[Colloquium] TTIC Talks: Or Zuk, Broad Institute

[Liv Leader]

When:     Friday, May 4 @ 11 a.m.

Where:    TTIC, 6045 S. Kenwood Avenue, Room #526

Who:       Or Zuk, Broad Institute

Title:        Missing Heritability: New algorithmic and statistical
approaches

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.

Host: Jinbo Xu, j3xu at ttic.edu


-- 
Liv Leader
Human Resources Coordinator

Toyota Technological Institute Chicago
6045 S Kenwood Ave
Chicago, IL 60637
Phone- (773) 702-5033
Fax-     (773) 834-9881
Email-  lleader at ttic.edu <jam at ttic.edu>
Web-   www.ttic.edu
<http://www.ttic.edu/>
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