Genome-Wide Association Studies for Dynamic Plant Height and Number of Nodes on the Main Stem in Summer Sowing Soybeans

Abstract

Plant height (PH) and the number of nodes on the main stem (NN) serve as major plant architecture traits affecting soybean seed yield. Although many quantitative trait loci for the two traits have been reported, their genetic controls at different developmental stages in soybeans remain unclear. Here, 368 soybean breeding lines were genotyped using 62,423 single nucleotide polymorphism (SNP) markers and phenotyped for the two traits at three different developmental stages over two locations in order to identify their quantitative trait nucleotides (QTNs) using compressed mixed linear model (CMLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) approaches. As a result, 11 and 13 QTNs were found by CMLM to be associated with PH and NN, respectively. Among these QTNs, 8, 3, and 4 for PH and 6, 6, and 8 for NN were found at the three stages, and 3 and 6 were repeatedly detected for PH and NN. In addition, 34 and 30 QTNs were found by mrMLM to be associated with PH and NN, respectively. Among these QTNs, 11, 13, and 16 for PH and 11, 15, and 8 for NN were found at the three stages. A majority of these QTNs overlapped with the previously reported loci. Moreover, one QTN within the known E2 locus for flowering time was detected for the two traits at all three stages, and another that overlapped with the Dt1 locus for stem growth habit was also identified for the two traits at the mature stage. This may explain the highly significant correlation between the two traits. Our findings provide evidence for mixed major plus polygenes inheritance for dynamic traits and an extended understanding of their genetic architecture for molecular dissection and breeding utilization in soybeans.

Exploring the QTL–allele constitution of main stem node number and its differentiation among maturity groups in a Northeast China soybean population

“…Although MSN is an important plant architecture trait, there have not been many reports involving the trait. After a careful search of SoyBase (http://soybase.org) and journals, altogether 65 MSN QTLs were published in 12 reports, each with 1–14 QTLs, including those identified from biparental populations, such as recombinant inbred line (RIL) populations, and those identified from germplasm populations (Chang et al., 2018; Chen et al., 2007; Fang et al., 2017; Gai, Wang, Wu, & Chen, 2007; Li, Sun, Han, Teng, & Li, 2010; Liu, Li, Fan. et al., 2017; Liu et al., 2011; Liu, Li, Hu, et al., 2017; Moongkanna et al., 2011; Wang et al., 2012; Yao et al., 2015; Zhang et al., 2004).…”

“…The results of our study and those found in the literature are different for the following reasons: Different mapping populations were used in different studies. Among the 12 reports, nine of used biparental populations with only 13 parents involved, whereas another three reports (Chang et al., 2018; Fang et al., 2017; Liu, Li, Fan, et al., 2017) used germplasm populations. The materials in the literature are mainly from later MGs than those in the present study. Different mapping procedures were used in different studies.…”

Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat

“…GWAS is considered as a significant approach to study genetic variants in a large population as it saves time and cost of developing a bi-parental population, deduces multi-allelic variations to help identify the most favorable alleles of a target trait in a single analysis, and it is more powerful and easy to fine map QTL due to a higher resolution resulting from a high genetic diversity (Breseghello and Sorrells, 2006;Atwell et al, 2010). GWAS takes complete advantage of all the recombination events occurring in the evolution of a natural population (Chang et al, 2018). GWAS has been used to understand the genetic basis of complex traits in various plant and animal species (Hirschhorn and Daly, 2005;McCarthy et al, 2008;Ingvarsson and Street, 2011;Bush and Moore, 2012).…”

“…However, most complex traits such as nutrient use efficiency are usually controlled by multiple loci, and thus MLM based models are never accurate to estimate the marker effects for such traits. Another problem with MLM based models is that the stringent criterion of significance for marker selection such as Bonferroni correction does not allow many significant markers to be detected Chang et al, 2018). Multi-locus mixed linear models have been developed to address this problem because they can be used to detect powerful marker-trait associations (MTA) using lower significance criterion as no Bonferroni correction is applied Chang et al, 2018;Lü et al, 2018;Ma et al, 2018;Peng et al, 2018;.…”

“…Another problem with MLM based models is that the stringent criterion of significance for marker selection such as Bonferroni correction does not allow many significant markers to be detected Chang et al, 2018). Multi-locus mixed linear models have been developed to address this problem because they can be used to detect powerful marker-trait associations (MTA) using lower significance criterion as no Bonferroni correction is applied Chang et al, 2018;Lü et al, 2018;Ma et al, 2018;Peng et al, 2018;. Ever since the first multi-locus random-SNP-effect mixed linear model (mrMLM) was proposed by Wang et al (2016), a series of models has been published in various studies, e.g., pLARmEB (Zhang et al, 2017), FASTmrMLM (Zhang and Tamba, 2018), and FASTmrEMMA (Wen et al, 2018).…”