Genetic Analysis Workshop 17 mini-exome simulation

Almasy, Laura; Dyer, Thomas D.; Peralta, Juan M.; Kent, Jack W.; Charlesworth, Jac; Curran, Joanne E.; Blangero, John

doi:10.1186/1753-6561-5-s9-s2

Cited by 160 publications

(136 citation statements)

References 5 publications

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“…In summary, we found that, as expected, there was no uniform winner among the aSPU, SKAT, and SKAT-O tests; under some situations, the aSPU test could be more powerful than both SKAT and SKAT-O. Since all the causal genes contained only a relatively small number of noncausal RVs, and all causal RVs were deleterious (Almasy et al 2011), most often the SPU(3) test was most powerful among the SPU tests. In contrast, perhaps as expected, the SPU(N) was almost always least powerful.…”

Section: Data Examplementioning

confidence: 80%

A Powerful and Adaptive Association Test for Rare Variants

et al. 2014

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This article focuses on conducting global testing for association between a binary trait and a set of rare variants (RVs), although its application can be much broader to other types of traits, common variants (CVs), and gene set or pathway analysis. We show that many of the existing tests have deteriorating performance in the presence of many nonassociated RVs: their power can dramatically drop as the proportion of nonassociated RVs in the group to be tested increases. We propose a class of so-called sum of powered score (SPU) tests, each of which is based on the score vector from a general regression model and hence can deal with different types of traits and adjust for covariates, e.g., principal components accounting for population stratification. The SPU tests generalize the sum test, a representative burden test based on pooling or collapsing genotypes of RVs, and a sum of squared score (SSU) test that is closely related to several other powerful variance component tests; a previous study has demonstrated good performance of one, but not both, of the Sum and SSU tests in many situations. The SPU tests are versatile in the sense that one of them is often powerful, although its identity varies with the unknown true association parameters. We propose an adaptive SPU (aSPU) test to approximate the most powerful SPU test for a given scenario, consequently maintaining high power and being highly adaptive across various scenarios. We conducted extensive simulations to show superior performance of the aSPU test over several state-of-the-art association tests in the presence of many nonassociated RVs. Finally we applied the SPU and aSPU tests to the GAW17 mini-exome sequence data to compare its practical performance with some existing tests, demonstrating their potential usefulness.T HE recent advances in sequencing technologies have made it feasible to conduct global testing for association between complex traits and rare variants (RVs) (Bansal et al. 2010). The most popular approach in genome-wide association studies (GWASs) is to test on each single nucleotide variant (SNV) one by one and then select the SNVs meeting a stringent significance level after adjusting for multiple testing. However, such a strategy may be low powered due to the weak signal contained within each individual RV for its extremely low minor allele frequency (MAF). Hence, developing new association tests tailored to RVs has been an active research area in the past few years. Due to low MAFs of RVs, to achieve practically meaningful power, the majority of existing approaches focus on testing on a group of RVs, rather than on each individual RV (Capanu et al. 2011); the main idea is to boost power through aggregating information across multiple RVs in an analysis unit, such as a gene (e.g., Morgenthaler and Thilly 2007;Li and Leal 2008;Madsen and Browning 2009;Liu and Leal 2010;Han and Pan 2010;Hoffmann et al. 2010;Li et al. 2010;Price et al. 2010;Zhang et al. 2010;Zhu et al. 2010;Luo et al. 2011;Neale et al. 2011;Ionita-Laza et al....

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Section: Data Examplementioning

confidence: 80%

A Powerful and Adaptive Association Test for Rare Variants

et al. 2014

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“…The data consists of real genotype data (from the 1000 Genomes Project consortium) on which a disease phenotype was simulated. 10 We considered 25 genes which were known to contain causal variants for the simulated disease phenotype and showed variation in the sample of n = 321 unrelated Asian subjects. Given the small sample size and low power in this data set, 5 final disease status for each of the 321 individuals was averaged across 200 independent phenotype simulations, with individuals who were diseased in at least 100 of the 200 independent simulations identified as 'diseased,' and the rest not.…”

Section: Applicationmentioning

confidence: 99%

A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures

et al. 2015

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The widespread availability of genome sequencing data made possible by way of next-generation technologies has yielded a flood of different gene-based rare variant association tests. Most of these tests have been published because they have superior power for particular genetic architectures. However, for applied researchers it is challenging to know which test to choose in practice when little is known a priori about genetic architecture. Recently, tests have been proposed which combine two particular individual tests (one burden and one variance components) to minimize power loss while improving robustness to a wider range of genetic architectures. In our analysis we propose an expansion of these approaches, yielding a general method that works for combining any number of individual tests. We demonstrate that running multiple different tests on the same data set and using a Bonferroni correction for multiple testing is never better than combining tests using our general method. We also find that using a test statistic that is highly robust to the inclusion of non-causal variants (joint-infinity) together with a previously published combined test (sequence kernel adaptive test-optimal) provides improved robustness to a wide range of genetic architectures and should be considered for use in practice. Software for this approach is supplied. We support the increased use of combined tests in practice -as well as further exploration of novel combined testing approaches using the general framework provided here -to maximize robustness of rare variant testing strategies against a wide range of genetic architectures.

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“…GAW17 had regions with linkage and association evidence. 14,15 Using MRC-OB, linkage on 7q36 for triglycerides was identified. 16 After dense single-nucleotide polymorphism (SNP) genotyping, associations were found, but only a modest portion of the linkage was explained.…”

Section: Data Setsmentioning

confidence: 99%

“…17,18 Simulated data set -GAW17-simulated data set. GAW17 used the 1000 Genomes 19 exome sequence data 14,15 to generate a data set with 697 individuals in 8 pedigrees. Fully informative markers were used to compute identical-by-descent (IBD) allele sharing.…”

Section: Data Setsmentioning

confidence: 99%

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A novel method, the Variant Impact On Linkage Effect Test (VIOLET), leads to improved identification of causal variants in linkage regions

et al. 2013

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The Human Genome Project was expected to individualize medicine by rapidly advancing knowledge of common complex disease through discovery of disease-causing genetic variants. However, this has proved challenging. Although linkage analysis has identified replicated chromosomal regions, subsequent detection of causal variants for complex traits has been limited. One explanation for this difficulty is that utilization of association to follow up linkage is problematic given that linkage and association are not required to co-occur. Indeed, co-occurrence is likely to occur only in special circumstances, such as Mendelian inheritance, but cannot be universally expected. To overcome this problem, we propose a novel method, the Variant Impact On Linkage Effect Test (VIOLET), which differs from other quantitative methods in that it is designed to follow up linkage by identifying variants that influence the variance explained by a quantitative trait locus. VIOLET's performance was compared with measured genotype and combined linkage association in two data sets with quantitative traits. Using simulated data, VIOLET had high power to detect the causal variant and reduced false positives compared with standard methods. Using real data, VIOLET identified a single variant, which explained 24% of linkage; this variant exhibited only nominal association (P ¼ 0.04) using measured genotype and was not identified by combined linkage association. These results demonstrate that VIOLET is highly specific while retaining low false-negative results. In summary, VIOLET overcomes a barrier to gene discovery and thus may be broadly applicable to identify underlying genetic etiology for traits exhibiting linkage.

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Genetic Analysis Workshop 17 mini-exome simulation

Cited by 160 publications

References 5 publications

A Powerful and Adaptive Association Test for Rare Variants

A Powerful and Adaptive Association Test for Rare Variants

A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures

A novel method, the Variant Impact On Linkage Effect Test (VIOLET), leads to improved identification of causal variants in linkage regions

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