Dataset Information

Combining identity by descent and association in genetic case-control studies.

ABSTRACT: In human case-control association studies, one of the chi-square tests typically carried out is based on a 2 x 3 table of genotypes (homogeneity of three genotype frequencies in case and control individuals). We formulate the two degrees of freedom associated with a given genotype distribution in terms of two biologically relevant parameters, (1) the probability F that an individual's two alleles are identical by descent (IBD) and (2) the frequency p of one of the alleles.Imposing the restriction, F > or = 0, makes some of the genotype frequencies invalid thereby reducing noise. We propose a new statistical association test, the FP test, by focusing on allele frequency differences between case and control individuals while allowing for suitable IBD probabilities. Power calculations show that (1) the practice of generally carrying out two association tests (allele and genotype test) has an increased type I error and (2) our test is more powerful than conventional genotype and allele tests under recessive trait inheritance, and at least as powerful as these conventional tests under dominant inheritance.For dominant and recessive modes of inheritance, any apparent power gain by an allele test when carried out in conjunction with a genotype test tends to be purchased entirely by an increased rate of false positive results due to omission of a multiple testing correction. As an alternative to these two standard association tests, our FP test represents a convenient and more powerful alternative.

SUBMITTER: Zhang Q

PROVIDER: S-EPMC2483716 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Combining identity by descent and association in genetic case-control studies.

Zhang Qingrun Q Wang Shuang S Ott Jurg J

BMC genetics 20080705

<h4>Background</h4>In human case-control association studies, one of the chi-square tests typically carried out is based on a 2 x 3 table of genotypes (homogeneity of three genotype frequencies in case and control individuals). We formulate the two degrees of freedom associated with a given genotype distribution in terms of two biologically relevant parameters, (1) the probability F that an individual's two alleles are identical by descent (IBD) and (2) the frequency p of one of the alleles.<h4> ...[more]

PMID: 18601744

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Project description:Genome-wide association studies (GWAS) explore the relationship between genome variability and disease susceptibility with either population- or family-based data. Here, we have evaluated the utility of combining population- and family-based statistical association tests and have proposed a method for reducing the burden of multiple testing. Unrelated singleton and parent-offspring trio cases and controls from the Genetics of Kidneys in Diabetes (GoKinD) study were analyzed for genetic association with diabetic nephropathy (DN) in type 1 diabetics (T1D). The Cochran-Armitage test for trend and the family-based association test were employed using either unrelated cases and controls or trios, respectively. In addition to combining single nucleotide polymorphism (SNP) p-values across these tests via Fisher's method, we employed a novel screening approach to rank SNPs based on conditional power for more efficient testing. Using either the population-based or family-based subset alone predictably limited resolution to detect DN SNPs. For 384,197 SNPs passing quality control (QC), none achieved strict genome-wide significance (1.4 × 10(-7)) using 1171 singletons (577/594 cases/controls) or 1738 pooled singletons and offspring probands (841/897). Similarly, none of the 352,004 SNPs passing QC in 567 family trios (264/303 case/control proband trios) reached genome-wide significance. Testing the top 10 SNPs ranked using aggregated conditional power resulted in two SNPs reaching genome-wide significance, rs11645147 on chromosome 16 (p = 1.74 × 10(-4) < 0.05/10 = 0.005) and rs7866522 on chromosome 9 (p = 0.0033). Efficient usage of mixed designs incorporating both unrelated and family-based data may help to uncover associations otherwise difficult to detect in the presence of massive multiple testing corrections. Capitalizing on the strengths of both types while using screening approaches may be useful especially in light of large-scale, next-generation sequencing and rare variant studies.

Dataset Information

Combining identity by descent and association in genetic case-control studies.

Publications

Combining identity by descent and association in genetic case-control studies.

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