The advantages and limitations of trait analysis with GWAS: a review
Arthur Korte,Ashley Farlow +1 more
TLDR
The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented.Abstract:
Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.read more
Citations
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A practical guide to environmental association analysis in landscape genomics
TL;DR: Expected future directions in the field of landscape genomics are summarized, such as the extension of statistical approaches, environmental association analysis for ecological gene annotation, and the need for replication and post hoc validation studies.
Journal ArticleDOI
Natural Variations and Genome-Wide Association Studies in Crop Plants
Xuehui Huang,Bin Han +1 more
TL;DR: The development of sequencing-based genotyping and genome-wide association studies in crops are described and the advent of high-throughput sequencing technology enables rapid and accurate resequencing of a large number of crop genomes to detect the genetic basis of phenotypic variations in crops.
Journal ArticleDOI
Nitrate Transport, Sensing, and Responses in Plants
José Antonio O’Brien,Andrea Vega,Eléonore Bouguyon,Eléonore Bouguyon,Gabriel Krouk,Alain Gojon,Gloria M. Coruzzi,Rodrigo A. Gutiérrez +7 more
TL;DR: An updated overview of mechanisms by which nitrate is sensed and transported throughout the plant is provided, and signaling components and how nitrate sensing crosstalks with hormonal pathways for developmental responses locally and globally in the plant are discussed.
Journal ArticleDOI
Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean
Chao Fang,Yanming Ma,Shiwen Wu,Zhi Liu,Zheng Wang,Rui Yang,Guanghui Hu,Zhengkui Zhou,Hong Yu,Min Zhang,Pan Yi,Guoan Zhou,Haixiang Ren,Weiguang Du,Hongrui Yan,Yanping Wang,Dezhi Han,Yanting Shen,Shu-Lin Liu,Tengfei Liu,Jixiang Zhang,Hao Qin,Jia Yuan,Xiaohui Yuan,Fanjiang Kong,Baohui Liu,Jiayang Li,Zhiwu Zhang,Guodong Wang,Baoge Zhu,Zhixi Tian +30 more
TL;DR: This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.
Journal ArticleDOI
A Combinatorial View on Speciation and Adaptive Radiation
David Alexander Marques,David Alexander Marques,Joana I. Meier,Joana I. Meier,Joana I. Meier,Ole Seehausen,Ole Seehausen +6 more
TL;DR: Why old variants are particularly good fuel for rapid speciation, and hypothesize that variation in access to such old variants might contribute to the large variation in speciation rates observed in nature.
References
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Lucia A. Hindorff,Praveen Sethupathy,Heather Junkins,Erin M. Ramos,Jayashri P. Mehta,Francis S. Collins,Teri A. Manolio +6 more
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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
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Genome-wide association studies for common diseases and complex traits
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Efficient Control of Population Structure in Model Organism Association Mapping
Hyun Min Kang,Noah Zaitlen,Claire M. Wade,Claire M. Wade,Andrew Kirby,Andrew Kirby,David Heckerman,Mark J. Daly,Mark J. Daly,Eleazar Eskin +9 more
TL;DR: A new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping and takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows for substantially increase the computational speed and reliability of the results.