A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis

Rui Chen; Qiang Wei; Xiaowei Zhan; Xue Zhong; James S Sutcliffe; Nancy J Cox; Edwin H Cook; Chun Li; Wei Chen; Bingshan Li

doi:10.1093/bioinformatics/btu860

A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis

Bioinformatics. 2015 May 1;31(9):1452-9. doi: 10.1093/bioinformatics/btu860. Epub 2015 Jan 6.

Authors

Affiliations

¹ Department of Molecular Physiology and Biophysics, Vanderbilt University, TN, 37221, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, TN, 37221, USA, Department of Medicine, University of Chicago, Chicago, IL, USA, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA, Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
² Department of Molecular Physiology and Biophysics, Vanderbilt University, TN, 37221, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, TN, 37221, USA, Department of Medicine, University of Chicago, Chicago, IL, USA, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA, Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA Department of Molecular Physiology and Biophysics, Vanderbilt University, TN, 37221, USA, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA, Center for Quantitative Sciences, Vanderbilt University, TN, 37221, USA, Department of Medicine, University of Chicago, Chicago, IL, USA, Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA, Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA and Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.

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: bingshan.li@vanderbilt.edu or wei.chen@chp.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Autistic Disorder / genetics
Computer Simulation
Data Interpretation, Statistical
Exome
Genetic Association Studies / methods*
Genetic Variation*
Haplotypes*
Humans
Linkage Disequilibrium*
Models, Genetic
Sequence Analysis, DNA

Abstract

Publication types

MeSH terms

Grants and funding