This course provides an introduction to the models and methods of Statistical Genetics for students with little Genetics background but with some knowledge of Probability and Statistics. The course provides a basis for further study in Statistical Genetics, whether in Quantitative Genetics, Human and Medical Genetics, Population and Evolutionary Genetics, or Computational Molecular Genetics.
The course material includes:
Mendelian segregation, meiosis and recombination.
Simple Mendelian traits; dominant and recessive traits.
Allele, genotype and haplotype frequencies.
Testing Hardy-Weinberg equilibrium, likelihood
estimation of allele frequencies,
the EM algorithm.
Population structure, linkage disequilibrium, and haplotyping.
Patterns of inheritance for simple Mendelian traits. Kinship and gene identity by descent; probabilities on pedigrees. Two-locus kinship and gene identity, Two-locus linkage analysis, the probabilities of meiosis patterns. Simple designs for two-locus linkage; testing for linkage, expected lod scores and power to detect linkage, homozygosity mapping.
Genetic map functions. Multipoint linkage analysis; the hidden Markov model for multipoint linkage. The Baum algorithms; the EM algorithm for map estimation. Likelihoods for linkage on large pedigrees: the Elston-Stewart algorithm.
Some useful books include: