Multi-locus Test and Correction for Confounding Effects in Genome-Wide Association Studies

Chen Donglai, Liu Chuanhai and Xie Jun ()
Additional contact information
Chen Donglai: Department of Statistics, Purdue University, 250 N. University Street West Lafayette, IN 47907, USA
Liu Chuanhai: Department of Statistics, Purdue University, 250 N. University Street West Lafayette, IN 47907, USA
Xie Jun: Department of Statistics, Purdue University, 250 N. University Street West Lafayette, IN 47907, USA

The International Journal of Biostatistics, 2016, vol. 12, issue 2, 13

Abstract: Genome-wide association studies (GWAS) examine a large number of genetic variants, e. g., single nucleotide polymorphisms (SNP), and associate them with a disease of interest. Traditional statistical methods for GWASs can produce spurious associations, due to limited information from individual SNPs and confounding effects. This paper develops two statistical methods to enhance data analysis of GWASs. The first is a multiple-SNP association test, which is a weighted chi-square test derived for big contingency tables. The test assesses combinatorial effects of multiple SNPs and improves conventional methods of single SNP analysis. The second is a method that corrects for confounding effects, which may come from population stratification as well as other ambiguous (unknown) factors. The proposed method identifies a latent confounding factor, using a profile of whole genome SNPs, and eliminates confounding effects through matching or stratified statistical analysis. Simulations and a GWAS of rheumatoid arthritis demonstrate that the proposed methods dramatically remove the number of significant tests, or false positives, and outperforms other available methods.

Keywords: confounding effect; genome-wide association studies; multi-locus association test (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2015-0091 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:12:y:2016:i:2:p:13:n:10

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2015-0091

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().