Linking common and rare disease genetics through gene regulatory networks

Abstract

Genetic variants identified through genome-wide association studies (GWAS) are typically non-coding and exert small regulatory effects on downstream genes, but which downstream genes are ultimately impacted and how they confer risk remains mostly unclear. Conversely, variants that cause rare Mendelian diseases are often coding and have a more direct impact on disease development. We demonstrate that common and rare genetic diseases can be linked by studying the gene regulatory networks impacted by common disease-associated variants. We implemented this in the 'Downstreamer' method and applied it to 44 GWAS traits and find that predicted downstream "key genes" are enriched with Mendelian disease genes, e.g. key genes for height are enriched for genes that cause skeletal abnormalities and Ehlers-Danlos syndromes. We find that 82% of these key genes are located outside of GWAS loci, suggesting that they result from complex trans regulation rather than being impacted by disease-associated variants in cis. Finally, we discuss the challenges in reconstructing gene regulatory networks and provide a roadmap to improve identification of these highly connected genes for common traits and diseases.

Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

“…31-33) using a similar approach to our previously developed multi-tissue GeneNetwork (n = 31,499) 54,55 . We applied Downstreamer 56 to SCZ 57 , PD 58 , MS 29 , AD 59 and amyotrophic lateral sclerosis (ALS) GWAS summary statistics 60 , using these networks to prioritize genes that are co-regulated with genes in their GWAS loci (Supplementary Note, Supplementary Fig. 34 and Supplementary Tables 25-30).…”

An efficient not-only-linear correlation coefficient based on machine learning

“…Recent theories, like the omnigenic model for complex traits [16,17], argue that these observations are explained by highly-interconnected gene regulatory networks, with some core genes having a more direct effect on the phenotype than others. Using this omnigenic perspective, we and others [19,20,23] have shown that integrating gene co-expression networks in genetic studies could potentially identify core genes that are missed by linear-only models alone like GWAS. Our results suggest that building these networks with more advanced and efficient correlation coefficients could better estimate gene co-expression profiles and thus more accurately identify these core genes.…”

“…The analysis of large RNA-seq datasets [10,11] can also reveal complex transcriptional mechanisms underlying human diseases [2,12,13,14,15]. Since the introduction of the omnigenic model of complex traits [16,17], gene-gene relationships are playing an increasingly important role in genetic studies of human diseases [18,19,20,21], even in specific fields such as polygenic risk scores [22]. In this context, recent approaches combine disease-associated genes from genome-wide association studies (GWAS) with gene co-expression networks to prioritize “core” genes directly affecting diseases [19,20,23].…”

“…Since the introduction of the omnigenic model of complex traits [16,17], gene-gene relationships are playing an increasingly important role in genetic studies of human diseases [18,19,20,21], even in specific fields such as polygenic risk scores [22]. In this context, recent approaches combine disease-associated genes from genome-wide association studies (GWAS) with gene co-expression networks to prioritize “core” genes directly affecting diseases [19,20,23]. These core genes are not captured by standard statistical methods but are believed to be part of highly-interconnected, disease-relevant regulatory networks.…”

RNetDys: identification of disease-related impaired regulatory interactions due to SNPs

“…Leveraging multi-OMICS data to construct and exploit the regulatory landscape in order to gather additional mechanistic insights would significantly contribute to a better understanding of the impact of disease-related SNPs on gene regulation and disease development. Notably, GRNs have been widely used to gain insights into diseases (Emmert-Streib et al, 2014; Ament et al, 2018; Bakker et al, 2021) but the characterization of underlying regulatory mechanisms dysregulated due to SNPs and the cell (sub)types specifically impaired remains elusive. The resolution of cell (sub)type specific regulatory mechanisms impaired due to SNPs in disease would provide additional mechanistic insights and pave the way towards the development of gene-based therapies for disease prevention and treatment (Uddin et al, 2020).…”