Dataset Information

Multi-trait multi-locus SEM model discriminates SNPs of different effects.

ABSTRACT:

Background

There is a plethora of methods for genome-wide association studies. However, only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes.

Results

We propose a multi-trait multi-locus model which employs structural equation modeling (SEM) to describe complex associations between SNPs and traits - multi-trait multi-locus SEM (mtmlSEM). The structure of our model makes it possible to discriminate pleiotropic and single-trait SNPs of direct and indirect effect. We also propose an automatic procedure to construct the model using factor analysis and the maximum likelihood method. For estimating a large number of parameters in the model, we performed Bayesian inference and implemented Gibbs sampling. An important feature of the model is that it correctly copes with non-normally distributed variables, such as some traits and variants.

Conclusions

We applied the model to Vavilov's collection of 404 chickpea (Cicer arietinum L.) accessions with 20-fold cross-validation. We analyzed 16 phenotypic traits which we organized into five groups and found around 230 SNPs associated with traits, 60 of which were of pleiotropic effect. The model demonstrated high accuracy in predicting trait values.

SUBMITTER: Igolkina AA

PROVIDER: S-EPMC7385891 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Multi-trait multi-locus SEM model discriminates SNPs of different effects.

Igolkina Anna A AA Meshcheryakov Georgy G Gretsova Maria V MV Nuzhdin Sergey V SV Samsonova Maria G MG

BMC genomics 20200728 Suppl 8

<h4>Background</h4>There is a plethora of methods for genome-wide association studies. However, only a few of them may be classified as multi-trait and multi-locus, i.e. consider the influence of multiple genetic variants to several correlated phenotypes.<h4>Results</h4>We propose a multi-trait multi-locus model which employs structural equation modeling (SEM) to describe complex associations between SNPs and traits - multi-trait multi-locus SEM (mtmlSEM). The structure of our model makes it pos ...[more]

PMID: 32723302

Similar Datasets

Project description:A genome-wide association study (GWAS) for 10 yield and yield component traits was conducted using an association panel comprising 225 diverse spring wheat genotypes. The panel was genotyped using 10,904 SNPs and evaluated for three years (2016-2019), which constituted three environments (E1, E2 and E3). Heritability for different traits ranged from 29.21 to 97.69%. Marker-trait associations (MTAs) were identified for each trait using data from each environment separately and also using BLUP values. Four different models were used, which included three single trait models (CMLM, FarmCPU, SUPER) and one multi-trait model (mvLMM). Hundreds of MTAs were obtained using each model, but after Bonferroni correction, only 6 MTAs for 3 traits were available using CMLM, and 21 MTAs for 4 traits were available using FarmCPU; none of the 525 MTAs obtained using SUPER could qualify after Bonferroni correction. Using BLUP, 20 MTAs were available, five of which also figured among MTAs identified for individual environments. Using mvLMM model, after Bonferroni correction, 38 multi-trait MTAs, for 15 different trait combinations were available. Epistatic interactions involving 28 pairs of MTAs were also available for seven of the 10 traits; no epistatic interactions were available for GNPS, PH, and BYPP. As many as 164 putative candidate genes (CGs) were identified using all the 50 MTAs (CMLM, 3; FarmCPU, 9; mvLMM, 6, epistasis, 21 and BLUP, 11 MTAs), which ranged from 20 (CMLM) to 66 (epistasis) CGs. In-silico expression analysis of CGs was also conducted in different tissues at different developmental stages. The information generated through the present study proved useful for developing a better understanding of the genetics of each of the 10 traits; the study also provided novel markers for marker-assisted selection (MAS) to be utilized for the development of wheat cultivars with improved agronomic traits.Supplementary informationThe online version contains supplementary material available at 10.1007/s11032-021-01240-1.

Dataset Information

Multi-trait multi-locus SEM model discriminates SNPs of different effects.

Background

Results

Conclusions

Publications

Multi-trait multi-locus SEM model discriminates SNPs of different effects.

Similar Datasets

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