Dataset Information

Designing candidate gene and genome-wide case-control association studies.

ABSTRACT: This protocol describes how to appropriately design a genetic association case-control study, either focusing on a candidate gene (CG) or region or implementing a genome-wide approach. The steps described involve: (i) defining the case phenotype in adequate detail; (ii) checking the heritability of the disease in question; (iii) considering whether a population-based study is the appropriate design for the research question; (iv) the appropriate selection of controls; (v) sample size calculations and (vi) giving due consideration to whether it is a de novo or replication study. General guidelines are given, as well as specific examples of a CG and a genome-wide association study into type 2 diabetes. Software and websites used in this protocol include the International HapMap Consortium website, Genetic Power Calculator, CaT, and SNPSpD. Running each of the programs takes only a few seconds; the rate-limiting steps involve thinking through the designs and parameters in the disease models.

SUBMITTER: Zondervan KT

PROVIDER: S-EPMC4180089 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Designing candidate gene and genome-wide case-control association studies.

Zondervan Krina T KT Cardon Lon R LR

Nature protocols 20070101 10

This protocol describes how to appropriately design a genetic association case-control study, either focusing on a candidate gene (CG) or region or implementing a genome-wide approach. The steps described involve: (i) defining the case phenotype in adequate detail; (ii) checking the heritability of the disease in question; (iii) considering whether a population-based study is the appropriate design for the research question; (iv) the appropriate selection of controls; (v) sample size calculation ...[more]

PMID: 17947991

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Project description:BackgroundGenome-wide association (GWA) studies have led to a paradigm shift in how researchers study the genetics underlying disease. Many GWA studies are now publicly available and can be used to examine whether or not previously proposed candidate genes are supported by GWA data. This approach is particularly important for the field of alcoholism because the contribution of many candidate genes remains controversial.MethodsUsing the Human Genome Epidemiology (HuGE) Navigator, we selected candidate genes for alcoholism that have been frequently examined in scientific articles in the past decade. Specific candidate loci as well as all the reported single nucleotide polymorphisms (SNPs) in candidate genes were examined in the Study of Addiction: Genetics and Environment (SAGE), a GWA study comparing alcohol-dependent and nondependent subjects.ResultsSeveral commonly reported candidate loci, including rs1800497 in DRD2, rs698 in ADH1C, rs1799971 in OPRM1, and rs4680 in COMT, are not replicated in SAGE (p > 0.05). Among candidate loci available for analysis, only rs279858 in GABRA2 (p = 0.0052, OR = 1.16) demonstrated a modest association. Examination of all SNPs reported in SAGE in over 50 candidate genes revealed no SNPs with large frequency differences between cases and controls, and the lowest p-value of any SNP was 0.0006.ConclusionsWe provide evidence that several extensively studied candidate loci do not have a strong contribution to risk of developing alcohol dependence in European and African ancestry populations. Owing to the lack of coverage, we were unable to rule out the contribution of other variants, and these genes and particular loci warrant further investigation. Our analysis demonstrates that publicly available GWA results can be used to better understand which if any of previously proposed candidate genes contribute to disease. Furthermore, we illustrate how examining the convergence of candidate gene and GWA studies can help elucidate the genetic architecture of alcoholism and more generally complex diseases.

Dataset Information

Designing candidate gene and genome-wide case-control association studies.

Publications

Designing candidate gene and genome-wide case-control association studies.

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