Skip to main content
construction release_alert
Scholars@Duke will be undergoing maintenance April 11-15. Some features may be unavailable during this time.
cancel
Journal cover image

Assessment of LD matrix measures for the analysis of biological pathway association.

Publication ,  Journal Article
Crosslin, DR; Qin, X; Hauser, ER
Published in: Stat Appl Genet Mol Biol
2010

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.

Duke Scholars

Published In

Stat Appl Genet Mol Biol

DOI

EISSN

1544-6115

Publication Date

2010

Volume

9

Issue

1

Start / End Page

Article35

Location

Germany

Related Subject Headings

  • White People
  • Software
  • Models, Genetic
  • Linkage Disequilibrium
  • Leukotrienes
  • Humans
  • Haplotypes
  • Epistasis, Genetic
  • Disease
  • Computer Simulation
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Crosslin, D. R., Qin, X., & Hauser, E. R. (2010). Assessment of LD matrix measures for the analysis of biological pathway association. Stat Appl Genet Mol Biol, 9(1), Article35. https://doi.org/10.2202/1544-6115.1561
Crosslin, David R., Xuejun Qin, and Elizabeth R. Hauser. “Assessment of LD matrix measures for the analysis of biological pathway association.Stat Appl Genet Mol Biol 9, no. 1 (2010): Article35. https://doi.org/10.2202/1544-6115.1561.
Crosslin DR, Qin X, Hauser ER. Assessment of LD matrix measures for the analysis of biological pathway association. Stat Appl Genet Mol Biol. 2010;9(1):Article35.
Crosslin, David R., et al. “Assessment of LD matrix measures for the analysis of biological pathway association.Stat Appl Genet Mol Biol, vol. 9, no. 1, 2010, p. Article35. Pubmed, doi:10.2202/1544-6115.1561.
Crosslin DR, Qin X, Hauser ER. Assessment of LD matrix measures for the analysis of biological pathway association. Stat Appl Genet Mol Biol. 2010;9(1):Article35.
Journal cover image

Published In

Stat Appl Genet Mol Biol

DOI

EISSN

1544-6115

Publication Date

2010

Volume

9

Issue

1

Start / End Page

Article35

Location

Germany

Related Subject Headings

  • White People
  • Software
  • Models, Genetic
  • Linkage Disequilibrium
  • Leukotrienes
  • Humans
  • Haplotypes
  • Epistasis, Genetic
  • Disease
  • Computer Simulation