Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines

Copley, Tanya; Duceppe, Marc‐Olivier; O’Donoughue, Louise S.

doi:10.1186/s12864-018-4558-4

Cited by 46 publications

(46 citation statements)

References 64 publications

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“…Our previous study based on evaluation of 120 soybean accessions in three different regions of Kazakhstan (Abugalieva et al, 2016) has confirmed the results of observations in other parts of the world (ContrerasSoto et al, 2018;Copley et al, 2018), which underline the importance of suitable flowering time for plant adaptation in a particular environmental niche.…”

Section: Gwas Of a Soybean Breeding Collection From South East And Sosupporting

confidence: 69%

GWAS of a soybean breeding collection from South East and South Kazakhstan for resistance to fungal diseases

et al. 2018

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Soybean (Glycine max(L.) Merr) is an essential food, feed, and technical culture. In Kazakhstan the area under soybean is increasing every year, helping to solve the problem of protein deficiency in human nutrition and animal feeding. One of the main problems of soybean production is fungal diseases causing yields losses of up to 30 %. Modern genomic studies can be applied to facilitate efficient breeding research for improvement of soybean fungal disease tolerance. Therefore, the objective of this genome-wide association study (GWAS) was analysis of a soybean collection consisting of 182 accessions in relation to fungal diseases in the conditions of South East and South Kazakhstan. Field evaluation of the soybean collection suggested thatFusariumspp. andCercospora sojinaaffected plants in the South region (RIBSP), andSeptoria glycines– in the South East region (KRIAPP). The major objective of the study was identification of QTL associated with resistance to fusarium root rot (FUS), frogeye leaf spot (FLS), and brown spot (BS). GWAS using 4 442 SNP (single nucleotide polymorphism) markers of Illumina iSelect array allowed for identification of fifteen marker trait associations (MTA) resistant to the three diseases at two different stages of growth. Two QTL both for FUS (chromosomes 13 and 17) and BS (chromosomes 14 and 17) were genetically mapped, including one presumably novel QTL for BS (chromosome 17). Also, five presumably novel QTL for FLS were genetically mapped on chromosomes 2, 7, and 15. The results can be used for improvement of the local breeding projects based on marker-assisted selection approach.

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Section: Gwas Of a Soybean Breeding Collection From South East And Sosupporting

confidence: 69%

GWAS of a soybean breeding collection from South East and South Kazakhstan for resistance to fungal diseases

et al. 2018

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“…GWAS has been conducted to identify markers associated with agronomic traits in soybean [2,3]. To date, more than 60 loci found through GWAS have been reported to be associated with maturity in soybean [4][5][6][7][8][9]. These loci are distributed across the soybean genome with chromosome 16 having the highest number of signi cant loci (https://www.soybase.org/).…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola¹,

Qin²,

Shi³

et al. 2020

Preprint

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BackgroundSoybean [Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy.Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). Population structure was inferred using STRUCTURE 2.3.4, GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that: a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci (E2, E4, and Dt1) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900, Glyma.19g200800, Glyma.09g196700, and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP (if no annotated genes found close the top one) for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent.Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits in soybean through marker-assisted selection (MAS) and GS in breeding programs. After validation, the candidate genes can be transferred to new cultivars using the linked SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.

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“…GWAS has been conducted to identify markers associated with agronomic traits in soybean (Cao et al 2017;Yao et al 2015). To date, more than 60 loci found through GWAS have been reported to be associated with maturity in soybean (Copley et al 2018;Fang et al 2017;Hu et al 2014;Sonah et al 2015;Zhang et al 2015a;Zuo et al 2013). These loci are distributed across the soybean genome with chromosome 16 having the highest number of significant loci (https://www.soybase.org/).…”

Section: Introductionmentioning

confidence: 99%

“…Molecular markers associated with seed weight have been previously investigated in soybean. GWAS identified a total of more than 95 loci controlling seed weight with chromosome 4 harboring 12 loci (Contreras-Soto et al 2017;Copley et al 2018;Hao et al 2012;Hu et al 2014;Sonah et al 2015;Wang et al 2016;Yan et al 2017;Zhang et al 2015b;Zhang et al 2016;Zhou et al 2015). Glyma11g03360, Glyma11g03430, Glyma11g05760, Glyma18g05240, Glyma18g43500, Glyma18g43500, Glyma.03g166700, Glyma.11g164700, Glyma.11g164700, LOC100784416, Glyma05g09390, Glyma.07g076800, Glyma10g28250, Glyma14g08050, Glyma14g08280, Glyma19g33421, Glyma19g33550, and Glyma19g35180 have been reported as potential candidate genes for seed weight in soybean (Contreras-Soto et al 2017;Wang et al 2016;Zhang et al 2015;Zhang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

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Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola

Qin²,

Shi

et al. 2019

Preprint

View full text Add to dashboard Cite

Background Soybean [ Glycine max (L.) Merr.] is a legume of great interest worldwide. Enhancing genetic gain for agronomic traits via molecular approaches has been long considered as the main task for soybean breeders and geneticists. The objectives of this study were to evaluate maturity, plant height, seed weight, and yield in a diverse soybean accession panel, to conduct a genome-wide association study (GWAS) for these traits and identify SNP markers associated with the four traits, and to assess genomic selection (GS) accuracy. Results A total of 250 soybean accessions were evaluated for maturity, plant height, seed weight, and yield over three years. This panel was genotyped with a total of 10,259 high quality SNPs postulated from genotyping by sequencing (GBS). GWAS was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model, and GS was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that a total of 20, 31, 37, 31, and 23 SNPs were significantly associated with the average 3-year data for maturity, plant height, seed weight, and yield, respectively; some significant SNPs were mapped into previously described loci ( E2 , E4 , and Dt1 ) affecting maturity and plant height in soybean and a new locus mapped on chromosome 20 was significantly associated with plant height; Glyma.10g228900 , Glyma.19g200800 , Glyma.09g196700 , and Glyma.09g038300 were candidate genes found in the vicinity of the top or the second best SNP for maturity, plant height, seed weight, and yield, respectively; a 11.5-Mb region of chromosome 10 was associated with both seed weight and yield; and GS accuracy was trait-, year-, and population structure-dependent. Conclusions The SNP markers identified from this study for plant height, maturity, seed weight and yield can be used to improve the four agronomic traits through marker-assisted selection (MAS) and GS in soybean breeding programs. After validation, the candidate genes can be transferred to new cultivars using SNP markers through MAS. The high GS accuracy has confirmed that the four agronomic traits can be selected in molecular breeding through GS.

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Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines

Cited by 46 publications

References 64 publications

GWAS of a soybean breeding collection from South East and South Kazakhstan for resistance to fungal diseases

GWAS of a soybean breeding collection from South East and South Kazakhstan for resistance to fungal diseases

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

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