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
Year: 2014
Type: article
Abstract: 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.
Source: BMC Proceedings
Authors: Jian Wang, Robert Yu, Sanjay Shete
Institution: The University of Texas MD Anderson Cancer Center
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,290
Awards: Multiethnic Study of Type 2 Diabetes Genes, Identifying T2D Variants by DNA Sequencing in Multiethnic Samples, G2053005029, Identification and Replication of Type 2 Diabetes Genes in Mexican Americans, Discovery of Functional Variants in Type 2 Diabetes Genes in Mexican Americans, Identifying variants causal for Type 2 Diabetes in Major human populations, Genome Wide Association Study of Head and Neck Cancer, Genetic Analysis of Common Diseases: An Evaluation, G9000183473, G9000244037
OpenAlex ID: W2061402125