Gene‐based genetic association test with adaptive optimal weights

Chen, Zhongxue; Lu, Yuchao; Lin, Tong; Liu, Qingzhong; Wang, Kai

doi:10.1002/gepi.22098

Cited by 10 publications

(9 citation statements)

References 38 publications

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“…Alternatively, one could choose to re-weight these contributions by specifying A otherwise [12,20,105,107,108]. For example, if SNP j has a small effect size but is known to be functionally associated with the trait of interest, then increasing A jj will reflect this knowledge.…”

Section: Using the Snp-level Null Threshold To Detect Enriched Genesmentioning

confidence: 99%

Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

2020

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Traditional univariate genome-wide association studies generate false positives and negatives due to difficulties distinguishing associated variants from variants with spurious nonzero effects that do not directly influence the trait. Recent efforts have been directed at identifying genes or signaling pathways enriched for mutations in quantitative traits or casecontrol studies, but these can be computationally costly and hampered by strict model assumptions. Here, we present gene-ε, a new approach for identifying statistical associations between sets of variants and quantitative traits. Our key insight is that enrichment studies on the gene-level are improved when we reformulate the genome-wide SNP-level null hypothesis to identify spurious small-to-intermediate SNP effects and classify them as noncausal. gene-ε efficiently identifies enriched genes under a variety of simulated genetic architectures, achieving greater than a 90% true positive rate at 1% false positive rate for polygenic traits. Lastly, we apply gene-ε to summary statistics derived from six quantitative traits using European-ancestry individuals in the UK Biobank, and identify enriched genes that are in biologically relevant pathways.

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Section: Using the Snp-level Null Threshold To Detect Enriched Genesmentioning

confidence: 99%

Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

2020

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“…The goal of a genetic association study is to identify candidate genetic factors that are associated with specific disease status [1][2][3][4][5][6]. Toward this goal, a variety of statistical methods have been developed to test the association between disease and genetic variants [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates

Chen

Zang

2021

Genes

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The additive genetic model as implemented in logistic regression has been widely used in genome-wide association studies (GWASs) for binary outcomes. Unfortunately, for many complex diseases, the underlying genetic models are generally unknown and a mis-specification of the genetic model can result in a substantial loss of power. To address this issue, the MAX3 test (the maximum of three separate test statistics) has been proposed as a robust test that performs plausibly regardless of the underlying genetic model. However, the original implementation of MAX3 utilizes the trend test so it cannot adjust for any covariates such as age and gender. This drawback has significantly limited the application of the MAX3 in GWASs, as covariates account for a considerable amount of variability in these disorders. In this paper, we extended the MAX3 and proposed the CMAX3 (covariate-adjusted MAX3) based on logistic regression. The proposed test yielded a similar robust efficiency as the original MAX3 while easily adjusting for any covariate based on the likelihood framework. The asymptotic formula to calculate the p-value of the proposed test was also developed in this paper. The simulation results showed that the proposed test performed desirably under both the null and alternative hypotheses. For the purpose of illustration, we applied the proposed test to re-analyze a case-control GWAS dataset from the Collaborative Studies on Genetics of Alcoholism (COGA). The R code to implement the proposed test is also introduced in this paper and is available for free download.

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“…Wang has proposed to increase the power of SKAT through a better estimate for the null distribution using selected subjects . SKAT can also be improved by replacing the eigenvalues with other weights or by choosing better ways to form the kernel of genotype …”

Section: Introductionmentioning

confidence: 99%

Gene‐based sequential burden association test

Chen

Wang

2019

Statistics in Medicine

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Detecting the association between a set of variants and a phenotype of interest is the first and important step in genetic and genomic studies. Although it attracted a large amount of attention in the scientific community and several related statistical approaches have been proposed in the literature, powerful and robust statistical tests are still highly desired and yet to be developed in this area.In this paper, we propose a powerful and robust association test, which combines information from each individual single-nucleotide polymorphisms based on sequential independent burden tests. We compare the proposed approach with some popular tests through a comprehensive simulation study and real data application. Our results show that, in general, the new test is more powerful; the gain in detecting power can be substantial in many situations, compared to other methods.

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Gene‐based genetic association test with adaptive optimal weights

Cited by 10 publications

References 38 publications

Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits

CMAX3: A Robust Statistical Test for Genetic Association Accounting for Covariates

Gene‐based sequential burden association test

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