Online Program

Return to main conference page
Friday, October 4
Fri, Oct 4, 2:30 PM - 4:00 PM
Evergreen A
Concurrent Session - Innovations in Big Data

Withdrawn - Small and Large Sample Bias of Genomic Heritability Estimation: An Assessment Through Big Data (306576)

Gustavo de los Campos, Michigan State University 
Tapabrata Maiti, Michigan State University 
*Raka Mandal, Michigan State University 

Keywords: Big Data, Likelihood Estimation, UKBiobank, Heritability

In genetics, the trait heritability represents the proportion of variance of a phenotype that can be explained by genetic factors. Recently, there has been an increased interest on the estimation of genomic heritability, that is the proportion of variance of a trait or in disease risk that can be explained by regression on large sets of molecular markers (e.g., SNPs).The debate about the methodology has been largely based on results from simulation studies which can produce, depending on the simulation settings, from nearly unbiased to seriously biased estimators. The recent availability of very large biomedical datasets present numerous opportunities for assessing the sampling properties of REML estimates. In this study we use real data from UKBiobank to investigate the effects of sample size and model complexity on estimates of genomic heritability using human height as an example trait. We use recursive partitioning of the training data and show that the average estimator of the genomic heritability decreases with sample size. Our conclusion being that the popular REML estimates of genomic heritability can be seriously biased.