Back to Search
Start Over
Blocking Approach for Identification of Rare Variants in Family-Based Association Studies
- Source :
- PLoS ONE, PLoS ONE, Vol 9, Iss 1, p e86126 (2014)
- Publication Year :
- 2014
- Publisher :
- Public Library of Science, 2014.
-
Abstract
- With the advent of next-generation sequencing technology, rare variant association analysis is increasingly being conducted to identify genetic variants associated with complex traits. In recent years, significant effort has been devoted to develop powerful statistical methods to test such associations for population-based designs. However, there has been relatively little development for family-based designs although family data have been shown to be more powerful to detect rare variants. This study introduces a blocking approach that extends two popular family-based common variant association tests to rare variants association studies. Several options are considered to partition a genomic region (gene) into "independent" blocks by which information from SNVs is aggregated within a block and an overall test statistic for the entire genomic region is calculated by combining information across these blocks. The proposed methodology allows different variants to have different directions (risk or protective) and specification of minor allele frequency threshold is not needed. We carried out a simulation to verify the validity of the method by showing that type I error is well under control when the underlying null hypothesis and the assumption of independence across blocks are satisfied. Further, data from the Genetic Analysis Workshop [Formula: see text] are utilized to illustrate the feasibility and performance of the proposed methodology in a realistic setting.
- Subjects :
- Population
lcsh:Medicine
Genome-wide association study
Computational biology
Biology
Polymorphism, Single Nucleotide
Linkage Disequilibrium
Engineering
Gene Frequency
Genome Analysis Tools
Test statistic
Genetics
Humans
Computer Simulation
lcsh:Science
education
Genetic Association Studies
Statistical hypothesis testing
Genetic association
education.field_of_study
Multidisciplinary
Models, Genetic
Software Tools
Applied Mathematics
lcsh:R
Statistics
Computational Biology
Software Engineering
Genomics
Minor allele frequency
ROC Curve
Data Interpretation, Statistical
Computer Science
lcsh:Q
Null hypothesis
Algorithms
Mathematics
Type I and type II errors
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 9
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....05ea004ae56cc834df9cc18710681dc5