Testing Implications of the Omnigenic Model for the Genetic Analysis of Loci Identified through Genome-wide Association

Rapid response of fly populations to gene dosage across development and generations

Although the effects of genetic and environmental perturbations on multicellular organisms are rarely restricted to single phenotypic layers, our current understanding of how developmental programs react to these challenges remains limited. Here, we have examined the phenotypic consequences of disturbing the bicoid regulatory network in early Drosophila embryos. We generated flies with two extra copies of bicoid, which causes a posterior shift of the network's regulatory outputs and a decrease in fitness. We subjected these flies to EMS mutagenesis, followed by experimental evolution. After only 8-15 generations, experimental populations have normalized patterns of gene expression and increased survival. Using a phenomics approach, we find that populations were normalized through rapid increases in embryo size driven by maternal changes in metabolism and ovariole development. We extend our results to additional populations of flies, demonstrating predictability. Together, our results necessitate a broader view of regulatory network evolution at the systems level.

Unraveling the Molecular Basis of Stabilizing Selection by Experimental Evolution

Stabilizing selection provides a challenge to molecular population genetics. Although stabilizing selection is ubiquitous, its genomic signature is difficult to distinguish from demographic signals. Experimental evolution provides a promising approach to characterize genomic regions exposed to stabilizing selection. A recent experimental evolution study of Aedes aegypti populations evolving either with or without sexual selection found a pattern of genetic differentiation suggestive of relaxed stabilizing selection. I argue that this study could not have detected the signal of relaxed stabilizing selection. I highlight why incorrect statistical methods resulted in a high number of false positive candidate single nucleotide polymorphism (SNPs) and discuss the fallacy of functional validation of candidate SNPs for polygenic traits by RNA-mediated knockdown.

Implementing Core Genes and an Omnigenic Model for Behaviour Traits Prediction in Genomics

A high number of genome variants are associated with complex traits, mainly due to genome-wide association studies (GWAS). Using polygenic risk scores (PRSs) is a widely accepted method for calculating an individual's complex trait prognosis using such data. Unlike monogenic traits, the practical implementation of complex traits by applying this method still falls behind. Calculating PRSs from all GWAS data has limited practical usability in behaviour traits due to statistical noise and the small effect size from a high number of genome variants involved. From a behaviour traits perspective, complex traits are explored using the concept of core genes from an omnigenic model, aiming to employ a simplified calculation version. Simplification may reduce the accuracy compared to a complete PRS encompassing all trait-associated variants. Integrating genome data with datasets from various disciplines, such as IT and psychology, could lead to better complex trait prediction. This review elucidates the significance of clear biological pathways in understanding behaviour traits. Specifically, it highlights the essential role of genes related to hormones, enzymes, and neurotransmitters as robust core genes in shaping these traits. Significant variations in core genes are prominently observed in behaviour traits such as stress response, impulsivity, and substance use.

Genetic architecture of natural variation of cardiac performance from flies to humans

Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP). Genome-wide associations studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were used to gain insights as to the molecular and cellular processes affected. Non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict transcription factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart-specific knockdown. Additionally, we showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability was also associated with natural variation of gene regulatory networks. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans.

Bacterial Metabolism and Transport Genes Are Associated with the Preference of Drosophila melanogaster for Dietary Yeast

Many animal traits are influenced by their associated microorganisms ("microbiota"). To expand our understanding of the relationship between microbial genotype and host phenotype, we report an analysis of the influence of the microbiota on the dietary preference of the fruit fly Drosophila melanogaster. First, we confirmed through experiments on flies reared bacteria-free ("axenic") or in monoassociation with two different strains of bacteria that the microbiota significantly influences fruit fly dietary preference across a range of ratios of dietary yeast:dietary glucose. Then, focusing on microbiota-dependent changes in fly dietary preference for yeast (DPY), we performed a metagenome-wide association (MGWA) study to define microbial species specificity for this trait and to predict bacterial genes that influence it. In a subsequent mutant analysis, we confirmed that disrupting a subset of the MGWA-predicted genes influences fly DPY, including for genes involved in thiamine biosynthesis and glucose transport. Follow-up tests revealed that the bacterial influence on fly DPY did not depend on bacterial modification of the glucose or protein content of the fly diet, suggesting that the bacteria mediate their effects independent of the fly diet or through more specific dietary changes than broad ratios of protein and glucose. Together, these findings provide additional insight into bacterial determinants of host nutrition and behavior by revealing specific genetic disruptions that influence D. melanogaster DPY. IMPORTANCE Associated microorganisms ("microbiota") impact the physiology and behavior of their hosts, and defining the mechanisms underlying these interactions is a major gap in the field of host-microbe interactions. This study expands our understanding of how the microbiota can influence dietary preference for yeast (DPY) of a model host, Drosophila melanogaster. First, we show that fly preferences for a range of different dietary yeast:dietary glucose ratios vary significantly with the identity of the microbes that colonize the fruit flies. We then performed a metagenome-wide association study to identify candidate bacterial genes that contributed to some of these bacterial influences. We confirmed that disrupting some of the predicted genes, including genes involved in glucose transport and thiamine biosynthesis, resulted in changes to fly DPY and show that the influence of two of these genes is not through changes in dietary ratios of protein to glucose. Together, these efforts expand our understanding of the bacterial genetic influences on a feeding behavior of a model animal host.

Using singleton densities to detect recent selection in Bos taurus

Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the "Singleton Density Score" (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans-milk protein content, milk fat content, and stature-have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies.

Genome-environment association methods comparison supports omnigenic adaptation to ecological niche in malaria vector mosquitoes

The concept of a fundamental ecological niche is central to questions of geographic distribution, population demography, species conservation, and evolutionary potential. However, robust inference of genomic regions associated with evolutionary adaptation to particular environmental conditions remains difficult due to the myriad of potential confounding processes that can generate heterogeneous patterns of variation across the genome. Here, we interrogate the potential role of genome environment association (GEA) testing as an initial step in building an understanding of the genetic basis of ecological niche. We leverage publicly available genomic data from the Anopheles gambiae 1000 Genomes (Ag1000g) Consortium to test the ability of multiple analytically unique GEA methods to handle confounding patterns of genetic variation, control false positive rates, and discern associations with broadly relevant climate variables from random allele frequency patterns throughout the genome. We found evidence supporting the ability of commonly implemented GEA methods to account for confounding patterns of spatial and genetic variation, and control false positive rates. However, we fail to find evidence supporting the ability of GEA tests to reject signals of adaptation to randomly simulated environmental variables, indicating that discerning between true signals of genome environment adaptation and genome environment correlations resulting from alternative evolutionary processes, remains challenging. Because signals of environmental adaptation are so diffuse and confounded throughout the genome, we argue that genomic adaptation to ecological niche is likely best understood under an omnigenic model wherein highly interconnected, genome-wide gene regulatory networks shape genomic adaptation to key environmental conditions.

Population Genetics: How Many Variable Genes Affect Variable Traits?

How many genes control a given trait? And are genes with defined knockout phenotypes affecting a given trait the same genes that also underlie population-wide variation in that trait? A new study in Drosophila melanogaster has some surprising answers.