Tutorial: Genetic analysis of complex traits

Jing Hua Zhao, MRC Epidemiology Unit, Cambridge, United Kingdom

Abstract and outline

A large quantity of genetic data is currently available to investigate their association with complex traits such as common human diseases and other quantitative measurements with contributions of many genes having small effects. There have been extensive international collaborations and vigorous developments in appropriate statistical methods and computational tools for this investigation.

The tutorial begins with an outline of genetic dissection of complex traits with focus on design and analysis in genetic association studies, particularly genome-wide association studies (GWAS) which involve large numbers of individuals and GeneChip data containing ~1,000,000 or more single nucleotide polymorphisms, the most abundant genetic variants in the human genome. Topics range from quality controls and descriptive analysis to assessing genotype-phenotype relationships and inference of pathways. Genotype imputation, metaanalysis and graphical presentation will be illustrated with case studies involving population-based and family-based samples and use of data in publicly available projects, such as HapMap, 1000 genomes and dbGaP. They will enable the participants to become familiar with the computational and statistical problems, and with popular specialized software and relevant packages in R, e.g., genetics, haplo.stats, gap, kinship, and with packages designed for GWAS such as GenABEL, snpMatrix, SNPassoc, and NCBI2R.

The tutorial is based on considerable theoretical and practical work in statistical genetics and genetic epidemiology, especially design and analysis for GWAS in several large epidemiological cohorts and contributions to a variety of consortia. It incorporates materials from a series of presentations including two previous useR! tutorials (in 2008 and 2009).

Potential attendees

The tutorial will be appropriate for researchers with basic knowledge in statistics and computing who wish to get involved with genetic data analysis in humans while generating interest to researchers in plant and animal sciences. It will also be useful to professionals and researchers actively engaged in analysis of genetic data and/or development of computational tools in environments such as R. Those who wish to learn how to fit mixed-effects models using lme4 will also be interested.


Tutorial Materials

Slides are here.