Analysis of Genetic Association Studies by Gang Zheng

By Gang Zheng

Analysis of Genetic organization experiences is either a graduate point textbook in statistical genetics and genetic epidemiology, and a reference ebook for the research of genetic organization reviews. scholars, researchers, and execs will locate the subjects brought in Analysis of Genetic organization Studies really correct. The booklet is acceptable to the learn of records, biostatistics, genetics and genetic epidemiology.

In addition to offering derivations, the e-book makes use of genuine examples and simulations to demonstrate step by step purposes. Introductory chapters on likelihood and genetic epidemiology terminology give you the reader with useful historical past wisdom. The association of this paintings allows either informal reference and shut examine.

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The FDR approach is to control the expected proportion of true null hypotheses among the rejected null hypotheses. Suppose there are M0 non-true null hypotheses among a total of M hypotheses. Assume R > 0 null hypotheses are rejected, among which V are true null hypotheses. Then the FDR is defined as E(V /R). To control the FDR, we keep E(V /R) below a given threshold α. One simple procedure to control the FDR is as follows. Assume the M test statistics are independent or positively correlated.

Other terms that are not covered here will be discussed in later chapters. Designs of genetic association studies are then introduced, including case-control and family-based designs. We will focus here on case-control designs and family-based designs will be discussed in Chap. 13. The Hardy-Weinberg law plays an important role in population genetics and the analysis of genetic data. Hardy-Weinberg equilibrium in a population is reviewed and the implications of departure from Hardy-Weinberg equilibrium are also demonstrated.

An estimate of θ , denoted by θ , is the MLE for θ if it maximizes the likelihood function. , Θ = (0, 1) for the binomial probability p. Then the MLE θ satisfies L(θ ) = max L(θ ). 6) We may also write θ = arg max L(θ ) = arg max l(θ ), θ∈Θ θ∈Θ where l(θ ) = log L(θ ) is the log-likelihood function. If the base of the log function is not specified, the natural log is used throughout this book. 6) may not be trivial. It is often solved from the following equation d l(θ ) = 0. dθ Note that when θ contains multiple parameters, the derivative d/dθ in the above equation becomes a partial derivative, which is evaluated for each element of θ .

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