Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect

8Citations
Citations of this article
23Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background: To evaluate statistical methods for genome-wide genetic analyses, one needs to be able to simulate realistic genotypes. We here describe a method, applicable to a broad range of association study designs, that can simulate autosome-wide single-nucleotide polymorphism data with realistic linkage disequilibrium and with spiked in, user-specified, single or multi-SNP causal effects. Results: Our construction uses existing genome-wide association data from unrelated case-parent triads, augmented by including a hypothetical complement triad for each triad (same parents but with a hypothetical offspring who carries the non-transmitted parental alleles). We assign offspring qualitative or quantitative traits probabilistically through a specified risk model and show that our approach destroys the risk signals from the original data. Our method can simulate genetically homogeneous or stratified populations and can simulate case-parents studies, case-control studies, case-only studies, or studies of quantitative traits. We show that allele frequencies and linkage disequilibrium structure in the original genome-wide association sample are preserved in the simulated data. We have implemented our method in an R package (TriadSim) which is freely available at the comprehensive R archive network. Conclusion: We have proposed a method for simulating genome-wide SNP data with realistic linkage disequilibrium. Our method will be useful for developing statistical methods for studying genetic associations, including higher order effects like epistasis and gene by environment interactions.

Cite

CITATION STYLE

APA

Shi, M., Umbach, D. M., Wise, A. S., & Weinberg, C. R. (2018). Simulating autosomal genotypes with realistic linkage disequilibrium and a spiked-in genetic effect. BMC Bioinformatics, 19(1). https://doi.org/10.1186/s12859-017-2004-2

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free