Journal Article

Solving the missing heritability problem

Young, Alexander I.

2019

Overview

[...]there were many common variants with relatively weak effects on height that had been missed by GWAS due to a lack of statistical power. The methodology assumes that effect sizes are normally distributed within each bin, where the variants have been divided into bins based upon their frequency and the strength of their correlations with other variants (LD). Since GREML makes inferences about the distribution of effect sizes, GREML heritability estimates can become biased when assumptions about the distribution of effect sizes are violated [17]. Family data is required to adjust for indirect genetic effects from relatives. [...]solving the problem of missing heritability for traits like educational attainment will require large samples of families with WGS data. While the correlation between the polygenic score and educational attainment suggests that it can predict around 11–13% of the variation in educational attainment, within-family analyses suggest that at least half of this predictive ability comes from indirect genetic effects from relatives, population stratification, and assortative mating [35, 39].

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Assortative mating / Bias / Biology and Life Sciences / Epigenetic inheritance / Estimates / Genetic Predisposition to Disease / Genetic research / Genome-Wide Association Study - statistics & numerical data / Genomes / Genomics / Heritability / Humans / Linkage Disequilibrium - genetics / Opinion Piece / Physical Sciences / Polygenic inheritance / Polymorphism, Single Nucleotide - genetics / Population / Population genetics / Power / Research and Analysis Methods / Studies / Twin Studies as Topic / Twins / Whole Genome Sequencing / Wills - statistics & numerical data