Genetics Workshop

Statistical Design and Analysis of Human Genetic Data

Yuehua Cui
Dept. of Statistics and Probability, Michigan State University

With the recent radical breakthrough in biotechnology, massive amounts of genetic data are produced almost with no limit. With the availability of high throughput genomewide single nucleotide polymorphism (SNP) data, we are experiencing a shift in analyzing genetic data from traditional approaches in mapping chromosome segments to new approaches centering on specific nucleotides underlying complex multifactorial disorders. Recent developments in genomewide association study have yielded impressive successes in discovery of genes involved in complex disorders such as type 2 diabetes and cardiovascular disease. Assessing disease gene association has been one of the major goals in modern biomedical research. This short course aims to introduce fundamental concepts in human genetic data analysis, basic principles and analytical approaches in association studies focusing on single SNP, haplotype- and gene-based analysis, as well as the state of art techniques in design and analysis of genomewide SNP data. The short course will be fairly introductory and will target general audiences with diverse backgrouds. The presented approaches also have great implications in analyzing genetic data collected from plants and animals.

Michigan State University | Department of Statistics and Probability | Statistical Genetics Lab