Dataset Information

Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies.

ABSTRACT:

Motivation

In the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution.

Results

We propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case-control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant-phenotype associations.

Availability and implementation

The summary statistics and code generated in this study are available at https://github.com/styvon/TAPE.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Zhuang Y

PROVIDER: S-EPMC9477535 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Publications

Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies.

Zhuang Yongwen Y Wolford Brooke N BN Nam Kisung K Bi Wenjian W Zhou Wei W Willer Cristen J CJ Mukherjee Bhramar B Lee Seunggeun S

Bioinformatics (Oxford, England) 20220901 18

<h4>Motivation</h4>In the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution.<h4>Results</h4>We propose a new method for genetic association test with family ...[more]

PMID: 35876838

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Project description:In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.

Dataset Information

Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Incorporating family disease history and controlling case-control imbalance for population-based genetic association studies.

Similar Datasets

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