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Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies
BACKGROUND: Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933893/ https://www.ncbi.nlm.nih.gov/pubmed/31881907 http://dx.doi.org/10.1186/s12859-019-3300-9 |
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author | Wang, Haohan Yue, Tianwei Yang, Jingkang Wu, Wei Xing, Eric P. |
author_facet | Wang, Haohan Yue, Tianwei Yang, Jingkang Wu, Wei Xing, Eric P. |
author_sort | Wang, Haohan |
collection | PubMed |
description | BACKGROUND: Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. RESULTS: In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. CONCLUSIONS: After validating the performance of our method using simulation experiments, we further apply it to Alzheimer’s disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer’s disease. |
format | Online Article Text |
id | pubmed-6933893 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69338932019-12-30 Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies Wang, Haohan Yue, Tianwei Yang, Jingkang Wu, Wei Xing, Eric P. BMC Bioinformatics Methodology BACKGROUND: Genome-wide Association Studies (GWAS) have contributed to unraveling associations between genetic variants in the human genome and complex traits for more than a decade. While many works have been invented as follow-ups to detect interactions between SNPs, epistasis are still yet to be modeled and discovered more thoroughly. RESULTS: In this paper, following the previous study of detecting marginal epistasis signals, and motivated by the universal approximation power of deep learning, we propose a neural network method that can potentially model arbitrary interactions between SNPs in genetic association studies as an extension to the mixed models in correcting confounding factors. Our method, namely Deep Mixed Model, consists of two components: 1) a confounding factor correction component, which is a large-kernel convolution neural network that focuses on calibrating the residual phenotypes by removing factors such as population stratification, and 2) a fixed-effect estimation component, which mainly consists of an Long-short Term Memory (LSTM) model that estimates the association effect size of SNPs with the residual phenotype. CONCLUSIONS: After validating the performance of our method using simulation experiments, we further apply it to Alzheimer’s disease data sets. Our results help gain some explorative understandings of the genetic architecture of Alzheimer’s disease. BioMed Central 2019-12-27 /pmc/articles/PMC6933893/ /pubmed/31881907 http://dx.doi.org/10.1186/s12859-019-3300-9 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Methodology Wang, Haohan Yue, Tianwei Yang, Jingkang Wu, Wei Xing, Eric P. Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies |
title | Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies |
title_full | Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies |
title_fullStr | Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies |
title_full_unstemmed | Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies |
title_short | Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies |
title_sort | deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6933893/ https://www.ncbi.nlm.nih.gov/pubmed/31881907 http://dx.doi.org/10.1186/s12859-019-3300-9 |
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