4.3 Article

Dissection of the genetic architecture for soybean seed weight across multiple environments

期刊

CROP & PASTURE SCIENCE
卷 68, 期 4, 页码 358-365

出版社

CSIRO PUBLISHING
DOI: 10.1071/CP16462

关键词

additive effect; epistatic effect; marker-assisted selection

资金

  1. National Key R & D Program for Crop Breeding [2016YFD0100300]
  2. Heilongjiang Provincial Natural Science Foundation [C2015011]
  3. 948 project
  4. National Supporting Project [2014BAD22B01]
  5. Youth Leading Talent Project of the Ministry of Science and Technology in China [2015RA228]
  6. Chinese National Natural Science Foundation [31471517, 31301339, 31201227, 31671717]
  7. Provincial/National Education Ministry Project [1252G014, 1253-NCET-005, 20122325120012]
  8. Northeast Agricultural University [15XG04, 14QC27]

向作者/读者索取更多资源

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, 20.26g) and L-100 (4.84g). 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 similar to 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 additivexenvironment 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.

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