Genome Wide Association Studies And Genomic Prediction

With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.

With the detailed genomic information that is now becoming available, we have a plethora of data that allows researchers to address questions in a variety of areas. Genome-wide association studies (GWAS) have become a vital approach to identify candidate regions associated with complex diseases in human medicine, production traits in agriculture, and variation in wild populations. Genomic prediction goes a step further, attempting to predict phenotypic variation in these traits from genomic information. Genome-Wide Association Studies and Genomic Prediction pulls together expert contributions to address this important area of study. The volume begins with a section covering the phenotypes of interest as well as design issues for GWAS, then moves on to discuss efficient computational methods to store and handle large datasets, quality control measures, phasing, haplotype inference, and imputation. Later chapters deal with statistical approaches to data analysis where the experimental objective is either to confirm the biology by identifying genomic regions associated to a trait or to use the data to make genomic predictions about a future phenotypic outcome (e.g. predict onset of disease). As part of the Methods in Molecular Biology series, chapters provide helpful, real-world implementation advice.

This book examines the utility of genome-wide association studies (GWAS) in the era of next-generation sequencing and big data, identifies limitations and potential means of overcoming them, and looks to the future of GWAS and what may lay beyond. GWAS are among the most powerful tools for elucidating the genetic aspects of human and disease diversity. In Genome-Wide Association Studies, experts in the field explore in depth the impacts of GWAS on genomic research into a variety of common diseases, including cardiovascular, autoimmune, diabetic, cancer, and infectious diseases. The book will equip readers with a sound understanding both of the types of disease and phenotypes that are suited for GWAS and of the ways in which a road map resulting from GWAS can lead to the realization of personalized/precision medicine: functional analysis, drug seeds, pathway analysis, disease mechanism, risk prediction, and diagnosis.

Wheat (Triticum aestivum L.) is one of the major food crops in the world that is grown on more land area than any other commercial crop. The demand for wheat is expected to increase by 60% by 2050 which cannot be met with the current yield gain of 1%. Hence, it is important to evaluate different strategies for increasing the genetic gain in wheat. With this focus, we evaluated two strategies, genomic prediction and genome-wide association studies (GWAS) for disease resistance in CIMMYT’s international bread wheat screening nurseries (IBWSN). Our objective was to compare different prediction models for resistance to leaf rust (LR), stem rust (SR), stripe rust (STR), Septoria tritici blotch (STB), Stagonospora nodorum blotch (SNB) and tan spot (TS) in the 45th and 46th IBWSN entries. The prediction models tested include: Least-squares (LS), genomic-BLUP (G-BLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes C (BC), Bayesian least absolute shrinkage and selection operator (BL), reproducing kernel Hilbert spaces (RKHS) markers (RKHS-M), RKHS pedigree (RKHS-P) and RKHS markers and pedigree (RKHS-MP). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing markers. For the rusts, the mean prediction accuracies were 0.74 for LR seedling, 0.56 for LR APR, 0.65 for SR APR, 0.78 for YR seedling and 0.71 for YR APR. For the leaf spotting diseases, the mean genomic prediction accuracies were 0.45 for STB APR, 0.55 for SNB seedling, 0.66 for TS seedling and 0.48 for TS APR. Using genome-wide marker based models resulted in an average of 42-48% increase in accuracy over LS. Overall, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GWAS was also performed on these traits and several significant markers and candidate genes were identified. We conclude that implementing GWAS and genomic selection in breeding for these diseases would help to achieve higher accuracies and rapid gains from selection. ...

The field of genetics is rapidly evolving and new medical breakthroughs are occuring as a result of advances in knowledge of genetics. This series continually publishes important reviews of the broadest interest to geneticists and their colleagues in affiliated disciplines. Five sections on the latest advances in complex traits Methods for testing with ethical, legal, and social implications Hot topics include discussions on systems biology approach to drug discovery; using comparative genomics for detecting human disease genes; computationally intensive challenges, and more

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Experts from academia and industry highlight the potential of genome-wide association studies from basic science to clinical and biotechnological/pharmaceutical applications.