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

Zhang Q, Wang S, Ott J - BMC Genet. (2008)

Bottom Line: 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.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.As an alternative to these two standard association tests, our FP test represents a convenient and more powerful alternative.

View Article: PubMed Central - HTML - PubMed

Affiliation: Beijing Institute of Genomics, Chinese Academy of Sciences, No. 7 Bei Tu Cheng West Road, Beijing 100029, PR China. zhangqr@big.ac.cn

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

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Power for recessive disease models. Power (y-axis) as a function of the penetrance ratio, γ, (x-axis), for recessive disease model. The FP test is most powerful while the MaxGA test (- - -) is slightly more powerful than the genotype test (--- --- ---).
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Figure 1: Power for recessive disease models. Power (y-axis) as a function of the penetrance ratio, γ, (x-axis), for recessive disease model. The FP test is most powerful while the MaxGA test (- - -) is slightly more powerful than the genotype test (--- --- ---).

Mentions: Figure 1 shows power curves for recessive disease models. Clearly, our FP test has better power than any of the other association tests. For dominant traits (Figure 2), if researchers carried out only the allele test, that test has higher power than the FP and genotype tests. As both the allele and genotype tests are usually carried out at the same time, the proper comparison is between the MaxGA statistic and our FP test, where the latter exhibits a slight advantage over the former. Thus, it is fair to say that the FP test is more powerful than conventional test statistics (allele and genotype tests) under our recessive and dominant inheritance models. We have implemented it in a computer program, FPtest, which is freely available on our website [13].


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

Zhang Q, Wang S, Ott J - BMC Genet. (2008)

Power for recessive disease models. Power (y-axis) as a function of the penetrance ratio, γ, (x-axis), for recessive disease model. The FP test is most powerful while the MaxGA test (- - -) is slightly more powerful than the genotype test (--- --- ---).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2483716&req=5

Figure 1: Power for recessive disease models. Power (y-axis) as a function of the penetrance ratio, γ, (x-axis), for recessive disease model. The FP test is most powerful while the MaxGA test (- - -) is slightly more powerful than the genotype test (--- --- ---).
Mentions: Figure 1 shows power curves for recessive disease models. Clearly, our FP test has better power than any of the other association tests. For dominant traits (Figure 2), if researchers carried out only the allele test, that test has higher power than the FP and genotype tests. As both the allele and genotype tests are usually carried out at the same time, the proper comparison is between the MaxGA statistic and our FP test, where the latter exhibits a slight advantage over the former. Thus, it is fair to say that the FP test is more powerful than conventional test statistics (allele and genotype tests) under our recessive and dominant inheritance models. We have implemented it in a computer program, FPtest, which is freely available on our website [13].

Bottom Line: 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.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.As an alternative to these two standard association tests, our FP test represents a convenient and more powerful alternative.

View Article: PubMed Central - HTML - PubMed

Affiliation: Beijing Institute of Genomics, Chinese Academy of Sciences, No. 7 Bei Tu Cheng West Road, Beijing 100029, PR China. zhangqr@big.ac.cn

ABSTRACT

Background: 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.

Results: 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.

Conclusion: 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.

Show MeSH