ORCID Profile
0000-0003-4204-8734
Current Organisations
University of Chicago
,
Vanderbilt University Medical Center
,
University of Amsterdam
,
University of Cambridge Clare Hall
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Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222786
Abstract: Bipolar disorder is a complex neuropsychiatric disorder presenting with episodic mood disturbances. In this study we use a transcriptomic imputation approach to identify novel genes and pathways associated with bipolar disorder, as well as three diagnostically and genetically distinct subtypes. Transcriptomic imputation approaches leverage well-curated and publicly available eQTL reference panels to create gene-expression prediction models, which may then be applied to “impute” genetically regulated gene expression (GREX) in large GWAS datasets. By testing for association between phenotype and GREX, rather than genotype, we hope to identify more biologically interpretable associations, and thus elucidate more of the genetic architecture of bipolar disorder. We applied GREX prediction models for 13 brain regions (derived from CommonMind Consortium and GTEx eQTL reference panels) to 21,488 bipolar cases and 54,303 matched controls, constituting the largest transcriptomic imputation study of bipolar disorder (BPD) to date. Additionally, we analyzed three specific BPD subtypes, including 14,938 in iduals with subtype 1 (BD-I), 3,543 in iduals with subtype 2 (BD-II), and 1,500 in iduals with schizoaffective subtype (SAB). We identified 125 gene-tissue associations with BPD, of which 53 represent independent associations after FINEMAP analysis. 29/53 associations were novel i.e., did not lie within 1Mb of a locus identified in the recent PGC-BD GWAS. We identified 37 independent BD-I gene-tissue associations (10 novel), 2 BD-II associations, and 2 SAB associations. Our BPD, BD-I and BD-II associations were significantly more likely to be differentially expressed in post-mortem brain tissue of BPD, BD-I and BD-II cases than we might expect by chance. Together with our pathway analysis, our results support long-standing hypotheses about bipolar disorder risk, including a role for oxidative stress and mitochondrial dysfunction, the post-synaptic density, and an enrichment of circadian rhythm and clock genes within our results.
Publisher: Cold Spring Harbor Laboratory
Date: 09-09-2016
DOI: 10.1101/074450
Abstract: Expression quantitative trait locus (eQTL) mapping provides a powerful means to identify functional variants influencing gene expression and disease pathogenesis. We report the identification of cis-eQTLs from 7,051 post-mortem s les representing 44 tissues and 449 in iduals as part of the Genotype-Tissue Expression (GTEx) project. We find a cis-eQTL for 88% of all annotated protein-coding genes, with one-third having multiple independent effects. We identify numerous tissue-specific cis-eQTLs, highlighting the unique functional impact of regulatory variation in erse tissues. By integrating large-scale functional genomics data and state-of-the-art fine-mapping algorithms, we identify multiple features predictive of tissue-specific and shared regulatory effects. We improve estimates of cis-eQTL sharing and effect sizes using allele specific expression across tissues. Finally, we demonstrate the utility of this large compendium of cis-eQTLs for understanding the tissue-specific etiology of complex traits, including coronary artery disease. The GTEx project provides an exceptional resource that has improved our understanding of gene regulation across tissues and the role of regulatory variation in human genetic diseases.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 08-05-2015
Abstract: Human genomes show extensive genetic variation across in iduals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within in iduals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem s les covering 54 body sites from 175 in iduals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science , this issue p. 648 , p. 660 , p. 666 see also p. 640
Publisher: Elsevier BV
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 12-10-2017
DOI: 10.1038/NATURE24277
Abstract: Characterization of the molecular function of the human genome and its variation across in iduals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across in iduals and erse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-in idual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease.
Publisher: Wiley
Date: 23-12-2021
DOI: 10.1002/AJMG.B.32829
Publisher: Springer Science and Business Media LLC
Date: 19-10-2015
DOI: 10.1038/SREP15145
Abstract: Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred in iduals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed and some essential tissues (e.g., heart and lung) show much stronger “co-aging” than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues it also presents a tissue-specific view of the link between aging and age-related diseases.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 11-09-2020
Abstract: Sex differences in the human transcriptome are widespread and tissue specific, and they contribute to complex traits.
Publisher: Hindawi Limited
Date: 27-11-2013
DOI: 10.1002/HUMU.22475
Publisher: American Association for Cancer Research (AACR)
Date: 14-08-2011
DOI: 10.1158/1078-0432.CCR-11-0724
Abstract: Purpose: Cell-based approaches were used to identify genetic markers predictive of patients' risk for poor response prior to chemotherapy. Experimental Design: We conducted genome-wide association studies (GWAS) to identify single-nucleotide polymorphisms (SNP) associated with cellular sensitivity to carboplatin through their effects on mRNA expression using International HapMap lymphoblastoid cell lines (LCL) and replicated them in additional LCLs. SNPs passing both stages of the cell-based study were tested for association with progression-free survival (PFS) in patients. Phase 1 validation was based on 377 ovarian cancer patients receiving at least four cycles of carboplatin and paclitaxel from the Australian Ovarian Cancer Study (AOCS). Positive associations were then assessed in phase 2 validation analysis of 1,326 patients from the Ovarian Cancer Association Consortium and The Cancer Genome Atlas. Results: In the initial GWAS, 342 SNPs were associated with carboplatin-induced cytotoxicity, of which 18 unique SNPs were retained after assessing their association with gene expression. One SNP (rs1649942) was replicated in an independent LCL set (Bonferroni adjusted P & 0.05). It was found to be significantly associated with decreased PFS in phase 1 AOCS patients (Pper-allele = 2 × 10−2), with a stronger effect in the subset of women with optimally debulked tumors (Pper-allele = 4 × 10−3). rs1649942 was also associated with poorer overall survival in women with optimally debulked tumors (Pper-allele = 9 × 10−3). However, this SNP was not significant in phase 2 validation analysis with patients from numerous cohorts. Conclusion: This study shows the potential of cell-based, genome-wide approaches to identify germline predictors of treatment outcome and highlights the need for extensive validation in patients to assess their clinical effect. Clin Cancer Res 17(16) 5490–500. ©2011 AACR.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 11-09-2020
Abstract: Outliers in the human transcriptome reveal the functional effects of rare genetic variants.
Publisher: Elsevier BV
Date: 10-2022
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2021
DOI: 10.1101/2021.11.18.21266545
Abstract: With the increasing availability of biobank-scale datasets that incorporate both genomic data and electronic health records, many associations between genetic variants and phenotypes of interest have been discovered. Polygenic risk scores (PRS), which are being widely explored in precision medicine, use the results of association studies to predict the genetic component of disease risk by accumulating risk alleles weighted by their effect sizes. However, few studies have thoroughly investigated best practices for PRS in global populations across different diseases. In this study, we utilize data from the Global-Biobank Meta-analysis Initiative (GBMI), which consists of in iduals from erse ancestries and across continents, to explore methodological considerations and PRS prediction performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRS using heuristic (pruning and thresholding, P+T) and Bayesian (PRS-CS) methods. We found that the genetic architecture, such as SNP-based heritability and polygenicity, varied greatly among endpoints. For both PRS construction methods, using a European ancestry LD reference panel resulted in comparable or higher prediction accuracy compared to several other non-European based panels this is largely attributable to European descent populations still comprising the majority of GBMI participants. PRS-CS overall outperformed the classic P+T method, especially for endpoints with higher SNP-based heritability. For ex le, substantial improvements are observed in East-Asian ancestry (EAS) using PRS- CS compared to P+T for heart failure (HF) and chronic obstructive pulmonary disease (COPD). Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma which has known variation in disease prevalence across global populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using the GBMI and highlight the importance of best practices for PRS in the biobank-scale genomics era.
Publisher: Oxford University Press (OUP)
Date: 24-02-2021
DOI: 10.1093/BIOINFORMATICS/BTAB115
Abstract: Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information. We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1. We performed 10 simulations per gene. The eQTL-h2 (i.e. the proportion of variation explained by the eQTLs) was set at 1%, 2% and 5%. We found E-MAGMA outperforms other gene-based approaches across a range of simulated parameters (e.g. the number of identified causal genes). When applied to genome-wide association summary statistics for five neuropsychiatric disorders, E-MAGMA identified more putative candidate causal genes compared to other eQTL-based approaches. By integrating tissue-specific eQTL information, these results show E-MAGMA will help to identify novel candidate causal genes from genome-wide association summary statistics and thereby improve the understanding of the biological basis of complex disorders. A tutorial and input files are made available in a github repository: skederks/eMAGMA-tutorial. Supplementary data are available at Bioinformatics online.
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2017
DOI: 10.1101/222596
Abstract: Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.
Publisher: Elsevier BV
Date: 07-2018
DOI: 10.1016/J.DRUGALCDEP.2018.03.026
Abstract: Alcohol and tobacco use are heritable phenotypes. However, only a small number of common genetic variants have been identified, and common variants account for a modest proportion of the heritability. Therefore, this study aims to investigate the role of low-frequency and rare variants in alcohol and tobacco use. We meta-analyzed ExomeChip association results from eight discovery cohorts and included 12,466 subjects and 7432 smokers in the analysis of alcohol consumption and tobacco use, respectively. The ExomeChip interrogates low-frequency and rare exonic variants, and in addition a small pool of common variants. We investigated top variants in an independent s le in which ICD-9 diagnoses of "alcoholism" (N = 25,508) and "tobacco use disorder" (N = 27,068) had been assessed. In addition to the single variant analysis, we performed gene-based, polygenic risk score (PRS), and pathway analyses. The meta-analysis did not yield exome-wide significant results. When we jointly analyzed our top results with the independent s le, no low-frequency or rare variants reached significance for alcohol consumption or tobacco use. However, two common variants that were present on the ExomeChip, rs16969968 (p = 2.39 × 10 Low-frequency and rare exonic variants with large effects do not play a major role in alcohol and tobacco use, nor does the aggregate effect of ExomeChip variants. However, our results confirmed the role of the CHRNA5-CHRNA3-CHRNB4 cluster of nicotinic acetylcholine receptor subunit genes in tobacco use.
Publisher: Springer Science and Business Media LLC
Date: 20-07-2017
DOI: 10.1038/S41598-017-05744-9
Abstract: We performed a whole-genome scan of genetic variants in splicing regulatory elements (SREs) and evaluated the extent to which natural selection has shaped extant patterns of variation in SREs. We investigated the degree of differentiation of single nucleotide polymorphisms (SNPs) in SREs among human populations and applied long-range haplotype- and multilocus allelic differentiation-based methods to detect selection signatures. We describe an approach, s ling a large number of loci across the genome from functional classes and using the consensus from multiple tests, for identifying candidates for selection signals. SRE SNPs in various SNP functional classes show different patterns of population differentiation compared with their non-SRE counterparts. Intronic regions display a greater enrichment for extreme population differentiation among the potentially tissue-dependent transcript ratio quantitative trait loci (trQTLs) than SRE SNPs in general and includ outlier trQTLs for cross-population composite likelihood ratio, suggesting that incorporation of context annotation for regulatory variation may lead to improved detection of signature of selection on these loci. The proportion of extremely rare SNPs disrupting SREs is significantly higher in European than in African s les. The approach developed here will be broadly useful for studies of function and disease-associated variation in the human genome.
Publisher: Cold Spring Harbor Laboratory
Date: 28-03-2019
DOI: 10.1101/591693
Abstract: Major depression is a common and severe psychiatric disorder with a highly polygenic genetic architecture. Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with major depression, but the exact causal genes and biological mechanisms are largely unknown. Tissue-specific network approaches may identify molecular mechanisms underlying major depression and provide a biological substrate for integrative analyses. We provide a framework for the identification of in idual risk genes and gene co-expression networks using genome-wide association summary statistics and gene expression information across multiple human brain tissues and whole blood. We developed a novel gene-based method called eMAGMA that leverages multi-tissue eQTL information to identify 99 biologically plausible risk genes associated with major depression, of which 58 are novel. Among these novel associations is Complement Factor 4A (C4A), recently implicated in schizophrenia through its role in synaptic pruning during postnatal development. Major depression risk genes were enriched in gene co-expression modules in multiple brain tissues and the implicated gene modules contained genes involved in synaptic signalling, neuronal development, and cell transport pathways. Modules enriched with major depression signals were strongly preserved across brain tissues, but were weakly preserved in whole blood, highlighting the importance of using disease-relevant tissues in genetic studies of psychiatric traits. We identified tissue-specific genes and gene co-expression networks associated with major depression. Our novel analytical framework can be used to gain fundamental insights into the functioning of the nervous system in major depression and other brain-related traits. Although genome-wide association studies have identified genetic risk variants associated with major depression, our understanding of the mechanisms through which they influence disease susceptibility remain largely unknown. Genetic risk variants are highly enriched in non-coding regions of the genome and affect gene expression. Genes are known to interact and regulate the activity of one another and form highly organized (co-expression) networks. Here, we generate tissue-specific gene co-expression networks, each containing groups of functionally related genes or “modules”, to delineate interactions between genes and thereby facilitate the identification of gene processes in major depression. We developed and applied a novel research methodology (called “eMagma”) which integrates genetic and transcriptomic information in a tissue-specific analysis and tests for their enrichment in gene co-expression modules. Using this novel approach, we identified gene modules in multiple tissues that are both enriched with major depression genetic association signals and biologically meaningful pathways. We also show gene modules are strongly preserved across brain regions, but not in whole blood, suggesting blood may not be a useful tissue surrogate for the genetic dissection of major depression. Our novel analytical framework provides fundamental insights into the functional genetics major depression and can be applied to other neuropsychiatric disorders.
Publisher: American Psychiatric Association Publishing
Date: 2015
Publisher: Cold Spring Harbor Laboratory
Date: 03-10-2019
DOI: 10.1101/787903
Abstract: The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing s les from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans , showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large ersity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.
Publisher: Springer Science and Business Media LLC
Date: 10-02-2017
Publisher: Springer Science and Business Media LLC
Date: 12-10-2017
DOI: 10.1038/NATURE24265
Abstract: X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of ‘escape’ from inactivation varying between genes and in iduals 1,2 . The extent to which XCI is shared between cells and tissues remains poorly characterized 3,4 , as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression 5 and phenotypic traits 6 . Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 in iduals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify ex les of heterogeneity between tissues, in iduals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic ersity 6,7 . Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.
Publisher: Springer Science and Business Media LLC
Date: 13-05-2019
Publisher: Springer Science and Business Media LLC
Date: 12-10-2017
DOI: 10.1038/NATURE24267
Abstract: Rare genetic variants are abundant in humans and are expected to contribute to in idual disease risk 1,2,3,4 . While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants 1,5 . Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles 1,6,7 , but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues 8,9,10,11 , but their effects across tissues are unknown. Here we identify gene expression outliers, or in iduals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release 12 . We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in in idual genomes.
Publisher: Springer Science and Business Media LLC
Date: 25-03-2019
Publisher: Public Library of Science (PLoS)
Date: 13-05-2015
Publisher: Springer Science and Business Media LLC
Date: 25-02-2022
Publisher: Springer Science and Business Media LLC
Date: 11-09-2020
DOI: 10.1186/S13059-020-02122-Z
Abstract: Allele expression (AE) analysis robustly measures cis -regulatory effects. Here, we present and demonstrate the utility of a vast AE resource generated from the GTEx v8 release, containing 15,253 s les spanning 54 human tissues for a total of 431 million measurements of AE at the SNP level and 153 million measurements at the haplotype level. In addition, we develop an extension of our tool phASER that allows effect sizes of cis -regulatory variants to be estimated using haplotype-level AE data. This AE resource is the largest to date, and we are able to make haplotype-level data publicly available. We anticipate that the availability of this resource will enable future studies of regulatory variation across human tissues.
Publisher: Elsevier BV
Date: 2023
Publisher: Cold Spring Harbor Laboratory
Date: 24-11-2019
DOI: 10.1101/853580
Abstract: Alzheimer’s disease is a highly heritable and severe neuropsychiatric condition. Genome-wide association studies have identified multiple genetic risk factors underlying susceptibility to Alzheimer’s disease, however their functional impact remains poorly understood. To overcome this shortcoming, we integrated genome-wide association summary statistics (71,880 cases, 338,378 controls) with tissue-specific gene co-expression networks derived from GTEx to identify functional gene co-expression networks underlying the disease. We found genetic variants associated with Alzheimer’s disease are enriched in gene co-expression networks involved in immune response-related biological processes. The implicated gene co-expression networks are preserved across multiple brain and peripheral tissues, highlighting the potential utility of peripheral tissues in genetic studies of Alzheimer’s disease. We also performed a computational drug repositioning analysis by integrating gene expression changes within Alzheimer’s disease modules with drug-gene signatures from the Connectivity Map, and show disease implicated networks retrieve known Alzheimer’s disease drugs and novel drug repurposing candidates for follow-up functional studies. Our results improve the biological interpretation of recent genetic data for Alzheimer’s disease and provide a list of potential anti-dementia drug repositioning candidates of which the efficacy should be investigated in functional validation studies.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 11-09-2020
Abstract: The Genotype-Tissue Expression (GTEx) project dissects how genetic variation affects gene expression and splicing.
Publisher: Elsevier BV
Date: 10-2020
Publisher: Public Library of Science (PLoS)
Date: 24-10-2013
Publisher: Springer Science and Business Media LLC
Date: 12-02-2016
DOI: 10.1038/NCOMMS10635
Abstract: Paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) is the most common cancer of childhood, yet little is known about BCP-ALL predisposition. In this study, in 2,187 cases of European ancestry and 5,543 controls, we discover and replicate a locus indexed by rs77728904 at 9p21.3 associated with BCP-ALL susceptibility ( P combined =3.32 × 10 −15 , OR=1.72) and independent from rs3731217, the previously reported ALL-associated variant in this region. Of correlated SNPs tagged by this locus, only rs662463 is significant in African Americans, suggesting it is a plausible causative variant. Functional analysis shows that rs662463 is a cis -eQTL for CDKN2B , with the risk allele associated with lower expression, and suggests that rs662463 influences BCP-ALL risk by regulating CDKN2B expression through CEBPB signalling. Functional analysis of rs3731217 suggests it is associated with BCP-ALL by acting within a splicing regulatory element determining CDKN2A exon 3 usage ( P =0.01). These findings provide new insights into the critical role of the CDKN2 locus in BCP-ALL aetiology.
Publisher: Springer Science and Business Media LLC
Date: 20-07-2021
DOI: 10.1038/S41467-021-24387-Z
Abstract: Studies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a s le prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.
Publisher: Mary Ann Liebert Inc
Date: 10-2015
Publisher: Public Library of Science (PLoS)
Date: 15-07-2019
Publisher: Springer Science and Business Media LLC
Date: 11-07-2016
DOI: 10.1038/NATURE18642
Publisher: Elsevier BV
Date: 10-2022
DOI: 10.1016/J.BIOPSYCH.2022.03.001
Abstract: Global genetic correlation analysis has provided valuable insight into the shared genetic basis between psychiatric and substance use disorders. However, little is known about which regions disproportionately contribute to the global correlation. We used Local Analysis of [co]Variant Annotation to calculate bivariate local genetic correlations across 2495 approximately equal-sized, semi-independent genomic regions for 20 psychiatric and substance use phenotypes. We performed a transcriptome-wide association study using expression weights from the prefrontal cortex to identify risk genes for each phenotype, followed by probabilistic fine-mapping to prioritize credible causal genes within each bivariate locus. We detected 80 significant (p < 2.08 × 10 Our study reveals previously unreported local bivariate genetic correlations between psychiatric and substance use phenotypes, which we fine-mapped to identify shared credible causal genes underlying genetically correlated phenotypes.
Publisher: Springer Science and Business Media LLC
Date: 14-12-2016
DOI: 10.1038/NATURE16068
Abstract: Thousands of transiting exoplanets have been discovered, but spectral analysis of their atmospheres has so far been dominated by a small number of exoplanets and data spanning relatively narrow wavelength ranges (such as 1.1-1.7 micrometres). Recent studies show that some hot-Jupiter exoplanets have much weaker water absorption features in their near-infrared spectra than predicted. The low litude of water signatures could be explained by very low water abundances, which may be a sign that water was depleted in the protoplanetary disk at the planet's formation location, but it is unclear whether this level of depletion can actually occur. Alternatively, these weak signals could be the result of obscuration by clouds or hazes, as found in some optical spectra. Here we report results from a comparative study of ten hot Jupiters covering the wavelength range 0.3-5 micrometres, which allows us to resolve both the optical scattering and infrared molecular absorption spectroscopically. Our results reveal a erse group of hot Jupiters that exhibit a continuum from clear to cloudy atmospheres. We find that the difference between the planetary radius measured at optical and infrared wavelengths is an effective metric for distinguishing different atmosphere types. The difference correlates with the spectral strength of water, so that strong water absorption lines are seen in clear-atmosphere planets and the weakest features are associated with clouds and hazes. This result strongly suggests that primordial water depletion during formation is unlikely and that clouds and hazes are the cause of weaker spectral signatures.
Publisher: Cold Spring Harbor Laboratory
Date: 11-10-2017
Abstract: Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a erse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 in iduals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues.
Publisher: Springer Science and Business Media LLC
Date: 28-06-2018
Publisher: Springer Science and Business Media LLC
Date: 14-08-2012
DOI: 10.1038/MP.2012.85
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 13-09-2021
DOI: 10.1212/NXG.0000000000000622
Abstract: To integrate genome-wide association study data with tissue-specific gene expression information to identify coexpression networks, biological pathways, and drug repositioning candidates for Alzheimer disease. We integrated genome-wide association summary statistics for Alzheimer disease with tissue-specific gene coexpression networks from brain tissue s les in the Genotype-Tissue Expression study. We identified gene coexpression networks enriched with genetic signals for Alzheimer disease and characterized the associated networks using biological pathway analysis. The disease-implicated modules were subsequently used as a molecular substrate for a computational drug repositioning analysis, in which we (1) imputed genetically regulated gene expression within Alzheimer disease implicated modules (2) integrated the imputed gene expression levels with drug-gene signatures from the connectivity map to identify compounds that normalize dysregulated gene expression underlying Alzheimer disease and (3) prioritized drug compounds and mechanisms of action based on the extent to which they normalize dysregulated expression signatures. Genetic factors for Alzheimer disease are enriched in brain gene coexpression networks involved in the immune response. Computational drug repositioning analyses of expression changes within the disease-associated networks retrieved known Alzheimer disease drugs (e.g., memantine) as well as biologically meaningful drug categories (e.g., glutamate receptor antagonists). Our results improve the biological interpretation of genetic data for Alzheimer disease and provide a list of potential antidementia drug repositioning candidates for which the efficacy should be investigated in functional validation studies.
Publisher: Cold Spring Harbor Laboratory
Date: 21-11-2021
DOI: 10.1101/2021.11.19.21266436
Abstract: Biobanks are being established across the world to understand the genetic, environmental, and epidemiological basis of human diseases with the goal of better prevention and treatments. Genome-wide association studies (GWAS) have been very successful at mapping genomic loci for a wide range of human diseases and traits, but in general, lack appropriate representation of erse ancestries - with most biobanks and preceding GWAS studies composed of in iduals of European ancestries. Here, we introduce the Global Biobank Meta-analysis Initiative (GBMI) -- a collaborative network of 19 biobanks from 4 continents representing more than 2.1 million consented in iduals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS generated using harmonized genotypes and phenotypes from member biobanks. GBMI brings together results from GWAS analysis across 6 main ancestry groups: approximately 33,000 of African ancestry either from Africa or from admixed-ancestry diaspora (AFR), 18,000 admixed American (AMR), 31,000 Central and South Asian (CSA), 341,000 East Asian (EAS), 1.4 million European (EUR), and 1,600 Middle Eastern (MID) in iduals. In this flagship project, we generated GWASs from across 14 exemplar diseases and endpoints, including both common and less prevalent diseases that were previously understudied. Using the genetic association results, we validate that GWASs conducted in biobanks worldwide can be successfully integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics between biobanks. We demonstrate the value of this collaborative effort to improve GWAS power for diseases, increase representation, benefit understudied diseases, and improve risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of the studied traits.
Publisher: Springer Science and Business Media LLC
Date: 12-10-2017
DOI: 10.1038/NATURE24041
Publisher: Elsevier BV
Date: 10-2022
Publisher: Cold Spring Harbor Laboratory
Date: 11-10-2017
Abstract: The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes ( cis -eQTLs). More research is needed to identify effects of genetic variation on distant genes ( trans -eQTLs) and understand their biological mechanisms. One common trans -eQTLs mechanism is “mediation” by a local ( cis ) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “ cis -mediators” of trans -eQTLs, including those “ cis -hubs” involved in regulation of many trans -genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans -eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis -mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among erse s les. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis -hubs and trans -eQTL regulation across tissue types.
Publisher: Proceedings of the National Academy of Sciences
Date: 07-04-2014
Abstract: We show that the genetic susceptibility to the euphoric effects of d - hetamine also influences the genetic predisposition to schizophrenia and attention deficit hyperactivity disorder (ADHD). These results reinforce the idea that dopamine plays a role in schizophrenia and ADHD this so-called dopamine hypothesis has been debated for several decades. Specifically, we found that the alleles associated with increased euphoric response to d - hetamine were associated with decreased risk for schizophrenia and ADHD. These results illustrate how an acute challenge with a pharmacological agent can reveal a genetic predisposition that will manifest itself as psychiatric illness over the lifetime of an in idual. Finally, our study offers a relatively novel paradigm for the analysis of endophenotypes for which large s le sizes are not typically available.
Publisher: Wiley
Date: 10-01-2020
DOI: 10.1002/WPS.20702
Publisher: American Association for the Advancement of Science (AAAS)
Date: 08-05-2015
Abstract: Human genomes show extensive genetic variation across in iduals, but we have only just started documenting the effects of this variation on the regulation of gene expression. Furthermore, only a few tissues have been examined per genetic variant. In order to examine how genetic expression varies among tissues within in iduals, the Genotype-Tissue Expression (GTEx) Consortium collected 1641 postmortem s les covering 54 body sites from 175 in iduals. They identified quantitative genetic traits that affect gene expression and determined which of these exhibit tissue-specific expression patterns. Melé et al. measured how transcription varies among tissues, and Rivas et al. looked at how truncated protein variants affect expression across tissues. Science , this issue p. 648 , p. 660 , p. 666 see also p. 640
Publisher: Springer Science and Business Media LLC
Date: 29-05-2013
DOI: 10.1038/NG.2653
Publisher: Elsevier BV
Date: 08-2023
Publisher: Springer Science and Business Media LLC
Date: 15-04-2019
DOI: 10.1038/S41467-018-08053-5
Abstract: Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.
Publisher: Springer Science and Business Media LLC
Date: 19-12-2017
Abstract: To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European in iduals and exome sequencing of 12,940 in iduals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1–5%) non-coding variants in the whole-genome sequenced in iduals and 99.7% of low-frequency coding variants in the whole-exome sequenced in iduals. Each variant was tested for association with T2D in the sequenced in iduals, and, to increase power, most were tested in larger numbers of in iduals ( % of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Eric Gamazon.