Detecting Association of Rare and Common Variants by Testing an Optimally Weighted Combination of Variants

Sha, Qiuying; Wang, Xuexia; Wang, Xinli; Zhang, Shuanglin

doi:10.1002/gepi.21649

Cited by 77 publications

(197 citation statements)

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“…We compare the performance of the proposed method with three existing methods: WSS, 12 sibpair-based weighted sum statistic (SPWSS), 28 and TOW. 17 WSS and TOW are based on unrelated cases and controls, whereas SPWSS is based on affected sib-pairs, unrelated cases, and unrelated controls.…”

Section: ?ð0þmentioning

confidence: 99%

“…[15][16][17][18] Quadratic tests include C-alpha test, 19 sequence kernel association test, 15 and the test for Testing the effects of the Optimally Weighted combination of variants (TOW). 17 Quadratic tests also include adaptive weighting methods [20][21][22][23][24] since, as pointed out by Derkach et al, 18 adaptive weighting methods are operationally similar to quadratic tests. Quadratic tests are robust to the directions of the effects of causal variants and are less affected by neutral variants than burden tests are.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the optimal weights, TOW-sib is robust to the directions of the effects of causal variants and is less affected by neutral variants than existing tests are. We use extensive simulation studies to compare the performance of the proposed method with that of existing methods based on unrelated individuals 12,17 and existing methods based on affected sib-pairs. 28 Our simulation results show that, in all the cases, the proposed method is substantially more powerful than existing methods based on either unrelated individuals or affected sib-pairs.…”

Section: Introductionmentioning

confidence: 99%

“…If these assumptions are true, burden tests can be powerful tests; otherwise, burden tests can perform poorly. [15][16][17][18] Quadratic tests include C-alpha test, 19 sequence kernel association test, 15 and the test for Testing the effects of the Optimally Weighted combination of variants (TOW). 17 Quadratic tests also include adaptive weighting methods 20-24 since, as pointed out by Derkach et al, 18 adaptive weighting methods are operationally similar to quadratic tests.…”

mentioning

confidence: 99%

“…We choose Sgene because the distributions of MAFs in the 100 variants in Sgene and in the 24 487 variants in all the 3205 genes are very similar. 17 In our simulation studies, we generate genotypes based on the genotypes of 697 individuals in Sgene. We use the program fastPHASE to infer haplotypic phase for the 697 individuals and calculate haplotype frequencies.…”

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Test of rare variant association based on affected sib-pairs

Sha

Zhang

2014

Eur J Hum Genet

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With the development of sequencing techniques, there is increasing interest to detect associations between rare variants and complex traits. Quite a few statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. Statistical methods for detecting rare variant associations under family-based designs have not received as much attention as methods for unrelated individuals. Recent studies show that rare disease variants will be enriched in family data and thus family-based designs may improve power to detect rare variant associations. In this article, we propose a novel test to test association between the optimally weighted combination of variants and trait of interests for affected sib-pairs. The optimal weights are analytically derived and can be calculated from sampled genotypes and phenotypes. Based on the optimal weights, the proposed method is robust to the directions of the effects of causal variants and is less affected by neutral variants than existing methods are. Our simulation results show that, in all the cases, the proposed method is substantially more powerful than existing methods based on unrelated individuals and existing methods based on affected sib-pairs. INTRODUCTIONRecent studies show that the large number of disease-associated variants identified through genome-wide association studies account for only a small portion of the presumed phenotypic variation. 1 One of the potential sources of missing heritability is the contribution of rare variants. [2][3][4][5][6][7] The recent advances of sequencing technology have made directly testing rare variants possible. 8,9 Therefore, there is increasing interest to detect associations between rare variants and complex traits.Recently, several statistical methods to detect associations between rare variants and complex traits have been developed for unrelated individuals. These methods can be roughly divided into three groups: burden tests, quadratic tests, and combined tests. Burden tests include the cohort allelic sums test, 10 the combined multivariate and collapsing method, 11 the weighted sum statistic (WSS), 12 the variable minor allele frequency (MAF) threshold method, 13 and the cumulative minor-allele test 14 among others. Burden tests implicitly assume that all the rare variants are causal and the directions of the effects are all the same. If these assumptions are true, burden tests can be powerful tests; otherwise, burden tests can perform poorly. [15][16][17][18] Quadratic tests include C-alpha test, 19 sequence kernel association test, 15 and the test for Testing the effects of the Optimally Weighted combination of variants (TOW). 17 Quadratic tests also include adaptive weighting methods 20-24 since, as pointed out by Derkach et al, 18 adaptive weighting methods are operationally similar to quadratic tests. Quadratic tests are robust to the directions of the effects of causal variants and are less affected by neutral variants than burden tests are. If most of the ra...

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Section: ?ð0þmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Test of rare variant association based on affected sib-pairs

Sha

Zhang

2014

Eur J Hum Genet

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Identification of Grouped Rare and Common Variants via Penalized Logistic Regression

Ayers

Cordell

2013

Genetic Epidemiology

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In spite of the success of genome-wide association studies in finding many common variants associated with disease, these variants seem to explain only a small proportion of the estimated heritability. Data collection has turned toward exome and whole genome sequencing, but it is well known that single marker methods frequently used for common variants have low power to detect rare variants associated with disease, even with very large sample sizes. In response, a variety of methods have been developed that attempt to cluster rare variants so that they may gather strength from one another under the premise that there may be multiple causal variants within a gene. Most of these methods group variants by gene or proximity, and test one gene or marker window at a time. We propose a penalized regression method (PeRC) that analyzes all genes at once, allowing grouping of all (rare and common) variants within a gene, along with subgrouping of the rare variants, thus borrowing strength from both rare and common variants within the same gene. The method can incorporate either a burden-based weighting of the rare variants or one in which the weights are data driven. In simulations, our method performs favorably when compared to many previously proposed approaches, including its predecessor, the sparse group lasso [Friedman et al., 2010].

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A Novel Test for Testing the Optimally Weighted Combination of Rare and Common Variants Based on Data of Parents and Affected Children

Sha

Zhang

2013

Genetic Epidemiology

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With the development of sequencing technologies, the direct testing of rare variant associations has become possible. Many statistical methods for detecting associations between rare variants and complex diseases have recently been developed, most of which are population-based methods for unrelated individuals. A limitation of population-based methods is that spurious associations can occur when there is a population structure. For rare variants, this problem can be more serious, since the spectrum of rare variation can be very different in diverse populations, as well as the current nonexistence of methods to control for population stratification in population-based rare variant associations. A solution to the problem of population stratification is to use family-based association tests, which use family members to control for population stratification. In this article, we propose a novel test for Testing the Optimally Weighted combination of variants based on data of Parents and Affected Children (TOW-PAC). TOW-PAC is a family-based association test that tests the combined effect of rare and common variants in a genomic region, and is robust to the directions of the effects of causal variants. Simulation studies confirm that, for rare variant associations, family-based association tests are robust to population stratification while population-based association tests can be seriously confounded by population stratification. The results of power comparisons show that the power of TOW-PAC increases with an increase of the number of affected children in each family and TOW-PAC based on multiple affected children per family is more powerful than TOW based on unrelated individuals.

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Detecting Association of Rare and Common Variants by Testing an Optimally Weighted Combination of Variants

Cited by 77 publications

References 45 publications

Test of rare variant association based on affected sib-pairs

Test of rare variant association based on affected sib-pairs

Identification of Grouped Rare and Common Variants via Penalized Logistic Regression

A Novel Test for Testing the Optimally Weighted Combination of Rare and Common Variants Based on Data of Parents and Affected Children

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