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

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255761
Author(s):  
Waltram Ravelombola ◽  
Jun Qin ◽  
Ainong Shi ◽  
Qijian Song ◽  
Jin Yuan ◽  
...  
Keyword(s):  
Association Study ◽  
Genomic Selection ◽  
Plant Height ◽  
Seed Weight ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide ◽  
A Genome

Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.

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

2019 ◽  
Author(s):  
Waltram Ravelombola ◽  
Jun Qin ◽  
Ainong Shi ◽  
Fengmin Wang ◽  
Yan Feng ◽  
...  
Keyword(s):  
Association Study ◽  
Genomic Selection ◽  
Candidate Genes ◽  
Plant Height ◽  
Seed Weight ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide

Abstract 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.

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

2020 ◽  
Author(s):  
Waltram Ravelombola ◽  
Jun Qin ◽  
Ainong Shi ◽  
Fengmin Wang ◽  
Yan Feng ◽  
...  
Keyword(s):  
Population Structure ◽  
Association Study ◽  
Genomic Selection ◽  
Plant Height ◽  
Seed Weight ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide

Abstract 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.

scholarly journals A Genome-Wide Association Study for Agronomic Traits in Soybean Using SNP Markers and SNP-Based Haplotype Analysis

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171105 ◽  
Author(s):  
Rodrigo Iván Contreras-Soto ◽  
Freddy Mora ◽  
Marco Antônio Rott de Oliveira ◽  
Wilson Higashi ◽  
Carlos Alberto Scapim ◽  
...  
Keyword(s):  
Association Study ◽  
Haplotype Analysis ◽  
Genome Wide Association Study ◽  
Agronomic Traits ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide ◽  
A Genome

scholarly journals MULTI-TRAIT MODELS FOR GENOMIC REGIONS ASSOCIATED WITH MAL DE RÍO CUARTO AND BACTERIAL DISEASE IN MAIZE

2021 ◽  
Vol 32 (Issue 1) ◽  
pp. 25-33
Author(s):  
M. Ruiz ◽  
E.A. Rossi ◽  
N.C. Bonamico ◽  
M.G. Balzarini
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Mixed Linear Model ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Bacterial Disease ◽  
Maize Germplasm ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Regions

Maize (Zea Mays L.) production has been greatly benefited from the improvement of inbred lines in regard to the resistance to diseases. However, the absence of resistant genotypes to bacteriosis is remarkable. The aim of the study was to identify genomic regions for resistance to Mal de Río Cuarto (MRC) and to bacterial disease (BD) in a diverse maize germplasm evaluated in the Argentinian region where MRC virus is endemic. A maize diverse population was assessed for both diseases during the 2019-2020 crop season. Incidence and severity of MRC and BD were estimated for each line and a genome wide association study (GWAS) was conducted with 78,376 SNP markers. A multi-trait mixed linear model was used for simultaneous evaluation of resistance to MRC and BD in the scored lines. The germplasm showed high genetic variability for both MRC and BD resistance. No significant genetic correlation was observed between the response to both diseases. Promising genomic regions for resistance to MRC and BD were identified and will be confirmed in further trials. Key words: maize disease; genome wide association study; SNP; multi-trait model

scholarly journals MULTI-TRAIT MODELS FOR GENOMIC REGIONS ASSOCIATED WITH MAL DE RÍO CUARTO AND BACTERIAL DISEASE IN MAIZE

2021 ◽  
Vol 32 (Issue 1) ◽  
pp. 25-33
Author(s):  
M. Ruiz ◽  
E.A. Ross ◽  
N.C. Bonamico ◽  
M.G. Balzarini
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Mixed Linear Model ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Bacterial Disease ◽  
Maize Germplasm ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Regions

Maize (Zea Mays L.) production has been greatly benefited from the improvement of inbred lines in regard to the resistance to diseases. However, the absence of resistant genotypes to bacteriosis is remarkable. The aim of the study was to identify genomic regions for resistance to Mal de Río Cuarto (MRC) and to bacterial disease (BD) in a diverse maize germplasm evaluated in the Argentinian region where MRC virus is endemic. A maize diverse population was assessed for both diseases during the 2019-2020 crop season. Incidence and severity of MRC and BD were estimated for each line and a genome wide association study (GWAS) was conducted with 78,376 SNP markers. A multi-trait mixed linear model was used for simultaneous evaluation of resistance to MRC and BD in the scored lines. The germplasm showed high genetic variability for both MRC and BD resistance. No significant genetic correlation was observed between the response to both diseases. Promising genomic regions for resistance to MRC and BD were identified and will be confirmed in further trials. Key words: maize disease; genome wide association study; SNP; multi-trait model

scholarly journals A Genome-Wide Association Study for Resistance to the Insect Pest Leptocybe invasa in Eucalyptus grandis Reveals Genomic Regions and Positional Candidate Defense Genes

2020 ◽  
Vol 61 (7) ◽  
pp. 1285-1296
Author(s):  
Lorraine Mhoswa ◽  
Marja M O’Neill ◽  
Makobatjatji M Mphahlele ◽  
Caryn N Oates ◽  
Kitt G Payn ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Eucalyptus Grandis ◽  
Nucleotide Binding ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Genome Wide ◽  
A Genome ◽  
Leptocybe Invasa ◽  
Genomic Regions

Abstract The galling insect, Leptocybe invasa, causes significant losses in plantations of various Eucalyptus species and hybrids, threatening its economic viability. We applied a genome-wide association study (GWAS) to identify single-nucleotide polymorphism (SNP) markers associated with resistance to L. invasa. A total of 563 insect-challenged Eucalyptus grandis trees, from 61 half-sib families, were genotyped using the EUChip60K SNP chip, and we identified 15,445 informative SNP markers in the test population. Multi-locus mixed-model (MLMM) analysis identified 35 SNP markers putatively associated with resistance to L. invasa based on four discreet classes of insect damage scores: (0) not infested, (1) infested showing evidence of oviposition but no gall development, (2) infested with galls on leaves, midribs or petioles and (3) stunting and lethal gall formation. MLMM analysis identified three associated genomic regions on chromosomes 3, 7 and 8 jointly explaining 17.6% of the total phenotypic variation. SNP analysis of a validation population of 494 E. grandis trees confirmed seven SNP markers that were also detected in the initial association analysis. Based on transcriptome profiles of resistant and susceptible genotypes from an independent experiment, we identified several putative candidate genes in associated genomic loci including Nucleotide-binding ARC- domain (NB-ARC) and toll-interleukin-1-receptor-Nucleotide binding signal- Leucine rich repeat (TIR-NBS-LRR) genes. Our results suggest that Leptocybe resistance in E. grandis may be influenced by a few large-effect loci in combination with minor effect loci segregating in our test and validation populations.

scholarly journals The Minnesota Center for Twin and Family Research Genome-Wide Association Study

2012 ◽  
Vol 15 (6) ◽  
pp. 767-774 ◽  
Author(s):  
Michael B. Miller ◽  
Saonli Basu ◽  
Julie Cunningham ◽  
Eleazar Eskin ◽  
Steven M. Malone ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Generalized Least Squares ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Family Research ◽  
Genome Wide ◽  
A Genome ◽  
Array Quality ◽  
The Rich

As part of the Genes, Environment and Development Initiative, the Minnesota Center for Twin and Family Research (MCTFR) undertook a genome-wide association study, which we describe here. A total of 8,405 research participants, clustered in four-member families, have been successfully genotyped on 527,829 single nucleotide polymorphism (SNP) markers using Illumina's Human660W-Quad array. Quality control screening of samples and markers as well as SNP imputation procedures are described. We also describe methods for ancestry control and how the familial clustering of the MCTFR sample can be accounted for in the analysis using a Rapid Feasible Generalized Least Squares algorithm. The rich longitudinal MCTFR assessments provide numerous opportunities for collaboration.

Identification of a single genomic region associated with seasonal river return timing in adult Scottish Atlantic salmon (Salmo salar), using a genome-wide association study

2018 ◽  
Vol 75 (9) ◽  
pp. 1427-1435 ◽  
Author(s):  
Eef Cauwelier ◽  
John Gilbey ◽  
James Sampayo ◽  
Lee Stradmeyer ◽  
Stuart J. Middlemas
Keyword(s):  
Atlantic Salmon ◽  
Association Study ◽  
Salmo Salar ◽  
Genome Wide Association Study ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Atlantic Salmon Salmo Salar ◽  
Genome Wide ◽  
A Genome ◽  
Run Timing

Examination of the genetic basis of the timing of the return migration of Atlantic salmon (Salmo salar) to fresh water from the sea, a trait of economic and conservation interest, was carried out using a genome-wide association study. We examined genotype data of 52 731 single nucleotide polymorphic (SNP) markers from 73 early and 49 late running two-sea-winter salmon from five rivers in eastern Scotland. A single region of the Atlantic salmon chromosome Ssa09 was identified, containing nine SNP markers significantly associated with run timing, a region previously linked to variation in sea age at maturity. Validation of the markers in a group of 233 one- and two-sea-winter fish, including adults from a novel river, again showed significant associations between the trait and the Ssa09 region, explaining ∼24% of the trait variance. The SNP loci identified provide the ability to examine trait variation in populations of Atlantic salmon and so help facilitate conservation management of the differing run timing phenotypes.

Sign in / Sign up

Export Citation Format

Share Document