel pigment content of representative Arido-American maize accessions conserved by Native Seeds/SEARCH which maintains land races. Kernel samples obtained from 48 accessions were planted in replicated nurseries at two locations (Ohio and Arizona) in 2008. We examined multiple plant, ear, and kernel traits, including total carotenoid and anthocyanin pigment content.

Flinty (hard), followed by floury (soft) kernel types and non-pigmented (white), followed by yellow kernel colors were most abundant. Days to silking, plant and ear height, 100- kernel weight, protein, oil, and carotenoid content were significantly affected by location.

Carotenoid content was highest in orange and yellow colored, and pop type kernels, whereas blue and purple colored, and floury and flint type kernels contained high levels of anthocyanin. Samples with high carotenoid pigment content (i.e. above 40 μg/g) were not in evidence, whereas many accessions produced ears with mixtures of red, purple and

blue kernels containing anthocyanin pigments – some with concentrations above 50 mg/100g. Plant and ear traits were consistent with provisional racial designations of all accessions. Arido-American maize accessions with high carotenoid pigment content were not found, but this material did contain germplasm expressing high kernel anthocyanin content. Superior individuals in this group could be employed by breeding programs to increase anthocyanin pigment levels in culturally-adapted, commercially-viable, maize lines, providing health and nutritional benefits to those who consume it.

Key Words: Maize, carotenoid, anthocyanin, Arido-America,

3.2. Introduction

As natural antioxidants, dietary carotenoid and anthocyanin pigments play a beneficial role in human nutrition and health. Carotenoids have important physiological functions as precursors to vitamin A, antioxidants, immune system enhancers, and protect against age-related blindness (Chitchumroonchokchai et al., 2004; DellaPenna and

Pogson, 2006; Grotewold, 2006; Wong et al., 2004). Anthocyanins also function as antioxidants and inhibit cancer cell growth (Cevallos-Casals and Cisneros-Zevallos, 2003;

Hagiwara et al., 2001; Jing et al., 2008; Lieberman, 2007; Oki et al., 2002).

Carotenoid and anthocyanin pigments are synthesized ubiquitously by species throughout the plant kingdom. Animals and human beings are unable to synthesize these pigments and subsequently, need to obtain them from their diet. Carotenoids are C40

tetraterpenoids derived from phytoene and are associated with yellow, orange and red coloration of diverse plant tissues. These compounds are commonly found in fruits and vegetables, but maize is the only major cereal crop that contains appreciable amounts of naturally occurring carotenoids (Wurtzel, 2004). Two key roles of carotenoids in plants are absorption of light energy for photosynthesis and protection of chlorophyll from photodamage (Lefsrud et al., 2006). Anthocyanins are water-soluble flavonoids and are usually manifested by red, purple, and blue colors (Abdel-Aal et al., 2006; Moreno et al.,

2005). Main roles of anthocyanins in plants are attraction of animals for pollination, eating, or seed dispersion and protection of cells from high light damage (Steyn et al.,

2002).

Modern maize varieties show little variation for kernel color. The majority of maize products consumed by human beings and livestock are derived from white or yellow maize varieties. Maize breeding programs in temperate regions have mainly focused on the development of yellow maize for animal feedstocks; however, white maize varieties are developed for areas where this kernel color is preferred for human consumption. Breeding efforts have led to remarkable maize varieties with increased production efficiency, but have also resulted in the loss of considerable genetic diversity for pigments in commercial breeding varieties. Reversing these losses within the gene pool will likely require introgression of genes from less-developed, but highly diverse maize land races. Once high pigment genes are reintroduced, Corn Belt dent lines may be

developed for specialty food and feed products that enhance food animal and human nutrition and health.

Native maize germplasm resources still cultivated in the southwestern USA and northwestern Mexico (Arido-Ameica) show considerable diversity for many kernel traits including coloration. Committed to the preservation of this diversity, Native

Seeds/SEARCH (NSS) a nonprofit conservation organization based in Tucson, Arizona conserves and distributes the adapted and diverse farmer varieties from Arido-America

(NSS, 2010). NSS maintains a core collection of 550 land race accessions (NSS AC) representing 25 races of maize from Arido-America. These represent collections of well known germplasm such as Hopi and Navajo blue corn, but also rare varieties that are nearly extinct. As a prelude to incorporating improved nutritional characteristics into

Corn Belt-adapted maize, a group of Ohio State University (OSU) and NSS scientists examined selected NSS land races in order to discover accessions with the highest concentrations of kernel anthocyanin and carotenoid pigments. Because of the potential importance of the food matrix, protein and oil contents of all samples were also determined. The racial identity of these accessions was unclear so morphological traits were also examined to assist in their classification.

3.3. Materials and Methods

3.3.1. Selection of accessions and field evaluation

We selected 48 representative accessions from 18 putative races in the NSS core collection. These selected accessions and two Corn Belt adapted entries [one synthetic

OhS12(C1) (Pratt, 1994) and a single-cross B73  Mo17] were planted at the NSS conservation farm near Patagonia, Arizona and at the OARDC Schaffter Farm near

Wooster, Ohio. Maize followed plow-down of green manure crop consisting of grass/legume mixture, plots were flood-irrigated when drought conditions were considered imminent, and pheromone traps were placed in the field to manage corn earworms (Helicoverpa zea) in Arizona. Nitrogen was applied at a rate of 112 kg/ha

(Barker et al., 2005), weeds were controlled by pre- and post-plant herbicides (Loux et al.,

2007), and plots were irrigated using overhead sprinklers when drought conditions appeared imminent in Ohio. A randomized complete block design with two replications for each respective location was utilized. During the field season, data were taken on days to silking, plant height, and ear height.

3.3.2. Harvest, milling and kernel oil and protein composition

Sample ears were harvested at eight weeks after pollination. We harvested hand- pollinated ears to analyze kernel compositional traits and open-pollinated ears to measure ear length, ear width, and the number of kernel rows. For the former group, ears including the husk were placed into the tassel bag to reduce possible pigment degradation

by sun light during drying under continuous forced air (Ohio) or forced air for 48 hours followed by ambient conditions (Arizona). Two well-filled representative ears per plot were obtained and individual ears were hand-shelled. If several kernel colors were present on one ear, kernels were partitioned into two groups based upon the predominant color classes present. Samples from the two predominant color classes were analyzed for pigment, protein, and oil content using protocols described below.

Samples were completely cleaned to eliminate foreign materials and broken kernels. We milled about 25 g of each sample by Cyclone Sample Mill (Model 3010-014,

UDY Corp., Fort Collins, CO, USA) with 0.5 mm sieve. The ground samples were analyzed for protein and oil content with a near-infrared reflectance (NIR) instrument, the

InfraXact (FOSS NIRSystems Inc., Laurel, MD, USA).

3.3.3. Total carotenoid pigment analysis

Total carotenoid pigment content in kernel samples was determined spectrophotometrically using a method modified from Kimura et al. (2007), Kurilich and

Juvik (1999) and Schaub et al. (2004). Kernel samples were prepared under dim light to protect the carotenoids from light-induced degradation. Briefly, for each sample, 1 g of ground material was placed in a 50 ml Falcon tube (Thermo Fisher Scientific, Pittsubrgh,

PA, USA) which was wrapped in aluminum foil to exclude light, and followed by the addition of 15 ml dH2O. Starch-degrading enzymes (0.5 ml of an aqueous solution containing 10 % amyloglucosidase, and 10% α-amylase; products A9913 and A3306,

respectively for use in Total Dietary Fiber Assay, TDF-100A; Sigma-Aldrich Inc., St.

Louis, MO, USA) were added to the tube and then the tube was placed in 70℃ water bath for hydration for 30 min. Each sample was centrifuged and supernatant containing degraded starch and other water-soluble components was decanted. A 15 ml aliquot of extraction solution containing 0.1% butylated hydroxytoluene (BHT) in an acetone- ethanol mixture (2:1 v/v) was added to the sample and the sample was extracted in a 70℃ water bath for 5 min. Following the extraction period, 250 ㎕ of 80% KOH was added to the sample and then the sample was saponified in a 70℃ water bath for 10 min. The sample was centrifuged and the supernatant was transferred to a clean tube. Carotenoid pigment was extracted from the supernatant by partitioning the aqueous solution 2 times with 15 ml hexane-methyl tertiary butyl ether (MTBE) (2:1 v/v) and 7.5 ml 2% acetic acid. The combined hexane-MTBE-acetic acid solution was brought to volume and the total carotenoid content was measured at 445 nm in a spectrophotometer (Model

Spectronic 20D+, Thermo Scientific Inc., West Palm Beach, FL, USA). The total carotenoid content of each sample was calculated from a standard curve of β-carotene

(Sigma-Aldrich, St. Louis, MO, USA) using the Lambert-Beer equation.

3.3.4. Total anthocyanin pigment analysis

Total anthocyanin pigment content in samples was determined spectrophotometrically by the method from Li et al. (2008). Briefly, 2 g of ground kernel sample was placed in a 50 ml Falcon tube followed by the addition of 20 ml of acidified 34

(1% HCl) methanol. The tube was placed in a refrigerator (4℃) for one hour. Following this initial extraction, the sample was centrifuged at 7500 rpm for 15 min and the supernatant was collected. A 2nd and 3rd extraction was completed with 20 ml and 10 ml of acidified methanol, respectively, and the extracts were combined and brought to a standard volume. Total anthocyanin content of samples was measured at 530 nm (the absorption maximum) in a spectrophotometer (Model DU 730, Beckman Coulter Inc.,

Brea, CA, USA) and the anthocyanin levels were calculated as milligrams of cyanidin 3- glucoside equivalents per 100 g of dry weight using the reported molar absorptivity coefficient of 26900 L cm-1 mg-1 and a molecular mass of 449.2 g/L (Jing et al., 2008).

3.3.5. Statistical analysis

Analyses of variance for agronomic traits were performed for both locations to see the difference across locations. Analyses of variance for kernel traits were also performed for each location because most traits were significantly affected by accession x location interaction when data analyses across locations were performed. Means were compared by the minimum significant difference (MSD) using Waller-Duncan mean separation. Statistical analysis of traits measured was conducted with SAS V9.1 (SAS

Institute, Inc., 2002). Analysis of variance using the command PROC GLM procedure was performed on the phenotypic data and all effects were considered as fixed in the model.

3.4. Results

We measured not only kernel traits but also agronomic characteristics of accessions in both locations (Tables 3.1 and 3.2). Traits observed were consistent with the provisional racial classifications designated by NSS. Days to silking (DS, p = 0.01), plant height (PH, p = 0.01) and ear height (EH, p = 0.01) were significantly affected by location, but ear length (EL), ear width (EW) and kernel row number (KR) were generally stable across locations. There were significant differences among accessions within locations for all agronomic characteristics measured. Significant Spearman‟s rank correlation coefficients suggested that the pattern of these differences was consistent across locations for all variables (Table 3.3). Moreover, the range of accession means was similar at both locations for all variables.

Kernel traits such as 100-kernel weight, oil and carotenoid content were significantly affected by accession x location interaction indicating that environmental effects have influenced kernel traits; hence we analyzed the data for each location separately. Kernel traits of Arido-American land races varied considerably across accessions in both locations as shown in Tables 3.4 and 3.5. Protein and carotenoid content were significantly different across replicates in Ohio and anthocyanin content was also different across replicates in Arizona. The standard deviation of protein in

Arizona was between 0.2 and 3.9 and in Ohio was between 0.1 and 1.5. The standard deviation of anthocyanin content was high indicating large variation among accessions;

0.0 to 26.9 in Ohio and 0.0 to 50.5 in Arizona. The average protein content of accessions

was higher than controls in Ohio, and was similar in Arizona. The majority of accessions had higher oil content than controls and the highest content (4.8%) was detected in NSS

AC ZM14-010 in Ohio and in NSS ACs ZM01-007, ZM02-148, and ZM02-185 in

Arizona.

Frequency distributions by kernel color and type in two locations are shown in

Figure 3.1. White, followed by yellow, kernel color (A) and flint, followed by floury, kernel type (B) were predominant. Under controlled self-pollination, ears with mixed colors were frequently observed in both locations. Brown kernel color was observed in accession „Chapolote‟ in Arizona, but it was photoperiod sensitive and ears did not form in Ohio.

The frequency ranges of carotenoid and anthocyanin contents of all accessions are shown in Figure 3.2. The lowest concentration class had the largest number of samples for both carotenoid and anthocyanin contents. The highest carotenoid content (34 μg/g) was detected in the control (B73  Mo17, yellow color, dent type) and the highest levels found among Arido-American accessions were 18.9 μg/g in Ohio and 22.9 μg/g in

Arizona, both associated with NSS AC ZM04-026. The highest anthocyanin content (112 mg/100g) was detected in „Flor del Rio‟ (NSS AC ZM02-141) in Arizona. The control contained the lowest concentration of anthocyanin in both locations.

Carotenoid and anthocyanin contents differed significantly by kernel color and type (Tables 3.6 and 3.7). Orange and yellow colored kernels contained high levels of carotenoids, and kernels in these color classes were significantly different from those in

other color classes. Pop kernel types contained higher carotenoid, but floury kernel types contained lower carotenoid contents. High levels of anthocyanins were detected in blue and purple colored kernels representing both floury and flint kernel types. Brown, pink, orange, yellow, and white kernels contained only small amounts of anthocyanin.

3.5. Discussion

We carried out this project at two locations in 2008; the Arizona site typified the

Arido-American environment under which many of our NSS accessions evolved, and the

Ohio site represented the temperate Eastern Corn Belt where commercial dent lines are well-adapted. The environmental conditions at each site were quite different; the average high temperature in Patagonia, AZ was 11℃ hotter than it was in Wooster, OH, and average precipitation in Wooster was 483 mm more than that measured in Patagonia

(Figure 3.3). Arid atmospheric conditions and high temperatures at the Arizona site may have resulted in accessions with diminished stature (plant height and ear height) and reduced kernel weight when compared with these traits measured in their Ohio-grown counterparts. Some accessions like NSS ACs ZM05-006, ZM06-005, ZM07-001, and

ZM08-001 were photoperiod sensitive in Ohio and longer day length prevented plants advancing to reproductive stage; therefore, we could not harvest ears from those accessions.

Some kernel traits, protein and carotenoid in Ohio, and anthocyanin in Arizona, were different across replicates (Tables 3.4 and 3.5). The standard deviation of those

traits was also high at the designated location. The NSS accessions were not genetically uniform; therefore, color variations were displayed within accessions. The sample number from accessions containing various colors was increased because ears and kernels were separated by color. These color and sampling differences may have affected the variation across replicates. The difference of protein content across replicates in Ohio suggested that the field fertilizer level might be different between replicates.

As expected, yellow and orange kernel color representing approximately 25% of our germplasm, contained high levels carotenoid pigments (Table 3.6). Menkir et al.

(2008) reported the carotenoid content of tropical-adapted yellow maize inbreds varied among lines with the highest containing 53.4 ug/g. Kurilich and Juvik (1999) also observed total carotenoid content of Midwest-grown yellow dent and sweet maize to vary significantly ranging between 0.2 and 31.4 μg/g and between 1.9 and 33.1 μg/g, respectively. Carotenoid content ranges for yellow dent accessions in our experiment were similar to those reported by Kurilich and Juvik (1999), but among our lines, values above 30 ug/g were only found in the Corn Belt control (B73  Mo17, 34 ug/g). We assume high carotenoid levels in Corn Belt yellow dent and the modest concentrations of carotenoid in our Arido-American germplasm result primarily from selection pressure applied by breeders over time, or farmer preferences, respectively.

The highest anthocyanin content in our experiment was 112 mg/100g detected in the Arido-American accession „Flor del Rio‟ exhibiting purple kernel color. Abdel-Aal et al. (2006) reported anthocyanin composition among lines with blue, pink, purple, and red

colored kernel ranged between 5.1 and 127.7 mg/100g, and they also found purple- colored kernels to contain the highest anthocyanin content. Moreno et al. (2005) characterized pigments using purplish-red races from Mexico and presented the highest total anthocyanin content containing 115 mg/100g from the degermed whole kernels. The highest total anthocyanin level in our materials was similar to the anthocyanin levels reported by those investigators. Among the various colors, blue and purple colors had high anthocyanin content. Floury and flint kernel types had higher anthocyanin content than pop and dent. The average anthocyanin content was high in floury types, but the highest content was detected in a flint type. It seems that kernel pigments are not conditioned by kernel hardness.

As evidenced by our data, native Arido-American farmers have developed and maintained genetic maize stocks exhibiting substantial diversity in kernel color and texture. Xolocotzi (1985) reported that cultures indigenous to the Arido-America considered suitability for food production to be an important factor for choosing grains, and that the various textures and colors of corn provided relief from a monotonous diet.

In addition, they may have considered land races with specific kernel characteristics as medicinal resources, or as foods with potential health benefits. These factors may have contributed to the development and survival of maize kernel diversity in Arido-American germplasm.

Over the last two decades, the general American consumer has been introduced to a variety of foods containing pigmented maize or maize products. Blue corn tortillas are

one of the most popular foods in traditional markets and a new generation of specialty products such as chips, pancakes, muffins, and flakes are emerging in markets (Dickerson,

1998). Maize is also one of most effective sources of food-grade coloring agents, and foods such as candies and soft drinks are often colored with natural extracts of purple maize (Tsuda et al., 2003). Although consumer demand is growing and the food industry continues to develop new products and uses for pigmented maize, sourcing raw materials for these products continues to be a challenge. Pigmented maize with red, purple, blue or mixed color kernels such as “Indian corn” is currently produced only in small amounts for specialty foods or for seasonal ornamentation in the USA. „Hope Blue‟ and „Navajo

Blue‟, the initial open-pollinated maize varieties used in production of blue corn chips and tortillas are low-yielding (Dickerson, 1998). These varieties, produced predominantly in southwestern USA, are not adapted to growing conditions in the majority of the Corn

Belt. Incorporating genes for high pigment levels from land races into well-adapted commercial varieties for the major maize producing regions will likely support industry expansion and may increase the variety and availability of health-beneficial, pigmented maize products for the American consumer.

In conclusion, Arido-American accessions exhibit extensive diversity in kernel color and in pigment content. The germplasm containing low carotenoid does not appear to constitute a potential resource for improvement of carotenoid content in modern varieties. However, anthocyanin content, associated with blue or purple kernel colors, was substantial in some with levels reaching above 50 mg/100g. These accessions could

be used for improvement of anthocyanin content in Corn Belt germplasm for the development of health-beneficial specialty maize products. For example, we have an

„Ohio Blue‟ population which performs well in Ohio; we expect that we might improve

„Ohio Blue‟ by introducing genes for high anthocyanin production from Arido-American accessions.

Table 3.1. Agronomic traits of Arido-American maize accessions in Ohio, 2008.

NSS AC† Name Race DS PH EH EL EW KR ZM01-007 Tohono O'odham 60-day O‟odham and Yoeme corns 60 203 115 20 3.7 14 ZM01-011 Yoeme Blue O‟odham and Yoeme corns 65 105 38 - - - ZM02-042 Navajo Red op Puebloan-12 row Southwestern and Hopi 77 265 120 19 3.7 12 ZM02-043 Hispanic Pueblo Red Puebloan-12 row Southwestern and Hopi 69 218 125 18 3.8 15 ZM02-126 Hernandez Muli-Color Puebloan-12 row Southwestern and Hopi 70 258 120 22 3.5 9 ZM02-141 OPNB Flor del Rio Puebloan-12 row Southwestern and Hopi 70 243 130 13 3.8 16 ZM02-162 Zea Mix Puebloan-12 row Southwestern and Hopi 71 205 130 19 3.3 12 ZM02-177 Navajo Red Puebloan-12 row Southwestern and Hopi 73 238 98 20 3.3 13 ZM02-185 San Felipe White Puebloan-12 row Southwestern and Hopi 74 260 160 22 4.3 16 ZM04-026 Yoeme Sweet corn 65 193 75 18 3.7 8 ZM06-013 Tarahumara Maiz Caliente Onaveno 83 288 198 28 3.5 12 ZM08-002 Tarahumara Palomitas Popcorn 67 240 145 14 3.2 14 ZM08-007 Flor del Rio Popcorn 73 250 153 17 3.3 16 ZM08-009 Palimero de Chihuahua Popcorn 70 225 125 17 3.4 16 ZM09-006 Mt. Pima Blando de Sonora Blando de Sonora 65 245 143 22 4.4 12 ZM13-006 Tarahumara Apachito Apachito 73 240 115 18 3.2 12 ZM13-017 - Apachito 63 275 123 18 3.9 13 ZM13-018 - Apachito 64 248 148 20 3.4 10 ZM13-020 Tarahumara Apachito Apachito 58 245 120 21 3.1 8 ZM13-024 Tarahumara Apachito Apachito 60 240 105 13 3.3 12 ZM14-007 Gordo Gordo 72 243 130 19 3.9 12 ZM14-010 Tarahumara Rosari Gordo 73 260 158 16 3.5 7 ZM15-021 - Maiz Azul 71 230 135 17 3.7 12 ZM15-022 - Maiz Azul 68 235 145 25 4.0 13 ZM15-030 - Maiz Azul 72 253 148 11 3.3 10 ZM15-039 Tarahumara Maiz Azul Maiz Azul 74 265 160 17 3.1 10 ZM15-042 Tarahumara Maiz Azul Maiz Azul 75 245 145 18 3.8 12 ZM16-003 Mt. Pima Lloron Bofo and Elote Occidentales 69 300 150 20 3.5 9 ZM17-004 Tarahumara Serape Cristalino de Chihuahua 70 210 113 16 3.4 13 ZM17-009 Tarahumara Pink Bola Cristalino de Chihuahua 64 260 130 22 3.6 11 ZM17-010 Tarahumara Golden Cristalino Cristalino de Chihuahua 62 263 150 19 3.5 13 ZM18-003 Tuxpeno Norte Tuxpeno complex 70 298 175 22 4.1 15 ZM19-012 Pepitilla Conico complex (Pepitilla) 63 228 110 15 4.1 13 ZM19-025 Tarahumara Amarillo Conico complex (Pepitilla) 72 293 168 17 4.1 15 ZM19-026 Tarahumara Conico Amarillo Conico complex (Pepitilla) 76 293 210 20 3.9 16 ZM20-005 Tablilla de Ocho Tablilla de Ocho 60 253 135 19 3.3 10 ZM20-008 Serrano Maiz Tablilla de Ocho 69 283 178 16 3.7 11 ZM21-018 Onaveno Misc. highland 66 235 115 20 3.8 14 ZM22-002 Tepehuan Maiz Rosero Serrano de Jalisco 67 238 135 18 3.1 11 ZM22-005 Tarahumara Serrano Serrano de Jalisco 64 233 133 19 3.5 12 ZM22-007 Tarahumara Serrano Serrano de Jalisco 69 253 143 17 3.6 14 B73/M017 B73  M017 Corn Belt Dent 68 250 143 20 4.9 15 OhS12(C1) OhS12(C1) Mixed 68 255 130 18 4.6 15 mean 69 242 135 19 3.7 13 LSD(5%) 4.3 44.0 42.3 4.8 0.7 4.1 †NSS AC, Native Seeds/SEARCH accession; DS, days to silking (d); PH, plant height

(cm); EH, ear height (cm); EL, ear length (cm); EW, ear width (cm); KR, kernel rows

(row).

Table 3.2. Agronomic traits of Arido-American maize accessions in Arizona, 2008.

NSS AC† Name Race DS PH EH EL EW KR ZM01-007 Tohono O'odham 60-day O‟odham and Yoeme corns 65 161 72 18 3.9 12 ZM01-011 Yoeme Blue O‟odham and Yoeme corns 68 104 44 19 3.6 12 ZM02-042 Navajo Red op Puebloan-12 row Southwestern and Hopi 82 219 104 23 3.8 14 ZM02-043 Hispanic Pueblo Red Puebloan-12 row Southwestern and Hopi 74 208 96 15 3.3 12 ZM02-126 Hernandez Muli-Color Puebloan-12 row Southwestern and Hopi 70 169 72 19 3.7 11 ZM02-141 OPNB Flor del Rio Puebloan-12 row Southwestern and Hopi 75 228 120 19 4.4 12 ZM02-148 Hopi Hominy Puebloan-12 row Southwestern and Hopi 73 109 34 17 4.3 13 ZM02-162 Zea Mix Puebloan-12 row Southwestern and Hopi 71 200 100 24 4.0 11 ZM02-177 Navajo Red Puebloan-12 row Southwestern and Hopi 76 203 144 23 3.6 14 ZM02-185 San Felipe White Puebloan-12 row Southwestern and Hopi 80 232 123 17 4.3 15 ZM04-002 Guarijio Red Sweet corn 93 271 159 21 3.7 10 ZM04-026 Yoeme Sweet corn 67 151 51 16 3.8 11 ZM05-006 Chapalote Chapalote 92 246 139 23 4.2 18 ZM06-005 Onaveno Onaveno 89 283 140 26 4.2 14 ZM06-013 Tarahumara Maiz Caliente Onaveno 86 293 169 22 4.2 14 ZM07-001 Reventador Reventador 88 273 164 21 3.6 19 ZM08-001 Cochito Pop Popcorn 82 142 65 19 4.3 22 ZM08-002 Tarahumara Palomitas Popcorn 71 202 102 16 3.0 12 ZM08-007 Flor del Rio Popcorn 80 168 108 14 3.3 14 ZM08-009 Palimero de Chihuahua Popcorn 71 175 93 15 3.5 16 ZM09-006 Mt. Pima Blando de Sonora Blando de Sonora 70 164 75 22 4.1 11 ZM13-006 Tarahumara Apachito Apachito 71 191 94 17 3.1 11 ZM13-017 - Apachito 69 195 80 22 3.6 12 ZM13-018 - Apachito 66 171 95 17 3.7 11 ZM13-020 Tarahumara Apachito Apachito 68 135 44 19 2.9 9 ZM13-024 Tarahumara Apachito Apachito 64 187 90 18 3.4 12 ZM14-007 Gordo Gordo 75 202 130 20 4.1 14 ZM14-010 Tarahumara Rosari Gordo 81 178 72 19 3.7 10 ZM15-021 - Maiz Azul 72 220 119 19 3.9 12 ZM15-022 - Maiz Azul 75 200 105 20 3.3 10 ZM15-030 - Maiz Azul 76 211 114 22 3.6 10 ZM15-039 Tarahumara Maiz Azul Maiz Azul 80 247 146 22 3.7 12 ZM16-003 Mt. Pima Lloron Bofo and Elote Occidentales 72 222 103 20 3.9 9 ZM17-004 Tarahumara Serape Cristalino de Chihuahua 72 197 104 22 3.7 13 ZM17-009 Tarahumara Pink Bola Cristalino de Chihuahua 71 183 95 22 3.6 10 ZM17-010 Tarahumara Golden Cristalino Cristalino de Chihuahua 69 172 90 16 3.2 11 ZM18-003 Tuxpeno Norte Tuxpeno complex 77 249 100 21 3.5 12 ZM19-012 Pepitilla Conico complex (Pepitilla) 67 180 68 15 4.1 15 ZM19-025 Tarahumara Amarillo Conico complex (Pepitilla) 77 243 148 18 4.4 16 ZM19-026 Tarahumara Conico Amarillo Conico complex (Pepitilla) 79 230 109 21 3.7 14 ZM20-005 Tablilla de Ocho Tablilla de Ocho 67 182 98 17 3.0 9 ZM20-008 Serrano Maiz Tablilla de Ocho 74 230 140 19 4.3 12 ZM21-018 Onaveno Misc. highland 74 172 82 22 3.6 10 ZM22-002 Tepehuan Maiz Rosero Serrano de Jalisco 68 185 113 17 3.0 11 ZM22-005 Tarahumara Serrano Serrano de Jalisco 70 200 109 17 3.8 16 ZM22-007 Tarahumara Serrano Serrano de Jalisco 76 214 122 18 3.2 12 B73/M017 B73  M017 Corn Belt Dent 73 231 117 21 5.1 14 OhS12(C1) OhS12(C1) Mixed 71 191 71 17 4.7 15 mean 74 199 102 19 3.7 13 LSD(5%) 5.2 39.2 35.1 5.5 0.6 3.7 †NSS AC, Native Seeds/SEARCH accession; DS, days to silking (d); PH, plant height (cm); EH, ear height (cm); EL, ear length (cm); EW, ear width (cm); KR, kernel rows.

Table 3.3. Relationship among Arido-American accessions of maize across Arizona and Ohio locations and range of accession means within locations for agronomic traits. Range of values among accessions within Spearman‟s rank Trait† locations correlation coefficient (r) Arizona Ohio DS 0.62 *** 58 – 83 64 – 92 PH 0.50 *** 105 – 300 104 – 293 EH 0.43 *** 38 – 210 34 – 169 EL 0.33 ** 10.9 – 27.8 13.8 – 26.3 EW 0.45 *** 3.1 – 4.9 2.9 – 5.1 KR 0.43 *** 7 – 16 9 – 22 †DS, days to silking; PH, plant height (cm); EH, ear height (cm); EL, ear length (cm); EW, ear width (cm); KR, number of kernel rows. **Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

Table 3.4. Mean values and ANOVA for 100-kernel weight, kernel protein, oil, carotenoids, and anthocyanins of 48 accessions from Native Seed/SEARCH stocks, grown in Ohio in 2008.

100wt.† Protein Oil Carotenoid Anthocyanin (g) ------(%) ------(μg/g) (mg/100g) Accessions 28.3 9.8 4.3 4.8 15.6 Range among 9.8 – 46.8 8.5 – 11.9 3.7 – 4.8 1.6 – 12.8 0.4 – 65.0 accessions Controls‡ 34.4 9.2 3.6 26.0 0.6

LSD0.05 6.8 1.2 0.4 2.7 29.4

ANOVA Source of Variation Rep 1.62 12.60*** 0.02 0.07* 0.06 Accession 272.37*** 2.59*** 0.43*** 0.22*** 0.54*** Error 24.20 0.72 0.09 0.15 0.19 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

†100 wt., 100-kernel weight

‡Controls, two Corn Belt adapted controls including a single-cross B73  Mo17 and one synthetic OhS12(C1)

Table 3.5. Mean values and ANOVA mean square value for 100-kernel weight, kernel protein, oil, carotenoids, and anthocyanins of 48 accessions from Native Seed/SEARCH stocks, grown in Arizona in 2008.

100wt.† Protein Oil Carotenoid Anthocyanin (g) ------(%) ------(μg/g) (mg/100g) Accessions 26.3 10.0 4.3 4.2 14.4 Range among 13.7 – 40.0 8.1 – 12.7 3.4 – 4.8 1.7 – 14.2 0.2 – 43.2 accessions Controls‡ 34.2 10.1 3.5 24.2 0.7

LSD0.05 6.0 1.6 0.5 3.3 28.1

ANOVA Source of Variation Rep 4.64 2.74 0.04 0.01 0.67** Accession 146.84*** 2.06* 0.44*** 0.20*** 0.74*** Error 16.70 1.24 0.10 0.02 0.09 *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

***Significant at the 0.001 probability level.

†100 wt., 100-kernel weight

‡Controls, two Corn Belt adapted controls including a single-cross B73  Mo17 and one synthetic OhS12(C1)

80 A Ohio Arizona 60

No. of samples of No. 20

120 B 90

60 No. of samples of No. 30

0 Flint Flour Dent Pop Sweet

Figure 3.1. Frequency distributions of samples by kernel color (A) and kernel type (B) evaluated in Wooster, Ohio and Patagonia, Arizona in 2008.

120 A Ohio Arizona 100

40 No. of samples of No.

20 B73/Mo17 ↓ 0 0-3 4-7 8-11 12-15 16-19 20-23 24-27 28-31 32-35 ㎍ Carotenoid content ( /g)

70 B73/Mo17 B 60 ↓ 50 40 30

No. of samples of No. 20 10 0

Anthocyanin content(mg/100g)

Figure 3.2. Frequency ranges by samples of carotenoids (A) and anthocyanins (B) content in Wooster, Ohio and Patagonia, Arizona in 2008. Pigment Contents of the control (B73

 Mo17) are showed by arrows.

Table 3.6. Waller-Duncan grouping of carotenoid and anthocyanin contents by kernel colors. Corn Belt controls were excluded to identify color variation in NSS germplasms.

All blue, purple, red, and brown kernel samples and randomly selected samples of white, yellow, orange, and pink were used for anthocyanin analysis.

Carotenoid Anthocyanin Color n Range Color n Range (μg/g) (mg/100g) Orange 11 8.48 a 2.9 - 14.0 Blue 29 34.42 a 7.4 - 73.1

Yellow 67 8.36 a 3.4 - 18.3 Purple 40 19.80 ab 0.8 - 111.7

Mix† 26 4.63 b 1.6 - 10.2 Mix 23 14.27 bc 0.8 - 33.4

Purple 41 4.45 bc 1.7 - 11.0 Red 21 5.15 bc 0.8 - 24.4

Brown 4 4.17 bc 2.8 - 4.8 Red(str) 20 1.63 c 0.6 - 8.1

Pink 6 3.50 bcd 1.5 - 7.2 Brown 4 1.16 c 0.7 - 1.7

Blue 29 3.29 bcd 1.5 - 14.8 Pink 2 0.88 c 0.3 - 1.4

Red 22 3.21 bcd 1.6 - 9.5 Orange 4 0.59 c 0.3 - 1.1

White 137 3.00 cd 1.0 - 6.4 Yellow 4 0.53 c 0.4 - 6.7

Red(str)‡ 20 2.70 d 1.4 - 4.9 White 4 0.29 c 0.0 - 0.4 †Mix, two or three colors are present in kernel.

‡Red(str), streaked kernel with red color.

Table 3.7. Waller-Duncan grouping of carotenoid and anthocyanin contents by kernel types. Corn Belt controls were excluded to identify color variation in NSS germplasms.

All blue, purple, red, and brown kernel samples and randomly selected samples of white, yellow, orange, and pink were used for anthocyanin analysis.

Carotenoid Anthocyanin Type n Range Type n Range (μg/g) (mg/100g) Pop 38 6.05 a 1.8 - 17.9 Floury 54 23.58 a 0.6 - 73.1

Dent 52 4.91 b 1.4 - 18.3 Flint 69 12.47 ab 0.3 - 111.7

Flint 181 4.87 b 1.0 - 14.0 Pop 12 8.44 b 0.7 - 50.3

Floury 92 2.89 c 1.3 - 8.2 Dent 5 6.30 b 1.3 - 25.8

OH(mm) AZ(mm) OH(℃) AZ(℃) 40 120

) 35

℃ 100 30 80 25

20 60

15 40 10 20

5 (mm) precipitation Avg. Avg. high temperature ( temperature high Avg.

0 0 1 2 3 4 5 6 7 8 9 10 11 12 Month

Figure 3.3. Comparison of average high temperature and average precipitation in two environments, Patagonia, Arizona and Wooster, Ohio. Data obtained from http://www.weather.com/outlook/driving/interstate/wxclimatology/monthly/graph/44691

?role=.

Chapter 4: Improving Maize Kernel Anthocyanin and Carotenoid Content Using

Populations (Germplasm) of Diverse Origin

4.1. Abstract

Current commercial maize germplasm lacks diversity for carotenoids and anthocyanins, plant antioxidant pigments that are of potential health-benefit to humans and livestock. Increasing kernel pigment content and availability in commercial maize varieties could provide improved nutrition for humans and animals. We selected high and low carotenoid, oil, and protein progenies in populations (Oh43  Oh608)  Oh43

(designated BC) and B73  H140 (designated BH) and examined the relationships among these nutritional traits by reciprocal cross-pollination between selected progenies from the respective populations. We also selected six high anthocyanin progenies from Arido-

American germplasm, crossed them with „Ohio Blue‟, a Corn Belt-adapted open- pollinated population, and examined the pigment contents in hybrids. The objectives of this study were as follows: to evaluate relationships among carotenoid, protein, and oil contents, to measure the female or male effects when high and low pigment progenies are crossed reciprocally, and to introduce the high anthocyanin content into a temperate Corn

Belt dent. The relationships between carotenoid and oil contents, or between carotenoid and protein contents differed in BC and BH and their reciprocal progenies, suggesting

that selection for individual traits may simultaneously result in indirect selection for other traits in either a desirable or undesirable direction. Carotenoid contents in reciprocal crosses were intermediate between two parents, or more closely resembled the female parent phenotype. Carotenoid values estimated by an endosperm formation equation based on gene dosage effect were similar to measured values in the hybrid progeny.

Anthocyanin contents in reciprocal crosses were not significantly different from the contents of their female parents indicating a strong maternal effect. In kernel pigment expression, the selected high anthocyanin progenies from Arido-American germplasm may be considered as a donor source for development of high anthocyanin temperate maize lines.

4.2. Introduction

In maize, yellow and orange kernels result from the presence of carotenoid pigments and red, purple, or blue kernels arise when anthocyanin pigments are present.

Increasing levels of the health-beneficial anthocyanins and carotenoids in kernels may increase the nutritional quality of maize for animals and humans.

Carotenoids are lipid-soluble pigments that accumulate predominantly in the maize kernel endosperm during early developmental stages. In mature kernels, horny endosperm contains the largest portion of the carotenoid fraction, accounting for 74 –

87% of the total content (Blessin et al., 1963; Weber, 1987). Floury endosperm also contains an appreciable carotenoid fraction (9 – 23% of the total content), but the germ

and other kernel tissues accumulate only 1-5% of the carotenoids present in the mature kernel. Carotenoids provide nutrients for animals and human beings. In the early part of

20th century, scientists found yellow maize to be superior for animal feeds, resulting in a decline in white maize production from 50% in 1920 to 1% of total in 1970 (Troyer,

1999). Today, commercial maize hybrids are usually combined with pigment-containing supplements to meet animal feed nutritional requirements; the development of pigment rich maize lines could reduce the need for these supplements (Egesel et al., 2003; Weber,

1987).

Anthocyanins are water-soluble pigments that accumulate in the kernel pericarp

(Geisler-Lee and Gallie, 2005; Moreno et al., 2005) and in the vacuoles of aleurone cells comprising the epidermal layer of the endosperm during late maize kernel development

(Becraft and Asuncion-Crabb, 2000; Candela and Hake, 2008). Knievel et al. (2009) also reported that blue and purple pigments were located in aleurone and pericarp of wheat, respectively. Products containing high anthocyanin whole grains or grain fractions (e.g., tortillas and breads) are gaining popularity in the marketplace (Abdel-Aal et al., 2006).

Cevallos-Casals and Cisneros-Zevallos (2003) reported the anthocyanin content for whole Andean purple corn to be 1642 mg/100g, higher than levels found in red-fleshed sweet potato and blueberry, suggesting the potential of this material as a source of natural antioxidant pigments for the functional food and dietary supplement markets. Li et al.

(2008) also suggested breeding for high pigment levels in purple corn husks, a prospective anthocyanin source for “blue corn” products. Despite growing interest in

high-anthocyanin maize, breeding efforts to improve kernel anthocyanin content have been minimal, relatively less than those dedicated to improving maize carotenoid levels.

The expression of pigments in the kernel is conditioned by the genotype of the diploid pericarp (maternal sporophytic tissue) and by the hybrid origin of triploid endosperm resulting from the fusion of two maternal polar nuclei (2n) with one paternal pollen sperm nucleus (n). Dilkes and Comai (2004) consider the hybrid nature of the endosperm as one of five factors leading to the “parent-of-origin” effect as manifested by phenotypic differences in the developing seeds of reciprocal crosses. Roach and Wulff

(1987) also list the nuclear composition of the endosperm as one of three maternal influences resulting in “reciprocal-specific effects” in developing seeds. Although these authors and others (e.g., Kermicle, 1970) acknowledge the phenomenon to be complex, developmental differences in the seeds of reciprocal crosses may, in some cases, arise simply from differences in endosperm allelic ratios resulting from the process of fertilization. For instance, both Brunson and Quackenbush (1962) and Egesel et al.

(2003) reported a gene dosage effects for kernel carotenoid contents following reciprocal crosses between high and low carotenoid lines. Regardless of mechanism, differential pigment expression in reciprocal crosses may have implications for selection, hybrid seed production, and possible xenia effects in the production field.

Herein, we examined the variation of oil, protein, and carotenoid contents in two

Corn Belt-adapted populations, (Oh43  Oh608)  Oh43 and B73  H140, and selected eight partially inbred progenies from each population. We also selected high anthocyanin

Arido-American germplasm identified in the previous study (Chapter 3) and crossed the selected progenies with „Ohio Blue‟, a Corn Belt-adapted open pollinated population.

The objectives of this study were threefold: to evaluate relationships among carotenoids, protein, and oil contents, to measure the female or male effect when high and low pigments progenies are crossed, and to introduce the high anthocyanin alleles from the germplasm of southwestern USA and northwestern Mexico (Arido-America) into the

„Ohio Blue‟ population.

4.3. Materials and Methods

4.3.1. Entry selection and field evaluation for carotenoids

This experiment was conducted in 2009 to measure female or male effects influencing the total carotenoid content and to investigate relationships among levels of oil, protein, and carotenoids in the maize kernel. We have examined the variation of kernel compositional traits in two Corn Belt  tropical maize breeding populations;

(Oh43  Oh608)  Oh43 (BC, 25% tropical) and B73  H140 (BH, 50% tropical) which were developed in maize breeding program by R.C. Pratt in OARDC, Ohio. BC is a population developed to improve kernel oil content created by crosses between Oh43 and

Oh608. Oh43 is a well known temperate inbred line released from OARDC at Wooster in

1949 (Gethi et al., 2002) and Oh608, developed from a cross between tropical inbreds

Hi34 and Tzi17, is a more current OARDC release by R.C. Pratt. BH is a population developed to improve kernel protein content resulting from crosses between a temperate

inbred line (B73) (Russell, 1972) and a recombinant inbred line (H140) of tropical origin

(Moon et al., 1999). We have chosen these populations because BC and BH have large variation for oil and protein, respectively. We selected eight BC1F5 progenies from BC and eight F4 progenies from BH based on compositional traits representing all possible combinations of high and low kernel oil, protein and carotenoid contents (Table 4.1). The average contents of kernel oil (%), protein (%), and carotenoids (μg/g) were 6.6, 13.3, and 47.0 for high (H) and 3.1, 10.6, and 33.0 for low (L) in BC population and 4.7, 14.9, and 35.5 for high (H) and 3.6, 12.7, 23.4 for low (L) in BH population, respectively.

These selected entries were planted at the OARDC Schaffter Farm near Wooster,

Ohio. Nitrogen was applied at a rate of 112 kg/ha (Barker et al., 2005), weeds were controlled by pre- and post-plant herbicides (Loux et al., 2007), and plots were irrigated using overhead sprinklers when drought conditions appeared imminent in Ohio. A randomized complete block design with two replications was utilized. All entries from

BC population were cross-pollinated with all entries from BH population reciprocally and all entries were also self-pollinated. During the field season, data were taken on days to silking, stalk lodging, plant height, and ear height. Sample ears were harvested at eight weeks after pollination. Ears including the husk were placed into the tassel bag to reduce possible pigments degradation by sun light following harvest and were dried in a low- temperature (30°C) continuous forced air oven. Kernel samples from individual ears were obtained by hand-shelling the middle part of the ear, and several samples per replicate were combined.

In 2010, we planted kernels from one BC progeny (093001) displaying high oil, protein, and carotenoid content and from the reciprocal crosses between 093001 and eight

BH progenies to examine the agronomic characteristics of the F1 generation, including tiller number and stay green ratings not previously recorded. This field experiment was conducted using techniques described above for the 2009 season, except that the ear samples were produced by the full-sib pollinating method. The resulting F2 seed was collected for subsequent analysis of kernel compositional traits.

4.3.2. Entry selection and field evaluation for anthocyanins

Anthocyanin experiment was conducted in 2009 to introduce high anthocyanins from the Arido-American germplasm into „Ohio Blue‟, an open pollinated variety (OPV) developed from „Blue Clarage‟ and „Ned‟s Blue‟. White variants were found in early segregating populations of „Ohio Blue‟ and these genotypes were maintained as a separate population („Ohio Blue‟-white).

To initiate this experiment, we selected six ears containing over 60 mg/100g of total anthocyanins from NSS accessions in 2008. Approximately twenty-five kernels each from these six selected ears and five original parental accessions and two „Ohio Blue‟ populations („Ohio Blue‟-blue and „Ohio Blue‟-white) were planted at the OARDC

Schaffter Farm near Wooster, Ohio. A randomized complete block design with two replications and standard field production practices were utilized. All entries from selected Arido-American accessions were cross-pollinated with the „Ohio Blue‟

populations reciprocally and all entries were also self-pollinated. During the field season, we measured days to silking, lodging, plant height, ear height, ear length, ear width, and the number of kernel rows.

Sample ears were harvested at eight weeks after pollination. Ears including the husk were placed into the tassel bag to reduce possible pigment degradation by sun light during harvest and were dried under continuous forced air. Two well-filled representative ears per plot were obtained and individual ears were hand-shelled. If several kernel colors were present on one ear, kernels were classified into two majority color classes. Samples from the two predominant color classes were analyzed for anthocyanin content using protocols described below.

In 2010, we examined four high anthocyanin progenies (08206, 08208, 08209,

08232) selected from Arido-American germplasm and the reciprocal crosses between the four progenies and „Ohio Blue‟ to examine the agronomic characteristics in F1 generation, including tiller number and the stay green rating. This field experiment was conducted using techniques described above for the 2009 season, except that the ear samples were produced by the full-sib pollinating method. Again, the resulting F2 seed was collected for subsequent analysis of kernel compositional traits.

4.3.3. Milling and compositional analysis

We analyzed compositional traits with the Tecator 1225 near-infrared transmittance (NIT) whole-grain analyzer (Foss North America, Silver Spring, MD)

equipped with the ISU System One Calibration. Kernel samples were completely cleaned to eliminate foreign material and broken kernels. We analyzed at least 70 g of each sample with seven to ten subsamples scanned, and then the mean value was calculated for each replicate. For carotenoid analysis, we milled about 20g of each sample using a

Cyclone Sample Mill (Model 3010-014, UDY Corp., Fort Collins, Co, USA) with 0.5 mm sieve.

4.3.4. Total carotenoid pigment analysis

Total carotenoid pigment content in kernel samples was determined spectrophotometrically using a method modified from Kimura et al. (2007), Kurilich and

Louis, MO, USA) were added to the tube and then the tube was placed in 70°C water bath for hydration for 30 min. Each sample was centrifuged and supernatant containing degraded starch and other water-soluble components was decanted. A 15 ml aliquot of extraction solution containing 0.1% butylated hydroxyl toluene (BHT) in an acetone – 61

ethanol mixture (2:1 v/v) was added to the sample and the sample was extracted in a 70℃ water bath for 5 min. Following the extraction period, 250 ㎕ of 80% KOH was added to the sample and then the sample was saponified in a 70℃ water bath for 10 min. The sample was centrifuged and the supernatant was transferred to a clean tube. Carotenoid pigment was extracted from the supernatant by partitioning the aqueous solution 2 times with 15 ml hexane-methyl tert butyl ether (MTBE) (2:1 v/v) and 7.5 ml 2% acetic acid.

The combined hexane-MTBE-acetic acid solution was brought to volume and the total carotenoid content was measured at 445 nm in a spectrophotometer (Model Spectronic

20D+, Thermo Scientific Inc., West Palm Beach, FL, USA). The total carotenoid content of each sample was calculated from a standard curve of β-carotene (Sigma-Aldrich, St.

Louis, MO, USA) using the Lambert-Beer equation.

4.3.5. Total anthocyanin pigment analysis

(1% HCl) methanol. The tube was placed in 4℃ refrigerator for one hour. Following extraction, the sample was centrifuged at 7500 rpm for 15 min and the supernatant was collected. A 2nd and 3rd extraction was completed with 20 ml and 10 ml of acidified

methanol, respectively, and the extracts were combined and brought to a standard volume.

The samples were scanned on a spectrophotometer (Model DU 730, Beckman Coulter

Inc., Brea, CA, USA) to pick the maximum wavelength absorption which was detected at

530 nm. Total anthocyanin content of samples was measured at 530 nm and the anthocyanin levels were calculated as milligrams of cyanidin 3-glucoside equivalents per

100 g of dry weight using the reported molar absorptivity coefficient of 26900 L cm-1 mg-

1 and a molecular mass of 449.2 g/L (Jing et al., 2008).

4.3.6. Statistical analysis

Statistical analysis of traits measured was conducted with SAS V9 (SAS Institute,

Cary, NC). Analysis of variance using the command PROC GLM procedure was performed on the phenotypic data and all effects were considered as fixed in the model.

PROC CORR was used to determine Pearson correlation coefficients between traits and

Spearman correlation coefficients between populations or years. The comparison between measured and expected values was carried out with t test.

4.4. Results and Discussion

4.4.1. Carotenoid contents in self- and cross-pollinated seeds

Kernel compositional traits of self-pollinated progenies in two populations are shown in Table 4.1. Progenies of the BC and BH populations chosen for specific levels

of oil or carotenoid performed according to their designations of low or high in both 2008 and 2009 (r = 0.8 and 0.9, respectively). However, protein contents did not follow expected patterns (r = 0.2). Protein contents of 3004 and 4008, which were selected as low protein progenies, increased substantially in 2009, whereas those of 4003, 4005 and

4007, which were selected as high protein progenies, decreased in 2009. Inconsistencies in progeny performance with respect to protein classification were unexpected. Maize protein content is known to be affected by year to year environmental variation (Rysava,

1994); annual differences in site condition or microclimate during this study may account for differences in progeny protein levels between years. Other factors potentially affecting protein levels in 2009 included the following: Progeny 3003 did not germinate and was replanted, sample size was insufficient for progeny 3004 because of low germination rate, and progeny 4007 was harvested prior to kernel maturity because of stalk rot disease pressure and increased lodging. The earliest progeny (4004) silked in 77 days, whereas the latest progeny (4008) required 92 days to silk (Table 4.2). Progeny

3003 was replanted, developed slowly after replanting and its ears were not fully matured at harvest. These various factors described above seemed to affect the kernel traits.

Agronomic traits of two populations, BC and BH, are shown in Table 4.2.

Generally, the populations performed agronomically as expected. Population BH produced taller plants with greater ear height than population BC.

In a companion study (Chapter 3), we correlated carotenoid content with other kernel traits among a highly variable group of accessions of Arido-American maize.

Positive correlations detected between carotenoid and oil (0.11*) and carotenoid and protein (0.21**) suggested that carotenoid levels and levels of oil or more specifically protein could be improved simultaneously. We analyzed phenotypic correlation coefficients in this study to see if similar relationships among the three kernel traits existed for the BC and BH populations (Table 4.3). A positive correlation was detected between carotenoid and oil when we used all progenies self-pollinated in BC and BH populations, and was detected between carotenoid and protein when we used all hybrid samples cross-pollinated. Carotenoid and oil were positively correlated in the self- pollinated BC population, but were not significantly different in the self-pollinated BH population and reciprocal crosses. Carotenoid and protein were positively correlated in reciprocal crosses, but were not significantly different among self-pollinated samples.

Correlation between protein and oil was not significant in this experiment.

Relationships revealing pleiotropy or linkage among kernel traits are important factors for breeding programs. Unfortunately, reports of positive phenotypic correlations between carotenoid and other kernel traits are rare. Lozano-Alejo et al. (2007) reported that the correlation between kernel carotenoid and oil content was not significant. We can conclude that correlations between carotenoid content and other kernel traits are not consistently related, but may be associated with the particular population examined.

Mean values of kernel carotenoid contents in reciprocal crosses were intermediate between the two parental populations, but the carotenoid contents of reciprocal crosses were dependent on which line was the female parent (Figure 4.1). The average carotenoid

content of BC population was higher than that of BH population. In reciprocal crosses, when the high carotenoid BC population was used as female, the carotenoid content was higher than when the low carotenoid BH population was the seed parent. We calculated the expected carotenoid value using triploid (3n) endosperm formation equation produced by two maternal polar nuclei (2n) and one pollen nucleus (1n). The expected content for

BC/BH was nearly identical to the measured content (p = 0.53). The content for BH/BC was significantly different from the measured content (p < 0.01) as determined by the t- statistic, but this difference was not of practical significance.

Mean value of kernel carotenoid contents in hybrid seeds from crosses between high and low carotenoid progenies in 2009 are presented in Table 4.4. Hybrids crossed between two high carotenoid progenies produced the highest carotenoid contents and crossing between two low progenies produced the lowest contents. Reciprocal crosses between high and low carotenoid progenies produced intermediate content and the content was higher when high parent was used as a female. When we calculated the expected carotenoid content using the endosperm formation equation, the contents were close to the measured carotenoid content (p = 0.08 for H/H, p = 0.84 for H/L, p = 0.15 for

L/H) except low (L) x low (L) group. The expected carotenoid content of L/L was lower than the measured content (p < 0.01), but again, this difference was not of practical significance.

Agreement between the mean measured and mean expected carotenoid values for reciprocal crosses supported the 2 to 1 gene dosage model suggesting that the female (2n)

and male (n) contributions to the endosperm control kernel carotenoid content additively.

Egesel et al. (2003) reported a similar gene dosage effect on carotenoid content in yellow maize. In their study, carotenoid levels in reciprocal crosses were closer to the levels of female parents.

Agronomic traits measured in 2010 between one parent (093001) from BC population and eight parents from BH population are shown in Table 4.5. Hybrids displayed fewer days to silking and better stay green ratings than parents, but tall plant height and ear height resulted in more lodged plants. (The total carotenoid contents in F2 seeds were not yet analyzed.)

4.4.2. Anthocyanin contents in self- and cross-pollinated seeds

Total anthocyanin contents of self-pollinated progenies in 2009 are shown in

Table 4.6. Mean total anthocyanin values of the Arido-American germplasm from progenies 6001 to 6005 (S0) were similar to their respective contents of 2008 except progeny 6001. Progenies 6006 to 6011 (S1), selected for high anthocyanin values in 2008, were also high in 2009 except progeny 6007. Both plant or kernel traits of progenies may have been influenced by poor adaptation to the Ohio environment. For instance, rust infested nearly 100% of progenies 6001 and 6006 („Yoeme Blue‟ origin) substantially curtailing seed yield and a majority of plants of progeny 6011 did not produce silks and/or pollen properly. In addition, ears of progeny 6007 contained kernels that

segregated purple and red, resulting in low total anthocyanin values associated with this progeny.

Mean value of total kernel anthocyanin contents in self- and cross-pollinated groups between selected high anthocyanin S1 progenies (HAS1) and „Ohio Blue‟ („Ohio

Blue‟-blue or „Ohio Blue‟-white) are shown in Figure 4.2. Anthocyanin contents among self and crossed progeny of HAS1 and „Ohio Blue‟-blue were not significantly different.

However, anthocyanin levels within self-pollinated populations of HAS1 and „Ohio

Blue‟-white differed significantly by as much as fifteen-fold. Anthocyanin contents in reciprocal crosses between HAS1 and „Ohio Blue‟-white were equivalent to their female parents with a significance level of p = 0.05. The F1 seeds of W/HAS1 demonstrated a greater than five-fold increase in anthocyanin levels over their female parent. Hybrid seeds of W/HAS1 exhibited an increase in the number of kernels per ear which appeared to be blue-mottled and the intensity of this mottling was greater than that found in „Ohio

Blue‟-white

Anthocyanins are synthesized and deposited in the pericarp (maternal tissue) of the maize caryopsis and in the aleurone cells comprising the epidermal layer of the endosperm. As a result of this compartmentalization, kernel anthocyanin content is likely to be heavily influenced by maternal effects. Both Dilkes and Comai (2004) and Roach and Wulff (1987) discuss the hybrid nature of the endosperm derived from the fusion of two maternal polar nuclei (2n) with one paternal sperm nucleus as a major factor resulting in compositional or developmental differences in seed from reciprocal crosses.

Reciprocal crosses between HAS and „Ohio Blue‟-blue or between HAS and „Ohio

Blue‟-white do differ substantially and/or significantly with respect to kernel anthocyanin levels (Figure 4.2). But unlike the pattern expressed in reciprocal crosses of progenies with high and low carotenoid content, reciprocal crosses between anthocyanin selections did not follow the 2 to 1 gene dosage model. Roach and Wulff (1987) list the direct or indirect influence of maternal tissue surrounding the developing seed on seed phenotype.

With respect to kernel anthocyanin content in this study, the maternal tissue of the caryopsis itself likely played an important role. Other maternal or “parent of origin” effects include genes inherited via the maternal cytoplasm (i.e., DNA associated with organelles) (Dilkes and Comai, 2004; Roach and Wulff, 1987) and transcriptional imprinting, the differential expression of an allele in the developing seed depending upon whether its source was paternal or maternal (Dilkes and Comai, 2004; Kermicle, 1970).

Source-dependent differential expression can be exemplified in maize by the behavior of alleles of the R locus which control mottling in red-pigmented kernels. By employing detailed crossing schemes involving variants with multiple gene copies, Kermicle (1970) demonstrated that R alleles donated to the endosperm by the female parent resulted in solid-pigmented kernels exclusively, whereas those donated by the male parent to females with recessive alleles resulted in kernels that were mottled. This expression pattern was constant regardless of the dosage of either allele. Transcriptional imprinting, resulting from epigenetic allelic modifications that effect expression, may play an important role in maize kernel pigmentation as well as other parent of origin-affected

traits (Chandler and Stam, 2004; Dilkes and Comai, 2004). The extent to which transcriptional imprinting influenced kernel anthocyanin contents overall or the level of mottling exhibited by the W and W/HAS1 phenotypes in this study has yet to be clarified.

Agronomic traits of selected high anthocyanin progenies in 2009 are presented in

Table 4.7. Among the selected S1 progenies, 6008 has less lodging, 6009 has longer ear length and thicker ear width, and 6006 has larger kernel rows. Agronomic traits measured in 2010 between four parents from the selected high anthocyanin progenies originated

Arido-America and „Ohio Blue‟ are shown in Table 4.8. Hybrids displayed fewer days to silking and better stay-green than parents, but lodged more frequently. The selected progenies from Arido-America lodged more often and senesced prematurely; when used as parents, these progenies tended to confer negative agronomic characteristics to their hybrid offspring (e.g., 6018). Conversely, hybrids 6015, 6016, and 6022 and their parents seem to be better adapted to Ohio conditions and they may offer more potential for developing high anthocyanin progenies in temperate regions. (The total anthocyanin contents in F2 seeds were not yet analyzed.)

It is important to consider factors other than total anthocyanin but also individual anthocyanin contents in maize breeding program. Kurilich et al. (2005) noted that all anthocyanins are not equally bioavailable in the human diet. We carried out this research with total anthocyanins only; therefore, in a future study, one should consider the variation within the anthocyanin profile. Carotenoid and anthocyanins are distributed in different regions of the maize kernel (Ford, 2000). Carotenoids are mainly produced in

the endosperm and anthocyanins are produced in the pericarp and aleurone layers surrounding the endosperm and embryo. It may be possible to develop high carotenoid and anthocyanin lines and hybrids by crossing between high carotenoid and high anthocyanin lines because of their differential location on maize kernel.

Table 4.1. Kernel compositional traits of selected partially inbred progenies in two population, (Oh43  Oh608)  Oh43 BC1F5 and B73  H140 F4, in 2008 and 2009 grown in Ohio.

2008 2009 Population Entry Type† Oil Protein Carotenoid Oil Protein Carotenoid (%) (%) (μg/g) (%) (%) (μg/g) 3001 H-H-H 7.3 12.7 54.6 5.4 12.2 68.8 3002 H-L-H 7.0 9.8 48.0 5.2 11.0 62.7 3003 H-H-L 5.4 13.0 27.9 5.3 14.4 42.2 (Oh43  3004 H-L-L 6.8 11.0 37.5 5.2 12.9 51.1 Oh608)  Oh43 3005 L-H-H 3.6 13.0 43.4 3.8 12.7 50.5 3006 L-L-H 2.6 11.3 41.8 3.9 11.5 53.4 3007 L-H-L 3.3 14.3 31.1 3.6 12.7 34.7 3008 L-L-L 3.0 10.2 35.6 3.4 11.2 43.8 4001 H-H-H 4.6 14.8 35.2 4.7 13.2 38.9 4002 H-L-H 4.7 13.0 42.8 4.1 12.1 46.2 4003 H-H-L 4.6 14.0 27.2 4.4 10.3 20.5 B73  4004 H-L-L 4.8 11.9 20.4 4.6 9.6 16.0 H140 4005 L-H-H 3.6 15.0 29.8 3.5 10.1 27.5 4006 L-L-H 3.5 12.7 34.1 3.5 11.1 34.1 4007 L-H-L 3.4 15.6 21.6 3.5 12.2 21.8 4008 L-L-L 3.8 13.1 24.5 3.5 13.9 24.8 †Type: H = high, L = low, Oil - Protein - Carotenoid.

Table 4.2. Agronomic characteristics of two populations, (Oh43  Oh608)  Oh43 and

B73  H140 in Ohio 2009.

Population Entry DS†(d) Lodge (1-5) PH (cm) EH (cm) 3001 82.5 2.0 180.0 82.0 3002 89.5 2.0 158.0 73.0 3003 79.0 2.0 196.5 85.5 3004 93.5 3.0 164.0 68.0 (Oh43 3005 85.5 2.0 197.0 80.0 Oh608)  Oh43 3006 80.0 2.0 194.5 84.0 3007 84.5 2.0 191.5 78.5 3008 79.5 2.0 167.0 79.0 Mean 84.3 2.1 181.1 78.8 LSD(5%) 2.3 1.2 15.0 15.5 4001 86.5 2.0 218.0 117.0 4002 89.5 2.0 193.5 94.5 4003 90.5 1.0 208.0 95.0 4004 76.5 2.0 199.5 103.0 4005 83.5 3.0 235.5 126.0 B73  H140 4006 87.5 2.0 177.5 95.5 4007 85.0 4.5 204.5 104.0 4008 91.5 3.0 227.5 119.5 Mean 86.3 2.4 208.0 106.8 LSD(5%) 2.1 1.4 13.2 10.3 †DS = days to silking; Lodge = scale of 1 (none) to 5 (all lodged); PH = plant height; EH

= ear height.

Table 4.3. Correlation of kernel carotenoid concentration with oil and protein contents of selected progenies from two populations in 2009. Correlation was analyzed for each self- pollinated population and reciprocal cross-pollination.

Traits Population n Carotenoid / oil Carotenoid / protein BC† 19 0.56 * -0.28 ns‡ BH§ 16 0.13 ns 0.39 Ns All progenies 35 0.50 ** 0.23 Ns BC/BH 128 -0.07 ns 0.19 * BH/BC 127 -0.16 ns 0.45 ** All hybrids 255 0.06 ns 0.31 ** *Significant at the 0.05 probability level.

**Significant at the 0.01 probability level.

†BC, (Oh43  Oh608)  Oh43 population.

‡ns, not significant at the 0.05 probability level.

§BH, B73  H140 population.

60 51.3 a 50 43.6 b

g/g) 37.4 c

μ 40 28.7 d 30

20 Carotenoid Carotenoid ( 10

0 BC BC/BH BH/BC BH

Treatment BC BC/BH BH/BC BH

Measured 51.3 43.6 37.4 28.7 carotenoid (μg/g) Expected - 43.2 35.9 - (2n+1n) carotenoid (μg/g) Figure 4.1. Mean value of total kernel carotenoid content by self-pollinated and cross- pollinated group in two populations (Oh43  Oh608)  Oh43 (BC) and B73  H140 (BH) in 2009. Expected carotenoid values were calculated by endosperm formation equation (2 female value + 1 male value).

Table 4.4. Mean value of kernel carotenoid content in hybrid seeds from crosses between high and low carotenoid progenies in 2009.

Treat n H/H† H/L L/H L/L

Mesured carotenoid (μg/g) 64 46.0 a 42.4 b 38.2 c 35.5 d

Expected carotenoid‡ (μg/g) 64 47.5 42.2 36.9 31.6 (2n+1n)

†Treat, types of crosses: H=high carotenoid progenies, L=low carotenoid progenies.

‡Expected carotenoid content = 2 female value + 1 male value.

Table 4.5. Agronomic traits of parents and hybrids in carotenoid development progenies in 2010.

Entry Population DS† LD PH EH TL SG EL EW KR 3001 093001 68.5 2.0 212.0 86.5 0.2 4.0 13.4 3.9 16.7 3002 094001 70.0 3.0 271.5 132.5 0.0 5.0 15.0 4.8 16.7 3003 094002 73.0 1.5 245.0 115.0 0.2 3.0 11.1 4.5 16.4 3004 094003 74.0 1.0 232.0 93.5 0.2 4.5 15.0 4.3 17.7 3005 094004 62.0 2.0 232.5 103.5 0.1 8.0 14.0 3.8 16.4 3006 094005 67.0 3.0 251.5 115.0 0.5 6.0 15.6 4.2 16.0 3007 094006 66.0 1.5 204.5 95.5 0.2 4.5 14.9 3.9 13.0 3008 094007 70.0 3.5 242.5 109.5 0.2 8.5 13.2 4.3 16.4 3009 094008 72.5 1.0 272.5 132.0 0.1 6.5 15.2 4.1 14.4 Mean 69.2 2.1 240.4 109.2 0.2 5.6 14.1 4.2 15.9 LSD(5%) 1.8 1.0 9.7 7.1 0.3 1.3 2.7 0.4 2.4 3011 093001/4001 64.0 2.0 299.0 145.0 0.1 3.0 18.9 5.1 18.0 3012 093001/4002 65.0 3.5 314.0 157.0 0.1 2.0 18.4 5.2 18.0 3013 093001/4003 66.5 3.0 305.5 137.5 0.1 3.0 17.8 4.8 17.0 3014 093001/4004 61.5 2.5 275.0 135.5 0.2 4.0 17.6 4.3 16.7 3015 093001/4005 64.5 2.5 291.5 135.5 0.1 3.5 16.9 4.7 17.0 3016 093001/4006 64.5 2.0 289.0 137.0 0.1 2.5 18.5 4.6 15.7 3017 093001/4007 64.0 2.5 292.5 140.0 0.1 3.0 17.1 4.7 17.4 3018 093001/4008 66.0 2.5 295.0 142.0 0.3 3.0 16.8 4.6 17.4 3019 094001/3001 63.5 2.5 291.5 148.0 0.1 2.5 16.1 4.9 19.0 3020 094002/3001 64.0 2.0 306.0 145.0 0.1 2.0 15.5 4.6 17.7 3021 094003/3001 64.5 2.0 305.0 142.5 0.1 2.5 18.6 4.9 20.0 3022 094004/3001 62.0 3.0 298.0 148.0 0.2 2.0 18.2 4.3 16.0 3023 094005/3001 64.0 2.5 284.0 141.5 0.1 4.0 16.0 4.7 17.7 3024 094006/3001 63.5 3.0 291.0 144.5 0.1 3.0 18.5 4.3 15.7 3025 094007/3001 64.5 3.5 308.0 148.5 0.1 2.5 15.5 4.6 17.7 3026 094008/3001 65.0 2.0 308.5 150.5 0.0 2.5 18.5 5.0 18.4 Mean 64.2 2.6 297.1 143.6 0.1 2.8 17.4 4.7 17.4 LSD(5%) 0.9 1.1 13.6 11.9 0.2 1.2 3.2 0.4 1.8 †DS = days to silking (d); LD = lodging scale of 1 (none) to 5 (all lodged); PH = plant height (cm); EH = ear height (cm); TL = tiller/plant; SG = stay green scale of 1(green) to

9(senesced); EL = ear length (cm); EW = ear width (cm); KR = kernel rows (row).

Table 4.6. Total anthocyanin contents of self-pollinated progenies in 2008 and 2009.

6001 to 6005 (S0) were parental accessions from Arido-American germplasm and 6006 to

6011 (S1) were the selected high anthocyanin progenies from 2008. 6012 was blue kernel of „Ohio Blue‟ and 6013 was white kernel of „Ohio Blue‟.

Anthocyanin (mg/100g)† 09 Entry Population 2008 2009 6001 Yoeme Blue (08202) 39.3 74.9 6002 OPNB Flor del Rio (08206) 32.9 30.6 6003 Zea Mix (08208) 33.5 35.0 6004 Navajo Red (08209) 14.3 14.3 6005 Tara. Maiz Azul (08232) 37.2 34.0 6006 OH08202-1 65.0 103.0 6007 OH08206-1 63.9 39.8 6008 AZ08206-2 111.7 102.4 6009 AZ08208-2 77.9 99.3 6010 OH08209-1 66.3 135.0 6011 AZ08232-2 73.1 128.4 6012 „Ohio Blue‟-blue 65.0 42.2 6013 „Ohio Blue‟-white 18.8 3.6 †Anthocyanin contents = mean value of 6001 to 6005, 6012, and 6013, highest value of 6006 to 6011.

60 54.8 a 54.8 a 53.6 a 52.2 a 50 41.5 a 42.2 a 40

30 20.2 b 20

Anthocyanin (mg/100g) Anthocyanin 3.6 b 0 HAS1 HAS1/B B/HAS1 B HAS1 HAS1/W W/HAS1 W

Figure 4.2. Mean value of total kernel anthocyanin content of self-pollinated and cross- pollinated groups derived from crosses between selected high anthocyanin progenies and

„Ohio Blue‟-blue or „Ohio Blue‟-white in 2009. HAS1: selected high anthocyanin progenies 6006 – 6011, B: blue kernel of „Ohio Blue‟, W: white kernel of „Ohio Blue‟.

Table 4.7. Agronomic traits of selected high anthocyanin progenies and „Ohio Blue‟ in

Ohio in 2009. 6001 to 6005 (S0) were the original NSS accessions and 6006 to 6011 (S1) were the selected high anthocyanin progenies from 2008. 6012 was blue kernel of „Ohio Blue‟ and 6013 was white kernel of „Ohio Blue‟. DS† Lodge PH EH EL EW KR Entry (D) (1-5) ------(cm) ------

6001 73.0 3.5 114.0 40.5 - - - 6002 75.5 3.0 237.5 126.5 18.6 3.9 16.0 6003 71.5 3.0 232.0 117.0 20.4 3.5 12.0 6004 76.5 3.5 242.0 128.5 22.9 3.7 13.0 6005 79.5 3.0 271.0 144.5 21.1 3.9 12.0 6006 78.0 3.5 128.5 42.5 17.4 3.1 16.0 6007 81.0 3.0 253.5 140.5 16.1 3.6 14.0 6008 82.5 1.5 265.0 121.5 17.3 3.4 12.0 6009 83.0 2.5 238.5 101.5 25.2 4.0 10.0 6010 84.5 3.0 251.5 112.5 20.0 3.8 14.0 6011 84.0 3.5 250.5 122.0 11.7 3.1 9.0 6012 77.0 2.5 242.5 111.0 19.0 4.9 15.0 6013 77.0 2.5 240.0 133.0 17.9 4.3 14.0 Mean 78.7 2.9 a 228.2 a 110.9 a 19.0 ns 3.8 ns 13.0 ns LSD(5%) 3.4 1.5 30.5 21.3 3.4 0.6 4.0

†DS = days to silking; Lodge = scale of 1 (none) to 5 (all lodged); PH = plant height; EH

= ear height; EL = ear length; EW = ear width; KR = kernel rows.

Table 4.8. Agronomic traits of parents and hybrids in antocyanin development progenies in Ohio in 2010.

Entry Population DS† LD PH EH TL SG EL EW KR 6001 096008 69.0 3.5 230.0 94.5 0.1 9.0 13.4 2.6 13.0 6002 096008 63.0 4.0 220.0 82.5 0.6 9.0 13.0 2.8 13.0 6003 096009 64.0 3.0 246.0 101.5 0.3 8.0 20.8 3.9 12.0 6004 096009 66.0 2.0 254.5 104.5 0.3 7.5 16.4 3.7 11.4 6005 096010 69.5 3.5 242.5 95.0 0.3 8.0 16.0 3.1 13.4 6006 096010 75.0 2.5 254.0 111.5 0.5 8.5 18.4 3.0 12.0 6007 096011 76.0 2.5 286.5 142.0 0.7 3.0 15.2 3.1 12.0 6008 096011 66.0 2.5 281.0 140.0 0.5 4.0 14.7 2.8 8.7 6009 OH-B 65.0 2.0 245.5 104.0 0.0 7.0 14.8 3.9 12.4 6010 OH-W 63.0 4.0 234.0 112.5 0.2 7.0 13.9 4.5 15.7 6011 OH-B('08) 61.5 3.0 258.0 124.5 0.1 5.0 18.3 4.5 15.7 6012 OH-W('08) 62.0 3.0 253.5 112.0 0.1 5.0 15.7 4.4 15.7 Mean 66.7 3.0 250.5 110.4 0.3 6.8 15.7 3.5 12.9 LSD(5%) 2.1 1.2 24.0 19.3 0.2 1.1 3.3 0.4 2.2 6015 096009/OH-B 64.0 2.5 297.5 143.0 0.0 3.5 17.4 4.6 16.4 6016 096010/OH-B 68.0 3.0 315.0 147.5 0.4 3.0 19.2 4.3 15.0 6017 096011/OH-B 63.5 4.0 317.5 166.5 0.9 2.0 20.6 3.9 11.3 6018 OH-B/096008 63.0 2.5 283.0 129.5 0.2 7.0 17.6 4.1 17.4 6019 OH-B/096009 65.5 4.0 315.0 149.5 0.1 4.5 21.2 4.1 12.4 6020 OH-B/096010 66.5 3.5 310.0 143.5 0.3 5.0 19.1 4.3 13.7 6021 OH-B/096011 65.0 4.5 327.0 154.5 0.8 3.0 26.5 3.8 11.4 6022 096009/OH-W 60.0 2.0 294.5 138.0 0.2 4.0 21.3 4.2 12.0 6023 096010/OH-W 66.5 4.0 316.0 148.5 0.4 5.5 16.3 4.2 16.0 6024 096011/OH-W 63.0 4.0 308.0 134.5 0.9 4.0 22.7 4.0 12.7 6025 OH-W/096008 62.5 3.5 278.0 127.5 0.3 7.0 17.4 3.8 15.0 6026 OH-W/096009 59.5 4.0 297.5 145.5 0.2 5.0 18.2 4.4 15.7 6027 OH-W/096010 66.0 3.5 305.5 147.5 0.3 5.0 18.8 4.3 15.0 6028 OH-W/096011 61.0 5.0 305.0 173.5 0.9 2.0 20.6 4.2 14.7 Mean 63.9 3.6 305.0 146.4 0.4 4.3 19.8 4.1 14.2 LSD(5%) 1.9 1.0 18.9 14.7 0.3 1.1 2.3 0.2 1.0 †DS = days to silking (d); LD = lodging scale of 1 (none) to 5 (all lodged); PH = plant height (cm); EH = ear height (cm); TL = tiller/plant; SG = stay green scale of 1(green) to

9(senesced); EL = ear length (cm); EW = ear width (cm); KR = kernel rows (row).

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