Molecular characterization of QTL-allele system for drought tolerance at seedling stage and optimal genotype design using multi-locus multi-allele genome-wide association analysis in a half-sib population of soybean (Glycine max (L.) Merr.)

Abstract

Characterizing the whole genetic architecture of drought tolerance (DT) is a persistent challenge for the breeders. Here we developed a half-sib population comprising of 404 lines of two recombinant inbred line (RIL) populations with M8206 as the joint parent (M8206 × TongShan and ZhengYang × M8206) and tested for its DT under sand culture. The population was sequenced using restriction-site-associated DNA sequencing filtered with minor allele frequency ≥0.01; 55,936 single nucleotide polymorphisms (SNPs) were obtained and assembled into 6137 SNPLDBs (SNP linkage disequilibrium blocks). The restricted two-stage multi-locus genome-wide association analysis characterized with error and false-positive control identified 40 QTLs with 93 alleles on an average of 34.75% of the phenotypic variance (PV) collectively for relative root length (RRL) and relative shoot length (RSL) that served as potential DT indicators. Among these, eight loci corresponded to previously reported QTLs, whereas 32 loci were therefore novel. The identified QTLs with their corresponding alleles for RRL and RSL were organized into QTL-allele matrices, depicting the comprehensive DT genetic architecture of the three parents/half-sib population. From the matrices, we predicted the possible best/optimal genotype with weighted average value (WAV) 1.553 over two indicators, while for the top 10 single crosses among RILs with 95th percentile WAV was 1.218–1.257, transgressive over the parents (0.693–0.794) yet much less than 1.553. From the detected QTL-allele system, 65 potential candidate genes collectively for both indicators explaining on an average of 24.41% PV were annotated and χ2-tested as a DT candidate gene system involving nine biological processes.

Uncovering the Novel QTLs and Candidate Genes of Salt Tolerance in Rice with Linkage Mapping, RTM-GWAS, and RNA-seq

“…We found that RTM-GWAS was effective in identifying QTLs in a bi-parental RILs population and identified more QTLs that overlapped with previous Meta-QTLs than that in linkage mapping. Similarly, RTM-GWAS showed a more effective QTL detection rate in soybean bi-parental RILs populations, and lower false positives compared to linkage mapping or traditional GWAS (Pan et al 2018 ; Khan et al 2020 ; Chang et al 2021 ; Zou et al 2021 ). Our result proved that integration of linkage mapping and RTM-GWAS can highlight important QTLs, such as qST-5.1 and qST-6.1 in this study.…”