‘Location, Location, Location’: a spatial approach for rare variant analysis and an application to a study on non-syndromic cleft lip with or without cleft palate

Abstract

Motivation: For the analysis of rare variants in sequence data, numerous approaches have been suggested. Fixed and flexible threshold approaches collapse the rare variant information of a genomic region into a test statistic with reduced dimensionality. Alternatively, the rare variant information can be combined in statistical frameworks that are based on suitable regression models, machine learning, etc. Although the existing approaches provide powerful tests that can incorporate information on allele frequencies and prior biological knowledge, differences in the spatial clustering of rare variants between cases and controls cannot be incorporated. Based on the assumption that deleterious variants and protective variants cluster or occur in different parts of the genomic region of interest, we propose a testing strategy for rare variants that builds on spatial cluster methodology and that guides the identification of the biological relevant segments of the region. Our approach does not require any assumption about the directions of the genetic effects.Results: In simulation studies, we assess the power of the clustering approach and compare it with existing methodology. Our simulation results suggest that the clustering approach for rare variants is well powered, even in situations that are ideal for standard methods. The efficiency of our spatial clustering approach is not affected by the presence of rare variants that have opposite effect size directions. An application to a sequencing study for non-syndromic cleft lip with or without cleft palate (NSCL/P) demonstrates its practical relevance. The proposed testing strategy is applied to a genomic region on chromosome 15q13.3 that was implicated in NSCL/P etiology in a previous genome-wide association study, and its results are compared with standard approaches.Availability: Source code and documentation for the implementation in R will be provided online. Currently, the R-implementation only supports genotype data. We currently are working on an extension for VCF files.Contact: heide.fier@googlemail.com

On Rare-Variant Analysis in Population-Based Designs: Decomposing the Likelihood to Two Informative Components

“…For instance, assuming that there is at least one DSV, the chance of observing one or more rare alleles at disease susceptibility loci is much higher than the chance at a null locus for multiplicative diseases, which would contribute to the nonrandom configuration of rare alleles. In the simulated data, we confirmed the informativity of p T 2 for the analysis of rare variants, and the configuration of rare alleles combined with genomic distance information has been also shown to be informative [17] . Therefore, we combined p T 1 and p T 2 into our overall statistic.…”

Detecting genomic clustering of risk variants from sequence data: cases versus controls

“…The cluster statistic of Fier et al is explained in detail elsewhere [Fier, et al 2012], so we give only a brief overview. The physical distances among the variants are used to detect the clustering, while using weights that depend on both physical distances as well as allele frequencies, in order to account for uneven distribution of allele frequencies.…”

“…Intuitively, this method computes a likelihood ratio statistic to compare the frequency of variants carried among cases and controls within a genomic window vs. those frequencies outside of a genomic window, and scans the genomic region of interest by sliding the window along the genome while evaluating a range of window sizes. Fier et al [Fier, et al 2012] also developed a method based on spatial clustering, emphasizing physical distances between variants. They combined physical distances between variants with minor allele frequencies of the variants to create weighted distances between variants, to then compare the distributions of these measures between cases and controls using the nonparametric Ansari-Bradley statistic, which is sensitive to differences in scale of the two distributions.…”

Regularized Rare Variant Enrichment Analysis for Case‐Control Exome Sequencing Data

“…To address this, variance component tests such as C-alpha test [Neale et al, 2011] and the sequence kernel association test (SKAT) [Kwee et al, 2008;Wu et al, 2011], a generalized form of the C-alpha, were developed to evaluate the association of a genomic ROI with a trait. Other methods include scan-based clustering approaches, which use a sliding window to localize variant clustering over a much larger genomic segment [Fier et al, 2012;Ionita-Laza et al, 2012;Schaid et al, 2013].…”