Detecting Rare Variant Effects Using Extreme Phenotype Sampling in Sequencing Association Studies

Barnett, Ian; Lee, Seunggeun; Lin, Xihong

doi:10.1002/gepi.21699

Cited by 123 publications

(130 citation statements)

References 39 publications

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“…This selection strategy is not only boosting the typical power gains from selection but also concentrating the functional variants in the sample. As previously reported, 16 this is the critical step to the design of a rare variant association study. If individuals are not selected for the functional variants, there will be no power to detect the association.…”

Section: Discussionmentioning

confidence: 89%

“…14 More recently, it has been established that extreme sampling performs better than random population-based sampling for single rare variants, with the apparent effect size increasing with more and more stringent selection thresholds, 15 but limited studies have explored the effect when variants are aggregated within a gene. Studies have shown that extreme sampling can enrich for the presence of causal variants 16,17 and, furthermore, that extreme phenotypic sampling and/or a twostage analysis can lead to gains in power. [17][18][19] Lee et al 20 compare available gene-based tests and discuss design strategies for RVASs.…”

Section: Introductionmentioning

confidence: 99%

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Phenotypic extremes in rare variant study designs

et al. 2015

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Currently, next-generation sequencing studies aim to identify rare and low-frequency variation that may contribute to disease. For a given effect size, as the allele frequency decreases, the power to detect genes or variants of interest also decreases. Although many methods have been proposed for the analysis of such data, study design and analytic issues still persist in data interpretation. In this study we present sequencing data for ABCA1 that has known rare variants associated with high-density lipoprotein cholesterol (HDL-C). We contrast empirical findings from two study designs: a phenotypic extreme sample and a population-based random sample. We found differing strengths of association with HDL-C across the two study designs (P = 0.0006 with n = 701 phenotypic extremes vs P = 0.03 with n = 1600 randomly sampled individuals). To explore this apparent difference in evidence for association, we performed a simulation study focused on the impact of phenotypic selection on power. We demonstrate that the power gain for an extreme phenotypic selection study design is much greater in rare variant studies than for studies of common variants. Our study confirms that studying phenotypic extremes is critical in rare variant studies because it boosts power in two ways: the typical increases from extreme sampling and increasing the proportion of relevant functional variants ascertained and thereby tested for association. Furthermore, we show that when combining statistical evidence through meta-analysis from an extreme-selected sample and a second separate population-based random sample, power is lower when a traditional sample size weighting is used compared with weighting by the noncentrality parameter.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Phenotypic extremes in rare variant study designs

et al. 2015

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“…This observation suggests that higher power may be achieved to contrast case-case dis/similarity against control-control dis/similarity instead of contrasting within-group comparisons against between-group comparisons. The suggestion echoes the implications in several recent work that the contrast of two extreme phenotype groups may improve the statistical power of rare-variant association tests (Guey et al 2011;Li et al 2011;Barnett et al 2013;Lee et al 2014).…”

Section: Introductionmentioning

confidence: 78%

Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples

Wang

Tzeng

et al. 2018

Genetics

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A properly designed distance-based measure can capture informative genetic differences among individuals with different phenotypes and be used to detect variants responsible for the phenotypes. To detect associated variants, various tests have been designed to contrast genetic dis/similarity scores of certain subject groups in different ways, among which the most widely used strategy is to quantify the difference between the within-group genetic dis/similarities (i.e., case-case and control-control similarities) and the between-group dis/similarities (i.e., case-control similarities). While it has been noted that for common variants, the within-group and the between-group measures should all be included, in this work, we show that for rare variants, comparison based on the two within-group measures can more effectively quantify the genetic difference between cases and controls. The between-group measure tends to overlap with one of the two within-group measures for rare variants, although such overlap is not present for common variants. Consequently, a dis/similarity test that includes the between-group information in the contrast tends to attenuate the association signals and leads to power loss. Based on the findings, we propose a dissimilarity test that compares the degree of SNP dissimilarity within cases to that within controls to better characterize the difference between two disease phenotypes.We provide the statistical properties, asymptotic distribution, and computation details for small sample size of the proposed test. We use simulated and real sequence data to assess the performance of the proposed test, comparing with other rare variant methods including those similarity-based tests that utilize both within-group and between-group information. As similarity-based approaches serve as one of the dominating approaches in rare variant analysis, our results would provide some insight for effective detection of rare variants.3

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“…In extreme phenotype sampling, individuals from the extremes of the trait distribution [39][40][41], individuals with large residuals after adjustment for nongenetic covariates [42] [39,42], particularly for small MAF. Analysis of selected samples is more complex, as specialized methods are required to adjust for the effects of ascertainment.…”

Section: Study Design and Rare Variantsmentioning

confidence: 99%

“…Analysis of selected samples is more complex, as specialized methods are required to adjust for the effects of ascertainment. Both burden-type tests and variance-based SKAT-type tests have been extended to account for extreme sampling [39,40]. Analysis of a secondary phenotype that is correlated with the phenotype defining the sampling scheme also requires a specialized regression model [44].…”

Section: Study Design and Rare Variantsmentioning

confidence: 99%

A sequence of methodological changes due to sequencing

Burkett

Greenwood²

2013

Current Opinion in Allergy &Amp; Clinical Immunology

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Many rare variant association tests have been developed to analyze the genetic variation discovered with large-scale DNA sequencing; however, no single approach outperforms others under all disease models and power tends to be low. Sequencing data are also contributing to improved imputation of uncommon genetic variants, although imputation of rare variants remains a challenge. The appropriate correction for population structure in rare variant analyses remains unclear; specialized adjustment techniques may be necessary.

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Detecting Rare Variant Effects Using Extreme Phenotype Sampling in Sequencing Association Studies

Cited by 123 publications

References 39 publications

Phenotypic extremes in rare variant study designs

Phenotypic extremes in rare variant study designs

Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples

A sequence of methodological changes due to sequencing

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