Utilizing population controls in rare-variant case-parent association tests.
There is great interest in detecting associations between human traits and rare genetic variation. To address the low power implicit in single-locus tests of rare genetic variants, many rare-variant association approaches attempt to accumulate information across a gene, often by taking linear combinations of single-locus contributions to a statistic. Using the right linear combination is key-an optimal test will up-weight true causal variants, down-weight neutral variants, and correctly assign the direction of effect for causal variants. Here, we propose a procedure that exploits data from population controls to estimate the linear combination to be used in an case-parent trio rare-variant association test. Specifically, we estimate the linear combination by comparing population control allele frequencies with allele frequencies in the parents of affected offspring. These estimates are then used to construct a rare-variant transmission disequilibrium test (rvTDT) in the case-parent data. Because the rvTDT is conditional on the parents' data, using parental data in estimating the linear combination does not affect the validity or asymptotic distribution of the rvTDT. By using simulation, we show that our new population-control-based rvTDT can dramatically improve power over rvTDTs that do not use population control information across a wide variety of genetic architectures. It also remains valid under population stratification. We apply the approach to a cohort of epileptic encephalopathy (EE) trios and find that dominant (or additive) inherited rare variants are unlikely to play a substantial role within EE genes previously identified through de novo mutation studies.
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA.
Yu
Y
Jiang
Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Glen A
GA
Satten
Center for Human Genome Variation, Duke University School of Medicine, Durham, NC 27708, USA.
Yujun
Y
Han
Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322, USA.
Michael P
MP
Epstein
Center for Human Genome Variation, Duke University School of Medicine, Durham, NC 27708, USA.
Erin L
EL
Heinzen
Center for Human Genome Variation, Duke University School of Medicine, Durham, NC 27708, USA.
David B
DB
Goldstein
Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA; Center for Human Genome Variation, Duke University School of Medicine, Durham, NC 27708, USA. Electronic address: andrew.s.allen@duke.edu.
Andrew S
AS
Allen
D003198:Computer Simulation; D035061:Control Groups; D004827:Epilepsy / Q000235:genetics*; D005787:Gene Frequency; D056426:Genetic Loci; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D010290:Parents; D010641:Phenotype; D035583:Rare Diseases / Q000175:diagnosis / Q000235:genetics*