Family-based association tests using genotype data with uncertainty

Abstract

Family-based association studies have been widely used to identify association between diseases and genetic markers. It is known that genotyping uncertainty is inherent in both directly genotyped or sequenced DNA variations and imputed data in silico. The uncertainty can lead to genotyping errors and missingness and can negatively impact the power and Type I error rates of family-based association studies even if the uncertainty is independent of disease status. Compared with studies using unrelated subjects, there are very few methods that address the issue of genotyping uncertainty for family-based designs. The limited attempts have mostly been made to correct the bias caused by genotyping errors. Without properly addressing the issue, the conventional testing strategy, i.e. family-based association tests using called genotypes, can yield invalid statistical inferences. Here, we propose a new test to address the challenges in analyzing case-parents data by using calls with high accuracy and modeling genotype-specific call rates. Our simulations show that compared with the conventional strategy and an alternative test, our new test has an improved performance in the presence of substantial uncertainty and has a similar performance when the uncertainty level is low. We also demonstrate the advantages of our new method by applying it to imputed markers from a genome-wide case-parents association study.

A General Efficient and Flexible Approach for Genome-Wide Association Analyses of Imputed Genotypes in Family-Based Designs

“…We showed, through extensive simulation studies, that the distribution of the FBATdosage statistic was consistent with a χ 2 distribution with one degree of freedom, whatever the MAF of the imputed marker, the imputation quality, and the imputation approach (in particular whether or not the family information is used at the imputation step). By contrast, the use of best-guess genotype approaches resulted in largely inflated type I errors for selected samples (e.g., the affected trios design), as previously reported [Taub et al, 2012;Yu, 2012]. Yu [2012] proposed a likelihood-ratio test (FBAT-LRT) incorporating the genotype-specific call rates into the likelihood function, to reduce the bias introduced by best-guess genotype calling.…”

“…By contrast, the use of best-guess genotype approaches resulted in largely inflated type I errors for selected samples (e.g., the affected trios design), as previously reported [Taub et al, 2012;Yu, 2012]. Yu [2012] proposed a likelihood-ratio test (FBAT-LRT) incorporating the genotype-specific call rates into the likelihood function, to reduce the bias introduced by best-guess genotype calling. However, we show here that FBAT-LRT did not fully control the type I error rate in the presence of genotype uncertainty, for low-to-moderate imputation quality.…”

“…Replicates were analyzed with FBATdosage, FBAT [Laird et al., ; Rabinowitz and Laird, ], and FBAT‐LRT [Yu, ] when applicable.…”

“…Imputation methods estimate genotype probabilities at variants not genotyped by identifying haplotype segments common to the individuals studied and a reference panel of more densely typed individuals. Reference panels are provided by the International HapMap project [The International HapMap Consortium, 2003, the 1,000 genomes project [The 1000Genomes Project Consortium, 2010, 2012 or are obtained by sequencing a subset of study individuals (as in reference [Jallow et al, 2009]). Genotype imputation is now widely used to refine association signals, to boost the power of GWAS by increasing the likelihood to identify a causal variant and to combine studies genotyped with different marker sets in meta-analyses [Marchini and Howie, 2010].…”

An Adaptive Weighted Sum Test for Family-Based Multi-Marker Association Studies

“…Imputed allele dosages are used in FBATdosage [7]. To correct the bias introduced by genotype uncertainty, FBAT-LRT is proposed [8]. In this article, we introduce an adaptive weighted sum association test to capture more important information from multiple loci in family-based studies by considering the genetic effect from both within-family and between-family variation while maintaining robustness to population stratification.…”