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The omnigenic model posits that genetic risk for traits with complex heritability involves cumulative effects of peripheral genes on mechanistic "core genes," suggesting that in a network of genes, those closer to clusters including core genes should have higher GWAS signals. In gene co-expression networks, we confirmed that GWAS signals accumulate in genes more connected to risk-enriched gene clusters, highlighting across-network risk convergence. This was strongest in adult psychiatric disorders, especially schizophrenia (SCZ), spanning 70% of network genes, suggestive of super-polygenic architecture. In snRNA-seq cell type networks, SCZ risk convergence was strongest in L2/L3 excitatory neurons. We prioritized genes most connected to SCZ-GWAS genes, which showed robust association to a CRISPRa measure of PGC3 regulation and were consistently identified across several brain regions. Several genes, including dopamine-associated ones, were prioritized specifically in the striatum. This strategy thus retrieves current drug targets and can be used to prioritize other potential drug targets. • We test risk gradients in gene co-expression networks based on the omnigenic model • Across psychiatric disorders, schizophrenia risk best fits the model's predictions • Findings hold in networks from excitatory neurons and in iPSC-derived neurons • Prioritizing genes most connected to schizophrenia risk reveals potential targets Genetic risk for schizophrenia spans the entire genome. Borcuk et al. show that network neighbors of risk genes also harbor risk, most evidently in excitatory neurons. They leverage this property to propose potential risk genes that are "guilty by association." Stem cell data support the link between these genes and schizophrenia risk. [ABSTRACT FROM AUTHOR]

The continued expansion of the world population, increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding. Fortunately, the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars. Many genes have been described in papers as being beneficial for yield increase. However, few of them have been translated into increased yield on farms. In contrast, commercial breeders are facing gene decidophobia, i.e., puzzled about which gene to choose for breeding among the many identified, a huge chasm between gene discovery and cultivar innovation. The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation. The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds, experimental validation in relevant environments, appropriate crop management, and data reusability. The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security.

Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single‐locus genome‐wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this "missing heritability" phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single‐locus GWA methods). Consistent with a polygenic architecture, our single‐locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher‐order community‐ and ecosystem‐level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel. [ABSTRACT FROM AUTHOR]

Abstract Intraspecific genetic variation in foundation species such as aspen (Populus tremuloides Michx.) shapes their impact on forest structure and function. Identifying genes underlying ecologically important traits is key to understanding that impact. Previous studies, using single‐locus genome‐wide association (GWA) analyses to identify candidate genes, have identified fewer genes than anticipated for highly heritable quantitative traits. Mounting evidence suggests that polygenic control of quantitative traits is largely responsible for this “missing heritability” phenomenon. Our research characterized the genetic architecture of 30 ecologically important traits using a common garden of aspen through genomic and transcriptomic analyses. A multilocus association model revealed that most traits displayed a highly polygenic architecture, with most variation explained by loci with small effects (likely below the detection levels of single‐locus GWA methods). Consistent with a polygenic architecture, our single‐locus GWA analyses found only 38 significant SNPs in 22 genes across 15 traits. Next, we used differential expression analysis on a subset of aspen genets with divergent concentrations of salicinoid phenolic glycosides (key defense traits). This complementary method to traditional GWA discovered 1243 differentially expressed genes for a polygenic trait. Soft clustering analysis revealed three gene clusters (241 candidate genes) involved in secondary metabolite biosynthesis and regulation. Our work reveals that ecologically important traits governing higher‐order community‐ and ecosystem‐level attributes of a foundation forest tree species have complex underlying genetic structures and will require methods beyond traditional GWA analyses to unravel.

The development of severe COVID-19, which is a complex multisystem disease, is thought to be associated with many genes whose action is modulated by numerous environmental and genetic factors. In this study, we focused on the ideas of the omnigenic model of heritability of complex traits, which assumes that a small number of core genes and a large pool of peripheral genes expressed in disease-relevant tissues contribute to the genetics of complex traits through interconnected networks. We hypothesized that primary immunodeficiency disease (PID) genes may be considered as core genes in severe COVID-19, and their functional partners (FPs) from protein–protein interaction networks may be considered as peripheral near-core genes. We used whole-exome sequencing data from patients aged ≤ 45 years with severe (n = 9) and non-severe COVID-19 (n = 11), and assessed the cumulative contribution of rare high-impact variants to disease severity. In patients with severe COVID-19, an excess of rare high-impact variants was observed at the whole-exome level, but maximal association signals were detected for PID + FP gene subsets among the genes intolerant to LoF variants, haploinsufficient and essential. Our exploratory study may serve as a model for new directions in the research of host genetics in severe COVID-19. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rare variants affecting host defense against pathogens may be involved in COVID- 19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies. [ABSTRACT FROM AUTHOR]

Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.

Underlying variation in height are regulatory changes to chondrocytes, cartilage cells comprising long-bone growth plates. Currently, we lack knowledge on epigenetic regulation and gene expression of chondrocytes sampled across the human skeleton, and therefore we cannot understand basic regulatory mechanisms controlling height biology. We first rectify this issue by generating extensive epigenetic and transcriptomic maps from chondrocytes sampled from different growth plates across developing human skeletons, discovering novel regulatory networks shaping human bone/joint development. Next, using these maps in tandem with height genome-wide association study (GWAS) signals, we disentangle the regulatory impacts that skeletal element-specific versus global-acting variants have on skeletal growth, revealing the prime importance of regulatory pleiotropy in controlling height variation. Finally, as height is highly heritable, and thus often the test case for complex-trait genetics, we leverage these datasets within a testable omnigenic model framework to discover novel chondrocyte developmental modules and peripheral-acting factors shaping height biology and skeletal growth., Competing Interests: Declaration of interests E.B. is a recent employee of Empirico Inc, a position that was obtained after his contributions were made to the paper. J.H. has equity in Camp4 Therapeutics., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Despite abundant genomic and phenotypic data across individuals and environments, the functional impact of most mutations on phenotype remains unclear. Here, we bridge this gap by linking genome to proteome in 800 meiotic progeny from an intercross between two closely related Saccharomyces cerevisiae isolates adapted to distinct niches. Modest genetic distance between the parents generated remarkable proteomic diversity that was amplified in the progeny and captured by 6,476 genotype-protein associations, over 1,600 of which we resolved to single variants. Proteomic adaptation emerged through the combined action of numerous cis - and trans -regulatory mutations, a regulatory architecture that was conserved across the species. Notably, trans -regulatory variants often arose in proteins not traditionally associated with gene regulation, such as enzymes. Moreover, the proteomic consequences of mutations predicted fitness under various stresses. Our study demonstrates that the collective action of natural genetic variants drives dramatic proteome diversification, with molecular consequences that forecast phenotypic outcomes., Competing Interests: Declaration of Interests M. Ralser is founder and shareholder of Eliptica Ltd. The other authors declare no competing interests.

The development of severe COVID-19, which is a complex multisystem disease, is thought to be associated with many genes whose action is modulated by numerous environmental and genetic factors. In this study, we focused on the ideas of the omnigenic model of heritability of complex traits, which assumes that a small number of core genes and a large pool of peripheral genes expressed in disease-relevant tissues contribute to the genetics of complex traits through interconnected networks. We hypothesized that primary immunodeficiency disease (PID) genes may be considered as core genes in severe COVID-19, and their functional partners (FPs) from protein–protein interaction networks may be considered as peripheral near-core genes. We used whole-exome sequencing data from patients aged ≤ 45 years with severe (n = 9) and non-severe COVID-19 (n = 11), and assessed the cumulative contribution of rare high-impact variants to disease severity. In patients with severe COVID-19, an excess of rare high-impact variants was observed at the whole-exome level, but maximal association signals were detected for PID + FP gene subsets among the genes intolerant to LoF variants, haploinsufficient and essential. Our exploratory study may serve as a model for new directions in the research of host genetics in severe COVID-19.

This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neural networks' capabilities to efficiently handle genetic interactions. A locally connected network initially routes input data from variants to their corresponding genes. The second step employs an Encoder-Decoder to capture relationships among identified genes. The final model integrates knowledge from the first two and incorporates a parallel component to consider the effects of additional genes. This expansion enhances prediction scores by considering a larger number of genes. Trained models achieved an average AUC of 0.83, surpassing other genotype-trained models and matching gene expression dataset-based approaches. Additionally, tests on held-out sets reported an average sensitivity of 0.72 and an accuracy of 0.76, aligning with schizophrenia heritability predictions. Moreover, the study addresses genetic heterogeneity challenges by considering diverse population subsets. [Display omitted] • Novel deep model to predict schizophrenia (SCZ) phenotype solely from genotypes. • Deep model is trained on multiple sub-cohorts, addressing genetic heterogeneity. • Inspired on the omnigenic model, the method prioritizes SCZ-related pathways. • The model yields better prediction performance for SCZ than the literature. [ABSTRACT FROM AUTHOR]

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks. [ABSTRACT FROM AUTHOR]

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.

Human induced environmental change may require rapid adaptation of plant populations and crops, but the genomic basis of environmental adaptation remain poorly understood. We analysed polymorphic loci from the perennial crop Medicago sativa (alfalfa or lucerne) and the annual legume model species M. truncatula to search for a common set of candidate genes that might contribute to adaptation to abiotic stress in both annual and perennial Medicago species. We identified a set of candidate genes of adaptation associated with environmental gradients along the distribution of the two Medicago species. Candidate genes for each species were detected in homologous genomic linkage blocks using genome‐environment (GEA) and genome‐phenotype association analyses. Hundreds of GEA candidate genes were species‐specific, of these, 13.4% (M. sativa) and 24% (M. truncatula) were also significantly associated with phenotypic traits. A set of 168 GEA candidates were shared by both species, which was 25.4% more than expected by chance. When combined, they explained a high proportion of variance for certain phenotypic traits associated with adaptation. Genes with highly conserved functions dominated among the shared candidates and were enriched in gene ontology terms that have shown to play a central role in drought avoidance and tolerance mechanisms by means of cellular shape modifications and other functions associated with cell homeostasis. Our results point to the existence of a molecular basis of adaptation to abiotic stress in Medicago determined by highly conserved genes and gene functions. We discuss these results in light of the recently proposed omnigenic model of complex traits. [ABSTRACT FROM AUTHOR]

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait's realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes' regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions. [ABSTRACT FROM AUTHOR]

The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms. [ABSTRACT FROM AUTHOR]

How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks. [Display omitted] • Complex traits are controlled by all genes of the organism and its environment • Nonlinear interactions of genes and the environment are key for biological robustness • We propose a model for reconstructing multilayer networks driving trait variation • The model unveils directed and signed epistasis for genotype-environment interplay Wang et al. develop an interdisciplinary model for reconstructing multilayer genetic networks involving all loci genotyped in a GWAS. Such omnigenic networks of bidirectional, signed, and weighted interactions can extract the unknown genetic mechanisms of G-E interactions, providing a paradigm shift from GWAS to genome-wide by environment interaction studies (GWEISs). [ABSTRACT FROM AUTHOR]

Organismal phenotypes usually have a quantitative distribution, and their genetic architecture can be studied by genome-wide association (GWA) mapping approaches. In most of such studies, it has become clear that many genes of moderate or small effects contribute to the phenotype. 1–4 Hence, the attention has turned toward the loci falling below the GWA cut-off, which may contribute to the phenotype through modifier interactions with a set of core genes, as proposed in the omnigenic model. 5 One can thus predict that both moderate effect GWA-derived candidate genes and randomly chosen genes should have a similar likelihood to affect a given phenotype when they are analyzed via gene disruption assays. We have tested this hypothesis by using an automated phenotyping system for Drosophila pupal phenotypes. 6,7 We first identified candidate genes for pupal length in a GWA based on the Drosophila Genetic Reference Panel (DGRP) 8,9 and showed that most of these candidate genes are indeed involved in the phenotype. We then randomly chose genes below a GWA significance threshold and found that three-quarters of them had also an effect on the trait with comparable effect sizes as the GWA candidate genes. We further tested the effects of these knockout lines on an independent behavioral pupal trait (pupation site choice) and found that a similar fraction had a significant effect as well. Our data thus confirm the implication that a large number of genes can influence independent quantitative traits. • GWA analysis identifies genes associating with Drosophila pupal case length • Randomly chosen genes can similarly affect pupal case length as those from GWA • A large number of genes can influence independent quantitative pupal traits • Current strategies for genetic confirmation of GWA hits need to be revisited Zhang et al. show for a standard GWA mapping approach in Drosophila that not only the GWA candidate genes can be confirmed by testing knockouts in isogenic lines, but also a set of randomly chosen genes can affect the phenotype. This supports an implication of the omnigenic model that most genes contribute to most quantitative trait phenotypes. [ABSTRACT FROM AUTHOR]

Early genome-wide association studies (GWASs) led to the surprising discovery that, for typical complex traits, most of the heritability is due to huge numbers of common variants with tiny effect sizes. Previously, we argued that new models are needed to understand these patterns. Here, we provide a formal model in which genetic contributions to complex traits are partitioned into direct effects from core genes and indirect effects from peripheral genes acting in trans. We propose that most heritability is driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral genes to impact the expression of core genes. In particular, if the core genes for a trait tend to be co-regulated, then the effects of peripheral variation can be amplified such that nearly all of the genetic variance is driven by weak trans effects. Thus, our model proposes a framework for understanding key features of the architecture of complex traits., (Copyright © 2019 Elsevier Inc. All rights reserved.)