A genome-wide association study of seed protein and oil content in soybean

Abstract

BackgroundAssociation analysis is an alternative to conventional family-based methods to detect the location of gene(s) or quantitative trait loci (QTL) and provides relatively high resolution in terms of defining the genome position of a gene or QTL. Seed protein and oil concentration are quantitative traits which are determined by the interaction among many genes with small to moderate genetic effects and their interaction with the environment. In this study, a genome-wide association study (GWAS) was performed to identify quantitative trait loci (QTL) controlling seed protein and oil concentration in 298 soybean germplasm accessions exhibiting a wide range of seed protein and oil content.ResultsA total of 55,159 single nucleotide polymorphisms (SNPs) were genotyped using various methods including Illumina Infinium and GoldenGate assays and 31,954 markers with minor allele frequency >0.10 were used to estimate linkage disequilibrium (LD) in heterochromatic and euchromatic regions. In euchromatic regions, the mean LD (r 2 ) rapidly declined to 0.2 within 360 Kbp, whereas the mean LD declined to 0.2 at 9,600 Kbp in heterochromatic regions. The GWAS results identified 40 SNPs in 17 different genomic regions significantly associated with seed protein. Of these, the five SNPs with the highest associations and seven adjacent SNPs were located in the 27.6-30.0 Mbp region of Gm20. A major seed protein QTL has been previously mapped to the same location and potential candidate genes have recently been identified in this region. The GWAS results also detected 25 SNPs in 13 different genomic regions associated with seed oil. Of these markers, seven SNPs had a significant association with both protein and oil.ConclusionsThis research indicated that GWAS not only identified most of the previously reported QTL controlling seed protein and oil, but also resulted in narrower genomic regions than the regions reported as containing these QTL. The narrower GWAS-defined genome regions will allow more precise marker-assisted allele selection and will expedite positional cloning of the causal gene(s).

Regulation of gene expression is associated with tolerance of the Arctic copepod Calanus glacialis to CO2‐acidified sea water

“…Similarly, gene expression of an Antarctic fish, whose adaptation to cold waters involves constitutively elevated stress‐related genes, shows a downregulation of those genes when exposed to temperature stress (Huth & Place, 2016). Heat‐tolerant strains of Daphnia pulex showed a general downregulation of genes involved in transcription, translation, DNA replication, DNA repair, and core metabolic pathways in response to heat stress, a response not present in heat‐sensitive strains (Yampolsky et al., 2014). Downregulation of helicase, involved in DNA replication, was also involved in tolerance to low pH in Pseudocalanus acuspes copepods which had gained some tolerance to low pH after being exposed for three generations (De Wit, Dupont, & Thor, 2015).…”

Selection of reference genes for qRT‐PCR and expression analysis of high‐altitude‐related genes in grassland caterpillars (Lepidoptera: Erebidae: Gynaephora) along an altitude gradient

“…Expression of the optimal RGs should be stable under various conditions, such as in different tissues, treatment conditions, and cell lines (Robledo et al., 2014). However, due to the noncommonality of optimal RG and NF numbers in qRT‐PCR experiments, these should be determined under specific experimental conditions (Bustin et al., 2009; Vandesompele et al., 2002).…”

“…Ideal RGs should be stably expressed under various experimental conditions (Chapman & Waldenström, 2014). Several traditional RGs, such as elongation factor 1 alpha ( EF1‐ α), β‐actin (β ‐ACT ), 18S ribosomal RNA ( 18S ), and glyceraldehyde‐3‐phosphate dehydrogenase ( GAPDH ), have been widely used in qRT‐PCR studies (Koramutla, Aminedi, & Bhattacharya, 2016; Nakamura et al., 2016; Robledo et al., 2014). However, previous studies have shown that these RGs are not always stably expression under various experimental conditions (Chapman & Waldenström, 2014; Thellin, Elmoualij, Heinen, & Zorzi, 2009).…”

A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits

“…The density of the 6K chip used here was too low to effectively detect fine‐scale patterns of among population divergence and within population homozygosity in response to population‐specific directional selection. More precise detection of long haplotype blocks from selective sweeps is now possible using the 220K SNP chip (Yáñez et al., 2016) and the high‐density 132K SNP chip (Houston et al., 2014) that have been developed for European Atlantic salmon.…”

“…Application of genome scans to Atlantic salmon populations has been facilitated by the greatly increased availability of SNPs for Atlantic salmon (Houston et al., 2014; Lien et al., 2011) and the publication of the genome (Lien et al., 2016). Recent outlier locus studies of salmonid fishes have primarily compared wild populations at different spatial scales (Freamo, O'Reilly, Berg, Lien, & Boulding, 2011; Vasemägi & Primmer, 2005) or have correlated outlier loci of wild populations with specific environmental variables (Bourret, Kent, et al., 2013; Bourret, Dionne, Kent, Lien, & Bernatchez, 2013; Narum, Campbell, Kozfkay, & Meyer, 2010; Perrier, Bourret, Kent, & Bernatchez et al., 2013).…”