Schedule

PUBH 8878, Fall ’25

Article links direct to files hosted on the Zotero group library

Week	Lecture	Readings	Assignment
1	Foundations: Mendelian genetics & statistical basics Mendel’s laws, Hardy–Weinberg equilibrium, \(\chi^2\)goodness‑of‑fit. Sampling distributions; linking population parameters to sample estimates. One‑locus likelihood: building and interpreting likelihood functions.	Sorensen Chapter 1 Edwards, A. W. F. (2008), “G. H. Hardy (1908) and Hardy–Weinberg Equilibrium,” Genetics. Introduction to Probability Theory	Problem Set 01
2	Heritability, segregation, and the gene-mapping toolkit Narrow and broad sense heritability; variance-component interpretation. Segregation analysis and modelling genetic inheritance without marker data.	Sorensen Chapter 2.1-2.2, 2.4, 2.8 Visscher, P. M., et al., (2008), “Heritability in the genomics era — concepts and misconceptions,” Nature Reviews Genetics.	Problem Set 02
3	Likelihood algorithms & practical gene mapping Newton-Raphson, EM, and stochastic gradient algorithms for complex likelihoods. Pedigree linkage analysis, LOD-score calculation, and missing-data EM steps. Single-marker and haplotype association tests with basic quality control	Sorensen, Chapter 3 Lander, E. S., and Green, P. (1987), “Construction of multilocus genetic linkage maps in humans.,” Proceedings of the National Academy of Sciences of the United States of America.	Problem Set 03
4	Population structure & Bayesian fundamentals Detecting and correcting for population stratification and admixture confounding. Priors, posteriors, and the Bayes-frequentist debate in genetic inference. Bayesian admixture/STRUCTURE-style modelling implemented in Stan.	Sorensen 4.1-4.5, 4.7-4.8, 5.1 Porras-Hurtado, L. et al., (2013), “An overview of STRUCTURE: applications, parameter settings, and supporting software,” Frontiers in Genetics. Lawson, D. J. et al., (2018), “A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots,” Nature Communications.	Problem Set 04
5	GWAS at Scale End to end GWAS workflow: sample QC, variant QC. Linear mixed models. Genomic inflation and calibration.	Loh, P.-R. et al., (2015), “Efficient Bayesian mixed model analysis increases association power in large cohorts,” Nature Genetics.
6	Prediction models in genetics	Sorensen Chapters 6, 7.1-7.2, 10.1, 10.5, 11.3-11.5 Wu, T. T., Chen, Y. F., Hastie, T., Sobel, E., and Lange, K. (2009), “Genome-wide association analysis by lasso penalized logistic regression,” Bioinformatics, 25, 714–721. https://doi.org/10.1093/bioinformatics/btp041.
7	Multiple testing & false-discovery control	Sorensen Chapter 8 Otani, T., Noma, H., Nishino, J., and Matsui, S. (2018), “Re-assessment of multiple testing strategies for more efficient genome-wide association studies,” European Journal of Human Genetics, Nature Publishing Group, 26, 1038–1048. https://doi.org/10.1038/s41431-018-0125-3.	Assignment 05
8	Binary traits	Sorensen Chapter 9 Zhou, W., Bi, W., Zhao, Z., Dey, K. K., Jagadeesh, K. A., Karczewski, K. J., Daly, M. J., Neale, B. M., and Lee, S. (2022), “SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests,” Nature Genetics, Nature Publishing Group, 54, 1466–1469. https://doi.org/10.1038/s41588-022-01178-w.
9	Causal inference (Mendelian randomization)	Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., and Davey Smith, G. (2022), “Mendelian randomization,” Nature Reviews Methods Primers, 2, 1–21. https://doi.org/10.1038/s43586-021-00092-5.	Assignment 06
10	Advanced AI Topics in Statistical Genetics: Language Models for Genomics	Recommended: Ji et al., 2021 Avsec et al., 2021 Cheng et al., 2023 Baghbanzadeh et al., 2025 Mollerus et al., 2025