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Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping

Motivation: Association tests based on next-generation sequencing data are often under-powered due to the presence of rare variants and large amount of neutral or protective variants. A successful strategy is to aggregate genetic information within meaningful single-nucleotide polymorphism (SNP) set...

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Autores principales: Zhu, Hongjie, Li, Lexin, Zhou, Hua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3436833/
https://www.ncbi.nlm.nih.gov/pubmed/22962455
http://dx.doi.org/10.1093/bioinformatics/bts406
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author Zhu, Hongjie
Li, Lexin
Zhou, Hua
author_facet Zhu, Hongjie
Li, Lexin
Zhou, Hua
author_sort Zhu, Hongjie
collection PubMed
description Motivation: Association tests based on next-generation sequencing data are often under-powered due to the presence of rare variants and large amount of neutral or protective variants. A successful strategy is to aggregate genetic information within meaningful single-nucleotide polymorphism (SNP) sets, e.g. genes or pathways, and test association on SNP sets. Many existing methods for group-wise tests require specific assumptions about the direction of individual SNP effects and/or perform poorly in the presence of interactions. Results: We propose a joint association test strategy based on two key components: a nonlinear supervised dimension reduction approach for effective SNP information aggregation and a novel kernel specially designed for qualitative genotype data. The new test demonstrates superior performance in identifying causal genes over existing methods across a large variety of disease models simulated from sequence data of real genes. In general, the proposed method provides an association test strategy that can (i) detect both rare and common causal variants, (ii) deal with both additive and interaction effect, (iii) handle both quantitative traits and disease dichotomies and (iv) incorporate non-genetic covariates. In addition, the new kernel can potentially boost the power of the entire family of kernel-based methods for genetic data analysis. Availability: The method is implemented in MATLAB. Source code is available upon request. Contact: hongjie.zhu@duke.edu
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spelling pubmed-34368332012-12-12 Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping Zhu, Hongjie Li, Lexin Zhou, Hua Bioinformatics Original Papers Motivation: Association tests based on next-generation sequencing data are often under-powered due to the presence of rare variants and large amount of neutral or protective variants. A successful strategy is to aggregate genetic information within meaningful single-nucleotide polymorphism (SNP) sets, e.g. genes or pathways, and test association on SNP sets. Many existing methods for group-wise tests require specific assumptions about the direction of individual SNP effects and/or perform poorly in the presence of interactions. Results: We propose a joint association test strategy based on two key components: a nonlinear supervised dimension reduction approach for effective SNP information aggregation and a novel kernel specially designed for qualitative genotype data. The new test demonstrates superior performance in identifying causal genes over existing methods across a large variety of disease models simulated from sequence data of real genes. In general, the proposed method provides an association test strategy that can (i) detect both rare and common causal variants, (ii) deal with both additive and interaction effect, (iii) handle both quantitative traits and disease dichotomies and (iv) incorporate non-genetic covariates. In addition, the new kernel can potentially boost the power of the entire family of kernel-based methods for genetic data analysis. Availability: The method is implemented in MATLAB. Source code is available upon request. Contact: hongjie.zhu@duke.edu Oxford University Press 2012-09-15 2012-09-03 /pmc/articles/PMC3436833/ /pubmed/22962455 http://dx.doi.org/10.1093/bioinformatics/bts406 Text en © The Author(s) (2012). Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Zhu, Hongjie
Li, Lexin
Zhou, Hua
Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
title Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
title_full Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
title_fullStr Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
title_full_unstemmed Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
title_short Nonlinear dimension reduction with Wright–Fisher kernel for genotype aggregation and association mapping
title_sort nonlinear dimension reduction with wright–fisher kernel for genotype aggregation and association mapping
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3436833/
https://www.ncbi.nlm.nih.gov/pubmed/22962455
http://dx.doi.org/10.1093/bioinformatics/bts406
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