Leiden University Student Repository

Kampert, M.M.D. (2011)

Statistical Analysis in Genome-Wide Association Studies on GenoType-Imputed Family Data: A Research Strategy to Compare Various Toolsets

Master thesis | Mathematics (MSc)

Freely available toolsets that can handle genome-wide association (GWA) studies on twin-family data and take into account imputed genotypes are growing in number. However, the documentation that comes with them (if available), does not facilitate the choice for a particular toolset. We propose a research strategy in which we compare ASSOC, EMMAX, MERLIN, PLINK and ProbABEL on feasibility and statistical accuracy for GWA studies on simulated traits. Feasibility comparison was based on install requirements, versatility on data input, command line interface, and help information. The comparison on statistical accuracy was performed on Type-I error, genomic inflation, power, and consistency and efficiency of estimated SNP-effects. We simulated 100 replicates of binary and quantitative phenotypic traits over heritability conditions of 5, 10, 20, 30, 50 and 80%, based on 3 effect-SNPs from 1557 samples from 597 nuclear twin-families from the Netherlands Twin Registry. Analyses on Type-I...Show moreFreely available toolsets that can handle genome-wide association (GWA) studies on twin-family data and take into account imputed genotypes are growing in number. However, the documentation that comes with them (if available), does not facilitate the choice for a particular toolset. We propose a research strategy in which we compare ASSOC, EMMAX, MERLIN, PLINK and ProbABEL on feasibility and statistical accuracy for GWA studies on simulated traits. Feasibility comparison was based on install requirements, versatility on data input, command line interface, and help information. The comparison on statistical accuracy was performed on Type-I error, genomic inflation, power, and consistency and efficiency of estimated SNP-effects. We simulated 100 replicates of binary and quantitative phenotypic traits over heritability conditions of 5, 10, 20, 30, 50 and 80%, based on 3 effect-SNPs from 1557 samples from 597 nuclear twin-families from the Netherlands Twin Registry. Analyses on Type-I error and genomic inflation were performed on 7757 pruned and unlinked SNPs that represented the null hypothesis. In the current design PLINK performs best on feasibility and statistical accuracy for the binary trait. On the quantitative trait ASSOC performs best on Type-I error control, EMMAX on statistical power, and PLINK on genomic inflation. Future research is needed for larger sample sizes and larger numbers of causal SNPs to compare the performance of the toolsets on complex traits.Show less

Author

Kampert, M.M.D.

Faculty

Faculty of Science

Supervisors

Meulman, J.J.
Hottenga, J.J.

Language

en