Genome-wide association studies dissect the genetic networks underlying agronomical traits in soybean

Chao Fang(Chinese Academy of Sciences), Yanming Ma(Chinese Academy of Sciences), Shiwen Wu(Chinese Academy of Sciences), Zhi Liu(Chinese Academy of Sciences), Zheng Wang(Chinese Academy of Sciences), Rui Yang(Chinese Academy of Sciences), Guanghui Hu(Heilongjiang Provincial Academy of Agricultural Sciences), Zhengkui Zhou(Chinese Academy of Agricultural Sciences), Hong Yu(Chinese Academy of Sciences), Min Zhang(Chinese Academy of Sciences), Yi Pan(Chinese Academy of Sciences), Guoan Zhou(Chinese Academy of Sciences), Haixiang Ren(Heilongjiang Provincial Academy of Agricultural Sciences), Du Weiguang(Heilongjiang Provincial Academy of Agricultural Sciences), Hongrui Yan(Heihe University), Yanping Wang(Heilongjiang Provincial Academy of Agricultural Sciences), Dezhi Han(Heilongjiang Provincial Academy of Agricultural Sciences), Yanting Shen(Chinese Academy of Sciences), Shulin Liu(Chinese Academy of Sciences), Tengfei Liu(Chinese Academy of Sciences), Jixiang Zhang(Chinese Academy of Sciences), Hao Qin(Chinese Academy of Sciences), Jia Yuan(Chinese Academy of Sciences), Xiaohui Yuan(Wuhan University of Technology), Fanjiang Kong(Chinese Academy of Sciences), Baohui Liu(Chinese Academy of Sciences), Jiayang Li(Chinese Academy of Sciences), Zhiwu Zhang(Washington State University), Guodong Wang(State Key Laboratory of Plant Genomics), Baoge Zhu(Chinese Academy of Sciences), Zhixi Tian(Institute of Genetics and Developmental Biology)
Genome biology
August 24, 2017
Cited by 571Open Access
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Abstract

BACKGROUND: Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. RESULTS: To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. CONCLUSIONS: This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design.


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