STATISTICS IN HUMAN GENETICS

GENETICS/MDGENET 565

Principles, problems, and methods of modern human genetics. Focuses on how researchers discover the genetics of diseases and how those discoveries are used to improve clinical practice. Surveys aspects of (i) the molecular function of the human genome, (ii) the basic principles of human genetics including statistical genetics, quantitative genetics, and genomic variation in human populations, (iii) the genetics of rare disorders and common diseases, and genomic analysis approaches, including genome-wide association studies and sequencing, and (iv) how genetics are used in medicine and discussions covering ethical considerations of human genomic data.

BMI/STAT 541

Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic.

BMI 826

Covers advanced topics in the areas of biostatistics and biomedical informatics. Includes reading and discussion of original literature and individual student projects.

BMI/STAT 877

Statistical and computational methods in statistical genomics for human and experimental populations. Review methods for quality control, experimental design, clustering, network analysis, and other downstream analysis of next-generation sequencing studies along with methods for genome wide association studies.

GENETICS 807

Combines review of key statistical concepts needed for genetics research with discussions of contemporary debates and common pitfalls in the application of quantitative methods specific to genetics research. Use statistical software to explore and apply statistics to genetic inference, with an emphasis on computationally intensive methods, including group coding exercises and case studies of genetics problems. Engage with contemporary ideas around study design, transparency, rigor and reproducibility in genetics research.