Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
Susanna Atwell,Yu S. Huang,Bjarni J. Vilhjálmsson,Glenda Willems,Matthew W. Horton,Yan Li,Dazhe Meng,Alexander Platt,Aaron M. Tarone,Tina T. Hu,Rong Jiang,N. Wayan Muliyati,Xu Zhang,Muhammad Ali Amer,Ivan Baxter,Benjamin Brachi,Joanne Chory,Joanne Chory,Caroline Dean,Marilyne Debieu,Juliette de Meaux,Joseph R. Ecker,Nathalie Faure,Joel M. Kniskern,Jonathan D. G. Jones,Todd P. Michael,Adnane Nemri,Fabrice Roux,Fabrice Roux,David E. Salt,Chunlao Tang,Marco Todesco,M. Brian Traw,Detlef Weigel,Paul Marjoram,Justin O. Borevitz,Joy Bergelson,Magnus Nordborg +37 more
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TLDR
This study demonstrates the feasibility of GWA studies in A. thaliana and suggests that the approach will be appropriate for many other organisms, particularly when inbred lines are available.Abstract:
Although pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases, genome-wide association (GWA) studies have, owing to advances in genotyping and sequencing technology, become an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available, because once these lines have been genotyped they can be phenotyped multiple times, making it possible (as well as extremely cost effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana, a widely distributed, predominantly self-fertilizing model plant known to harbour considerable genetic variation for many adaptively important traits. Our results are dramatically different from those of human GWA studies, in that we identify many common alleles of major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true associations from false. However, a-priori candidates are significantly over-represented among these associations as well, making many of them excellent candidates for follow-up experiments. Our study demonstrates the feasibility of GWA studies in A. thaliana and suggests that the approach will be appropriate for many other organisms.read more
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Genome-wide association studies of 14 agronomic traits in rice landraces
Xuehui Huang,Xinghua Wei,Tao Sang,Qiang Zhao,Qiang Zhao,Qi Feng,Yan Zhao,Canyang Li,Chuanrang Zhu,Tingting Lu,Zhiwu Zhang,Meng Li,Meng Li,Danlin Fan,Yunli Guo,Ahong Wang,Lu Wang,Liuwei Deng,Wenjun Li,Yiqi Lu,Qijun Weng,Kunyan Liu,Tao Huang,Taoying Zhou,Yufeng Jing,Wei Li,Zhang Lin,Edward S. Buckler,Edward S. Buckler,Qian Qian,Qifa Zhang,Jiayang Li,Bin Han,Bin Han +33 more
TL;DR: This study identifies ∼3.6 million SNPs by sequencing 517 rice landraces and constructed a high-density haplotype map of the rice genome using a novel data-imputation method, demonstrating that an approach integrating second-generation genome sequencing and GWAS can be used as a powerful complementary strategy to classical biparental cross-mapping for dissecting complex traits in rice.
Journal ArticleDOI
Development of High-Density Genetic Maps for Barley and Wheat Using a Novel Two-Enzyme Genotyping-by-Sequencing Approach
Jesse Poland,Jesse Poland,Patrick J. Brown,Mark E. Sorrells,Jean-Luc Jannink,Jean-Luc Jannink +5 more
TL;DR: The GBS approach presented here provides a powerful method of developing high-density markers in species without a sequenced genome while providing valuable tools for anchoring and ordering physical maps and whole-genome shotgun sequence.
Journal ArticleDOI
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
Matthew T. Weirauch,Matthew T. Weirauch,Ally Yang,Mihai Albu,Atina G. Cote,Alejandro Montenegro-Montero,Philipp Drewe,Hamed S. Najafabadi,Samuel A. Lambert,Ishminder Mann,Kate B. Cook,Hong Zheng,Alejandra Goity,Harm van Bakel,Harm van Bakel,Jean Claude Lozano,Mary Galli,Mathew G. Lewsey,Eryong Huang,Tuhin Mukherjee,Xiaoting Chen,John S. Reece-Hoyes,Sridhar Govindarajan,Gad Shaulsky,Albertha J.M. Walhout,François-Yves Bouget,Gunnar Rätsch,Luis F. Larrondo,Joseph R. Ecker,Timothy P. Hughes +29 more
TL;DR: DNA sequence preferences for >1,000 TFs encompassing 54 different DBD classes from 131 diverse eukaryotes are determined, finding that closely related DBDs almost always have very similar DNA sequence preferences, enabling inference of motifs for ∼34% of the ∼170,000 known or predicted eUKaryotic TFs.
Journal ArticleDOI
Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa
Keyan Zhao,Chih-Wei Tung,Georgia C. Eizenga,Mark Wright,M. Liakat Ali,Adam H. Price,Gareth J. Norton,S. M. Rafiqul Islam,Andrew R. Reynolds,Jason G. Mezey,Anna M. McClung,Carlos Bustamante,Carlos Bustamante,Susan R. McCouch +13 more
TL;DR: This work establishes an open-source translational research platform for genome-wide association studies in rice that directly links molecular variation in genes and metabolic pathways with the germplasm resources needed to accelerate varietal development and crop improvement.
Journal ArticleDOI
Phenomics: the next challenge.
TL;DR: Phenomics should be recognized and pursued as an independent discipline to enable the development and adoption of high-throughput and high-dimensional phenotyping.
References
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Principal components analysis corrects for stratification in genome-wide association studies
Alkes L. Price,Alkes L. Price,Nick Patterson,Robert M. Plenge,Robert M. Plenge,Michael E. Weinblatt,Nancy A. Shadick,David Reich,David Reich +8 more
TL;DR: This work describes a method that enables explicit detection and correction of population stratification on a genome-wide scale and uses principal components analysis to explicitly model ancestry differences between cases and controls.
Journal ArticleDOI
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
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TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Journal ArticleDOI
Finding the missing heritability of complex diseases
Teri A. Manolio,Francis S. Collins,Nancy J. Cox,David Goldstein,Lucia A. Hindorff,David J. Hunter,Mark I. McCarthy,Erin M. Ramos,Lon R. Cardon,Aravinda Chakravarti,Judy H. Cho,Alan E. Guttmacher,Augustine Kong,Leonid Kruglyak,Leonid Kruglyak,Elaine R. Mardis,Charles N. Rotimi,Montgomery Slatkin,David Valle,Alice S. Whittemore,Michael Boehnke,Andrew G. Clark,Evan E. Eichler,Greg Gibson,Jonathan L. Haines,Trudy F. C. Mackay,Steven A. McCarroll,Peter M. Visscher +27 more
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Journal ArticleDOI
A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
Jianming Yu,Gaël Pressoir,William H. Briggs,Irie Vroh Bi,Masanori Yamasaki,John Doebley,Michael D. McMullen,Michael D. McMullen,Brandon S. Gaut,Dahlia M. Nielsen,James B. Holland,James B. Holland,Stephen Kresovich,Edward S. Buckler,Edward S. Buckler +14 more
TL;DR: A unified mixed-model approach to account for multiple levels of relatedness simultaneously as detected by random genetic markers is developed and provides a powerful complement to currently available methods for association mapping.
Journal ArticleDOI
Genomic control for association studies.
Bernie Devlin,Kathryn Roeder +1 more
TL;DR: The performance of the genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.
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