Stratify or adjust? Dealing with multiple populations when evaluating rare variants

BMC Proceedings

Proceedings

Robert C Culverhouse¹  Anthony L Hinrichs²  Brian K Suarez³ 

[1] Department of Medicine, Washington University School of Medicine, 660 South Euclid Avenue, 63110, Saint Louis, MO, USA;Division of Biostatistics, Washington University School of Medicine, 660 South Euclid Avenue, 63110, Saint Louis, MO, USA;Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, 63110, Saint Louis, MO, USA;Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, 63110, Saint Louis, MO, USA;Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, 63110, Saint Louis, MO, USA;

关键词: Minor Allele Frequency;    Rare Variant;    Significance Threshold;    Population Stratification;    Causative Variant;   

DOI  :  10.1186/1753-6561-5-S9-S101

来源: Springer

【 摘 要 】

The unrelated individuals sample from Genetic Analysis Workshop 17 consists of a small number of subjects from eight population samples and genetic data composed mostly of rare variants. We compare two simple approaches to collapsing rare variants within genes for their utility in identifying genes that affect phenotype. We also compare results from stratified analyses to those from a pooled analysis that uses ethnicity as a covariate. We found that the two collapsing approaches were similarly effective in identifying genes that contain causative variants in these data. However, including population as a covariate was not an effective substitute for analyzing the subpopulations separately when only one subpopulation contained a rare variant linked to the phenotype.

【 授权许可】

CC BY   
© Culverhouse et al; licensee BioMed Central Ltd. 2011