Identification of domestication-related loci associated with flowering time and seed size in soybean with the RAD-seq genotyping method

Abstract

Flowering time and seed size are traits related to domestication. However, identification of domestication-related loci/genes of controlling the traits in soybean is rarely reported. In this study, we identified a total of 48 domestication-related loci based on RAD-seq genotyping of a natural population comprising 286 accessions. Among these, four on chromosome 12 and additional two on chromosomes 11 and 15 were associated with flowering time, and four on chromosomes 11 and 16 were associated with seed size. Of the five genes associated with flowering time and the three genes associated with seed size, three genes Glyma11g18720, Glyma11g15480 and Glyma15g35080 were homologous to Arabidopsis genes, additional five genes were found for the first time to be associated with these two traits. Glyma11g18720 and Glyma05g28130 were co-expressed with five genes homologous to flowering time genes in Arabidopsis, and Glyma11g15480 was co-expressed with 24 genes homologous to seed development genes in Arabidopsis. This study indicates that integration of population divergence analysis, genome-wide association study and expression analysis is an efficient approach to identify candidate domestication-related genes.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…These infrastructure networks enable access to the necessary tools for phenotyping, in particular robot-assisted image capture (Cooper et al, 2009;Fiorani and Schurr, 2013), statistical designs and models for extracting relevant physiological variables from raw data (Cabrera-Bosquet et al, 2016), and specialized information systems managing large datasets originating from phenotyping experiments (Tardieu et al, 2017). GrowScreen-PaGe is a non-invasive, high-throughput phenotyping system developed at the Institute of Biosciences Accelerating genetic gains in legumes | 3295 (Lee et al, 2015) • 286 (14 wild, 153 landraces and 119 elite; Zhou et al, 2015b) • Illumina 384 SNP VeraCode assays (Lee et al, 2015) • NJAU 355 K SoySNP array • Illumina Infinium SoySNP6K…”

The Impact of Genetic Changes during Crop Domestication

“…Flowering time control also plays important role in crops, and thus not surprisingly, central genes appear to be recurrent targets of artificial selection. Particularly, the flowering inducer FT has been shown to have an important role in the domestication and diversification of both monocot and eudicot crops (sunflower [64], soybean [65], rice [66], lupin [67], and chickpea [7]). …”

QTLomics in Soybean: A Way Forward for Translational Genomics and Breeding

“…Further, recent developments in NGS technologies make sequencing-based genotyping cost-effective and efficient (Deshmukh et al, 2014). These sequencing based genotyping approaches include restriction-site-associated DNA sequencing (Baird et al, 2008; Zhou L. et al, 2015), genotyping by sequencing (GBS) (Elshire et al, 2011; Sonah et al, 2013) and whole genome re-sequencing (Xu et al, 2013; Qi et al, 2014). …”

“…Genome-wide association study (GWAS), which uses high density marker genotyping in a population of unrelated genotypes that have accumulated a much larger number of crossing-over events since their last common progenitor, provides higher mapping resolution than conventional QTL mapping and enables one to predict or identify causal genes (Zhang et al, 2015a). GWAS has been successfully combined with sequencing based genotyping technologies for identification of QTLs in soybean (Bastien et al, 2014; Sonah et al, 2015; Iquira et al, 2015; Zhou L. et al, 2015) Novel approaches are being applied for QTL mapping and candidate gene identification in legumes which includes use of multi parent populations like nested association mapping (NAM) and multi-parent advanced generation inter-cross (MAGIC) populations, trait mapping through pooled sequencing methods such as QTL-Seq, MutMap, Seq-BSA, Indel-Seq, and BSR-Seq, a comprehensive review of which is given in Pandey et al (2016). In recent past, several omics resources has been developed in soybean and integration of omics approaches has been proposed for soybean improvement, in which QTLomics will be an integral part (Deshmukh et al, 2014; Chaudhary et al, 2015).…”

“…Wild species of soybean G. soja has very small seeds and selection for bold seeded lines during domestication has given commercial value to this crop. Using GWAS in 286 soybean accessions, Zhou L. et al (2015) identified two candidate genes on Gm011 and three candidate genes on Gm16 significantly associated with seed size. Three of the five candidate genes had shown relatively higher expression levels during seed development.…”