Combining Family- and Population-Based Imputation Data for Association Analysis of Rare and Common Variants in Large Pedigrees

• Published in: Genetic epidemiology Vol. 38; no. 7; pp. 579 - 590
• Main Authors: Saad, Mohamad, Wijsman, Ellen M.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.11.2014; Wiley Subscription Services, Inc
• Subjects: association analysis; burden test; Computer Simulation; Genetic Association Studies; Genetic Predisposition to Disease; Genotype; Genotype & phenotype; Humans; inheritance vectors; kernel statistic; Linkage Disequilibrium; MCMC; Methods; mixed linear model; Models, Genetic; Multivariate Analysis; Pedigree; Phenotype; Polymorphism, Single Nucleotide; Population; sequence data; Software; variance components; variance components; mixed linear model; sequence data; inheritance vectors; association analysis; kernel statistic; MCMC; burden test
• ISSN: 0741-0395; 1098-2272; 1098-2272
• DOI: 10.1002/gepi.21844

ABSTRACT In the last two decades, complex traits have become the main focus of genetic studies. The hypothesis that both rare and common variants are associated with complex traits is increasingly being discussed. Family‐based association studies using relatively large pedigrees are suitable for both rare and common variant identification. Because of the high cost of sequencing technologies, imputation methods are important for increasing the amount of information at low cost. A recent family‐based imputation method, Genotype Imputation Given Inheritance (GIGI), is able to handle large pedigrees and accurately impute rare variants, but does less well for common variants where population‐based methods perform better. Here, we propose a flexible approach to combine imputation data from both family‐ and population‐based methods. We also extend the Sequence Kernel Association Test for Rare and Common variants (SKAT‐RC), originally proposed for data from unrelated subjects, to family data in order to make use of such imputed data. We call this extension “famSKAT‐RC.” We compare the performance of famSKAT‐RC and several other existing burden and kernel association tests. In simulated pedigree sequence data, our results show an increase of imputation accuracy from use of our combining approach. Also, they show an increase of power of the association tests with this approach over the use of either family‐ or population‐based imputation methods alone, in the context of rare and common variants. Moreover, our results show better performance of famSKAT‐RC compared to the other considered tests, in most scenarios investigated here.