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Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. However, assessing the comparative heritability of multiple traits estimated in different cohorts may be misleading due to the population-specific nature of heritability. Here we report the SNP heritability for 551 complex traits derived from the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes, and examine the moderating effect of three major demographic variables (age, sex and socioeconomic status) on the heritability estimates. Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in comparing and interpreting heritability.
Phenomewide Heritability Analysis
We will use a novel software tool we have recently developed to compute the extent of the influence of human DNA on observable physical, clinical, and cognitive characteristics/traits (phenotype). Many of these traits are caused by genetic (heritable), environmental and life-style factors. Our primary aim is to identify those traits, where genetic factors play a significant role. This will enable us and other scientists to prioritize phenotypes for follow-up genetics studies. Our second aim will be to study the genetic overlap between phenotypes. Identifying the genetic factors that influence health-related, observable individual-level traits, such as disease diagnosis, will be critical for understanding the causal mechanisms of various clinical conditions, and developing prevention and treatment strategies. With rich phenotypic datasets such as the UK Biobank, it is going to be critical to prioritize phenotypes based on heritability. Those phenotypes which are largely determined by genetics (i.e., have large heritability) will be good candidates for further examining the underlying genetic causes. We will use a novel analytic strategy, which we recently published, to examine genome-wide marker (single nucleotide polymorphism, or SNP) data and phenotype data to examine the relationship between DNA and observable traits. We will use the full cohort.