Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.)

Abstract

Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.

Identification of superior and rare haplotypes to optimize branch number in soybean

“…For example, the SNP, viz., Chr18_32068331, was detected through all the five GWAS models, while in contrast, four significant SNPs among the total eleven were identified through only one GWAS model. Similar results were previously reported in different studies, such as in soybean (Bhat et al 2022b ), maize (Kaler et al 2020) and wheat (Merrick et al 2022 ). This can be explained by the fact that different GWAS models are based on different hypotheses involving varied QTL effect distribution characteristics (Bhat et al 2021 ).…”

“…However, branch number is a complex quantitative trait which is highly influenced by the environment; thus, conventional efforts have not met breeding demands (Shim et al 2019 ). In this regard, molecular breeding has emerged as a potential approach for breeding improved soybean cultivars with higher precision and accuracy (Bhat et al 2022b ). However, in molecular breeding, it is important first to know the genetic basis of the traits of interest, such as branch number, and use the detected QTLs/genes/haplotypes associated with such traits in soybean breeding.…”

“…Therefore, the use of haplotype markers in crop breeding will prevent the loss of important genetic variation and make it available for crop improvement (Bhat et al 2021 ). Recently the superior haplotypes have been identified in soybean for the plant height (Bhat et al 2022a ; Yu et al 2023 ), yield-related traits (Bhat et al 2022b ) and salt tolerance (Patil et al 2016 ). The superior haplotypes were also identified for different traits in other crops such as grain quality traits of rice (Wang et al 2017 ) and drought tolerance in pigeonpea (Sinha et al 2020 ).…”

“…In recent years, advances in sequencing and high-throughput genotyping have allowed the routine use of GWAS in crop plants. Multiple studies have demonstrated the performance of GWAS in gene mapping in wheat (Saini et al 2022 ), soybean (Bhat et al 2022b ; Yu et al 2023 ), maize (Ma et al 2022 ), Arabidopsis thaliana (Sasaki et al 2022 ), chickpea (Thudi et al 2021 ) and rice (Lv et al 2022 ).…”

Soybean genetics, genomics, and breeding for improving nutritional value and reducing antinutritional traits in food and feed

“…(2001) proposed the concept of genomic prediction for genomic selection, it was successfully implemented in an animal breeding program for complex quantitative traits (Schaeffer et al., 2006) and subsequently utilized in a plant breeding program (Massman et al., 2013). To date, genomic selection has been successfully applied in soybean breeding programs for key traits, including seed oil and protein (Hemingway et al., 2021; Jarquin et al., 2016; Stewart‐Brown et al., 2019), grain yield (Bhat et al., 2022; Ravelombola et al., 2021; Stewart‐Brown et al., 2019), agronomic traits (Ma et al., 2016; Zhang et al., 2016), and disease resistance (Bao et al., 2014; de Azevedo Peixoto et al., 2017; Shi et al., 2022). Further nutritional component applications of genomic prediction in soybean will be discussed in later sections.…”