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Raby, Benjamin

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Raby

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Benjamin

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Raby, Benjamin
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Now showing 1 - 10 of 42
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    Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation
    (Public Library of Science, 2015) Croteau-Chonka, Damien; Rogers, Angela J.; Raj, Towfique; McGeachie, Michael; Qiu, Weiliang; Ziniti, John P.; Stubbs, Benjamin J.; Liang, Liming; Martinez, Fernando D.; Strunk, Robert C.; Lemanske, Robert F.; Liu, Andrew H.; Stranger, Barbara E.; Carey, Vincent; Raby, Benjamin
    Disease-associated loci identified through genome-wide association studies (GWAS) frequently localize to non-coding sequence. We and others have demonstrated strong enrichment of such single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTLs), supporting an important role for regulatory genetic variation in complex disease pathogenesis. Herein we describe our initial efforts to develop a predictive model of disease-associated variants leveraging eQTL information. We first catalogued cis-acting eQTLs (SNPs within 100kb of target gene transcripts) by meta-analyzing four studies of three blood-derived tissues (n = 586). At a false discovery rate < 5%, we mapped eQTLs for 6,535 genes; these were enriched for disease-associated genes (P < 10−04), particularly those related to immune diseases and metabolic traits. Based on eQTL information and other variant annotations (distance from target gene transcript, minor allele frequency, and chromatin state), we created multivariate logistic regression models to predict SNP membership in reported GWAS. The complete model revealed independent contributions of specific annotations as strong predictors, including evidence for an eQTL (odds ratio (OR) = 1.2–2.0, P < 10−11) and the chromatin states of active promoters, different classes of strong or weak enhancers, or transcriptionally active regions (OR = 1.5–2.3, P < 10−11). This complete prediction model including eQTL association information ultimately allowed for better discrimination of SNPs with higher probabilities of GWAS membership (6.3–10.0%, compared to 3.5% for a random SNP) than the other two models excluding eQTL information. This eQTL-based prediction model of disease relevance can help systematically prioritize non-coding GWAS SNPs for further functional characterization.
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    Limited statistical evidence for shared genetic effects of eQTLs and autoimmune disease-associated loci in three major immune cell types
    (2017) Chun, Sung; Casparino, Alexandra; Patsopoulos, Nikolaos; Croteau-Chonka, Damien; Raby, Benjamin; De Jager, Philip; Sunyaev, Shamil; Cotsapas, Chris
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    A PRDX1 mutant allele causes a MMACHC secondary epimutation in cblC patients
    (Nature Publishing Group UK, 2018) Guéant, Jean-Louis; Chéry, Céline; Oussalah, Abderrahim; Nadaf, Javad; Coelho, David; Josse, Thomas; Flayac, Justine; Robert, Aurélie; Koscinski, Isabelle; Gastin, Isabelle; Filhine-Tresarrieu, Pierre; Pupavac, Mihaela; Brebner, Alison; Watkins, David; Pastinen, Tomi; Montpetit, Alexandre; Hariri, Fadi; Tregouët, David; Raby, Benjamin; Chung, Wendy K.; Morange, Pierre-Emmanuel; Froese, D. Sean; Baumgartner, Matthias R.; Benoist, Jean-François; Ficicioglu, Can; Marchand, Virginie; Motorin, Yuri; Bonnemains, Chrystèle; Feillet, François; Majewski, Jacek; Rosenblatt, David S.
    To date, epimutations reported in man have been somatic and erased in germlines. Here, we identify a cause of the autosomal recessive cblC class of inborn errors of vitamin B12 metabolism that we name “epi-cblC”. The subjects are compound heterozygotes for a genetic mutation and for a promoter epimutation, detected in blood, fibroblasts, and sperm, at the MMACHC locus; 5-azacytidine restores the expression of MMACHC in fibroblasts. MMACHC is flanked by CCDC163P and PRDX1, which are in the opposite orientation. The epimutation is present in three generations and results from PRDX1 mutations that force antisense transcription of MMACHC thereby possibly generating a H3K36me3 mark. The silencing of PRDX1 transcription leads to partial hypomethylation of the epiallele and restores the expression of MMACHC. This example of epi-cblC demonstrates the need to search for compound epigenetic-genetic heterozygosity in patients with typical disease manifestation and genetic heterozygosity in disease-causing genes located in other gene trios.
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    A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data
    (Nature Publishing Group UK, 2018) Pandey, Gaurav; Pandey, Om P.; Rogers, Angela J.; Ahsen, Mehmet E.; Hoffman, Gabriel E.; Raby, Benjamin; Weiss, Scott; Schadt, Eric E.; Bunyavanich, Supinda
    Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.
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    Publisher Correction: A PRDX1 mutant allele causes a MMACHC secondary epimutation in cblC patients
    (Nature Publishing Group UK, 2018) Guéant, Jean-Louis; Chéry, Céline; Oussalah, Abderrahim; Nadaf, Javad; Coelho, David; Josse, Thomas; Flayac, Justine; Robert, Aurélie; Koscinski, Isabelle; Gastin, Isabelle; Filhine-Tresarrieu, Pierre; Pupavac, Mihaela; Brebner, Alison; Watkins, David; Pastinen, Tomi; Montpetit, Alexandre; Hariri, Fadi; Tregouët, David; Raby, Benjamin; Chung, Wendy K.; Morange, Pierre-Emmanuel; Froese, D. Sean; Baumgartner, Matthias R.; Benoist, Jean-François; Ficicioglu, Can; Marchand, Virginie; Motorin, Yuri; Bonnemains, Chrystèle; Feillet, François; Majewski, Jacek; Rosenblatt, David S.
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    Multiancestry association study identifies new asthma risk loci that colocalize with immune cell enhancer marks
    (2018) Demenais, Florence; Margaritte-Jeannin, Patricia; Barnes, Kathleen C; Cookson, William OC; Altmüller, Janine; Ang, Wei; Barr, R Graham; Beaty, Terri H; Becker, Allan B; Beilby, John; Bisgaard, Hans; Bjornsdottir, Unnur Steina; Bleecker, Eugene; Bønnelykke, Klaus; Boomsma, Dorret I; Bouzigon, Emmanuelle; Brightling, Christopher E; Brossard, Myriam; Brusselle, Guy G; Burchard, Esteban; Burkart, Kristin M; Bush, Andrew; Chan-Yeung, Moira; Chung, Kian Fan; Alves, Alexessander Couto; Curtin, John A; Custovic, Adnan; Daley, Denise; de Jongste, Johan C; Del-Rio-Navarro, Blanca E; Donohue, Kathleen M; Duijts, Liesbeth; Eng, Celeste; Eriksson, Johan G; Farrall, Martin; Fedorova, Yuliya; Feenstra, Bjarke; Ferreira, Manuel A; Freidin, Maxim B; Gajdos, Zofia; Gauderman, Jim; Gehring, Ulrike; Geller, Frank; Genuneit, Jon; Gharib, Sina A; Gilliland, Frank; Granell, Raquel; Graves, Penelope E; Gudbjartsson, Daniel F; Haahtela, Tari; Heckbert, Susan R; Heederik, Dick; Heinrich, Joachim; Heliövaara, Markku; Henderson, John; Himes, Blanca E; Hirose, Hiroshi; Hirschhorn, Joel; Hofman, Albert; Holt, Patrick; Hottenga, Jouke Jan; Hudson, Thomas J; Hui, Jennie; Imboden, Medea; Ivanov, Vladimir; Jaddoe, Vincent WV; James, Alan; Janson, Christer; Jarvelin, Marjo-Riitta; Jarvis, Deborah; Jones, Graham; Jonsdottir, Ingileif; Jousilahti, Pekka; Kabesch, Michael; Kähönen, Mika; Kantor, David; Karunas, Alexandra S; Khusnutdinova, Elza; Koppelman, Gerard H; Kozyrskyj, Anita L; Kreiner, Eskil; Kubo, Michiaki; Kumar, Rajesh; Kumar, Ashish; Kuokkanen, Mikko; Lahousse, Lies; Laitinen, Tarja; Laprise, Catherine; Lathrop, Mark; Lau, Susanne; Lee, Young-Ae; Lehtimäki, Terho; Letort, Sébastien; Levin, Albert M; Li, Guo; Liang, Liming; Loehr, Laura R; London, Stephanie J; Loth, Daan W; Manichaikul, Ani; Marenholz, Ingo; Martinez, Fernando J; Matheson, Melanie C; Mathias, Rasika A; Matsumoto, Kenji; Mbarek, Hamdi; McArdle, Wendy L; Melbye, Mads; Melén, Erik; Meyers, Deborah; Michel, Sven; Mohamdi, Hamida; Musk, Arthur W; Myers, Rachel A; Nieuwenhuis, Maartje AE; Noguchi, Emiko; O'Connor, George T; Ogorodova, Ludmila M; Palmer, Cameron D; Palotie, Aarno; Park, Julie E; Pennell, Craig E; Pershagen, Göran; Polonikov, Alexey; Postma, Dirkje S; Probst-Hensch, Nicole; Puzyrev, Valery P; Raby, Benjamin; Raitakari, Olli T; Ramasamy, Adaikalavan; Rich, Stephen S; Robertson, Colin F; Romieu, Isabelle; Salam, Muhammad T; Salomaa, Veikko; Schlünssen, Vivi; Scott, Robert; Selivanova, Polina A; Sigsgaard, Torben; Simpson, Angela; Siroux, Valérie; Smith, Lewis J; Solodilova, Maria; Standl, Marie; Stefansson, Kari; Strachan, David P; Stricker, Bruno H; Takahashi, Atsushi; Thompson, Philip J; Thorleifsson, Gudmar; Thorsteinsdottir, Unnur; Tiesler, Carla MT; Torgerson, Dara G; Tsunoda, Tatsuhiko; Uitterlinden, André G; van der Valk, Ralf JP; Vaysse, Amaury; Vedantam, Sailaja; Von Berg, Andrea; Von Mutius, Erika; Vonk, Judith M; Waage, Johannes; Wareham, Nick J; Weiss, Scott; White, Wendy B; Wickman, Magnus; Widén, Elisabeth; Willemsen, Gonneke; Williams, L Keoki; Wouters, Inge M; Yang, James J; Zhao, Jing Hua; Moffatt, Miriam F; Ober, Carole; Nicolae, Dan L
    We examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 cases, 118,538 controls) from ethnically-diverse populations. We identified five new asthma loci, uncovered two additional novel associations at two known asthma loci, established asthma associations at two loci implicated previously in comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. Enrichment of asthma risk loci in enhancer marks, especially in immune cells, suggests a major role of these loci in the regulation of immune-related mechanisms.
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    ITGB5 and AGFG1 variants are associated with severity of airway responsiveness
    (BioMed Central, 2013) Himes, Blanca; Qiu, Weiliang; Klanderman, Barbara; Ziniti, John; Senter-Sylvia, Jody; Szefler, Stanley J; Lemanske, Jr, Robert F; Zeiger, Robert S; Strunk, Robert C; Martinez, Fernando D; Boushey, Homer; Chinchilli, Vernon M; Israel, Elliot; Mauger, David; Koppelman, Gerard H; Nieuwenhuis, Maartje AE; Postma, Dirkje S; Vonk, Judith M; Rafaels, Nicholas; Hansel, Nadia N; Barnes, Kathleen; Raby, Benjamin; Tantisira, Kelan; Weiss, Scott
    Background: Airway hyperresponsiveness (AHR), a primary characteristic of asthma, involves increased airway smooth muscle contractility in response to certain exposures. We sought to determine whether common genetic variants were associated with AHR severity. Methods: A genome-wide association study (GWAS) of AHR, quantified as the natural log of the dosage of methacholine causing a 20% drop in FEV1, was performed with 994 non-Hispanic white asthmatic subjects from three drug clinical trials: CAMP, CARE, and ACRN. Genotyping was performed on Affymetrix 6.0 arrays, and imputed data based on HapMap Phase 2, was used to measure the association of SNPs with AHR using a linear regression model. Replication of primary findings was attempted in 650 white subjects from DAG, and 3,354 white subjects from LHS. Evidence that the top SNPs were eQTL of their respective genes was sought using expression data available for 419 white CAMP subjects. Results: The top primary GWAS associations were in rs848788 (P-value 7.2E-07) and rs6731443 (P-value 2.5E-06), located within the ITGB5 and AGFG1 genes, respectively. The AGFG1 result replicated at a nominally significant level in one independent population (LHS P-value 0.012), and the SNP had a nominally significant unadjusted P-value (0.0067) for being an eQTL of AGFG1. Conclusions: Based on current knowledge of ITGB5 and AGFG1, our results suggest that variants within these genes may be involved in modulating AHR. Future functional studies are required to confirm that our associations represent true biologically significant findings.
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    Gene-by-environment effect of house dust mite on purinergic receptor P2Y12 (P2RY12) and lung function in children with asthma
    (Wiley-Blackwell, 2011) Bunyavanich, S.; Boyce, Joshua; Raby, Benjamin; Weiss, Scott
    Background— Distinct receptors likely exist for leukotriene(LT)E4, a potent mediator of airway inflammation. Purinergic receptor P2Y12 is needed for LTE4-induced airways inflammation, and P2Y12 antagonism attenuates house dust mite-induced pulmonary eosinophilia in mice. Although experimental data support a role for P2Y12 in airway inflammation, its role in human asthma has never been studied. Objective— To test for association between variants in the P2Y12 gene (P2RY12) and lung function in human subjects with asthma, and to examine for gene-by-environment interaction with house dust mite exposure. Methods— 19 single nucleotide polymorphisms (SNPs) in P2RY12 were genotyped in 422 children with asthma and their parents (n=1266). Using family-based methods, we tested for associations between these SNPs and five lung function measures. We performed haplotype association analyses and tested for gene-by-environment interactions using house dust mite exposure. We used the false discovery rate to account for multiple comparisons. Results— Five SNPs in P2RY12 were associated with multiple lung function measures (P values 0.006–0.025). Haplotypes in P2RY12 were also associated with lung function (P values 0.0055– 0.046). House dust mite exposure modulated associations between P2RY12 and lung function, with minor allele homozygotes exposed to house dust mite demonstrating worse lung function than those unexposed (significant interaction P values 0.0028–0.040). Conclusions and clinical relevance— P2RY12 variants were associated with lung function in a large family-based asthma cohort. House dust mite exposure caused significant gene-by- environment effects. Our findings add the first human evidence to experimental data supporting a role for P2Y12 in lung function. P2Y12 could represent a novel target for asthma treatment.
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    Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis
    (BioMed Central, 2014) Bunyavanich, Supinda; Schadt, Eric E; Himes, Blanca; Lasky-Su, Jessica; Qiu, Weiliang; Lazarus, Ross; Ziniti, John P; Cohain, Ariella; Linderman, Michael; Torgerson, Dara G; Eng, Celeste S; Pino-Yanes, Maria; Padhukasahasram, Badri; Yang, James J; Mathias, Rasika A; Beaty, Terri H; Li, Xingnan; Graves, Penelope; Romieu, Isabelle; Navarro, Blanca del Rio; Salam, M Towhid; Vora, Hita; Nicolae, Dan L; Ober, Carole; Martinez, Fernando D; Bleecker, Eugene R; Meyers, Deborah A; Gauderman, W James; Gilliland, Frank; Burchard, Esteban G; Barnes, Kathleen C; Williams, L Keoki; London, Stephanie J; Zhang, Bin; Raby, Benjamin; Weiss, Scott
    Background: Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis. Methods: We performed genome wide association studies (GWAS) of allergic rhinitis in 5633 ethnically diverse North American subjects. Next, we profiled gene expression in disease-relevant tissue (peripheral blood CD4+ lymphocytes) collected from subjects who had been genotyped. We then integrated the GWAS and gene expression data using expression single nucleotide (eSNP), coexpression network, and pathway approaches to identify the biologic relevance of our GWAS. Results: GWAS revealed ethnicity-specific findings, with 4 genome-wide significant loci among Latinos and 1 genome-wide significant locus in the GWAS meta-analysis across ethnic groups. To identify biologic context for these results, we constructed a coexpression network to define modules of genes with similar patterns of CD4+ gene expression (coexpression modules) that could serve as constructs of broader gene expression. 6 of the 22 GWAS loci with P-value ≤ 1x10−6 tagged one particular coexpression module (4.0-fold enrichment, P-value 0.0029), and this module also had the greatest enrichment (3.4-fold enrichment, P-value 2.6 × 10−24) for allergic rhinitis-associated eSNPs (genetic variants associated with both gene expression and allergic rhinitis). The integrated GWAS, coexpression network, and eSNP results therefore supported this coexpression module as an allergic rhinitis module. Pathway analysis revealed that the module was enriched for mitochondrial pathways (8.6-fold enrichment, P-value 4.5 × 10−72). Conclusions: Our results highlight mitochondrial pathways as a target for further investigation of allergic rhinitis mechanism and treatment. Our integrated approach can be applied to provide biologic context for GWAS of other diseases.
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    Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD
    (BioMed Central, 2014) Chu, Jen-Hwa; Hersh, Craig; Castaldi, Peter; Cho, Michael; Raby, Benjamin; Laird, Nan; Bowler, Russell; Rennard, Stephen; Loscalzo, Joseph; Quackenbush, John; Silverman, Edwin
    Background: The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes. Results: We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches. Conclusion: Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.