Multiple Genetic Variant Association Testing by Collapsing and Kernel Methods With Pedigree or Population Structured Data

Abstract

Searching for rare genetic variants associated with complex diseases can be facilitated by enriching for diseased carriers of rare variants by sampling cases from pedigrees enriched for disease, possibly with related or unrelated controls. This strategy, however, complicates analyses because of shared genetic ancestry, as well as linkage disequilibrium among genetic markers. To overcome these problems, we developed broad classes of “burden” statistics and kernel statistics, extending commonly used methods for unrelated case-control data to allow for known pedigree relationships, for autosomes and the X chromosome. Furthermore, by replacing pedigree-based genetic correlation matrices with estimates of genetic relationships based on large-scale genomic data, our methods can be used to account for population-structured data. By simulations, we show that the type I error rates of our developed methods are near the asymptotic nominal levels, allowing rapid computation of P-values. Our simulations also show that a linear weighted kernel statistic is generally more powerful than a weighted “burden” statistic. Because the proposed statistics are rapid to compute, they can be readily used for large-scale screening of the association of genomic sequence data with disease status.

Germline miRNA DNA variants and the risk of colorectal cancer by subtype

“…We performed both single‐SNP‐ and miRNA‐level analyses using an extension to commonly used gene‐based statistics to allow for known pedigree relationships (Schaid et al, 2013). For miRNA‐level tests, analyses were conducted using both a burden test (most powerful if variants in a gene have effects in the same direction) and kernel statistic (most powerful if variants have effects in opposite directions).…”

Adaptive-weight burden test for associations between quantitative traits and genotype data with complex correlations

“…Different with other burden tests, FBT is based on a retrospective model analogous to that in MASTOR, thus also possesses nice properties such as ability to incorporate partially missing data and robustness to misspecification of the phenotype model. A similar method can be found in Schaid et al (2013), with a different approach adopted for defining residuals in the null trait model and deriving covariances of the genetic burden score.…”

“…Burden tests group variants into a single variable called genetic burden score by some transformations or projections, and then perform association testing on the burden score. Typical collapsing methods, including rare variant indicator or weighted sum, have been developed for both unrelated (Morgenthaler and Thilly, 2007; Li and Leal, 2008; Madsen and Browning, 2009; Price et al, 2010a) and related (Chen, Meigs and Dupuis, 2013; Schaid et al, 2013) individuals. Other dimension reduction techniques, such as Fourier transformation (Wang and Elston, 2007), principal component analysis (Gauderman et al, 2007; Wang and Abbott, 2008), and partial least-squares regression (Chun et al, 2011), have also been applied in grouping multiple variants.…”

“…With its roots in kernel-machine regression (Wu et al, 2010) and mixed effects model, kernel tests adopt a statistic with a quadratic form, which essentially is a weighted sum of the score statistics for testing individual variant effects (Wu et al, 2011). Methods in this broad category include the C-alpha test (Neale et al, 2011), SKAT (Wu et al, 2011), and LSKM (Kwee et al, 2008) for unrelated individuals, and have been extended to allow related individuals (Schifano et al, 2012; Chen, Meigs and Dupuis, 2013; Schaid et al, 2013; Wang et al, 2013a, b). In particular, this approach assumes random effects for individual variants, and test the regression coefficients of the variants by a variance-component score test.…”

“…Since aggregation is on individual variant statistics instead of on variants themselves, kernel tests allow different directions and magnitude of effects for individual variants. It has been shown that burden tests are more powerful when the variants to be tested are most causal with effects of the same direction and similar magnitudes, whereas kernel tests are more powerful when the effects of causal variants are in different directions or a large proportion of neutral variants present (Wu et al, 2011; Chen, Meigs and Dupuis, 2013; Schaid et al, 2013; Lee, Wu and Lin, 2012; Wang, Chen and Yang, 2012). To borrow strength from both approaches and avoid loss of power in certain scenarios, it is possible to combine test statistics from the two categories (Lee, Wu and Lin, 2012; Lee et al, 2012; Jiang and McPeek, 2013), albeit determining the optimal combination weight may be problematic in real data applications, and the null distribution of the combined statistic is usually hard to derive.…”