E
Edward S. Buckler
Researcher at Cornell University
Publications - 313
Citations - 65092
Edward S. Buckler is an academic researcher from Cornell University. The author has contributed to research in topics: Population & Quantitative trait locus. The author has an hindex of 97, co-authored 294 publications receiving 55140 citations. Previous affiliations of Edward S. Buckler include Agricultural Research Service & United States Department of Agriculture.
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Journal ArticleDOI
TASSEL: software for association mapping of complex traits in diverse samples
Peter J. Bradbury,Zhiwu Zhang,Dallas E. Kroon,Terry M. Casstevens,Yogesh Ramdoss,Edward S. Buckler +5 more
TL;DR: TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure and allows for linkage disequilibrium statistics to be calculated and visualized graphically.
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A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species
Robert J. Elshire,Jeffrey C. Glaubitz,Qi-ying Sun,Jesse Poland,Ken Kawamoto,Edward S. Buckler,Edward S. Buckler,Sharon E. Mitchell +7 more
TL;DR: A procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs) is reported, which is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches.
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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.
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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.
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Mixed linear model approach adapted for genome-wide association studies.
Zhiwu Zhang,Elhan S. Ersoz,Chao-Qiang Lai,Rory J. Todhunter,Hemant K. Tiwari,Michael A. Gore,Peter J. Bradbury,Jianming Yu,Donna K. Arnett,Jose M. Ordovas,Edward S. Buckler,Edward S. Buckler +11 more
TL;DR: A compression approach is reported, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups and a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components.