David Golan is a PhD candidate in the department of statistics at Tel-Aviv University under the supervision of Prof. Saharon Rosset.
His research spans a wide range of problems in genetics and bioinformatics, ranging from modeling and analysis of deep sequencing data to population genetics problems such as heritability estimation using GWAS data. David is a Colton fellow at Tel-Aviv University and a fellow of the Edmund J. Safra Center for Bioinformatics.
Linear mixed effects models (LMMs) have recently gained popularity as the method of choice for estimating heritability from GWAS data. Recent results using LMMs suggest that much of the “missing” heritability can be found in common SNPs with small effects which are unidentified by current-day GWAS due to low power.
However, many of the interesting diseases and disorders studied are rare (typically affecting <1% of the population), and so case-control designs are used, wherein the proportion of cases in a study is usually considerably higher than the proportion of cases in the population.
We show that this over-representation of cases invalidates several key assumptions of LMMs, e.g. the normality and independence of the random effects, and show that ignoring these problems results in shrunken estimates of heritability.
We propose an alternative approach for estimating heritability. We derive the relationship between the genetic similarity and the phenotypic similarity of any two individuals as a function of the heritability, while explicitly conditioning on the fact that both individuals were selected for the study. Our method then entials regressing the pairwise phenotypic similarities on the pairwise genetic similarities and using the slope to obtain an estimate of the heritability. We show, using simulations, that our method yields unbiased estimates which are considerably more accurate than the current state-of-the-art methodology.
Applying our method to several well-studied GWAS yields heritability estimates which are considerably higher than previously published results.
Wednesday Feb 5th, 2014
12:45 PM Lunch: sign up sheet here.
1:15 PM Seminar starts.
Location: Clark Center S360
Host: Jonathan Pritchard
Schedule: Tara Trim (ttrim at stanford.edu)