A central goal of genetics is to understand the links between genetic variation and disease. Intuitively, one might expect disease-causing variants to cluster into key pathways that drive disease etiology. But for complex traits, association signals tend to be spread across most of the genome—including near many genes without an obvious connection to disease. We propose that gene regulatory networks are sufficiently interconnected such that all genes expressed in disease-relevant cells are liable to affect the functions of core disease-related genes and that most heritability can be explained by effects on genes outside core pathways. We refer to this hypothesis as an “omnigenic” model.

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The longest-standing question in genetics is to understand how genetic variation contributes to phenotypic variation. In the early 1900s, there was fierce debate between the Mendelians—who were inspired by Mendel’s work on pea genetics and focused on discrete, monogenic phenotypes—and the biometricians, who were interested in the inheritance of continuous traits such as height. The biometricians believed that Mendelian genetics could not explain the continuous distribution of variation observed for many traits in humans and other species.

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et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Despite the success of the infinitesimal model in describing inheritance patterns, especially in plant and animal breeding, it was unclear throughout the 20th century how many genes would actually be important for driving complex traits. Indeed, human geneticists expected that even complex traits would be driven by a handful of moderate-effect loci—thus giving rise to large numbers of mapping studies that were, in retrospect, greatly underpowered. For example, an elegant 1999 analysis of allele sharing in autistic siblings concluded from the lack of significant hits that there must be “a large number of loci (perhaps ≥15).” This prediction was strikingly high at the time but seems quaintly low now ().

But despite the success of these earlier studies, we argue that the enrichment of signal in relevant genes is surprisingly weak overall, suggesting that prevailing conceptual models for complex diseases are incomplete. We highlight some pertinent features of current data and discuss what these may tell us about the genetic architecture of complex diseases.

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Defining the role of common variation in the genomic and biological architecture of adult human height. As shown above for height, GWAS signals tend to be markedly enriched in predicted gene regulatory elements. In particular, many groups have shown that disease-associated SNPs are enriched in active chromatin and particularly in chromatin that is active in cell types relevant to disease (). Similarly, signals also aggregate near genes that are expressed in relevant cell types (). An intuitive interpretation is that the cell-type-based regulatory maps point us toward cell-type-specific regulatory elements that control specific functions of those cells and thereby drive disease biology. Indeed, the relevant papers often describe these analyses as highlighting “cell-type-specific” aspects of regulation. But given that the heritability signal is so widespread, we wanted to understand whether the signal is specifically concentrated in chromatin that is active in just the relevant (or related) cell types, as opposed to chromatin that is broadly active. Finucane et al., 2015 Finucane H.K.

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Partitioning heritability by functional annotation using genome-wide association summary statistics. To explore this question, we used active chromatin data measured in ten broadly defined cell-type groups (e.g., immune, central nervous system (CNS), cardiovascular, etc.). A region was considered active in a cell-type group if it was detected as active for any cell type in that group. We applied stratified LD score regression—a method that estimates how much different classes of SNPs contribute to heritability (). We focused on three well-powered GWAS studies that showed clear enrichment within a single cell-type group in a previous analysis: Crohn’s disease (immune), rheumatoid arthritis (RA, immune), and schizophrenia (CNS) (). Figure 2 Heritability Tends to Be Enriched in Regions that Are Transcriptionally Active in Relevant Tissues Show full caption (A) Contributions to heritability (relative to random SNPs) as a function of chromatin context. There is enrichment for signal among SNPs that are in chromatin active in the relevant tissue, regardless of the overall tissue breadth of activity. (B) Genes with brain-specific expression show the strongest enrichment of schizophrenia signal (left), but broadly expressed genes contribute more to total heritability due to their greater number (right). While there are strong cell-type effects, these are largely independent of the breadth of chromatin activity. For example, we observed that SNPs in chromatin that is broadly active across most cell types make substantial contributions to heritability. On average, SNPs in broadly active elements contribute roughly as much to heritability as do SNPs in cell-type-specific active chromatin (only for RA are these significantly different; Figure 2 A). Meanwhile, SNPs in chromatin that is inactive or is active only in irrelevant cell types contribute little or no heritability, thus providing an important negative control. GTEx Consortium, 2015 GTEx Consortium

Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. For an alternative viewpoint, we also considered breadth of gene expression. We estimated the contribution of SNPs in or near exons for genes with different expression profiles. Based on GTEx data, we identified genes that are particularly highly expressed in particular tissue groups, as well as broadly expressed genes (). As shown for schizophrenia ( Figure 2 B), SNPs near genes that are expressed in the brain contribute substantially to heritability, while genes that are specifically expressed in other tissues contribute little or nothing. Perhaps intuitively, SNPs near genes expressed specifically in brain contribute more heritability per SNP than SNPs near genes with broad expression profiles. However, only a modest fraction of all brain-expressed genes are specifically upregulated in brain. Hence, broadly expressed genes actually contribute more to the overall heritability than do brain-specific genes. In summary, genetic contribution to disease is heavily concentrated in regions that are transcribed or marked by active chromatin in relevant tissues, but there is little enrichment for cell-type-specific regulatory elements versus broadly actively regions. As expected, there appears to be little or no genetic contribution from regions that are inactive in these tissues. To investigate the question of GWAS specificity further, we next examined evidence for enrichment of associated genes in specific functional categories.