Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

AbstractExpression quantitative trait loci (eQTL) mapping has successfully resolved some genome-wide association study (GWAS) loci for complex traits1–6. However, there is a need for implementing additional “omic” approaches to untangle additional loci and provide a biological context for GWAS signals. We generated a detailed landscape of the genomic architecture of protein levels in multiple neurologically relevant tissues (brain, cerebrospinal fluid (CSF) and plasma), by profiling thousands of proteins in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. We demonstrated that cis-pQTL are more likely to be shared across tissues but trans-pQTL are tissue-specific. Between 78% to 87% of pQTL are not eQTL, indicating that protein levels have a different genetic architecture than gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. In the context of personalized medicine, these results highlight the need for implementing additional functional genomic approaches beyond gene expression in order to understand the biology of complex traits, and to identify novel biomarkers and potential drug targets for those traits.

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Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits

10.21203/rs.3.rs-70284/v1 ◽

2020 ◽

Author(s):

Carlos Cruchaga ◽

Chengran Yang ◽

Fabiana Geraldo Farias ◽

Laura Ibanez ◽

Brooke Sadler ◽

...

Keyword(s):

Protein Interactions ◽

Complex Traits ◽

Drug Targets ◽

Genetic Architecture ◽

Protein Protein Interactions ◽

Regulate Gene Expression ◽

Tissue Specific ◽

Protein Levels ◽

Novel Biomarkers ◽

Health And Disease

Abstract Understanding the tissue-specific genetic architecture of protein levels is instrumental to understand the biology of health and disease. We generated a genomic atlas of protein levels in multiple neurologically relevant tissues (380 brain, 835 cerebrospinal fluid (CSF) and 529 plasma), by profiling thousands of proteins (713 CSF, 931 plasma and 1079 brain) in a large and well-characterized cohort. We identified 274, 127 and 32 protein quantitative loci (pQTL) for CSF, plasma and brain respectively. cis-pQTL were more likely to be shared across tissues but trans-pQTL tend to be tissue-specific. Between 44% to 68.2% of the pQTL do not colocalize with expression, splicing, methylation or histone QTLs, indicating that protein levels have a different genetic architecture to those that regulate gene expression. By combining our pQTL with Mendelian Randomization approaches we identified potential novel biomarkers and drug targets for neurodegenerative diseases including Alzheimer disease and frontotemporal dementia. Here we present the first multi-tissue study yielding hundred of novel pQTLs. This data will be instrumental to identify the functional gene from GWAS signals, identify novel biological protein-protein interactions, identify novel potential biomarkers and drug targets for complex traits.

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A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

10.1101/563379 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tom G Richardson ◽

Gibran Hemani ◽

Tom R Gaunt ◽

Caroline L Relton ◽

George Davey Smith

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Mendelian Randomization ◽

Drug Repositioning ◽

Association Studies ◽

Thyroid Tissue ◽

Genome Wide Association Studies ◽

Tissue Specific ◽

Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

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Mendelian randomization identifies folliculin expression as a mediator of diabetic retinopathy

10.1101/2020.06.09.143164 ◽

2020 ◽

Author(s):

Andrew D. Skol ◽

Segun C. Jung ◽

Ana Marija Sokovic ◽

Siquan Chen ◽

Sarah Fazal ◽

...

Keyword(s):

Gene Expression ◽

Diabetic Retinopathy ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Meta Analysis ◽

Susceptibility Gene ◽

Glucose Response ◽

Aberrant Expression ◽

Genome Wide ◽

A Genome

AbstractThe goal of the study was to identify genes whose aberrant expression can contribute to diabetic retinopathy. We determined differential gene expression in response to high glucose in lymphoblastoid cell lines derived from matched individuals with type 1 diabetes (T1D) with and without retinopathy. Those genes exhibiting the largest difference in glucose response between individuals with diabetes with and without retinopathy were assessed for association to diabetic retinopathy utilizing genotype data from a genome-wide association study meta-analysis. All genetic variants associated with gene expression (expression Quantitative Trait Loci, eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the eQTLs from the glucose response genes among small association p-values and identified folliculin (FLCN) as a susceptibility gene for diabetic retinopathy. We show that expression of FLCN in response to glucose was greater in individuals with diabetic retinopathy compared to individuals with diabetes without retinopathy. Three large, independent cohorts of individuals with diabetes revealed an association of FLCN eQTLs to diabetic retinopathy. Mendelian randomization further confirmed a direct positive effect of increased FLCN expression on retinopathy in individuals with diabetes. Together, our studies integrating genetic association and gene expression implicate FLCN as a disease gene for diabetic retinopathy.

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Genetic determinants of daytime napping and effects on cardiometabolic health

10.1101/2020.06.12.20129858 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hassan S. Dashti ◽

Iyas Daghlas ◽

Jacqueline M. Lane ◽

Yunru Huang ◽

Miriam S. Udler ◽

...

Keyword(s):

Blood Pressure ◽

Drug Targets ◽

Genetic Basis ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Cardiometabolic Health ◽

Genetic Determinants ◽

Genome Wide ◽

A Genome ◽

The Uk

AbstractDaytime napping is a common, heritable behavior, but its genetic basis and causal relationship with cardiometabolic health remains unclear. Here, we performed a genome-wide association study of self-reported daytime napping in the UK Biobank (n=452,633) and identified 123 loci of which 60 replicated in 23andMe research participants (n=541,333). Findings included missense variants in established drug targets (HCRTR1, HCRTR2), genes with roles in arousal (TRPC6, PNOC), and genes suggesting an obesity-hypersomnolence pathway (PNOC, PATJ). Signals were concordant with accelerometer-measured daytime inactivity duration and 33 signals colocalized with signals for other sleep phenotypes. Cluster analysis identified 3 clusters suggesting distinct nap-promoting mechanisms with heterogeneous associations with cardiometabolic outcomes. Mendelian randomization showed potential causal links between more frequent daytime napping and higher systolic blood pressure, diastolic blood pressure, and waist circumference.

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The genetic architecture of Parkinson disease in Spain: characterizing population-specific risk, differential haplotype structures, and providing etiologic insight

10.1101/609016 ◽

2019 ◽

Cited By ~ 1

Author(s):

Sara Bandres-Ciga ◽

Sarah Ahmed ◽

Marya S. Sabir ◽

Cornelis Blauwendraat ◽

Astrid D. Adarmes-Gómez ◽

...

Keyword(s):

Parkinson Disease ◽

Genetic Architecture ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Age At Onset ◽

Genetic Correlations ◽

Risk Haplotype ◽

Specific Risk ◽

Genome Wide ◽

Gwas Signal

ABSTRACTBackgroundThe Iberian Peninsula stands out as having variable levels of population admixture and isolation, making Spain an interesting setting for studying the genetic architecture of neurodegenerative diseases.ObjectivesTo perform the largest Parkinson disease (PD) genome-wide association study (GWAS) restricted to a single country.MethodsWe performed a GWAS for both risk of PD and age-at-onset (AAO) in 7,849 Spanish individuals. Further analyses included population-specific risk haplotype assessments, polygenic risk scoring through machine learning, Mendelian randomization of expression and methylation data to gain insight into disease-associated loci, heritability estimates, genetic correlations and burden analyses.ResultsWe identified a novel population-specific GWAS signal atPARK2associated with AAO. We replicated four genome-wide independent signals associated with PD risk, includingSNCA, LRRK2, KANSL1/MAPTandHLA-DQB1. A significant trend for smaller risk haplotypes at known loci was found compared to similar studies of non-Spanish origin. Seventeen PD-related genes showed functional consequence via two-sample Mendelian randomization in expression and methylation datasets. Long runs of homozygosity at 28 known genes/loci were found to be enriched in cases versus controls.ConclusionsOur data demonstrate the utility of the Spanish risk haplotype substructure for future fine-mapping efforts, showing how leveraging unique and diverse population histories can benefit genetic studies of complex diseases. The present study points toPARK2as a major hallmark of PD etiology in Spain.

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GWIS: Genome Wide Inferred Statistics for non-linear functions of multiple phenotypes

10.1101/035329 ◽

2015 ◽

Cited By ~ 1

Author(s):

Harold Nieuwboer ◽

Rene Pool ◽

Conor V Dolan ◽

Dorret I Boomsma ◽

Michel G Nivard

Keyword(s):

Genetic Architecture ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Linear Functions ◽

Standard Errors ◽

Summary Statistics ◽

Polygenic Risk ◽

Multiple Phenotypes ◽

Genome Wide ◽

Risk Scoring

Here we present a method of genome wide inferred study (GWIS) that provides an approximation of genome wide association study (GWAS) summary statistics for a variable that is a function of phenotypes for which GWAS summary statistics, phenotypic means and covariances are available. GWIS can be performed regardless of sample overlap between the GWAS of the phenotypes on which the function depends. As GWIS provides association estimates and their standard errors for each SNP, GWIS can form the basis for polygenic risk scoring, LD score regression, Mendelian randomization studies, biological annotation and other analyses. Here, we replicate a body mass index (BMI) GWAS using GWIS based on a height GWAS and a weight GWAS. We proceed to use a GWIS to further our understanding of the genetic architecture of schizophrenia and bipolar disorder.

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Genome-wide association study of skin and soft tissue infection susceptibility

10.1101/2020.09.03.20187468 ◽

2020 ◽

Author(s):

Tormod Rogne ◽

Kristin V Liyanarachi ◽

Humaira Rasheed ◽

Laurent F Thomas ◽

Helene M Flatby ◽

...

Keyword(s):

Gene Expression ◽

Soft Tissue ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

Genome Wide Association ◽

P Value ◽

Uk Biobank ◽

Genome Wide ◽

Monocytes And Macrophages ◽

The Uk

Background: Skin and soft tissue infections (SSTIs) are common worldwide, but little is known about the genetic susceptibility and the causal effect of cardiometabolic risk factors. We therefore conducted the first genome-wide association study (GWAS) of SSTIs, with downstream analyses including Mendelian randomization analyses. Methods: The GWAS was conducted using the UK Biobank as discovery cohort, with 6,107 cases and 399,239 controls, and the Trondelag Health Study (HUNT) as replication cohort with 1,657 cases and 67,522 controls. Cases and controls were those who had or had not been hospitalized with an SSTI diagnosis, respectively. Findings: One locus, lead single-nucleotide polymorphism rs3749748 in LINC01184/SLC12A2, was associated with SSTIs in the UK Biobank (odds ratio [OR] 1.19, p-value = 7.6e-16) and replicated in HUNT (OR 1.15, p-value = 6.3e-4). Meta-analysis confirmed the lead variant (OR 1.18, p-value = 4.4e-18), as well as suggested two additional loci close to genome-wide significance (rs2007361 in PSMA1, OR 0.91, p-value = 5.1e-8; and rs78625038 in GAN, OR 1.86, p-value = 5.9e-8). Gene-based association tests identified four genes linked to SSTIs: SLC12A2, PSMA1, GAN, and IL6R. The minor allele of rs3749748 reduced the gene expression of SLC12A2 primarily in monocytes and macrophages. Mendelian randomization analyses showed that increasing body mass index and lifetime smoking habits increased risk of SSTIs. Interpretation: LINC0118/SLC12A2 was robustly associated with SSTI incidence and may exert its effect through reduced gene expression in monocytes and macrophages. Reducing tobacco smoking, overweight and obesity in the population may reduce the incidence of SSTIs.

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An integrative multi-omics analysis of 16 autoimmune diseases and cancer outcomes highlights immune-cell regulatory mechanisms and shared genetic architecture

10.1101/2020.11.27.20235663 ◽

2020 ◽

Author(s):

C Prince ◽

R. E Mitchell ◽

T. G. Richardson

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Genetic Architecture ◽

Immune Cell ◽

Mendelian Randomization ◽

Disease Risk ◽

Specific Cell ◽

Cell Type ◽

Cancer Outcomes ◽

Shared Genetic

AbstractBackgroundDeveloping functional understanding into the causal molecular drivers of immunological disease is a critical challenge in genomic medicine. Here we systematically apply Mendelian randomization (MR), genetic colocalization, immune cell-type enrichment and phenome-wide association methods to investigate the effect of genetically predicted gene expression on 12 autoimmune and 4 cancer outcomes.ResultsUsing whole blood derived estimates for regulatory variants from the eQTLGen consortium (n=31,684) we constructed genetic risk scores (r2<0.1) for 10,104 genes. Applying the inverse-variance weighted Mendelian randomization method transcriptome-wide whilst accounting for linkage disequilibrium structure identified 773 unique genes with evidence of a genetically predicted effect on at least one disease outcome (P<4.81 × 10−5). We next undertook genetic colocalization to investigate whether these effects may be confined to specific cell-types using gene expression data derived from 18 types of immune cells. This highlighted many cell-type dependent effects, such as PRKCQ expression and asthma risk (posterior probability of association (PPA)=0.998), which was T-cell specific, as well as TPM3 expression and prostate cancer risk (PPA=0.821), which was restricted to monocytes. Phenome-wide analyses on 320 complex traits allowed us to explore the shared genetic architecture and prioritize key drivers of disease risk, such as CASP10 which provided evidence of an effect on 7 cancer-related outcomes. Similarly, these evaluations of pervasive pleiotropy may be valuable for evaluations of therapeutic targets to help identify potential adverse effects.ConclusionsOur atlas of results can be used to characterize known and novel loci in autoimmune disease and cancer susceptibility, both in terms of developing insight into cell-type dependent effects as well as dissecting shared genetic architecture and disease pathways. As exemplar, we have highlighted several key findings in this study, although similar evaluations can be conducted interactively at http://mrcieu.mrsoftware.org/immuno_MR/.

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Using the MR-Base platform to investigate risk factors and drug targets for thousands of phenotypes

Wellcome Open Research ◽

10.12688/wellcomeopenres.15334.1 ◽

2019 ◽

Vol 4 ◽

pp. 113 ◽

Cited By ~ 1

Author(s):

Venexia M Walker ◽

Neil M Davies ◽

Gibran Hemani ◽

Jie Zheng ◽

Philip C Haycock ◽

...

Keyword(s):

Risk Factors ◽

Web Application ◽

Drug Targets ◽

Genome Wide Association Study ◽

Mendelian Randomization ◽

R Package ◽

Sensitivity Analyses ◽

Link Type ◽

Genome Wide ◽

Study Results

Mendelian randomization (MR) uses genetic information to strengthen causal inference concerning the effect of exposures on outcomes. This method has a broad range of applications, including investigating risk factors and appraising potential targets for intervention. MR-Base has become established as a freely accessible, online platform, which combines a database of complete genome-wide association study results with an interface for performing Mendelian randomization and sensitivity analyses. This allows the user to explore millions of potentially causal associations. MR-Base is available as a web application or as an R package. The technical aspects of the tool have previously been documented in the literature. The present article is complimentary to this as it focuses on the applied aspects. Specifically, we describe how MR-Base can be used in several ways, including to perform novel causal analyses, replicate results and enable transparency, amongst others. We also present three use cases, which demonstrate important applications of Mendelian randomization and highlight the benefits of using MR-Base for these types of analyses.

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Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

10.1101/377267 ◽

2018 ◽

Cited By ~ 4

Author(s):

Eleonora Porcu ◽

Sina Rüeger ◽

Kaido Lepik ◽

Federico A. Santoni ◽

Alexandre Reymond ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Mendelian Randomization ◽

Association Studies ◽

Regulation Of Gene Expression ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Genome Wide ◽

Biological Interpretation ◽

Independent Association

AbstractGenome-wide association studies (GWAS) identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs (eQTLs), indicating their potential involvement in the regulation of gene expression.Here, we propose an advanced transcriptome-wide summary statistics-based Mendelian Randomization approach (called TWMR) that uses multiple SNPs jointly as instruments and multiple gene expression traits as exposures, simultaneously.When applied to 43 human phenotypes it uncovered 2,369 genes whose blood expression is putatively associated with at least one phenotype resulting in 3,913 gene-trait associations; of note, 36% of them had no genome-wide significant SNP nearby in previous GWAS analysis. Using independent association summary statistics (UKBiobank), we confirmed that the majority of these loci were missed by conventional GWAS due to power issues. Noteworthy among these novel links is educational attainment-associated BSCL2, known to carry mutations leading to a mendelian form of encephalopathy. We similarly unraveled novel pleiotropic causal effects suggestive of mechanistic connections, e.g. the shared genetic effects of GSDMB in rheumatoid arthritis, ulcerative colitis and Crohn’s disease.Our advanced Mendelian Randomization unlocks hidden value from published GWAS through higher power in detecting associations. It better accounts for pleiotropy and unravels new biological mechanisms underlying complex and clinical traits.

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