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

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...

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Autores principales: Igolkina, Anna A., Meshcheryakov, Georgy, Gretsova, Maria V., Nuzhdin, Sergey V., Samsonova, Maria G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385891/
https://www.ncbi.nlm.nih.gov/pubmed/32723302
http://dx.doi.org/10.1186/s12864-020-06833-2
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author Igolkina, Anna A.
Meshcheryakov, Georgy
Gretsova, Maria V.
Nuzhdin, Sergey V.
Samsonova, Maria G.
author_facet Igolkina, Anna A.
Meshcheryakov, Georgy
Gretsova, Maria V.
Nuzhdin, Sergey V.
Samsonova, Maria G.
author_sort Igolkina, Anna A.
collection PubMed
description 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.
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spelling pubmed-73858912020-07-30 Multi-trait multi-locus SEM model discriminates SNPs of different effects Igolkina, Anna A. Meshcheryakov, Georgy Gretsova, Maria V. Nuzhdin, Sergey V. Samsonova, Maria G. BMC Genomics Research 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. BioMed Central 2020-07-28 /pmc/articles/PMC7385891/ /pubmed/32723302 http://dx.doi.org/10.1186/s12864-020-06833-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Igolkina, Anna A.
Meshcheryakov, Georgy
Gretsova, Maria V.
Nuzhdin, Sergey V.
Samsonova, Maria G.
Multi-trait multi-locus SEM model discriminates SNPs of different effects
title Multi-trait multi-locus SEM model discriminates SNPs of different effects
title_full Multi-trait multi-locus SEM model discriminates SNPs of different effects
title_fullStr Multi-trait multi-locus SEM model discriminates SNPs of different effects
title_full_unstemmed Multi-trait multi-locus SEM model discriminates SNPs of different effects
title_short Multi-trait multi-locus SEM model discriminates SNPs of different effects
title_sort multi-trait multi-locus sem model discriminates snps of different effects
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385891/
https://www.ncbi.nlm.nih.gov/pubmed/32723302
http://dx.doi.org/10.1186/s12864-020-06833-2
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