A Powerful Nonparametric Statistical Framework for Family-Based Association Analyses

Abstract

Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametricbased methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only withinfamily information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., betweenfamily information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.KEYWORDS population stratification; pedigree structure; within-family information; between-family information; nicotine dependence B OTH population-based and family-based designs have been commonly used in genetic association studies. Case-control study is a typical population-based design, by which unrelated cases are recruited and compared with healthy controls. When cases and controls come from different ethnicity backgrounds, they could have distinct ancestry distribution, leading to false-positive association results due to population stratification (PS) (Knowler et al. 1988;Freedman et al. 2004). Many statistical methods have been proposed to address the issue of PS (Devlin and Roeder 1999;Pritchard and Rosenberg 1999; Pritchard et al. 2000a,b;Satten et al. 2001;Chen et al. 2003;Price et al. 2006;Bauchet et al. 2007). However, these methods can infer only the average effect of PS based on a large number of genetic variants, but not the locus-specific PS. Population stratification related to a particular locus may vary and deviate from the average effect. In the presence of locus-specific PS, these approaches may overadjust or underadjust the PS effect, leading to either low power or inflated type I error (Marchini et al. 2004;Qin and Zhu 2012). Unlike population-based studies, family-based studies offer robust protection against PS (Weinberg et al. 1998;Cardon and Palmer 2003;Weinberg 2003). In a typical family-based association study, the alleles transmitted to affected individu...

Powerful and robust cross‐phenotype association test for case‐parent trios

“…For a more flexible test, another possibility to attempt is to implement the GAMuT test directly on offspring genotypes conditional on sufficient statistics (using the approach of Rabinowitz and Laird (2000)) and then account for within-family correlation of outcomes and conditional offspring genotypes using a statistical-whitening procedure (Kessy, Lewin, & Strimmer, 2017). Another possible technique would be to modify the robust family-based U-statistic approach of Li et al (2015), which currently handles univariate phenotype and genotype data, to handle multivariate phenotype and genotype data. As the authors’ approach uses a kernel function to model phenotype similarity between relatives, extension of the framework to handle multivariate phenotypes is straightforward.…”

“…As the authors’ approach uses a kernel function to model phenotype similarity between relatives, extension of the framework to handle multivariate phenotypes is straightforward. Extending the approach of Li et al (2015) to multiple genetic variants is more challenging but, by constructing the authors’ test statistic as a sum over the Euclidean distances across each variant, it may be possible to create a closed-form test for inference.…”

“…As the authors' approach uses a kernel function to model phenotype similarity between relatives, extension of the framework to handle multivariate phenotypes is straightforward. Extending the approach of Li et al (2015) to multiple genetic variants is more challenging but, by constructing the authors' test statistic as a sum over the Euclidean distances across each A reviewer pointed out a recent method by Won et al (2015) for gene-based association testing of common variants with multiple phenotypes in family-based studies. The approach constructs an omnibus statistic based on a quasilikelihood score function where phenotypes are adjusted with an offset based on incorporating the best linear unbiased predictor from a linear mixed model.…”

A multi-locus predictiveness curve and its summary assessment for genetic risk prediction

“…U-Statistics are well known for flexibility and robustness, 9 and have been recently applied to genetic association studies to evaluate the association between multiple genetic variants and disease risk. 10–15 For example, a Mann-Whitney U-statistic has been demonstrated to be an excellent summary statistic for ROC curves. 16 In this article, we first propose a predictiveness curve based on multiple genetic variants with the consideration of possible interactions and further define a U-statistics-based measurement, the U-Index, to summarize its overall performance.…”