Abstract:

The completion of the human genome project set a stepping stone in building
catalogs of common human genetic variation. These catalogs, in turn, enabled
the search for associations between common variants and complex human traits
and diseases, by performing Genome-Wide Association Studies (GWAS). GWAS
have been successful in discovering thousands of statistically significant,
reproducible, genotype-phenotype associations. However, the discovered
variants (genotypes) explain only a small fraction of the phenotypic
variance in the population for most human traits.  In contrast, the
heritability, defined as the proportion of phenotypic variance explained 
by all genetic factors, was estimated to be much larger for those same 
traits using indirect population-based estimators. This gap is referred to 
as 'missing heritability'.

Mathematically, heritability is defined by considering a function $F$ 
mapping a set of (Boolean) variables, $(x_1,.., x_n)$ representing genotypes, 
and additional environmental or 'noise' variables $\epsilon$, to a single (real 
or discrete) variable $z$, representing phenotype. We use the variance
decomposition of $F$, separating the linear term, corresponding to additive
(narrow-sense) heritability, and higher-order terms, representing
genetic-interactions (epistasis), to explore several explanations for 
the 'missing heritability' mystery. We show that genetic interactions can
significantly bias upwards current population-based heritability estimators,
creating a false impression of 'missing heritability'. We offer a solution
to this problem by providing a novel consistent estimator based on unrelated
individuals. We also use the Wright-Fisher process from population genetic
theory to develop and apply a novel power correction method for inferring
the relative contributions of rare and common variants to heritability.
Finally, we propose a novel algorithm for estimating the different variance
components (beyond additive) of heritability from GWAS data.

I will give the needed genetics background, and discuss the statistical
methods and algorithms used.


Short Bio:
I am currently a post-doc at the Broad Institute of MIT and Harvard,
in Eric Lander's lab. I work on computational and statistical
problems arising from genomics applications, in particular in human
genetics and comparative genomics.
I have completed my Ph.D. in Computer Science and Applied Mathematics
at the Weizmann Institute of Science under the supervision of Eytan Domany.

Refreshments served from 14:15 on,
 	Lecture starts at 14:30