Comparison of multilevel modeling and the family-based association test for identifying genetic variants associated with systolic and diastolic blood pressure using Genetic Analysis Workshop 18 simulated data

BMC Proceedings

Proceedings

Robert Yu¹  Jian Wang²  Sanjay Shete² 

[1] Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 77030, Houston, TX, USA;Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 77030, Houston, TX, USA;Department of Epidemiology, The University of Texas MD Anderson Cancer Center, 77030, Houston, TX, USA;

关键词: Rare Variant;    Multilevel Model;    Causal Variant;    Genetic Analysis Workshop;    Rare Genetic Variant;   

DOI  :  10.1186/1753-6561-8-S1-S30

来源: Springer

【 摘 要 】

Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data.

【 授权许可】

CC BY   
© Wang et al.; licensee BioMed Central Ltd. 2014