Statistical Modelling and Analysis of Complex Traits in Human Populations

Systematic Inflammatory Response Syndrome (SIRS) is an inflammatory response to a variety of severe clinical conditions such as infection, shock or trauma. An example of SIRS is sepsis, a systematic inflammatory response to infection. Sepsis is the most common diagnosis and cause of death among critically ill patients from intensive care units, occurring in about 1% of all hospitalized patients. SIRS is a complex genetic disorder involving a number of genes that act in conjunction with lifestyle and environmental factors to increase an individual's risk of developing the illness. Increasingly, researchers studying complex disorders are turning to genetic markers called single nucleotide polymorphisms (or SNPs) to assist in locating susceptibility mutations. Haplotypes, the combinations of genetic variants inherited together from a parent, may affect susceptibility either directly by influencing regulation and/or function of susceptibility genes, or indirectly through associations with unobserved genetic variants that confer susceptibility. Due to the limitations of current cost-effective genotyping technology, however, haplotypes are not observed unambiguously in all subjects. The team modelled and analysed the associations between SNP haplotypes and treatment outcomes in critically-ill sepsis patients. The SNPs investigated were in four genes thought to play a role in the inflammatory process. Currently, the popular practice for haplotype analyses is to substitute a best guess for the haplotypes, and ignore the extra source of variation due to the uncertainty in these guesses. However, ignoring this extra variation can lead to potential errors in interpretation of the scientific results. This project will serve as a guide for future analyses undertaken by iCAPTURE that account for haplotype uncertainty in a statistically valid way.

Faculty Supervisor:

Dr. Jinko Graham & Brad McNeney

Student:

Zhijian Chen

Partner:

iCapture Centre

Discipline:

Statistics / Actuarial sciences

Sector:

Life sciences

University:

Simon Fraser University