Unraveling the Genetic Architecture of Cryptic Genetic Variation

Abstract

Cryptic genetic variation (CGV), hidden under most conditions, is the repressed genetic potential that can facilitate adaptation and evolution. The conditional manifestation of CGV has been claimed to explain the background dependence of causal loci as well as missing heritability. However, despite being proposed over 60 years ago, the genetic architecture and regulation of CGV and its contribution towards regulation of complex traits remains unclear. Using linkage mapping of mean and variance effects, we have identified loci that regulate phenotypic manifestation of standing genetic variation in a previously published dataset of biparental Saccharomyces cerevisiae population grown in 34 diverse environments. Based on our results we propose the existence of a gradient of buffering states for a population determined by the environment. Most environments show a tight buffering with additive, independent causal loci with little epistasis. However, as this buffering is disrupted, the underlying highly interconnected environment-specific genetic interactome is revealed such that each causal locus is a part of this network. Interspersed within these networks are generalist capacitors that regulate CGV across multiple environments, with one allele behaving as a capacitor and the other as a potentiator. Our study demonstrates the connecting link between architecture of hidden and visible genetic variation and uncovers the genetic networks which potentially underlie all complex traits. Our study establishes CGV as a significant contributor to phenotypic variation, and provides evidence for a predictable pattern underlying gene-gene and gene-environment interactions that can explain background dependence and missing heritability in complex traits and diseases.\n\nSUMMARYThe phenotypic effects of cryptic genetic variation (CGV) are mostly hidden and manifested only under certain rare conditions and have the potential to facilitate adaptation. However, little is understood about its genetic regulation. We performed variance QTL mapping to understand the regulation of phenotypic manifestation of standing genetic variation in a biparental yeast population. We propose a model describing the connecting link between visible variation and CGV. We identify generalist capacitors and environment-specific networks that potentially underlie all phenotypes. This fresh approach of mapping causal loci can solve the long-standing mystery of missing heritability in complex traits and diseases.