Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido

6 Issues per year


IMPACT FACTOR 2017: 0.812
5-year IMPACT FACTOR: 1.104

CiteScore 2017: 0.86

SCImago Journal Rank (SJR) 2017: 0.456
Source Normalized Impact per Paper (SNIP) 2017: 0.527

Mathematical Citation Quotient (MCQ) 2017: 0.04

Online
ISSN
1544-6115
See all formats and pricing
More options …
Volume 9, Issue 1

Issues

Volume 10 (2011)

Volume 9 (2010)

Volume 6 (2007)

Volume 5 (2006)

Volume 4 (2005)

Volume 2 (2003)

Volume 1 (2002)

Assessment of LD Matrix Measures for the Analysis of Biological Pathway Association

David R. Crosslin / Xuejun Qin / Elizabeth R. Hauser
Published Online: 2010-10-02 | DOI: https://doi.org/10.2202/1544-6115.1561

Complex diseases will have multiple functional sites, and it will be invaluable to understand the cross-locus interaction in terms of linkage disequilibrium (LD) between those sites (epistasis) in addition to the haplotype-LD effects. We investigated the statistical properties of a class of matrix-based statistics to assess this epistasis. These statistical methods include two LD contrast tests (Zaykin et al., 2006) and partial least squares regression (Wang et al., 2008). To estimate Type 1 error rates and power, we simulated multiple two-variant disease models using the SIMLA software package. SIMLA allows for the joint action of up to two disease genes in the simulated data with all possible multiplicative interaction effects between them. Our goal was to detect an interaction between multiple disease-causing variants by means of their linkage disequilibrium (LD) patterns with other markers. We measured the effects of marginal disease effect size, haplotype LD, disease prevalence and minor allele frequency have on cross-locus interaction (epistasis).In the setting of strong allele effects and strong interaction, the correlation between the two disease genes was weak (r = 0.2). In a complex system with multiple correlations (both marginal and interaction), it was difficult to determine the source of a significant result. Despite these complications, the partial least squares and modified LD contrast methods maintained adequate power to detect the epistatic effects; however, for many of the analyses we often could not separate interaction from a strong marginal effect. While we did not exhaust the entire parameter space of possible models, we do provide guidance on the effects that population parameters have on cross-locus interaction.

Keywords: epistasis; linkage disequilibrium; complex disease; cardiovascular disease

About the article

Published Online: 2010-10-02


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 9, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1561.

Export Citation

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Kichan Lee, Seonggyun Han, Yeonjeong Tark, and Sangsoo Kim
Genomics & Informatics, 2014, Volume 12, Number 4, Page 165

Comments (0)

Please log in or register to comment.
Log in