Prediction and inference from population scale datasets
Thursdays 11/16 1:30-3:00 pm in MSOB x303
Population biobanks are a valuable resource for identifying genetic and environmental factors that contribute to disease. Recent advances in statistical methods and computational power have enabled the analysis of large-scale datasets from these biobanks, leading to the discovery of novel therapeutic targets and pathways. This seminar will present on the use of population biobank scale datasets for the analysis of renal, liver, and sex hormone biomarkers. In addition, I will discuss the path from statistical methodological development to target identification for glaucoma to therapeutic development using monoclonal antibodies to mimic effects of protective mutations in humans. Finally, I will present on approaches for disease risk prediction using genetics, metabolomics, and proteomics data. Together, the methods and applications presented in this talk demonstrate the value of population-scale cohorts to advance our understanding of disease and development of new treatments.
Suggested reading:
Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma, https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008682
Genetics of 35 blood and urine biomarkers, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867639/
Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, et al. (2022) Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet 18(3): e1010105. https://doi.org/10.1371/journal.pgen.1010105
Bayesian model comparison for rare-variant association studies
GR Venkataraman, C DeBoever, Y Tanigawa… – The American Journal of Human Genetics, 2021. Julia Carrasco-Zanini doi:
This weekly seminar (running during Fall, Winter and Spring quarters) doubles as a class “Workshops in Biostatistics (BIODS/STATS 260).”