Genetic analysis of blood molecular phenotypes reveals regulatory networks affecting complex traits: a DIRECT study.

Genetic variants identified by genome-wide association studies can contribute to disease risk by altering the production and abundance of mRNA, proteins and other molecules. However, the interplay between molecular intermediaries that define the pathway from genetic variation to disease is not well understood. Here, we evaluated the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3,029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant was associated with multiple molecular phenotypes over multiple genomic regions. We find varying proportions of shared genetic regulation across phenotypes, highest between expression and proteins (66.6%). We were able to recapitulate a substantial proportion of gene expression genetic regulation in a diverse set of 44 tissues, with a median of 88% shared associations for blood expression and 22.3% for plasma proteins. Finally, the genetic and molecular associations were represented in networks including 2,828 known GWAS variants. One sub-network shows the trans relationship between rs149007767 and RTEN, and identifies GRB10 and IKZF1 as candidates mediating genes. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants across different molecular phenotypes.

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Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases

10.1101/265553 ◽

2018 ◽

Cited By ~ 11

Author(s):

Sarvenaz Choobdar ◽

Mehmet E. Ahsen ◽

Jake Crawford ◽

Mattia Tomasoni ◽

Tao Fang ◽

...

Keyword(s):

Complex Traits ◽

Gene Networks ◽

Association Studies ◽

Molecular Network ◽

Molecular Networks ◽

Disease Genes ◽

Genome Wide Association Studies ◽

Identification Methods ◽

Module Identification ◽

Network Modules

AbstractIdentification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of gene and protein networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology, and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). Our critical assessment of 75 contributed module identification methods reveals novel top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

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Case-control association mapping without cases

10.1101/045831 ◽

2016 ◽

Cited By ~ 2

Author(s):

Jimmy Z Liu ◽

Yaniv Erlich ◽

Joseph K Pickrell

Keyword(s):

Association Mapping ◽

Complex Traits ◽

Disease Risk ◽

Association Studies ◽

Meta Analysis ◽

Large Population ◽

Case Control ◽

Genome Wide Association Studies ◽

The Uk ◽

Control Association

AbstractThe case-control association study is a powerful method for identifying genetic variants that influence disease risk. However, the collection of cases can be time-consuming and expensive; if a disease occurs late in life or is rapidly lethal, it may be more practical to identify family members of cases. Here, we show that replacing cases with their first-degree relatives enables genome-wide association studies by proxy (GWAX). In randomly-ascertained cohorts, this approach enables previously infeasible studies of diseases that are absent (or nearly absent) in the cohort. As an illustration, we performed GWAX of 12 common diseases in 116,196 individuals from the UK Biobank. By combining these results with published GWAS summary statistics in a meta-analysis, we replicated established risk loci and identified 17 newly associated risk loci: four in Alzheimer’s disease, eight in coronary artery disease, and five in type 2 diabetes. In addition to informing disease biology, our results demonstrate the utility of association mapping using family history of disease as a phenotype to be mapped. We anticipate that this approach will prove useful in future genetic studies of complex traits in large population cohorts.

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Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽

10.3390/genes13010087 ◽

2021 ◽

Vol 13 (1) ◽

pp. 87

Author(s):

Sean M. Burnard ◽

Rodney A. Lea ◽

Miles Benton ◽

David Eccles ◽

Daniel W. Kennedy ◽

...

Keyword(s):

Multiple Sclerosis ◽

Complex Traits ◽

Multiple Testing ◽

Large Scale ◽

Disease Risk ◽

Association Studies ◽

Meta Analysis ◽

Elastic Net ◽

Genome Wide Association Studies ◽

Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

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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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Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

10.1101/2021.03.19.436212 ◽

2021 ◽

Author(s):

Karthik A. Jagadeesh ◽

Kushal K Dey ◽

Daniel T. Montoro ◽

Steven Gazal ◽

Jesse M Engreitz ◽

...

Keyword(s):

Single Cell ◽

Disease Progression ◽

Genetic Variants ◽

Complex Traits ◽

Disease Risk ◽

Association Studies ◽

Cell Types ◽

Genome Wide Association Studies ◽

Cascade Process ◽

Cellular Processes

Cellular dysfunction is a hallmark of disease. Genome-wide association studies (GWAS) have provided a powerful means to identify loci and genes contributing to disease risk, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important both for our understanding of disease, and for developing therapeutic interventions. Here, we introduce a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression in cell types, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N=297K). The inferred disease enrichments recapitulated known biology and highlighted novel relationships for different conditions, including GABAergic neurons in major depressive disorder (MDD), disease progression programs in M cells in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

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Establishing gene regulatory networks from Parkinson's disease risk loci

10.1101/2021.04.08.439080 ◽

2021 ◽

Author(s):

Sophie L Farrow ◽

William Schierding ◽

Sreemol Gokuladhas ◽

Evgeniia Golovina ◽

Tayaza M. Fadason ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Disease Risk ◽

Association Studies ◽

Adult Brain ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Gene Regulatory

The latest meta-analysis of genome wide association studies (GWAS) identified 90 independent single nucleotide polymorphisms (SNPs) across 78 genomic regions associated with Parkinson's disease (PD), yet the mechanisms by which these variants influence the development of the disease remains largely elusive. To establish the functional gene regulatory networks associated with PD-SNPs, we utilised an approach combining spatial (chromosomal conformation capture) and functional (expression quantitative trait loci; eQTL) data. We identified 518 genes subject to regulation by 76 PD-SNPs across 49 tissues, that encompass 36 peripheral and 13 CNS tissues. Notably, one third of these genes were regulated via trans- acting mechanisms (distal; risk locus-gene separated by > 1Mb, or on different chromosomes). Of particular interest is the identification of a novel trans-eQTL-gene connection between rs10847864 and SYNJ1 in the adult brain cortex, highlighting a convergence between familial studies and PD GWAS loci for SYNJ1 (PARK20) for the first time. Furthermore, we identified 16 neuro-development specific eQTL-gene regulatory connections within the foetal cortex, consistent with hypotheses suggesting a neurodevelopmental involvement in the pathogenesis of PD. Through utilising Louvain clustering we extracted nine significant and highly intra-connected clusters within the entire gene regulatory network. The nine clusters are enriched for specific biological processes and pathways, some of which have not previously been associated with PD. Together, our results not only contribute to an overall understanding of the mechanisms and impact of specific combinations of PD-SNPs, but also highlight the potential impact gene regulatory networks may have when elucidating aetiological subtypes of PD.

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Genetic control of RNA splicing and its distinctive role in complex trait variation

10.21203/rs.3.rs-155233/v1 ◽

2021 ◽

Author(s):

Jian Yang ◽

Ting Qi ◽

Yang Wu ◽

Futao Zhang ◽

Jian Zeng

Keyword(s):

Complex Traits ◽

Rna Splicing ◽

Association Studies ◽

Genetic Regulation ◽

Complex Trait ◽

Regulation Of Transcription ◽

Genome Wide Association Studies ◽

Trait Variation ◽

Eqtl Data ◽

Distinctive Role

Abstract Most genetic variants identified from genome-wide association studies (GWAS) in humans are noncoding, indicating their role in gene regulation. Prior studies have shown considerable links of GWAS signals to expression quantitative trait loci (eQTLs), but the links to other genetic regulatory mechanisms such as splicing QTLs (sQTLs) are underexplored. Here, we introduce a transcript-based sQTL method (named THISTLE) with improved power for sQTL detection. Applying THISTLE along with LeafCutter, an event-based sQTL method, to brain transcriptomic data (n=1,073), we identified 7,491 genes with sQTLs with P<5×10^(-8) (the largest brain cis-sQTL collection to date), ~68% of which were distinct from eQTLs. Integrating the sQTL data into GWAS for ten brain-related complex traits (including diseases), we identified 107 genes associated with the traits through the sQTLs, ~68% of which could not be discovered using eQTL data. Our study demonstrates the distinctive role of most sQTLs in genetic regulation of transcription and complex trait variation.

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Modeling regulatory network topology improves genome-wide analyses of complex human traits

Nature Communications ◽

10.1038/s41467-021-22588-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Xiang Zhu ◽

Zhana Duren ◽

Wing Hung Wong

Keyword(s):

Network Topology ◽

Complex Traits ◽

Regulatory Networks ◽

Association Studies ◽

Biological Significance ◽

Cell Types ◽

Genome Wide Association Studies ◽

Coding Regions ◽

Genome Wide ◽

Complex Human Traits

AbstractGenome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and previously undescribed trait-associated genes revealing biological and therapeutic insights.

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Embracing polygenicity: a review of methods and tools for psychiatric genetics research

Psychological Medicine ◽

10.1017/s0033291717002318 ◽

2017 ◽

Vol 48 (7) ◽

pp. 1055-1067 ◽

Cited By ~ 33

Author(s):

R. M. Maier ◽

P. M. Visscher ◽

M. R. Robinson ◽

N. R. Wray

Keyword(s):

Psychiatric Disorders ◽

Genetic Risk ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Genetics Research ◽

Genome Wide ◽

Analyse Data ◽

Shared Genetic

AbstractThe availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

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Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation

10.1101/240275 ◽

2017 ◽

Cited By ~ 1

Author(s):

Oleksandr Frei ◽

Dominic Holland ◽

Olav B. Smeland ◽

Alexey A. Shadrin ◽

Chun Chieh Fan ◽

...

Keyword(s):

Educational Attainment ◽

Genetic Correlation ◽

Complex Traits ◽

Association Studies ◽

Venn Diagram ◽

Genome Wide Association Studies ◽

Statistical Tool ◽

Causal Variants ◽

Complex Human Traits ◽

Shared Genetic

ABSTRACTAccumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. While current cross-trait analytical methods focus on genetic correlation between traits, we developed a novel statistical tool (MiXeR), which quantifies polygenic overlap independent of genetic correlation, using summary statistics from GWAS. MiXeR results can be presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that more than 9K variants causally influence schizophrenia, 7K influence bipolar disorder, and out of those variants 6.9K are shared between these two disorders, which have high genetic correlation. Further, MiXeR uncovers extensive polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, these traits share more than 9K causal variants, while 3K additional variants only influence educational attainment. By considering the polygenicity, heritability and discoverability of complex phenotypes, MiXeR provides a more complete quantification of shared genetic architecture than offered by other available tools.

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