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STAT 578C; Fall 1999
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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: Simple Mendelian traits; Mendelian segregation, dominant and recessive traits, X-linked traits, patterns of inheritance. Population genetic issues; testing Hardy-Weinberg equilibrium, likelihood estimation of allele frequencies, the EM algorithm, mutation, selection, random genetic drift. Kinship and gene identity by descent; probabilities on pedigrees. Genetic linkage; two-locus kinship and gene identity, allelic associations, likelihood estimation of haplotype frequencies. Meiosis and recombination; 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. Multipoint linkage analysis; the hidden Markov model for multipoint linkage, the Baum algorithms. Monte Carlo linkage likelihoods; the Baum-Welch algorithm, importance sampling.
Useful books include: