Abstract
Genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. We describe UK Biobank GWAS results for three molecular traits-urate, IGF-1, and testosterone-with better-understood biology than most other complex traits. We find that many of the most significant hits are readily interpretable. We observe huge enrichment of associations near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of each trait, including differences in testosterone regulation between females and males. At the same time, even these molecular traits are highly polygenic, with many thousands of variants spread across the genome contributing to trait variance. In summary, for these three molecular traits we identify strong enrichment of signal in putative core gene sets, even while most of the SNP-based heritability is driven by a massively polygenic background.
Highlights
One of the central goals of genetics is to understand how genetic variation, environmental factors, and other sources of variation, map into phenotypes
We examined the genetic basis of three molecular traits measured in blood serum: a metabolic byproduct, a signaling protein (IGF-1), and a steroid hormone
Other aspects of the data are reminiscent of patterns for complex common diseases, including high polygenicity, little indication of allelic dominance or epistasis, and enrichment of signals in tissue-specific regulatory elements spread across the genome
Summary
One of the central goals of genetics is to understand how genetic variation, environmental factors, and other sources of variation, map into phenotypes. The nature of the genotype-to-phenotype mapping has been a key motivating question ever since the start of modern genetics in the early 1900s. In those early days, the genetic basis of phenotypic variation was debated between the Mendelians, who were interested in discrete monogenic phenotypes, and the biometricians, who believed that Mendelian genetics were incompatible with the continuous distributions observed for height and many other traits. This type of model is referred to as the ‘infinitesimal model’, and it laid the foundations for the growth of quantitative genetics in the 20th century (Lynch and Walsh, 1998)
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