Dissection of the genetic architecture for soybean seed weight across multiple environments
Weili Teng A , Lei Feng A , Wen Li A , Depeng Wu A , Xue Zhao A , Yingpeng Han A B and Wenbin Li A B
+ Author Affiliations
- Author Affiliations
A Key Laboratory of Soybean Biology in Chinese Ministry of Education (Northeastern Key Laboratory of Soybean Biology and Genetics & Breeding in Chinese Ministry of Agriculture), Northeast Agricultural University, Harbin 150030, China.
B Corresponding authors. Email: hyp234286@aliyun.com; wenbinli@neau.edu.cn
Crop and Pasture Science 68(4) 358-365 https://doi.org/10.1071/CP16462
Submitted: 20 December 2016 Accepted: 9 March 2017 Published: 7 April 2017
Abstract
Seed weight (SW), measured as mass per seed, significantly affects soybean (Glycine max (L.) Merr.) yield and the quality of soybean-derived food. The objective of the present study was to identify quantitative trait loci (QTLs) and epistatic QTLs associated with SW in soybean across 129 recombinant inbred lines (RILs) derived from a cross between Dongnong 46 (100-seed weight, 20.26 g) and ‘L-100 (4.84 g). Phenotypic data were collected from this population after it was grown in nine environments. A molecular genetic map including 213 simple sequence repeat (SSR) markers was constructed, which distributed in 18 of 20 chromosomes (linkage groups). This map encompassed ~3623.39 cM, with an average distance of 17.01 cM between markers. Nine QTLs associated with SW were identified. These QTLs explained 1.07–18.43% of the observed phenotypic variation in the nine different environments, and the phenotypic variation explained by most QTLs was 5–10%. Among these nine QTLs, qSW-3 (Satt192) and qSW-5 (Satt568) explained 2.33–9.96% and 7.26–15.11% of the observed phenotypic variation across eight tested environments, respectively. QTLs qSW-8 (Satt514) and qSW-9 (Satt163) were both identified in six environments and explained 8.99–16.40% and 3.68–18.43% of the observed phenotypic variation, respectively. Nine QTLs had additive and/or additive × environment interaction effects, and the environment-independent QTLs often had higher additive effects. Moreover, nine epistatic pairwise QTLs were identified in different environments. Understanding the existence of additive and epistatic effects of SW QTLs could guide the choice of which reasonable SW QTL to manipulate and could predict the outcomes of assembling a large number of SW QTLs with marker-assisted selection of soybean varieties with desirable SW.
Additional keywords: additive effect, epistatic effect, marker-assisted selection.
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