Applied Statistical Genetics with R: For Population-based Association Studies (Use R!)

Applied Statistical Genetics with R: For Population-based Association Studies (Use R!)

Andrea S. Foulkes

Language: English

Pages: 252

ISBN: 0387895531

Format: PDF / Kindle (mobi) / ePub


Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

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exception 16 1 Genetic Association Studies is in the context of cancer, in which DNA damage develops, resulting from environmental exposure to mutagens and resulting in uncontrolled cell proliferation. In the complex disease association studies described in this text, the genes under investigation do not vary within the timeframe of study. This is a marked difference from the viral genetic setting, in which multiple genetic polymorphisms can occur within a short period of time, typically in

resistin c180g and NDRM.CH. Interestingly, based on a Wald test, there appears to be an effect of the CG genotype compared with the referent CC genotype (t = −2.067, p = 0.0392); however, interpretation of this p-value needs to be in light of the two t-statistics generated unless there was an a priori hypothesis about the heterozygous genotype in this setting. Further discussion of the appropriate adjustment is given in Chapter 4. A Kruskal-Wallis (K-W) test can also be applied and is more

Define and contrast each of the following terms: (1) confounding, (2) effect mediation, (3) effect modification, (4) causal pathway, (5) interaction and (6) conditional association. 2.2. Based on the FAMuSS data, determine whether any of the four SNPs within the akt2 gene are associated with percentage change in non-dominant arm muscle strength as measured by NDRM.CH. Perform your analysis unadjusted and then adjusting for Race, Gender and Age. State clearly how you code all variables and justify

under study. 4. Record the vector of statistics Zn#1 = (βn# − βn )/sd(βn# ) (4.32) 5. Repeat steps (2)–(4) B times to get Zn#1 , . . . , Zn#B . The distribution of Zn# , given by Q# 0n , converges to Q0n conditional on the data. 6. Determine a single-step significance cutoff by choosing a vector c = (c1 , . . . , cm ) such that   m # | > cj I |Zjn Pr  ≥ k = α (4.33) j=1 # where Zjn is the jth element of Zn# . Here α is the level at which we want to control the type-1 error, and

> > + + + > + install.packages("haplo.stats") library(haplo.stats) attach(fms) Geno <- cbind(substr(actn3_r577x,1,1), substr(actn3_r577x,2,2), substr(actn3_rs540874,1,1), substr(actn3_rs540874,2,2), substr(actn3_rs1815739,1,1), substr(actn3_rs1815739,2,2), substr(actn3_1671064,1,1), substr(actn3_1671064,2,2)) SNPnames <- c("actn3_r577x", "actn3_rs540874", "actn3_rs1815739", "actn3_1671064") We then subset African Americans and Caucasians and apply the haplo.em() function to each group. This

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