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LEVERAGING LOCAL IDENTITY-BY-DESCENT INCREASES THE POWER OF CASE/CONTROL GWAS WITH RELATED INDIVIDUALS.

ABSTRACT: Large case/control genome-wide association studies (GWAS) often include groups of related individuals with known relationships. When testing for associations at a given locus, current methods incorporate only the familial relationships between individuals. Here, we introduce the chromosome-based Quasi Likelihood Score (cQLS) statistic that incorporates local Identity-By-Descent (IBD) to increase the power to detect associations. In studies robust to population stratification, such as those with case/control sibling pairs, simulations show that the study power can be increased by over 50%. In our example, a GWAS examining late-onset Alzheimers disease, the p-values among the most strongly associated SNPs in the APOE gene tend to decrease, with the smallest p-value decreasing from 1.23 × 10-8 to 7.70 × 10-9. Furthermore, as a part of our simulations, we reevaluate our expectations about the use of families in GWAS. We show that, although adding only half as many unique chromosomes, genotyping affected siblings is more efficient than genotyping randomly ascertained cases. We also show that genotyping cases with a family history of disease will be less beneficial when searching for SNPs with smaller effect sizes.

SUBMITTER: Sampson JN

PROVIDER: S-EPMC4275846 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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LEVERAGING LOCAL IDENTITY-BY-DESCENT INCREASES THE POWER OF CASE/CONTROL GWAS WITH RELATED INDIVIDUALS.

Sampson Joshua N JN Wheeler Bill B Li Peng P Shi Jianxin J

The annals of applied statistics 20140601 2

Large case/control genome-wide association studies (GWAS) often include groups of related individuals with known relationships. When testing for associations at a given locus, current methods incorporate only the familial relationships between individuals. Here, we introduce the chromosome-based Quasi Likelihood Score (cQLS) statistic that incorporates local Identity-By-Descent (IBD) to increase the power to detect associations. In studies robust to population stratification, such as those with ...[more]

PMID: 25544865

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Project description:Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 × 10(-9)). The improvement varied across diseases with a 16% median increase in ?(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.

Dataset Information

LEVERAGING LOCAL IDENTITY-BY-DESCENT INCREASES THE POWER OF CASE/CONTROL GWAS WITH RELATED INDIVIDUALS.

Publications

LEVERAGING LOCAL IDENTITY-BY-DESCENT INCREASES THE POWER OF CASE/CONTROL GWAS WITH RELATED INDIVIDUALS.

Similar Datasets

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