RESEARCH ARTICLE
Genetic Studies: The Linear Mixed Models in Genome-wide Association Studies
Gengxin Li1, *, Hongjiang Zhu2
Article Information
Identifiers and Pagination:
Year: 2013Volume: 7
Issue: Suppl-1, M2
First Page: 27
Last Page: 33
Publisher ID: TOBIOIJ-7-27
DOI: 10.2174/1875036201307010027
Article History:
Received Date: 06/08/2013Revision Received Date: 06/09/2013
Acceptance Date: 15/09/2013
Electronic publication date: 13/12/2013
Collection year: 2013
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
With the availability of high-density genomic data containing millions of single nucleotide polymorphisms and tens or hundreds of thousands of individuals, genetic association study is likely to identify the variants contributing to complex traits in a genome-wide scale. However, genome-wide association studies are confounded by some spurious associations due to not properly interpreting sample structure (containing population structure, family structure and cryptic relatedness). The absence of complete genealogy of population in the genome-wide association studies model greatly motivates the development of new methods to correct the inflation of false positive. In this process, linear mixed model based approaches with the advantage of capturing multilevel relatedness have gained large ground. We summarize current literatures dealing with sample structure, and our review focuses on the following four areas: (i) The approaches handling population structure in genome-wide association studies; (ii) The linear mixed model based approaches in genome-wide association studies; (iii) The performance of linear mixed model based approaches in genome-wide association studies and (iv) The unsolved issues and future work of linear mixed model based approaches.
