A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required

Dering, Carmen; König, Inke R.; Ramsey, Laura B.; Relling, Mary V.; Yang, Wenjian; Ziegler, Andreas

doi:10.3389/fgene.2014.00323

Cited by 13 publications

(19 citation statements)

References 39 publications

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“…For a prototype of our strategy, we selected genes as ROIs and selected all available RVs and common variants therein. As has been shown by Dering et al [17] , the choice of the included variants (non-synonymous, damaging, all variants) has a direct effect on the obtained association results. When only damaging variants are used for the analysis, the signals may be stronger compared to when all variants in the gene or only non-synonymous variants are included in the analysis.…”

Section: Phasementioning

confidence: 67%

“…However, even when power is acceptable, it is meaningless in the presence of high false positive rates. As reported by Dering et al [17] , most collapsing and similarity-based methods to date have inflated type I error rates, including SKAT and SKAT-O. An ideal method should have acceptable power, while keeping the false positive rate under control.…”

Section: Introductionmentioning

confidence: 99%

“…However, this depends on the particular RV association method used. Notably, there is a tendency for inflated type I error rates for the majority of RV association tests, when all variants in a gene are considered (rather than only non-synonymous ones) [17] .…”

Section: Phasementioning

confidence: 99%

“…CMC for quantitative traits and VT were implemented using the package STARSEQ [39] . The MAF cutoff was set to 0.01 for CMC and VT. No MAF cutoff was used for MB-MDR, SKAT and SKAT-O, since according to Dering et al [17] the default setting for SKAT and SKAT-O takes both common variants as well as RVs into account. Besides, it was shown by Ladouceur et al [30] that the addition of common causal variants to the presence of causal RVs did not improve the power for any of the methods, except for the SKAT method which demonstrates its advantage when combining RVs and common variants.…”

Section: Phasementioning

confidence: 99%

“…Each of these, as well as scientific contributions introducing novel methodologies for RV association analysis, adopt different criteria to classify the analytic approaches: for instance, collapsing methods, similarity-based methods and regression-based approaches [12] , (weighted) burden tests, variable-threshold tests, tests that allow variants with opposite effects to be grouped together [16] or simply burden versus non-burden tests [17] . We follow Dering et al [17] by referring to burden tests as those tests that involve collapsing over a region of interest and share the assumption that all variants in the region below a specific MAF threshold are causal with the same direction of effect. In this sense, collapsing methods include burden tests but not exclusively.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Phasementioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Section: Phasementioning

confidence: 99%

Section: Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis

et al. 2015

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The Translational Machine: A novel machine‐learning approach to illuminate complex genetic architectures

Askland

Strong

Wright

et al. 2021

Genetic Epidemiology

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The Translational Machine (TM) is a machine learning (ML)‐based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome‐scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model‐free, nonparametric ML‐based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset‐/pathways‐based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole‐exome schizophrenia case–control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.

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Assessing Rare Variation in Complex Traits

Kuchenbaecker

Appel

2018

Methods in Molecular Biology

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While genome-wide association studies have been very successful in identifying associations of common genetic variants with many different traits, the rarer frequency spectrum of the genome has not yet been comprehensively explored. Technological developments increasingly lift restrictions to access rare genetic variation. Dense reference panels enable improved genotype imputation for rarer variants in studies using DNA microarrays. Moreover, the decreasing cost of next generation sequencing makes whole exome and genome sequencing increasingly affordable for large samples. Large-scale efforts based on sequencing, such as ExAC, 100,000 Genomes, and TopMed, are likely to significantly advance this field.The main challenge in evaluating complex trait associations of rare variants is statistical power. The choice of population should be considered carefully because allele frequencies and linkage disequilibrium structure differ between populations. Genetically isolated populations can have favorable genomic characteristics for the study of rare variants.One strategy to increase power is to assess the combined effect of multiple rare variants within a region, known as aggregate testing. A range of methods have been developed for this. Model performance depends on the genetic architecture of the region of interest.

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A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required

Cited by 13 publications

References 39 publications

Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis

Model-Based Multifactor Dimensionality Reduction for Rare Variant Association Analysis

The Translational Machine: A novel machine‐learning approach to illuminate complex genetic architectures

Assessing Rare Variation in Complex Traits

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