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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]

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.)

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]

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., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

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.)