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Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships

BACKGROUND: There is growing interest in the role of rare variants in the variation of complex traits due to increasing evidence that rare variants are associated with quantitative traits. However, association methods that are commonly used for mapping common variants are not effective to map rare v...

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Autores principales: Zhang, Qianqian, Guldbrandtsen, Bernt, Calus, Mario P. L., Lund, Mogens Sandø, Sahana, Goutam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989328/
https://www.ncbi.nlm.nih.gov/pubmed/27534618
http://dx.doi.org/10.1186/s12711-016-0238-5
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author Zhang, Qianqian
Guldbrandtsen, Bernt
Calus, Mario P. L.
Lund, Mogens Sandø
Sahana, Goutam
author_facet Zhang, Qianqian
Guldbrandtsen, Bernt
Calus, Mario P. L.
Lund, Mogens Sandø
Sahana, Goutam
author_sort Zhang, Qianqian
collection PubMed
description BACKGROUND: There is growing interest in the role of rare variants in the variation of complex traits due to increasing evidence that rare variants are associated with quantitative traits. However, association methods that are commonly used for mapping common variants are not effective to map rare variants. Besides, livestock populations have large half-sib families and the occurrence of rare variants may be confounded with family structure, which makes it difficult to disentangle their effects from family mean effects. We compared the power of methods that are commonly applied in human genetics to map rare variants in cattle using whole-genome sequence data and simulated phenotypes. We also studied the power of mapping rare variants using linear mixed models (LMM), which are the method of choice to account for both family relationships and population structure in cattle. RESULTS: We observed that the power of the LMM approach was low for mapping a rare variant (defined as those that have frequencies lower than 0.01) with a moderate effect (5 to 8 % of phenotypic variance explained by multiple rare variants that vary from 5 to 21 in number) contributing to a QTL with a sample size of 1000. In contrast, across the scenarios studied, statistical methods that are specialized for mapping rare variants increased power regardless of whether multiple rare variants or a single rare variant underlie a QTL. Different methods for combining rare variants in the test single nucleotide polymorphism set resulted in similar power irrespective of the proportion of total genetic variance explained by the QTL. However, when the QTL variance is very small (only 0.1 % of the total genetic variance), these specialized methods for mapping rare variants and LMM generally had no power to map the variants within a gene with sample sizes of 1000 or 5000. CONCLUSIONS: We observed that the methods that combine multiple rare variants within a gene into a meta-variant generally had greater power to map rare variants compared to LMM. Therefore, it is recommended to use rare variant association mapping methods to map rare genetic variants that affect quantitative traits in livestock, such as bovine populations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0238-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-49893282016-08-19 Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships Zhang, Qianqian Guldbrandtsen, Bernt Calus, Mario P. L. Lund, Mogens Sandø Sahana, Goutam Genet Sel Evol Research Article BACKGROUND: There is growing interest in the role of rare variants in the variation of complex traits due to increasing evidence that rare variants are associated with quantitative traits. However, association methods that are commonly used for mapping common variants are not effective to map rare variants. Besides, livestock populations have large half-sib families and the occurrence of rare variants may be confounded with family structure, which makes it difficult to disentangle their effects from family mean effects. We compared the power of methods that are commonly applied in human genetics to map rare variants in cattle using whole-genome sequence data and simulated phenotypes. We also studied the power of mapping rare variants using linear mixed models (LMM), which are the method of choice to account for both family relationships and population structure in cattle. RESULTS: We observed that the power of the LMM approach was low for mapping a rare variant (defined as those that have frequencies lower than 0.01) with a moderate effect (5 to 8 % of phenotypic variance explained by multiple rare variants that vary from 5 to 21 in number) contributing to a QTL with a sample size of 1000. In contrast, across the scenarios studied, statistical methods that are specialized for mapping rare variants increased power regardless of whether multiple rare variants or a single rare variant underlie a QTL. Different methods for combining rare variants in the test single nucleotide polymorphism set resulted in similar power irrespective of the proportion of total genetic variance explained by the QTL. However, when the QTL variance is very small (only 0.1 % of the total genetic variance), these specialized methods for mapping rare variants and LMM generally had no power to map the variants within a gene with sample sizes of 1000 or 5000. CONCLUSIONS: We observed that the methods that combine multiple rare variants within a gene into a meta-variant generally had greater power to map rare variants compared to LMM. Therefore, it is recommended to use rare variant association mapping methods to map rare genetic variants that affect quantitative traits in livestock, such as bovine populations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0238-5) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-17 /pmc/articles/PMC4989328/ /pubmed/27534618 http://dx.doi.org/10.1186/s12711-016-0238-5 Text en © The Author(s) 2016 Open AccessThis 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 Research Article
Zhang, Qianqian
Guldbrandtsen, Bernt
Calus, Mario P. L.
Lund, Mogens Sandø
Sahana, Goutam
Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships
title Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships
title_full Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships
title_fullStr Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships
title_full_unstemmed Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships
title_short Comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships
title_sort comparison of gene-based rare variant association mapping methods for quantitative traits in a bovine population with complex familial relationships
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989328/
https://www.ncbi.nlm.nih.gov/pubmed/27534618
http://dx.doi.org/10.1186/s12711-016-0238-5
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