Genome-wide association study and genomic selection for yield and related traits in soybean

Ravelombola, Waltram; Qin, Jun; Shi, Ainong; Song, Qijian; Yuan, Jiangang; Wang, Fengmin; Chen, Pengyin; Long, Yan; Yan, Feng; Zhao, Tengteng; Yue, Meng; Guan, Kexin; Yang, Chen; Zhang, Mengchen

doi:10.1371/journal.pone.0255761

Cited by 39 publications

(29 citation statements)

References 57 publications

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“…The GS was performed on amino acid concentration ( Qin et al, 2019 ), soybean chlorophyll content, soybean cyst nematode tolerance ( Ravelombola et al, 2019 ), yield, and yield-related traits, such as maturity, plant height, and 100-seed weight ( Ravelombola et al, 2021 ). These studies have shown the feasibility of GS for soybean yield and quality-related traits ( Matei et al, 2018 ; Stewart-Brown et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…The results showed that the use of GWAS-derived SNPs for conducting GS significantly improved the accuracy of prediction, which was consistent with the results reported by Qin et al (2019) . The model selection criteria, SNP sets, and population training size were critical factors when conducting a GS, as reported in previous studies ( Ravelombola et al, 2019 , 2020 , 2021 ). Those studies had demonstrated that 1,000–2,000 genome-wide markers across all the lines/accessions were needed to reach maximum efficiency of genomic prediction in the populations, increasing marker density that would not improve prediction efficiency ( Poland et al, 2012 ; Bao et al, 2014 ; Zhang J. et al, 2016 ; Song et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

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Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

et al. 2022

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Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283–19315351) included 7 candidate genes and the Hap.XAA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

et al. 2022

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“…Genome-wide association studies have proved its efficiency in finding genomic regions linked with economically important agronomical features in several crops, including wheat [ 39 , 40 , 41 , 42 ], eggplant [ 36 ], potato [ 43 ], and soybean [ 44 , 45 ]. There are important agro-morphological traits to be improved in eggplant, including the development of prickleless varieties.…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Association Study for Agro-Morphological Traits in Eggplant Core Collection

Haile

Kim

et al. 2022

Plants

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Eggplant is one of the most economically and nutritionally important vegetables worldwide. The study of the association of phenotypic traits with genetic factors is vital for the rapid and efficient identification and selection of eggplant genetic resources for breeding purposes with desired traits. The eggplant resources (587) collected from different countries, including Korea, were used for establishing the core collection. A total of 288 accessions were selected from 587 Solanum accessions based on 52 single nucleotide polymorphisms (SNPs) markers together with 17 morphological traits. This core collection was further used to analyze the genetic associations of eggplant morphological variations. A large variation was found among the evaluated eggplant accessions for some agro-morphological traits. Stem prickles and leaf prickles showed a significant positive correlation (r = 0.83***), followed by days to flowering and days to maturity (r = 0.64***). A total of 114,981 SNPs were filtered and used for phylogenetic tree analysis, population structure analysis, and genome-wide association study (GWAS). Among the agro-morphological traits, significantly associated SNPs were found for six traits. A total of 377 significantly associated SNPs with six agro-morphological traits were identified. These six traits and the number of SNPs were: days to maturity (51), flower size (121), fruit width (20), harvest fruit color (42), leaf prickles (38), and stem prickles (105). The largest fraction of significant SNPs (11.94%) was obtained on chromosome Ch01, followed by Ch07 and Ch06 with 11.67% and 10.08%, respectively. This study will help to develop markers linked to the most important agro-morphological traits of eggplant genetic resources and support the selection of desirable traits for eggplant breeding programs.

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“…GS has been shown to reduce selection time in soybean breeding ( Matei et al, 2018 ) and the United States soybean germplasm collection has proven to be a valuable resource for creating GS models ( Jarquin et al, 2016 ). Promising results have displayed successful prediction of grain yield, protein and oil content, plant height, maturity, seed weight ( Ma et al, 2016 ; Duhnen et al, 2017 ; Stewart-Brown et al, 2019 ; Ravelombola et al, 2021 ) as well as soybean cyst nematode resistance ( Ravelombola et al, 2019 , 2020 ). However, only one study by Qin et al (2019) has evaluated GS for amino acid content in soybean seed, and it did not include Met concentrations.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association Study and Genomic Selection for Proteinogenic Methionine in Soybean Seeds

Singer

Shea

et al. 2022

Front. Plant Sci.

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Soybean [Glycine max (L.) Merr.] seeds have an amino acid profile that provides excellent viability as a food and feed protein source. However, low concentrations of an essential amino acid, methionine, limit the nutritional utility of soybean protein. The objectives of this study were to identify genomic associations and evaluate the potential for genomic selection (GS) for methionine content in soybean seeds. We performed a genome-wide association study (GWAS) that utilized 311 soybean accessions from maturity groups IV and V grown in three locations in 2018 and 2019. A total of 35,570 single nucleotide polymorphisms (SNPs) were used to identify genomic associations with proteinogenic methionine content that was quantified by high-performance liquid chromatography (HPLC). Across four environments, 23 novel SNPs were identified as being associated with methionine content. The strongest associations were found on chromosomes 3 (ss715586112, ss715586120, ss715586126, ss715586203, and ss715586204), 8 (ss715599541 and ss715599547) and 16 (ss715625009). Several gene models were recognized within proximity to these SNPs, such as a leucine-rich repeat protein kinase and a serine/threonine protein kinase. Identification of these linked SNPs should help soybean breeders to improve protein quality in soybean seeds. GS was evaluated using k-fold cross validation within each environment with two SNP sets, the complete 35,570 set and a subset of 248 SNPs determined to be associated with methionine through GWAS. Average prediction accuracy (r2) was highest using the SNP subset ranging from 0.45 to 0.62, which was a significant improvement from the complete set accuracy that ranged from 0.03 to 0.27. This indicated that GS utilizing a significant subset of SNPs may be a viable tool for soybean breeders seeking to improve methionine content.

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Genome-wide association study and genomic selection for yield and related traits in soybean

Cited by 39 publications

References 57 publications

Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

Genome-Wide Association Study for Agro-Morphological Traits in Eggplant Core Collection

Genome-Wide Association Study and Genomic Selection for Proteinogenic Methionine in Soybean Seeds

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