From Biostat
Rasmus Nielsen
Thursday, 1/31 16:00
Location: 177 Stanley
Title: Detecting interactions in association mapping studies
and the prospects of using evolutionary inferences to
inform such studies
Abstract:
For most common diseases with heritable components, not a single or a few
single-nucleotide polymorphisms (SNPs) explain most of the variance for
these disorders. Instead, much of the variance may be caused by interactions
(epistasis) among multiple SNPs or interactions with environmental conditions.
I will discuss a new statistical model for analyzing and interpreting genomic
data that influence multifactorial phenotypic traits with a complex and likely
polygenic inheritance. The new method is based on Markov chain Monte Carlo
(MCMC) and allows for identification of sets of SNPs and environmental factors
that when combined increase disease risk or change the distribution of a
quantitative trait. Using simulations, we show that the MCMC method can detect
disease association when multiple, interacting SNPs are present in the data.
When applying the method on real large-scale data from a Danish population-based
cohort, multiple interactions are identified that severely affect serum triglyceride
levels in the study individuals. The method is designed for quantitative traits
but can also be applied on qualitative traits. It is computationally feasible
even for a large number of possible interactions and differs fundamentally
from most previous approaches by entertaining nonlinear interactions and by
directly addressing the multiple-testing problem. I will also discuss an
approach for using evolutionary information to inform association mapping
studies. We have developed a Bayesian approach which combines structural,
population genetic and comparative genomic data to quantify the probability
that a particular mutation is deleterious. We validate the method on real
data and show that has better frequentist properties than previous method
for predicting the fitness effects of mutations.
Jeremy Sanford
Wednesday 2/06 16:00
Location: 101 LSA
Title: Cracking the splicing code: Genomic analysis of RNA binding protein
target specificity
Molly Przeworski
Wednesday 3/05 16:00
Location: 101 LSA
Title: An evolutionary perspective on human recombination
John Quackenbush
Thursday 4/24 16:00
Location: 1011 Evans
Title: TBA
Abstract: TBA
