Genome-wide marker-trait association analysis in a core set of Dolichos bean germplasm

2018 ◽  
Vol 17 (1) ◽  
pp. 1-11 ◽  
Author(s):  
P. V. Vaijayanthi ◽  
S. Ramesh ◽  
M. B. Gowda ◽  
A. M. Rao ◽  
C. M. Keerthi
Keyword(s):  
Linear Model ◽  
Ssr Markers ◽  
Natural Populations ◽  
Mixed Linear Model ◽  
Multiple Traits ◽  
False Discovery Rates ◽  
Genome Wide ◽  
Core Set ◽  
Fixation Indices ◽  
Genomic Regions

AbstractAssociation mapping (AM), an alternative method of quantitative trait loci (QTL) discovery, exploits historic linkage disequilibrium (LD) present in natural populations. AM is effective in self-pollinated crops such as Dolichos bean as LD extends over longer genomic distance driven-by low rate of recombination and thereby requiring fewer markers for exploring marker-traits associations. A core set of Dolichos bean germplasm consisting of 64 accessions was evaluated for nine quantitative traits (QTs) during 2014 and 2015 rainy seasons and genotyped using 234 simple sequence repeats (SSR) markers. Substantial diversity was observed among the core set accessions at loci controlling QTs and 95 of the 234 SSR markers were found polymorphic. The structure analysis and low magnitude of fixation indices suggested weak population structure, which in-turn indicated the low possibility of false discovery rates in the marker-QTs association. The marker allele's scores were regressed onto phenotypes at nine QTs following general linear model and mixed linear model for exploring marker-QTs associations. Significantly higher number of SSR markers was found associated with genomic regions controlling nine QTs. A few of the markers such as KT Dolichos (KTD) 200 for days to 50% flowering, KTD 273 for fresh pod yield per plant and KTD 130 for fresh pods per plant explained ≥10% of the trait variations. The study could also identify a few SSR markers such as KTD 273, KTD 271 and KTD 130 linked to multiple traits. These linked SSR markers are suggested for validation for their use in marker-assisted Dolichos bean improvement programmes.

scholarly journals Dissection of soybean populations according to selection signatures based on whole-genome sequences

GigaScience ◽  
2019 ◽  
Vol 8 (12) ◽  
Author(s):  
Jae-Yoon Kim ◽  
Seongmun Jeong ◽  
Kyoung Hyoun Kim ◽  
Won-Jun Lim ◽  
Ho-Yeon Lee ◽  
...  
Keyword(s):  
Seed Development ◽  
Soybean Cyst Nematode ◽  
Agronomic Traits ◽  
Cyst Nematode ◽  
Wild Type ◽  
Selection Signatures ◽  
Genetic Traits ◽  
Genome Wide ◽  
Core Set ◽  
Genomic Regions

Abstract Background Domestication and improvement processes, accompanied by selections and adaptations, have generated genome-wide divergence and stratification in soybean populations. Simultaneously, soybean populations, which comprise diverse subpopulations, have developed their own adaptive characteristics enhancing fitness, resistance, agronomic traits, and morphological features. The genetic traits underlying these characteristics play a fundamental role in improving other soybean populations. Results This study focused on identifying the selection signatures and adaptive characteristics in soybean populations. A core set of 245 accessions (112 wild-type, 79 landrace, and 54 improvement soybeans) selected from 4,234 soybean accessions was re-sequenced. Their genomic architectures were examined according to the domestication and improvement, and accessions were then classified into 3 wild-type, 2 landrace, and 2 improvement subgroups based on various population analyses. Selection and gene set enrichment analyses revealed that the landrace subgroups have selection signals for soybean-cyst nematode HG type 0 and seed development with germination, and that the improvement subgroups have selection signals for plant development with viability and seed development with embryo development, respectively. The adaptive characteristic for soybean-cyst nematode was partially underpinned by multiple resistance accessions, and the characteristics related to seed development were supported by our phenotypic findings for seed weights. Furthermore, their adaptive characteristics were also confirmed as genome-based evidence, and unique genomic regions that exhibit distinct selection and selective sweep patterns were revealed for 13 candidate genes. Conclusions Although our findings require further biological validation, they provide valuable information about soybean breeding strategies and present new options for breeders seeking donor lines to improve soybean populations.

scholarly journals Mixed linear model approach adapted for genome-wide association studies

2010 ◽  
Vol 42 (4) ◽  
pp. 355-360 ◽  
Author(s):  
Zhiwu Zhang ◽  
Elhan Ersoz ◽  
Chao-Qiang Lai ◽  
Rory J Todhunter ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Linear Model ◽  
Association Studies ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Model Approach

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 Genome-wide association study uncovers major genetic loci associated with seed flooding tolerance in soybean

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ripa Akter Sharmin ◽  
Benjamin Karikari ◽  
Fangguo Chang ◽  
G.M. Al Amin ◽  
Mashiur Rahman Bhuiyan ◽  
...  
Keyword(s):  
Association Study ◽  
Candidate Genes ◽  
Linear Model ◽  
Genetic Basis ◽  
Genome Wide Association Study ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Flooding Tolerance ◽  
Genome Wide ◽  
Germination Stage

Abstract Background Seed flooding stress is one of the threatening environmental stressors that adversely limits soybean at the germination stage across the globe. The knowledge on the genetic basis underlying seed-flooding tolerance is limited. Therefore, we performed a genome-wide association study (GWAS) using 34,718 single nucleotide polymorphism (SNPs) in a panel of 243 worldwide soybean collections to identify genetic loci linked to soybean seed flooding tolerance at the germination stage. Results In the present study, GWAS was performed with two contrasting models, Mixed Linear Model (MLM) and Multi-Locus Random-SNP-Effect Mixed Linear Model (mrMLM) to identify significant SNPs associated with electrical conductivity (EC), germination rate (GR), shoot length (ShL), and root length (RL) traits at germination stage in soybean. With MLM, a total of 20, 40, 4, and 9 SNPs associated with EC, GR, ShL and RL, respectively, whereas in the same order mrMLM detected 27, 17, 13, and 18 SNPs. Among these SNPs, two major SNPs, Gm_08_11971416, and Gm_08_46239716 were found to be consistently connected with seed-flooding tolerance related traits, namely EC and GR across two environments. We also detected two SNPs, Gm_05_1000479 and Gm_01_53535790 linked to ShL and RL, respectively. Based on Gene Ontology enrichment analysis, gene functional annotations, and protein-protein interaction network analysis, we predicted eight candidate genes and three hub genes within the regions of the four SNPs with Cis-elements in promoter regions which may be involved in seed-flooding tolerance in soybeans and these warrant further screening and functional validation. Conclusions Our findings demonstrate that GWAS based on high-density SNP markers is an efficient approach to dissect the genetic basis of complex traits and identify candidate genes in soybean. The trait associated SNPs could be used for genetic improvement in soybean breeding programs. The candidate genes could help researchers better understand the molecular mechanisms underlying seed-flooding stress tolerance in soybean.

Genome-Wide association study identifies new elements on the genetic basis of quality-related traits in wheat across multiple environments

2020 ◽  
Author(s):  
Zhien Pu ◽  
Xueling Ye ◽  
Yang Li ◽  
Zehou Liu ◽  
Bingxin Shi ◽  
...  
Keyword(s):  
Association Study ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Snp Array ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Phenotypic Analysis ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Regions

Abstract Backgrounds: Grain protein concentration (GPC), grain starch concentration (GSC), and wet gluten concentration (WGC) are complex traits that determine nutrient concentration, end-use quality, and yield in wheat. To identify the elite and stable loci or genomic regions conferring high GPC, GSC, and WGC, a genome-wide association study (GWAS) based on a mixed linear model (MLM) was performed using 55K single nucleotide polymorphism (SNP) array in a panel of 236 wheat accessions, including 160 commercial varieties and 76 landraces, derived from Sichuan Province, China. The panel was evaluated for GPC, GSC, and WGC at four different fields. Results: Phenotypic analysis showed variation in GPC, GSC, and WGC among the different genotypes and environments. GWAS identified 12 quantitative trait loci (QTL) (-log10(P) > 2.5) associated with these three quality traits in at least two environments and located on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 5D, and 7D; the phenotypic variation explained (PVE) by these QTL ranged from 4.2% to 10.7%. Among these, three, seven, and two QTL are associated with GPC, GSC, and WGC, respectively; five QTL (QGsc.sicau-1BL, QGsc.sicau-1DS, QGsc.sicau-2DL.1, QGsc.sicau-2DL.2, QWgc.sicau-5DL) were defined potentially novel Compared with the previously reported QTLs/genes by linkage or association mapping, 5 QTLs (QGsc.sicau-1BL, QGsc.sicau-1DS, QGsc.sicau-2DL.1, QGsc.sicau-2DL.2, QWgc.sicau-5DL) were potentially novel. Furthermore, 21 presumptive candidate genes, which are involved in the metabolism or transportation of all kinds of carbohydrates, photosynthesis, programmed cell death, the balance of abscisic acid and ethylene, within these potentially novel genomic regions were predicted. Conclusions: This study provided new genetic resources and valuable genetic information of nutritional quality to broaden the genetic background and laid the molecular foundation for marker-assisted selection in wheat quality breeding.

scholarly journals Linkage disequilibrium and association mapping of fibre quality traits in elite Asiatic cotton (Gossypium arboreum) germplasm populations

2017 ◽  
Vol 53 (No. 4) ◽  
pp. 159-167 ◽  
Author(s):  
K. Sethi ◽  
P. Siwach ◽  
S.K. Verma
Keyword(s):  
Linkage Disequilibrium ◽  
Association Mapping ◽  
Linear Model ◽  
Ssr Markers ◽  
Mixed Linear Model ◽  
Quality Traits ◽  
Fibre Quality ◽  
Gossypium Arboreum ◽  
Mixed Ancestry ◽  
Ssr Loci

Cotton productivity has been hindered by the narrow genetic base of cultivated cotton. Linkage disequilibrium-based association mapping has become a powerful molecular tool to dissect and exploit genetic diversity. In the present study, population structure and marker-trait associations for fibre quality traits in genotypes belonging to six races of Gossypium arboreum were assessed. Out of 300 simple sequence repeat (SSR) markers, 100 were found polymorphic, yielding a total of 240 alleles (all polymorphic). Structure analysis revealed allelic admixtures between genotypes. A Q-matrix exhibited mixed ancestry for the majority of genotypes, the race indicum forming a significant percent ancestry for almost all genotypes. At significant threshold values of r<sup>2</sup> ≥ 0.05, 7.37% of SSR loci showed significant linkage disequilibrium (LD), while at highly significant threshold of r<sup>2</sup> ≥ 0.1, the value was reduced to 5.31%. LD clearly decayed within the genetic distance of 9–10 cM, with r<sup>2</sup> ≥ 0.1. Twenty-eight SSR markers were found associated with six fibre quality traits using general linear model and mixed linear model.

scholarly journals Identification of QTN and Candidate Gene for Seed-flooding Tolerance in Soybean [Glycine max (L.) Merr.] using Genome-Wide Association Study (GWAS)

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 957 ◽  
Author(s):  
Yu ◽  
Chang ◽  
Lv ◽  
Sharmin ◽  
Wang ◽  
...  
Keyword(s):  
Association Study ◽  
Candidate Gene ◽  
Linear Model ◽  
Genetic Basis ◽  
Genome Wide Association Study ◽  
Gene Annotation ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Flooding Tolerance ◽  
Genome Wide

Seed-flooding stress is one of the major abiotic constraints severely affecting soybean yield and quality. Understanding the molecular mechanism and genetic basis underlying seed-flooding tolerance will be of greatly importance in soybean breeding. However, very limited information is available about the genetic basis of seed-flooding tolerance in soybean. The present study performed Genome-Wide Association Study (GWAS) to identify the quantitative trait nucleotides (QTNs) associated with three seed-flooding tolerance related traits, viz., germination rate (GR), normal seedling rate (NSR) and electric conductivity (EC), using a panel of 347 soybean lines and the genotypic data of 60,109 SNPs with MAF > 0.05. A total of 25 and 21 QTNs associated with all three traits were identified via mixed linear model (MLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) in three different environments (JP14, HY15, and Combined). Among these QTNs, three major QTNs, viz., QTN13, qNSR-10 and qEC-7-2, were identified through both methods MLM and mrMLM. Interestingly, QTN13 located on Chr.13 has been consistently identified to be associated with all three studied traits in both methods and multiple environments. Within the 1.0 Mb physical interval surrounding the QTN13, nine candidate genes were screened for their involvement in seed-flooding tolerance based on gene annotation information and available literature. Based on the qRT-PCR and sequence analysis, only one gene designated as GmSFT (Glyma.13g248000) displayed significantly higher expression level in all tolerant genotypes compared to sensitive ones under flooding treatment, as well as revealed nonsynonymous mutation in tolerant genotypes, leading to amino acid change in the protein. Additionally, subcellular localization showed that GmSFT was localized in the nucleus and cell membrane. Hence, GmSFT was considered as the most likely candidate gene for seed-flooding tolerance in soybean. In conclusion, the findings of the present study not only increase our knowledge of the genetic control of seed-flooding tolerance in soybean, but will also be of great utility in marker-assisted selection and gene cloning to elucidate the mechanisms of seed-flooding tolerance.

scholarly journals Integrative functional linear model for genome-wide association studies with multiple traits

Biostatistics ◽  
2020 ◽  
Author(s):  
Yang Li ◽  
Fan Wang ◽  
Mengyun Wu ◽  
Shuangge Ma
Keyword(s):  
Linear Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
High Dimensionality ◽  
Sufficient Information ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Functional Linear Model ◽  
Genome Wide ◽  
Good Prediction Accuracy

Summary In recent biomedical research, genome-wide association studies (GWAS) have demonstrated great success in investigating the genetic architecture of human diseases. For many complex diseases, multiple correlated traits have been collected. However, most of the existing GWAS are still limited because they analyze each trait separately without considering their correlations and suffer from a lack of sufficient information. Moreover, the high dimensionality of single nucleotide polymorphism (SNP) data still poses tremendous challenges to statistical methods, in both theoretical and practical aspects. In this article, we innovatively propose an integrative functional linear model for GWAS with multiple traits. This study is the first to approximate SNPs as functional objects in a joint model of multiple traits with penalization techniques. It effectively accommodates the high dimensionality of SNPs and correlations among multiple traits to facilitate information borrowing. Our extensive simulation studies demonstrate the satisfactory performance of the proposed method in the identification and estimation of disease-associated genetic variants, compared to four alternatives. The analysis of type 2 diabetes data leads to biologically meaningful findings with good prediction accuracy and selection stability.

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