Principal components ancestry adjustment for Genetic Analysis Workshop 17 data

BMC Proceedings

Proceedings

Jane E Cerise¹  Jing Jin¹  Nancy R Mendell¹  Eun Jung Yoon¹  Stephen J Finch¹  Sun Jung Kang²  Seungtai Yoon³ 

[1] Department of Applied Mathematics and Statistics, State University of New York at Stony Brook, 11733, Stony Brook, NY, USA;Henri Begleiter Neurodynamics Laboratory, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Medical Center, Box 1203, 450 Clarkson Avenue, 11203, Brooklyn, NY, USA;Seaver Autism Center and Department of Psychiatry, Mount Sinai School of Medicine, Box 1668, One Gustave L. Levy Place, 10029, New York, NY, USA;

关键词: Error Rate;    Causal Gene;    Population Stratification;    Nominal Level;    Genetic Analysis Workshop;   

DOI  :  10.1186/1753-6561-5-S9-S66

来源: Springer

【 摘 要 】

Statistical tests on rare variant data may well have type I error rates that differ from their nominal levels. Here, we use the Genetic Analysis Workshop 17 data to estimate type I error rates and powers of three models for identifying rare variants associated with a phenotype: (1) by using the number of minor alleles, age, and smoking status as predictor variables; (2) by using the number of minor alleles, age, smoking status, and the identity of the population of the subject as predictor variables; and (3) by using the number of minor alleles, age, smoking status, and ancestry adjustment using 10 principal component scores. We studied both quantitative phenotype and a dichotomized phenotype. The model with principal component adjustment has type I error rates that are closer to the nominal level of significance of 0.05 for single-nucleotide polymorphisms (SNPs) in noncausal genes for the selected phenotype than the model directly adjusting for population. The principal component adjustment model type I error rates are also closer to the nominal level of 0.05 for noncausal SNPs located in causal genes for the phenotype. The power for causal SNPs with the principal component adjustment model is comparable to the power of the other methods. The power using the underlying quantitative phenotype is greater than the power using the dichotomized phenotype.

【 授权许可】

Unknown   
© Jin et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.