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

Schaid, Daniel J.; Sinnwell, Jason P.; McDonnell, Shannon K.; Thibodeau, Stephen N.

doi:10.1007/s00439-013-1335-y

Cited by 15 publications

(34 citation statements)

References 14 publications

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“…Without correlation, SKAT tended to have the highest power among the tests they compared [16]. In fact, the correlation between rare variants is usually low [37], [38].…”

Section: Discussionmentioning

confidence: 98%

Association Testing of Clustered Rare Causal Variants in Case-Control Studies

Lin

2014

PLoS ONE

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Biological evidence suggests that multiple causal variants in a gene may cluster physically. Variants within the same protein functional domain or gene regulatory element would locate in close proximity on the DNA sequence. However, spatial information of variants is usually not used in current rare variant association analyses. We here propose a clustering method (abbreviated as “CLUSTER”), which is extended from the adaptive combination of P-values. Our method combines the association signals of variants that are more likely to be causal. Furthermore, the statistic incorporates the spatial information of variants. With extensive simulations, we show that our method outperforms several commonly-used methods in many scenarios. To demonstrate its use in real data analyses, we also apply this CLUSTER test to the Dallas Heart Study data. CLUSTER is among the best methods when the effects of causal variants are all in the same direction. As variants located in close proximity are more likely to have similar impact on disease risk, CLUSTER is recommended for association testing of clustered rare causal variants in case-control studies.

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“…Without correlation, SKAT tended to have the highest power among the tests they compared [16]. In fact, the correlation between rare variants is usually low [37], [38].…”

Section: Discussionmentioning

confidence: 98%

Association Testing of Clustered Rare Causal Variants in Case-Control Studies

Lin

2014

PLoS ONE

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Larson

Schaid

2013

Genetic Epidemiology

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Rare variants have recently garnered an immense amount of attention in genetic association analysis. However, unlike methods traditionally used for single marker analysis in GWAS, rare variant analysis often requires some method of aggregation, since single marker approaches are poorly powered for typical sequencing study sample sizes. Advancements in sequencing technologies have rendered next-generation sequencing platforms a realistic alternative to traditional genotyping arrays. Exome sequencing in particular not only provides base-level resolution of genetic coding regions, but also a natural paradigm for aggregation via genes and exons. Here we propose the use of penalized regression in combination with variant aggregation measures to identify rare variant enrichment in exome sequencing data. In contrast to marginal gene-level testing, we simultaneously evaluate the effects of rare variants in multiple genes, focusing on gene-based LASSO and exon-based sparse group LASSO models. By using gene membership as a grouping variable, the sparse group LASSO can be used as a gene-centric analysis of rare variants while also providing a penalized approach toward identifying specific regions of interest. We apply extensive simulations to evaluate the performance of these approaches with respect to specificity and sensitivity, comparing these results to multiple competing marginal testing methods. Finally, we discuss our findings and outline future research.

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“…Usually, spatial clustering is ignored in collapsing and similarity-based methods. Examples of this third class of RV analytic methods include IL-K [27] , KERNEL [28] and CLUSTER [29] . Schaid et al [28] have shown that IL-K outperforms KERNEL over a range of clustering scenarios, but KERNEL takes approximately half the computational time of IL-K. For the majority of the simulation settings considered by Lin [29] , CLUS-TER seems to outperform KERNEL and the VT approach [21] .…”

Section: Introductionmentioning

confidence: 99%

Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis

et al. 2015

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Genome-wide association studies have revealed a vast amount of common loci associated to human complex diseases. Still, a large proportion of heritability remains unexplained. The extent to which rare genetic variants (RVs) are able to explain a relevant portion of the genetic heritability for complex traits leaves room for several debates and paves the way to the collection of RV databases and the development of novel analytic tools to analyze these. To date, several statistical methods have been proposed to uncover the association of RVs with complex diseases, but none of them is the clear winner in all possible scenarios of study design and assumed underlying disease model. The latter may involve differences in the distributions of effect sizes, proportions of causal variants, and ratios of protective to deleterious variants at distinct regions throughout the genome. Therefore, there is a need for robust scalable methods with acceptable overall performance in terms of power and type I error under various realistic scenarios. In this paper, we propose a novel RV association analysis strategy, which satisfies several of the desired properties that a RV analysis tool should exhibit.

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

Cited by 15 publications

References 14 publications

Association Testing of Clustered Rare Causal Variants in Case-Control Studies

Association Testing of Clustered Rare Causal Variants in Case-Control Studies

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

Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis

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