Book: Applied Statistic Genetics with R with data & code!

Check this out! http://people.umass.edu/foulkes/asg/examples.html

This book is intended to provide fundamental statistical concepts and R tools relevant to the analysis of genetic data arising from population-based association studies.  The statistical methods described are broadly relevant to the field of statistical genetics and include a large array of tools for a wide variety of medical and public health applications.  Data analytic methods include approaches to handling multiplicity, ambiguity in haplotypic phase and underlying gene-gene and gene-environment interactions.  Several publicly available data sets are used for illustration

•           Chapter 1
              #  1.1:  Identifying the minor allele and its frequency
              Chapter 2            
•             #  2.1: Chi-squared test for association
              #  2.2: Fisher's exact test for association
              #  2.3: Chochran-Armitage (C-A) trend test for association
              #  2.4: Two-sample tests for association for a quantitative trait
              #  2.5: M-sample tests of association for a quantitative trait
              #  2.6: Linear Regression
•             Chapter 3
•             #  3.1: Measuring LD using D-prime
              #  3.2: Measuring LD for a group of SNPs
•             #  3.3: Measuring LD based on r^2 and the \chi^2-statistic
              #  3.4: Determining average LD across multiple SNPs
•             #  3.5: Population substructure and LD
              #  3.6: Testing for HWE using Pearsons \chi^2-test
•             #  3.7:  Testing for HWE using Fishers exact test
              #  3.8:  HWE and geographic origin
•             #  3.9: Generating a similarity matrix
              #  3.10: Multidimensional scaling (MDS) for identifying population substructure
•             #  3.11: Principal components analysis (PCA) for identifying population substructure 
•             Chapter 4
•             #  4.1: Bonferroni adjustment
              #  4.2: Tukeys single-step method
•             #  4.3: Banjamini and Hochberg (B-H) adjustment            #  4.4: Benjamini and Yekutieli (B-Y) adjustment
•             #  4.5: Calculation of the q-value
•             #  4.6: Free step down resampling adjustment
•             #  4.7: Null unrestricted bootstrap approach
•             Chapter 5
              #  5.1: EM approach to haplotype frequency estimation
•             #  5.2: Calculating posterior haplotype probabilities 
              #  5.3: Testing hypotheses about haplotype frequencies within the EM framework
•             #  5.4: Application of haplotype trend regression (HTR)
              #  5.5: Multiple imputation for haplotype effect estimation and testing
•             #  5.6: EM for estimation and testing of haplotype-trait association
•             Chapter 6
              #  6.2: Creating a classification tree
•             #  6.3: Generating a regression tree
              #  6.4: Categorical and ordinal predictors in a tree
              #  6.5: Cost-complexity pruning
•             Chapter 7
              #  7.1: An application of random forests
•             #  7.2: RF with missing SNP data - single imputation
              #  7.3: RF with missing SNP data - multiple imputation
•             #  7.4: MIRF
•             #  7.5: Application of logic regression
•             #  7.6: Monte Carol logic regression
•             #  7.7: An application of MARS