A Haplotype-based Framework for Group-wise Transmission/disequilibrium Tests for Rare Variant Association Analysis

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2015 Jan 9

PMID 25568282

Citations 10

Authors

Rui Chen

Qiang Wei

Xiaowei Zhan

Xue Zhong

James S Sutcliffe

Nancy J Cox

Edwin H Cook

Chun Li

Wei Chen

Bingshan Li

Affiliations

Soon will be listed here.

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.

Citing Articles

Recent advances and challenges of rare variant association analysis in the biobank sequencing era.

Chen W, Coombes B, Larson N Front Genet. 2022; 13:1014947.

PMID: 36276986 PMC: 9582646. DOI: 10.3389/fgene.2022.1014947.

A unifying framework for rare variant association testing in family-based designs, including higher criticism approaches, SKATs, and burden tests.

Hecker J, Townes F, Kachroo P, Laurie C, Lasky-Su J, Ziniti J Bioinformatics. 2020; 36(22-23):5432-5438.

PMID: 33367522 PMC: 8016468. DOI: 10.1093/bioinformatics/btaa1055.

A comparison of popular TDT-generalizations for family-based association analysis.

Hecker J, Laird N, Lange C Genet Epidemiol. 2019; 43(3):300-317.

PMID: 30609057 PMC: 6599182. DOI: 10.1002/gepi.22181.

Paternally inherited cis-regulatory structural variants are associated with autism.

Brandler W, Antaki D, Gujral M, Kleiber M, Whitney J, Maile M Science. 2018; 360(6386):327-331.

PMID: 29674594 PMC: 6449150. DOI: 10.1126/science.aan2261.

SV2: accurate structural variation genotyping and de novo mutation detection from whole genomes.

Antaki D, Brandler W, Sebat J Bioinformatics. 2018; 34(10):1774-1777.

PMID: 29300834 PMC: 5946924. DOI: 10.1093/bioinformatics/btx813.

References

Nejentsev S, Walker N, Riches D, Egholm M, Todd J . Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science. 2009; 324(5925):387-9. PMC: 2707798. DOI: 10.1126/science.1167728. View

Schaid D . General score tests for associations of genetic markers with disease using cases and their parents. Genet Epidemiol. 1996; 13(5):423-49. DOI: 10.1002/(SICI)1098-2272(1996)13:5<423::AID-GEPI1>3.0.CO;2-3. View

Kang H, Sul J, Service S, Zaitlen N, Kong S, Freimer N . Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 2010; 42(4):348-54. PMC: 3092069. DOI: 10.1038/ng.548. View

Price A, Kryukov G, de Bakker P, Purcell S, Staples J, Wei L . Pooled association tests for rare variants in exon-resequencing studies. Am J Hum Genet. 2010; 86(6):832-8. PMC: 3032073. DOI: 10.1016/j.ajhg.2010.04.005. View

Schaffner S, Foo C, Gabriel S, Reich D, Daly M, Altshuler D . Calibrating a coalescent simulation of human genome sequence variation. Genome Res. 2005; 15(11):1576-83. PMC: 1310645. DOI: 10.1101/gr.3709305. View

Price A, Patterson N, Plenge R, Weinblatt M, Shadick N, Reich D . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006; 38(8):904-9. DOI: 10.1038/ng1847. View

Morgenthaler S, Thilly W . A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat Res. 2006; 615(1-2):28-56. DOI: 10.1016/j.mrfmmm.2006.09.003. View

Gonzalez J, Carrasco J, Dudbridge F, Armengol L, Estivill X, Moreno V . Maximizing association statistics over genetic models. Genet Epidemiol. 2008; 32(3):246-54. DOI: 10.1002/gepi.20299. View

Ji W, Foo J, ORoak B, Zhao H, Larson M, Simon D . Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat Genet. 2008; 40(5):592-599. PMC: 3766631. DOI: 10.1038/ng.118. View

10.

Li B, Leal S . Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008; 83(3):311-21. PMC: 2842185. DOI: 10.1016/j.ajhg.2008.06.024. View

11.

Browning B, Browning S . A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009; 84(2):210-23. PMC: 2668004. DOI: 10.1016/j.ajhg.2009.01.005. View

12.

Madsen B, Browning S . A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 2009; 5(2):e1000384. PMC: 2633048. DOI: 10.1371/journal.pgen.1000384. View

13.

Han F, Pan W . A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered. 2010; 70(1):42-54. PMC: 2912645. DOI: 10.1159/000288704. View

14.

McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A . The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20(9):1297-303. PMC: 2928508. DOI: 10.1101/gr.107524.110. View

15.

Wang K, Li M, Hakonarson H . ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38(16):e164. PMC: 2938201. DOI: 10.1093/nar/gkq603. View

16.

Liu D, Leal S . A novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions. PLoS Genet. 2010; 6(10):e1001156. PMC: 2954824. DOI: 10.1371/journal.pgen.1001156. View

17.

Asimit J, Zeggini E . Rare variant association analysis methods for complex traits. Annu Rev Genet. 2010; 44:293-308. DOI: 10.1146/annurev-genet-102209-163421. View

18.

Li Y, Willer C, Ding J, Scheet P, Abecasis G . MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010; 34(8):816-34. PMC: 3175618. DOI: 10.1002/gepi.20533. View

19.

Tewhey R, Bansal V, Torkamani A, Topol E, Schork N . The importance of phase information for human genomics. Nat Rev Genet. 2011; 12(3):215-23. PMC: 3753045. DOI: 10.1038/nrg2950. View

20.

Neale B, Rivas M, Voight B, Altshuler D, Devlin B, Orho-Melander M . Testing for an unusual distribution of rare variants. PLoS Genet. 2011; 7(3):e1001322. PMC: 3048375. DOI: 10.1371/journal.pgen.1001322. View