By Gang Zheng
Analysis of Genetic organization stories is either a graduate point textbook in statistical genetics and genetic epidemiology, and a reference ebook for the research of genetic organization experiences. scholars, researchers, and pros will locate the subjects brought in Analysis of Genetic organization Studies really proper. The booklet is appropriate to the research of information, biostatistics, genetics and genetic epidemiology.
In addition to delivering derivations, the booklet makes use of actual examples and simulations to demonstrate step by step functions. Introductory chapters on likelihood and genetic epidemiology terminology give you the reader with worthy historical past wisdom. The association of this paintings makes it possible for either informal reference and shut research.
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Additional resources for Analysis of Genetic Association Studies
We denote Pr(case) = k as the prevalence of the disease. 1, in which one locus is a marker with alleles A/a and the other is a functional locus with alleles B/b. We will discuss two types of designs: population based and family based. In the population-based design, we focus on the retrospective case-control study. We discuss case-control designs and analyses from the epidemiological perspective. Other relevant designs are also mentioned. 1 Linkage Disequilibrium and Association Studies Because the functional locus (or disease locus) that has a causal relationship with a disease is unknown, a marker is genotyped and tested for association with the disease.
To apply a Bonferroni correction when testing M null hypotheses, each null hypothesis is tested at the level α/M. If one of the M tests is significant at the α/M level, the null hypothesis is rejected, and the overall Type I error would be controlled at the α level. Let Ti be the ith test for the ith null hypothesis with level α/M. Denote its critical value by Ci . Thus, under H0 , Pr(Ti > Ci ) = α/M. Hence, under H0 , the Type I error to incorrectly reject H0 is M Pr(reject H0 ) = Pr M (Ti > Ci ) ≤ i=1 M Pr(Ti > Ci ) = i=1 α/M = α.
24 1 Introduction to Probability Theory and Statistics For a given threshold level α, find the largest k such that p(k:M) ≤ kα/M. Finally, reject the k null hypotheses corresponding to p(1:M) , . . , p(k:M) . Note that, in the above simple approach, in order to reject at least one null hypothesis, the smallest p-value has to be smaller than the Bonferroni-corrected significance level α/M. Therefore, any null hypothesis that is rejected under Bonferroni correction will be rejected using the FDR when the same threshold level is used.
Analysis of Genetic Association Studies by Gang Zheng