Dataset Information

Incorporating covariates into multipoint association mapping in the case-parent design.

ABSTRACT: To improve the efficiency of disease locus localization in association mapping using case-parent designs and to assess or account for the main covariate effects and gene-covariate interaction effects, while localizing the disease locus.The present study extends a multipoint fine-mapping approach to incorporate covariates into the association mapping of case-parent designs through parametric and non-parametric modeling. This approach is based on the expected preferential-allele-transmission statistics for transmission from either parent to an affected child.Simulation studies indicate that the efficiency in estimating the disease locus increases considerably when incorporating a covariate associated with the disease. This is especially true when the genetic effect of the disease locus is small. The proposed approach was applied to a young-onset hypertension data sample. The relative efficiency of estimating the locus of young-onset hypertension increases 110-fold after incorporating triglyceride into the association mapping while localizing the disease variant in the lipoprotein lipase gene in the non-parametric model. By incorporating the information of SNP variants into the fine-mapping, the proposed method further assesses the gene-gene interactions between the SNP and the disease locus.With the incorporation of covariates, the proposed method cannot only improve efficiency in estimating disease loci, but can also elucidate the etiology of a complex disease.

SUBMITTER: Chiu YF

PROVIDER: S-EPMC2889259 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Incorporating covariates into multipoint association mapping in the case-parent design.

Chiu Yen-Feng YF Liang Kung-Yee KY Pan Wen-Harn WH

Human heredity 20100324 4

<h4>Background/aims</h4>To improve the efficiency of disease locus localization in association mapping using case-parent designs and to assess or account for the main covariate effects and gene-covariate interaction effects, while localizing the disease locus.<h4>Methods</h4>The present study extends a multipoint fine-mapping approach to incorporate covariates into the association mapping of case-parent designs through parametric and non-parametric modeling. This approach is based on the expecte ...[more]

PMID: 20332647

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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

Incorporating covariates into multipoint association mapping in the case-parent design.

Publications

Incorporating covariates into multipoint association mapping in the case-parent design.

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