Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

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Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

10.1101/2021.08.02.21261488 ◽

2021 ◽

Author(s):

Steven Gazal ◽

Omer Weissbrod ◽

Farhad Hormozdiari ◽

Kushal Dey ◽

Joseph Nasser ◽

...

Keyword(s):

Fine Mapping ◽

Complex Traits ◽

Target Genes ◽

Disease Risk ◽

Association Studies ◽

Common Disease ◽

Disease Genes ◽

Genome Wide Association Studies ◽

Functional Interpretation ◽

Genome Wide

Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies.

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Host Genetic Variants Potentially Associated with SARS-Cov-2: A Multi-Population Analysis

University of the Future: Re-Imagining Research and Higher Education ◽

10.29117/quarfe.2020.0298 ◽

2020 ◽

Author(s):

Maria K. Smatti ◽

Yasser Al-Sarraj ◽

Omar Albagha ◽

Hadi M. Yassine

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Population Analysis ◽

Fold Change ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Risk Alleles ◽

Risk Variants ◽

Genome Wide ◽

Ifn Γ

Background: Clinical outcomes of Coronavirus Disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) showed enormous inter-individual and interpopulation differences, possibly due to host genetics differences. Earlier studies identified single nucleotide polymorphisms (SNPs) associated with SARS-CoV-1 in Eastern Asian (EAS) populations. In this report, we aimed at exploring the frequency of a set of genetic polymorphisms that could affect SARS-CoV-2 susceptibility or severity, including those that were previously associated with SARS-CoV-1. Methods: We extracted the list of SNPs that could potentially modulate SARS-CoV-2 from the genome wide association studies (GWAS) on SARS-CoV-1 and other viruses. We also collected the expression data of these SNPs from the expression quantitative trait loci (eQTLs) databases. Sequences from Qatar Genome Programme (QGP, n=6,054) and 1000Genome project were used to calculate and compare allelic frequencies (AF). Results: A total of 74 SNPs, located in 10 genes: ICAM3, IFN-γ, CCL2, CCL5, AHSG, MBL, Furin, TMPRSS2, IL4, and CD209 promoter, were identified. Analysis of Qatari genomes revealed significantly lower AF of risk variants linked to SARS-CoV-1 severity (CCL2, MBL, CCL5, AHSG, and IL4) compared to that of 1000Genome and/or the EAS population (up to 25-fold change). Conversely, SNPs in TMPRSS2, IFN-γ, ICAM3, and Furin were more common among Qataris (average 2-fold change). Inter-population analysis showed that the distribution of risk alleles among Europeans differs substantially from Africans and EASs. Remarkably, Africans seem to carry extremely lower frequencies of SARS-CoV-1 susceptibility alleles, reaching to 32-fold decrease compared to other populations. Conclusion: Multiple genetic variants, which could potentially modulate SARS-CoV-2 infection, are significantly variable between populations, with the lowest frequency observed among Africans. Our results highlight the importance of exploring population genetics to understand and predict COVID-19 outcomes. Indeed, further studies are needed to validate these findings as well as to identify new genetic determinants linked to SARS-CoV-2.

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Association of CACNA1C polymorphisms with serum BDNF levels in bipolar disorder

The British Journal of Psychiatry ◽

10.1192/bjp.2019.173 ◽

2019 ◽

pp. 1-3

Author(s):

Erik Smedler ◽

Erik Pålsson ◽

Kenji Hashimoto ◽

Mikael Landén

Keyword(s):

Bipolar Disorder ◽

Genetic Variants ◽

Association Studies ◽

Serum Levels ◽

Genome Wide Association Studies ◽

Risk Alleles ◽

Voltage Gated Calcium Channels ◽

Genome Wide ◽

Tissue Plasminogen ◽

Serum Bdnf

Variation in the CACNA1C gene has been associated with bipolar disorder in several genome-wide association studies. This gene encodes the alpha 1C subunit of L-type voltage-gated calcium channels, which play an essential role in neurons. We analysed 39 biomarkers in either cerebrospinal fluid or serum in relation to six different CACNA1C variants in 282 patients with bipolar disorder and 90 controls. We report associations of CACNA1C risk alleles with serum levels of BDNF as well as tissue plasminogen activator, which converts pro-BDNF to mature BDNF. This sheds light on links between CACNA1C genetic variants and pathophysiological mechanisms in bipolar disorder.Declaration of interestNone.

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INFERNO - INFERring the molecular mechanisms of NOncoding genetic variants

10.1101/211599 ◽

2017 ◽

Cited By ~ 3

Author(s):

Alexandre Amlie-Wolf ◽

Mitchell Tang ◽

Elisabeth E. Mlynarski ◽

Pavel P. Kuksa ◽

Otto Valladares ◽

...

Keyword(s):

Genetic Variants ◽

Molecular Mechanisms ◽

Target Genes ◽

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Cellular Processes ◽

Genome Wide ◽

Causal Variants ◽

Novel Method

AbstractThe majority of variants identified by genome-wide association studies (GWAS) reside in the noncoding genome, where they affect regulatory elements including transcriptional enhancers. We propose INFERNO (INFERring the molecular mechanisms of NOncoding genetic variants), a novel method which integrates hundreds of diverse functional genomics data sources with GWAS summary statistics to identify putatively causal noncoding variants underlying association signals. INFERNO comprehensively infers the relevant tissue contexts, target genes, and downstream biological processes affected by causal variants. We apply INFERNO to schizophrenia GWAS data, recapitulating known schizophrenia-associated genes including CACNA1C and discovering novel signals related to transmembrane cellular processes.

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Post-GWAS era in genetics of schizophrenia

V M BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY ◽

10.31363/2313-7053-2019-4-1-6-7 ◽

2019 ◽

pp. 6-7

Author(s):

V. E. Golimbet ◽

A. K. Golov ◽

N. V. Kondratyev

Keyword(s):

Genetic Variants ◽

Target Genes ◽

Disease Risk ◽

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Biological Mechanisms ◽

Master Regulators ◽

Genome Wide

Genome-wide association studies (GWASs) discovered multiple genetic variants associated with schizophrenia. Te next step (post-GWAS analysis) is aimed at identifying the causal genetic variants and biological mechanisms underlying the associations with disease risk. Te following strategies are considered: the study of transcriptional regulation in neuronal human cells and the use of epigenomic information for searching for regulatory elements involved in the pathogenesis of schizophrenia. Te frst strategy includes identifcation of neuronal enhancers, mapping of potential target genes and functional confrmation of enhancer-promoter interactions. Te second approach is focused on the identifcation of transcriptional factors, which appear to be master regulators of expression.

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MicroRNA Variants and HLA-miRNA Interactions are Novel Rheumatoid Arthritis Susceptibility Factors

Frontiers in Genetics ◽

10.3389/fgene.2021.747274 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shicheng Guo ◽

Yehua Jin ◽

Jieru Zhou ◽

Qi Zhu ◽

Ting Jiang ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Target Genes ◽

Association Studies ◽

Genome Wide Association Studies ◽

Risk Alleles ◽

Novel Mirna ◽

Genome Wide ◽

Normal Individuals ◽

Target Binding ◽

Immune Related Genes

Genome-wide association studies have identified >100 genetic risk factors for rheumatoid arthritis. However, the reported genetic variants could only explain less than 40% heritability of rheumatoid arthritis. The majority of the heritability is still missing and needs to be identified with more studies with different approaches and populations. In order to identify novel function SNPs to explain missing heritability and reveal novel mechanism pathogenesis of rheumatoid arthritis, 4 HLA SNPs (HLA-DRB1, HLA-DRB9, HLA-DQB1, and TNFAIP3) and 225 common SNPs located in miRNA, which might influence the miRNA target binding or pre-miRNA stability, were genotyped in 1,607 rheumatoid arthritis and 1,580 matched normal individuals. We identified 2 novel SNPs as significantly associated with rheumatoid arthritis including rs1414273 (miR-548ac, OR = 0.84, p = 8.26 × 10−4) and rs2620381 (miR-627, OR = 0.77, p = 2.55 × 10−3). We also identified that rs5997893 (miR-3928) showed significant epistasis effect with rs4947332 (HLA-DRB1, OR = 4.23, p = 0.04) and rs2967897 (miR-5695) with rs7752903 (TNFAIP3, OR = 4.43, p = 0.03). In addition, we found that individuals who carried 8 risk alleles showed 15.38 (95%CI: 4.69–50.49, p < 1.0 × 10−6) times more risk of being affected by RA. Finally, we demonstrated that the targets of the significant miRNAs showed enrichment in immune related genes (p = 2.0 × 10−5) and FDA approved drug target genes (p = 0.014). Overall, 6 novel miRNA SNPs including rs1414273 (miR-548ac, p = 8.26 × 10−4), rs2620381 (miR-627, p = 2.55 × 10−3), rs4285314 (miR-3135b, p = 1.10 × 10−13), rs28477407 (miR-4308, p = 3.44 × 10−5), rs5997893 (miR-3928, p = 5.9 × 10−3) and rs45596840 (miR-4482, p = 6.6 × 10−3) were confirmed to be significantly associated with RA in a Chinese population. Our study suggests that miRNAs might be interesting targets to accelerate understanding of the pathogenesis and drug development for rheumatoid arthritis.

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Migraine: Genetic Variants and Clinical Phenotypes

Current Medicinal Chemistry ◽

10.2174/0929867325666180719120215 ◽

2019 ◽

Vol 26 (34) ◽

pp. 6207-6221 ◽

Cited By ~ 1

Author(s):

Innocenzo Rainero ◽

Alessandro Vacca ◽

Flora Govone ◽

Annalisa Gai ◽

Lorenzo Pinessi ◽

...

Keyword(s):

Genetic Variants ◽

Association Studies ◽

Mthfr Gene ◽

Genome Wide Association Studies ◽

Clinical Phenotypes ◽

Network Analyses ◽

Genome Wide ◽

Genomic Studies ◽

The Common ◽

The Relationship

Migraine is a common, chronic neurovascular disorder caused by a complex interaction between genetic and environmental risk factors. In the last two decades, molecular genetics of migraine have been intensively investigated. In a few cases, migraine is transmitted as a monogenic disorder, and the disease phenotype cosegregates with mutations in different genes like CACNA1A, ATP1A2, SCN1A, KCNK18, and NOTCH3. In the common forms of migraine, candidate genes as well as genome-wide association studies have shown that a large number of genetic variants may increase the risk of developing migraine. At present, few studies investigated the genotype-phenotype correlation in patients with migraine. The purpose of this review was to discuss recent studies investigating the relationship between different genetic variants and the clinical characteristics of migraine. Analysis of genotype-phenotype correlations in migraineurs is complicated by several confounding factors and, to date, only polymorphisms of the MTHFR gene have been shown to have an effect on migraine phenotype. Additional genomic studies and network analyses are needed to clarify the complex pathways underlying migraine and its clinical phenotypes.

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Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

Genome Medicine ◽

10.1186/s13073-021-00857-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shuquan Rao ◽

Yao Yao ◽

Daniel E. Bauer

Keyword(s):

Genome Editing ◽

Genetic Variants ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Functional Studies ◽

Functional Genetics ◽

Genome Wide ◽

Causal Variants ◽

Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

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Transcriptional Regulation of RUNX1: An Informatics Analysis

Genes ◽

10.3390/genes12081175 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1175

Author(s):

Amarni L. Thomas ◽

Judith Marsman ◽

Jisha Antony ◽

William Schierding ◽

Justin M. O’Sullivan ◽

...

Keyword(s):

Association Studies ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genome Wide ◽

Runx1 Gene ◽

Gene Regulatory Elements ◽

Important Regulator ◽

Aml1 Gene ◽

Regulatory Landscape

The RUNX1/AML1 gene encodes a developmental transcription factor that is an important regulator of haematopoiesis in vertebrates. Genetic disruptions to the RUNX1 gene are frequently associated with acute myeloid leukaemia. Gene regulatory elements (REs), such as enhancers located in non-coding DNA, are likely to be important for Runx1 transcription. Non-coding elements that modulate Runx1 expression have been investigated over several decades, but how and when these REs function remains poorly understood. Here we used bioinformatic methods and functional data to characterise the regulatory landscape of vertebrate Runx1. We identified REs that are conserved between human and mouse, many of which produce enhancer RNAs in diverse tissues. Genome-wide association studies detected single nucleotide polymorphisms in REs, some of which correlate with gene expression quantitative trait loci in tissues in which the RE is active. Our analyses also suggest that REs can be variant in haematological malignancies. In summary, our analysis identifies features of the RUNX1 regulatory landscape that are likely to be important for the regulation of this gene in normal and malignant haematopoiesis.

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CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

Nucleic Acids Research ◽

10.1093/nar/gkz1026 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jianhua Wang ◽

Dandan Huang ◽

Yao Zhou ◽

Hongcheng Yao ◽

Huanhuan Liu ◽

...

Keyword(s):

Fine Mapping ◽

Genetic Variants ◽

Association Studies ◽

Complex Trait ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide ◽

Credible Sets ◽

Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

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