Identification of QTNs and Their Candidate Genes for 100-Seed Weight in Soybean (Glycine max L.) Using Multi-Locus Genome-Wide Association Studies

Abstract

<jats:p>100-seed weight (100-SW) in soybeans is a yield component trait and controlled by multiple genes with different effects, but limited information is available for its quantitative trait nucleotides (QTNs) and candidate genes. To better understand the genetic architecture underlying the trait and improve the precision of marker-assisted selection, a total of 43,834 single nucleotide polymorphisms (SNPs) in 250 soybean accessions were used to identify significant QTNs for 100-SW in four environments and their BLUP values using six multi-locus and one single-locus genome-wide association study methods. As a result, a total of 218 significant QTNs were detected using multi-locus methods, whereas eight QTNs were identified by a single-locus method. Among 43 QTNs or QTN clusters identified repeatedly across various environments and/or approaches, all of them exhibited significant trait differences between their corresponding alleles, 33 were found in the genomic region of previously reported QTLs, 10 were identified as new QTNs, and three (qHSW-4-1, qcHSW-7-3, and qcHSW-10-4) were detected in all the four environments. The number of seed weight (SW) increasing alleles for each accession ranged from 8 (18.6%) to 36 (83.72%), and three accessions (Yixingwuhuangdou, Nannong 95C-5, and Yafanzaodou) had more than 35 SW increasing alleles. Among 36 homologous seed-weight genes in Arabidopsis underlying the above 43 stable QTNs, more importantly, Glyma05g34120, GmCRY1, and GmCPK11 had known seed-size/weight-related genes in soybean, and Glyma07g07850, Glyma10g03440, and Glyma10g36070 were candidate genes identified in this study. These results provide useful information for genetic foundation, marker-assisted selection, genomic prediction, and functional genomics of 100-SW.</jats:p>

Chitosan Modified Biochar Increases Soybean (Glycine max L.) Resistance to Salt-Stress by Augmenting Root Morphology, Antioxidant Defense Mechanisms and the Expression of Stress-Responsive Genes

“…Soybean ( Glycine max L.) is an important oilseed crop that provides 30% oil and 69% protein in the world wide food production [ 1 ]. The major portion of oil and soy meal is consumed as edible oil and in animal feed, respectively, while soybean seeds are used in different food items such as tofu, miso, and natto [ 2 ]. Salinity is among the greatest threats hindering global food security [ 3 ] and higher levels of salinity have a negative impact on soybean production and quality [ 4 ].…”

Genome-Wide Association Study Identifies Genomic Regions for Important Morpho-Agronomic Traits in Mesoamerican Common Bean

“…Considering that the genomic regions around the significant QTNs for the different traits assessed in this study overlapped one another, 74% of the identified genes were also detected for more than one trait. Due to the strong LD observed in common beans, it is difficult to assign a gene precisely to a trait, especially when it comes to polygenic traits (Ikram et al, 2020). The genes located in the genomic regions around the identified QTNs may serve as promising targets for studying molecular mechanisms responsible for morpho-agronomic traits in common beans.…”

“…Thus, understanding the relationship between these traits is very important for directing strategies and efforts in genetic improvement programs (Singh et al, 1991;Assefa et al, 2019). Traditional selection methods in plant breeding require intensive phenotyping fieldwork, with evaluations in several environments and years, resulting in high cost and a time-consuming process (Ikram et al, 2020). The use of molecular markers can increase efficiency and reduce the costs of phenotyping in plant breeding programs.…”

“…Using molecular tools makes it possible to identify genomic regions related to the productivity and its components, which can be used in marker-assisted selection (Kamfwa et al, 2015). Genome-Wide Association Studies (GWAS) represent a powerful option for the genetic characterization of quantitative traits and have been widely used to analyze agronomic characteristics in plants (Wang et al, 2012;Contreras-Soto et al, 2017;Zhang K. et al, 2017;Cui et al, 2018;Ward et al, 2019;Ikram et al, 2020).…”

Effect of Population Size on Genome-Wide Association Study of Agronomic Traits in Soybean

“…Still, the limitation of those studies was a small number of analysed accessions in the breeding pool, restricted to nearly a hundred lines. The analysis of the soybean genome-wide association studies that involve small (~100 accessions) Li et al, 2019), medium (~300 accessions) (Wang et al, 2019;Ikram et al, 2020), and large populations (500 and more accessions) (Fang et al, 2017;Lee et al, 2019) suggested that the effectiveness of GWAS was higher with enlargement of the size of the studied population. On the other hand, analysing a large population in multiple repetitions is costly and often is challenging for a small research group.…”