Dataset Information

Genome-wide association of breast cancer: composite likelihood with imputed genotypes.

ABSTRACT: We describe composite likelihood-based analysis of a genome-wide breast cancer case-control sample from the Cancer Genetic Markers of Susceptibility project. We determine 14?380 genome regions of fixed size on a linkage disequilibrium (LD) map, which delimit comparable levels of LD. Although the numbers of single-nucleotide polymorphisms (SNPs) are highly variable, each region contains an average of ?35 SNPs and an average of ?69 after imputation of missing genotypes. Composite likelihood association mapping yields a single P-value for each region, established by a permutation test, along with a maximum likelihood disease location, SE and information weight. For single SNP analysis, the nominal P-value for the most significant SNP (msSNP) requires substantial correction given the number of SNPs in the region. Therefore, imputing genotypes may not always be advantageous for the msSNP test, in contrast to composite likelihood. For the region containing FGFR2 (a known breast cancer gene) the largest ?(2) is obtained under composite likelihood with imputed genotypes (?(2)(2) increases from 20.6 to 22.7), and compares with a single SNP-based ?(2)(2) of 19.9 after correction. Imputation of additional genotypes in this region reduces the size of the 95% confidence interval for location of the disease gene by ?40%. Among the highest ranked regions, SNPs in the NTSR1 gene would be worthy of examination in additional samples. Meta-analysis, which combines weighted evidence from composite likelihood in different samples, and refines putative disease locations, is facilitated through defining fixed regions on an underlying LD map.

SUBMITTER: Politopoulos I

PROVIDER: S-EPMC3025787 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome-wide association of breast cancer: composite likelihood with imputed genotypes.

Politopoulos Ioannis I Gibson Jane J Tapper William W Ennis Sarah S Eccles Diana D Collins Andrew A

European journal of human genetics : EJHG 20101020 2

We describe composite likelihood-based analysis of a genome-wide breast cancer case-control sample from the Cancer Genetic Markers of Susceptibility project. We determine 14 380 genome regions of fixed size on a linkage disequilibrium (LD) map, which delimit comparable levels of LD. Although the numbers of single-nucleotide polymorphisms (SNPs) are highly variable, each region contains an average of ∼35 SNPs and an average of ∼69 after imputation of missing genotypes. Composite likelihood associ ...[more]

PMID: 20959865

Similar Datasets

Project description:Genetic and environmental covariances between pairs of complex traits are important quantitative measurements that characterize their shared genetic and environmental architectures. Accurate estimation of genetic and environmental covariances in genome-wide association studies (GWASs) can help us identify common genetic and environmental factors associated with both traits and facilitate the investigation of their causal relationship. Genetic and environmental covariances are often modeled through multivariate linear mixed models. Existing algorithms for covariance estimation include the traditional restricted maximum likelihood (REML) method and the recent method of moments (MoM). Compared to REML, MoM approaches are computationally efficient and require only GWAS summary statistics. However, MoM approaches can be statistically inefficient, often yielding inaccurate covariance estimates. In addition, existing MoM approaches have so far focused on estimating genetic covariance and have largely ignored environmental covariance estimation. Here we introduce a new computational method, GECKO, for estimating both genetic and environmental covariances, that improves the estimation accuracy of MoM while keeping computation in check. GECKO is based on composite likelihood, relies on only summary statistics for scalable computation, provides accurate genetic and environmental covariance estimates across a range of scenarios, and can accommodate SNP annotation stratified covariance estimation. We illustrate the benefits of GECKO through simulations and applications on analyzing 22 traits from five large-scale GWASs. In the real data applications, GECKO identified 50 significant genetic covariances among analyzed trait pairs, resulting in a twofold power gain compared to the previous MoM method LDSC. In addition, GECKO identified 20 significant environmental covariances. The ability of GECKO to estimate environmental covariance in addition to genetic covariance helps us reveal strong positive correlation between the genetic and environmental covariance estimates across trait pairs, suggesting that common pathways may underlie the shared genetic and environmental architectures between traits.

Dataset Information

Genome-wide association of breast cancer: composite likelihood with imputed genotypes.

Publications

Genome-wide association of breast cancer: composite likelihood with imputed genotypes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure