A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis
Year: 2015
Type: article
Abstract: MOTIVATION: A major focus of current sequencing studies for human genetics is to identify rare variants associated with complex diseases. Aside from reduced power of detecting associated rare variants, controlling for population stratification is particularly challenging for rare variants. Transmission/disequilibrium tests (TDT) based on family designs are robust to population stratification and admixture, and therefore provide an effective approach to rare variant association studies to eliminate spurious associations. To increase power of rare variant association analysis, gene-based collapsing methods become standard approaches for analyzing rare variants. Existing methods that extend this strategy to rare variants in families usually combine TDT statistics at individual variants and therefore lack the flexibility of incorporating other genetic models. RESULTS: In this study, we describe a haplotype-based framework for group-wise TDT (gTDT) that is flexible to encompass a variety of genetic models such as additive, dominant and compound heterozygous (CH) (i.e. recessive) models as well as other complex interactions. Unlike existing methods, gTDT constructs haplotypes by transmission when possible and inherently takes into account the linkage disequilibrium among variants. Through extensive simulations we showed that type I error was correctly controlled for rare variants under all models investigated, and this remained true in the presence of population stratification. Under a variety of genetic models, gTDT showed increased power compared with the single marker TDT. Application of gTDT to an autism exome sequencing data of 118 trios identified potentially interesting candidate genes with CH rare variants. AVAILABILITY AND IMPLEMENTATION: We implemented gTDT in C++ and the source code and the detailed usage are available on the authors' website (https://medschool.vanderbilt.edu/cgg). CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Source: Bioinformatics
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: University of Michigan, National Institutes of Health, National Institute of Mental Health, National Human Genome Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: ACE: Translational Studies of Insistence on Sameness in Autism, G2145549928, G2725772924, Design and analysis of sequencing studies for gene mapping in families, Re-Engineering Translational Research at the University of Chicago, Statistical methods for population and family-based whole-genome sequence data, Conte Center for Computational Systems Genomics of Neuropsychiatric Phenotypes, Diabetes Research and Training Center
OpenAlex ID: W2142095438