Dataset Information

ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure.

ABSTRACT: Genome-wide association studies are routinely conducted to identify genetic variants that influence complex disorders. It is well known that failure to properly account for population or pedigree structure can lead to spurious association as well as reduced power. We propose a method, ROADTRIPS, for case-control association testing in samples with partially or completely unknown population and pedigree structure. ROADTRIPS uses a covariance matrix estimated from genome-screen data to correct for unknown population and pedigree structure while maintaining high power by taking advantage of known pedigree information when it is available. ROADTRIPS can incorporate data on arbitrary combinations of related and unrelated individuals and is computationally feasible for the analysis of genetic studies with millions of markers. In simulations with related individuals and population structure, including admixture, we demonstrate that ROADTRIPS provides a substantial improvement over existing methods in terms of power and type 1 error. The ROADTRIPS method can be used across a variety of study designs, ranging from studies that have a combination of unrelated individuals and small pedigrees to studies of isolated founder populations with partially known or completely unknown pedigrees. We apply the method to analyze two data sets: a study of rheumatoid arthritis in small UK pedigrees, from Genetic Analysis Workshop 15, and data from the Collaborative Study of the Genetics of Alcoholism on alcohol dependence in a sample of moderate-size pedigrees of European descent, from Genetic Analysis Workshop 14. We detect genome-wide significant association, after Bonferroni correction, in both studies.

SUBMITTER: Thornton T

PROVIDER: S-EPMC2820184 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure.

Thornton Timothy T McPeek Mary Sara MS

American journal of human genetics 20100204 2

Genome-wide association studies are routinely conducted to identify genetic variants that influence complex disorders. It is well known that failure to properly account for population or pedigree structure can lead to spurious association as well as reduced power. We propose a method, ROADTRIPS, for case-control association testing in samples with partially or completely unknown population and pedigree structure. ROADTRIPS uses a covariance matrix estimated from genome-screen data to correct for ...[more]

PMID: 20137780

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Project description:BackgroundWith the advent of next-generation sequencing (NGS) technologies, researchers are now generating a deluge of data on high dimensional genomic variations, whose analysis is likely to reveal rare variants involved in the complex etiology of disease. Standing in the way of such discoveries, however, is the fact that statistics for rare variants are currently designed for use with population-based data. In this paper, we introduce a pedigree-based statistic specifically designed to test for rare variants in family-based data. The additional power of pedigree-based statistics stems from the fact that while rare variants related to diseases or traits of interest occur only infrequently in populations, in families with multiple affected individuals, such variants are enriched. Note that while the proposed statistic can be applied with and without statistical weighting, our simulations show that its power increases when weighting (WSS and VT) are applied.ResultsOur working hypothesis was that, since rare variants are concentrated in families with multiple affected individuals, pedigree-based statistics should detect rare variants more powerfully than population-based statistics. To evaluate how well our new pedigree-based statistics perform in association studies, we develop a general framework for sequence-based association studies capable of handling data from pedigrees of various types and also from unrelated individuals. In short, we developed a procedure for transforming population-based statistics into tests for family-based associations. Furthermore, we modify two existing tests, the weighted sum-square test and the variable-threshold test, and apply both to our family-based collapsing methods. We demonstrate that the new family-based tests are more powerful than corresponding population-based test and they generate a reasonable type I error rate.To demonstrate feasibility, we apply the newly developed tests to a pedigree-based GWAS data set from the Framingham Heart Study (FHS). FHS-GWAS data contain approximately 5000 uncommon variants with frequencies less than 0.05. Potential association findings in these data demonstrate the feasibility of the software PB-STAR (note, PB-STAR is now freely available to the public).ConclusionOur tests show that when analyzing for rare variants, a pedigree-based design is more powerful than a population-based case-control design. We further demonstrate that a pedigree-based statistic's power to detect rare variants increases in direct relation to the proportion of affected individuals within the pedigree.

Project description:BackgroundObesity has been inconsistently linked to prostate cancer, mainly with mortality rather than incidence. Few large-scale studies exist assessing obesity in relation to prostate-specific antigen (PSA)-detected prostate cancer.MethodsWe used cases and stratum-matched controls from the population-based PSA-testing phase of the Prostate testing for cancer and Treatment study to examine the hypothesis that obesity as measured by body mass index (BMI), waist circumference and waist-to-hip ratio (WHR) is associated with increased prostate cancer risk, and with higher tumour stage and grade. In all, 2167 eligible cases and 11 638 randomly selected eligible controls with PSA values were recruited between 2001 and 2008. A maximum of 960 cases and 4156 controls had measurement data, and also complete data on age and family history, and were included in the final analysis. BMI was categorised as <25.0, 25.0-29.9, ≥ 30.0 in kg m(-2).ResultsFollowing adjustment for age and family history of prostate cancer, we found little evidence that BMI was associated with total prostate cancer (odds ratio (OR): 0.83, 95% confidence interval (CI): 0.67, 1.03; highest vs lowest tertile; P-trend 0.1). A weak inverse association was evident for low-grade (OR: 0.76, 95% CI: 0.59, 0.97; highest vs lowest tertile; P-trend 0.045) prostate cancer. We found no association of either waist circumference (OR: 0.94, 95% CI: 0.80, 1.12; highest vs lowest tertile) or waist-to-hip ratio (WHR; OR: 0.93, 95% CI: 0.77, 1.11; highest vs lowest tertile) with total prostate cancer, and in analyses stratified by disease stage (all P-trend>0.35) or grade (all P-trend>0.16).ConclusionGeneral adiposity, as measured by BMI, was associated with a decreased risk of low-grade PSA-detected prostate cancer. However, effects were small and the confidence intervals had limits very close to one. Abdominal obesity (as measured by WHR/waist circumference) was not associated with PSA-detected prostate cancer.

Dataset Information

ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure.

Publications

ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure.

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