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Family-based samples can play an important role in genetic association studies.

ABSTRACT: Over the past 2 decades, DNA samples from thousands of families have been collected and genotyped for linkage studies of common complex diseases, such as type 2 diabetes, asthma, and prostate cancer. Unfortunately, little success has been achieved in identifying genetic susceptibility risk factors through these considerable efforts. However, significant success in identifying common disease risk-associated variants has been recently achieved from genome-wide association studies using unrelated case-control samples. These genome-wide association studies are typically done using population-based cases and controls that are ascertained irrespective of their family history for the disease of interest. Few genetic association studies have taken full advantage of the considerable resources that are available from the linkage-based family collections despite evidence showing cases that have a positive family history of disease are more likely to carry common genetic variants associated with disease susceptibility. Herein, we argue that population stratification is still a concern in case-control genetic association studies, despite the development of analytic methods designed to account for this source of confounding, for a subset of single nucleotide polymorphisms in the genome, most notably those single nucleotide polymorphisms in regions involved with natural selection. We note that current analytic approaches designed to address the issue of population stratification in case-control studies cannot definitively distinguish between true and false associations, and we argue that family-based samples can still serve an invaluable role in following up findings from case-control studies.

SUBMITTER: Lange EM

PROVIDER: S-EPMC2665689 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Family-based samples can play an important role in genetic association studies.

Lange Ethan M EM Sun Jielin J Lange Leslie A LA Zheng S Lilly SL Duggan David D Carpten John D JD Gronberg Henrik H Isaacs William B WB Xu Jianfeng J Chang Bao-Li BL

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20080901 9

Over the past 2 decades, DNA samples from thousands of families have been collected and genotyped for linkage studies of common complex diseases, such as type 2 diabetes, asthma, and prostate cancer. Unfortunately, little success has been achieved in identifying genetic susceptibility risk factors through these considerable efforts. However, significant success in identifying common disease risk-associated variants has been recently achieved from genome-wide association studies using unrelated c ...[more]

PMID: 18768484

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Project description:BackgroundWith the completion of the HapMap project, a variety of computational algorithms and tools have been proposed for haplotype inference, tag SNP selection and genome-wide association studies. Simulated data are commonly used in evaluating these new developed approaches. In addition to simulations based on population models, empirical data generated by perturbing real data, has also been used because it may inherit specific properties from real data. However, there is no tool that is publicly available to generate large scale simulated variation data by taking into account knowledge from the HapMap project.ResultsA computer program (gs) was developed to quickly generate a large number of samples based on real data that are useful for a variety of purposes, including evaluating methods for haplotype inference, tag SNP selection and association studies. Two approaches have been implemented to generate dense SNP haplotype/genotype data that share similar local linkage disequilibrium (LD) patterns as those in human populations. The first approach takes haplotype pairs from samples as inputs, and the second approach takes patterns of haplotype block structures as inputs. Both quantitative and qualitative traits have been incorporated in the program. Phenotypes are generated based on a disease model, or based on the effect of a quantitative trait nucleotide, both of which can be specified by users. In addition to single-locus disease models, two-locus disease models have also been implemented that can incorporate any degree of epistasis. Users are allowed to specify all nine parameters in a 3 x 3 penetrance table. For several commonly used two-locus disease models, the program can automatically calculate penetrances based on the population prevalence and marginal effects of a disease that users can conveniently specify.ConclusionThe program gs can effectively generate large scale genetic and phenotypic variation data that can be used for evaluating new developed approaches. It is freely available from the authors' web site at http://www.eecs.case.edu/~jxl175/gs.html.

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Family-based samples can play an important role in genetic association studies.

Publications

Family-based samples can play an important role in genetic association studies.

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