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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Principal components ancestry adjustment for Genetic Analysis Workshop 17 data.

Jing Jin¹, Jane E Cerise, Sun Jung Kang

¹Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, Stony Brook, NY 11733, USA. sjfinch@optonline.net.

BMC Proceedings

|March 1, 2012

Summary

Principal component analysis improves statistical test accuracy for rare variant association studies. This method offers better control of type I error rates compared to direct population adjustment, especially for single-nucleotide polymorphisms (SNPs).

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Area of Science:

Genetics
Statistical genetics
Bioinformatics

Background:

Statistical tests for rare variant data can exhibit type I error rates deviating from nominal levels.
Accurate identification of rare variants associated with phenotypes is crucial in genetic research.

Purpose of the Study:

To evaluate type I error rates and statistical power of different models for identifying rare variants associated with a phenotype.
To compare the performance of models with and without principal component (PC) adjustment for ancestry.

Main Methods:

Utilized the Genetic Analysis Workshop 17 data.
Assessed three models: (1) number of minor alleles, age, smoking status; (2) same as (1) plus population identity; (3) same as (1) plus 10 principal component scores for ancestry adjustment.
Examined both quantitative and dichotomized phenotypes.

Main Results:

The model incorporating principal component adjustment demonstrated type I error rates closer to the nominal level (0.05) for noncausal single-nucleotide polymorphisms (SNPs).
This was observed for SNPs in both noncausal and causal genes compared to a model directly adjusting for population.
Statistical power for causal SNPs was comparable across models, and using a quantitative phenotype yielded higher power than a dichotomized phenotype.

Conclusions:

Principal component adjustment is a more effective method for controlling type I error rates in rare variant association studies than direct population adjustment.
The use of a quantitative phenotype enhances statistical power compared to a dichotomized phenotype.