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Predicting Complex Phenotype-Genotype Relationships in Grasses: a Systems Genetics Approach" (2013)

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Predicting Complex Phenotype-Genotype Relationships in Grasses: a Systems Genetics Approach Clemson University TigerPrints All Dissertations Dissertations 5-2013 PREDICTING COMPLEX PHENOTYPE- GENOTYPE RELATIONSHIPS IN GRASSES: A SYSTEMS GENETICS APPROACH Stephen Ficklin Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_dissertations Part of the Bioinformatics Commons Recommended Citation Ficklin, Stephen, "PREDICTING COMPLEX PHENOTYPE-GENOTYPE RELATIONSHIPS IN GRASSES: A SYSTEMS GENETICS APPROACH" (2013). All Dissertations. 1087. https://tigerprints.clemson.edu/all_dissertations/1087 This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [email protected]. Title Page PREDICTING COMPLEX PHENOTYPE-GENOTYPE RELATIONSHIPS IN GRASSES: A SYSTEMS GENETICS APPROACH A Dissertation Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Plant & Environmental Sciences by Stephen Patrick Ficklin May 2013 Accepted by: Dr. F. Alex Feltus, Committee Chair Dr. Douglas G. Bielenberg Dr. Feng Luo Dr. Hong Luo Abstract It is becoming increasingly urgent to identify and understand the mechanisms underlying complex traits. Expected increases in the human population coupled with climate change make this especially urgent for grasses in the Poaceae family because these serve as major staples of the human and livestock diets worldwide. In particular, Oryza sativa (rice), Triticum spp. (wheat), Zea mays (maize), and Saccharum spp. (sugarcane) are among the top agricultural commodities. Molecular marker tools such as linkage-based Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWAS), Multiple Marker Assisted Selection (MMAS), and Genome Selection (GS) techniques offer promise for understanding the mechanisms behind complex traits and to improve breeding programs. These methods have shown some success. Often, however, they cannot identify the causal genes underlying traits nor the biological context in which those genes function. To improve our understanding of complex traits as well improve breeding techniques, additional tools are needed to augment existing methods. This work proposes a knowledge-independent systems-genetic paradigm that integrates results from genetic studies such as QTL mapping, GWAS and mutational insertion lines such as Tos17 with gene co-expression networks for grasses—in particular for rice. The techniques described herein attempt to overcome the bias of limited human knowledge by relying solely on the underlying signals within the data to capture a holistic representation of gene interactions for a species. Through integration of gene co- expression networks with genetic signal, modules of genes can be identified with ii potential effect for a given trait, and the biological function of those interacting genes can be determined. iii Acknowledgements Special thanks are extended to my advisor Dr. F. Alex Feltus for his training, teaching, patience, flexibility and friendship. He has been a wonderful advisor and supported me despite being the atypical student with a full time job and with a large family to support. I would also like to thank the members of my PhD committee, Drs. Doug Bielenberg, Feng Luo and Hong Luo for their willingness to meet, discuss my progress and share their thoughts. This also includes Dr. Bert Abbott who served on the committee during the first few years. A great amount of gratitude and love is expressed to my wife Beth, who supported my efforts, provided time for me to study and work, and listened to my scientific babblings. Without her support I would not have had this opportunity. Additional thanks go to my current employer Dr. Dorrie Main. Without her kindness and support in granting a flexible schedule I would not have been able to complete the PhD work. The same thanks are extended to Dr. Richard Hilderman and Dr. Keith Murphy who, during my time at the Clemson University Genomics Institute afforded the same flexibility. A big thank you is given to the other students in the Feltus Lab, Drs. Josh Vandenbrink and Jacob Spangler who provided comments and insight into this work. I wish them both the best in their future endeavors. iv Table of Contents Page Title Page ............................................................................................................................ i Abstract .............................................................................................................................. ii Acknowledgements .......................................................................................................... iv List of Tables .................................................................................................................... ix List of Figures .................................................................................................................... x Chapter 1. Introduction ............................................................................................................... 1 The Importance of Grasses .......................................................................................... 1 Rice as a Model for the Grasses .................................................................................. 3 Methods for Identifying Genes of Complex Traits ..................................................... 4 Breeding Tools for Complex Traits............................................................................. 9 Systems Genetics as a Technique for Exploring Complex Traits...................................................................................................... 11 Gene Co-expression Networks .................................................................................. 16 Examples with Integration of Genetic Resources and Networks .............................. 18 A Holistic Approach to Systems Genetics ................................................................ 20 Summary ................................................................................................................... 22 2. The Association of Multiple Interacting Genes with Specific Phenotypes In Rice (Oryza sativa) Using Gene Co-Expression Networks .................................................................................... 26 Abstract ..................................................................................................................... 27 Introduction ............................................................................................................... 28 Results ....................................................................................................................... 32 The Rice Network ..................................................................................................... 32 Mapping of Microarray Probe sets to Rice Loci ....................................................... 37 Functional Enrichment and Clustering ...................................................................... 38 v Table of Contents (Continued) Page Online Co-expression Network Browser .................................................................. 39 Functional Significance of Select Modules and Clusters .......................................... 41 Discussion ................................................................................................................. 48 Conclusion ................................................................................................................. 51 Materials & Methods ................................................................................................. 52 Raw Expression Data ................................................................................................ 52 Expression Profile Correlation .................................................................................. 53 Scale-free Behavior and Module Detection .............................................................. 53 Threshold Selection and Network Analysis .............................................................. 54 Functional Enrichment .............................................................................................. 55 Functional Clustering ................................................................................................ 57 3. Gene Co-expression Network Alignment and Conservation of Gene Modules between Two Grass Species: Maize and Rice .................................................................................................... 60 Abstract ..................................................................................................................... 61 Introduction ............................................................................................................... 62 Results ....................................................................................................................... 66 Maize Co-Expression Network Construction ............................................................ 66 Functional Enrichment and Clustering of Co-Expressed Maize Gene Modules ....................................................................................................... 70 Rice and Maize Co-expression Network Alignment ................................................. 73 Common Function in Conserved Maize-Rice Subgraphs ........................................
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