ANNOUNCEMENT: The required text “Principles of Population Genetics” is now available in the Brown bookstore

In the post genome-sequence phase of the Human Genome Project, the HapMap Project has been focusing on the study of inherited genetic variation, and its critical but as yet largely uncharacterized role in human disease. Most common diseases, such as diabetes, cancer, and heart disease are affected by many genes and environmental factors. Although any two unrelated people are the same at about 99.9% of their DNA sequences, the remaining 0.1% is important because it contains the genetic variants that influence how people differ in their risk of disease or their response to drugs. This course focuses on genome-wide disease association studies and the computational challenges of revealing the genetic determinants of disease. In this exploration we will use the haplotype map of the human genome, the HapMap, which describes the common patterns of human DNA sequence variation.

Life Science students (biological sciences and medical sciences) are welcome to take this course. They will be assigned personal classwork according to their backgrounds.

This course focuses on population genetics models, SNPs and haplotypes analysis, and disease associations. It presents the state of the art of the research area after the HapMap Project. The following topics will be covered: basic models of population genetics, linkage disequilibrium (LD), LD measures, LD theory and genetic determinants of disease, empirical state of LD patterns across populations, SNP challenges to genome assembly, haplotype blocks, and block-free methods, haplotype phasing (expectation maximization (EM) algorithms, Clark algorithm, parsimony algorithms, Bayesian methods, perfect phylogeny algorithms), proofs of NP-completeness for the haplotype phasing problem (EM, parsimony, Clark-type parsimony), SNP selection and the minimum informative subset, hypothesis testing and associations, disease associations tests of significance, Sir R.A. Fisher and the likelihood, genome-wide association studies for: cardiovascular disease, diabetes, and cancer, uses and misuses of tests of statistical significance, sample size and power calculations, haplotypes in association analysis, common disease common variant hypothesis, coalescent theory and the ancestor recombination graph problem.