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GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE Network.
Namjou, Bahram; Lingren, Todd; Huang, Yongbo; Parameswaran, Sreeja; Cobb, Beth L; Stanaway, Ian B; Connolly, John J; Mentch, Frank D; Benoit, Barbara; Niu, Xinnan; Wei, Wei-Qi; Carroll, Robert J; Pacheco, Jennifer A; Harley, Isaac T W; Divanovic, Senad; Carrell, David S; Larson, Eric B; Carey, David J; Verma, Shefali; Ritchie, Marylyn D; Gharavi, Ali G; Murphy, Shawn; Williams, Marc S; Crosslin, David R; Jarvik, Gail P; Kullo, Iftikhar J; Hakonarson, Hakon; Li, Rongling; Xanthakos, Stavra A; Harley, John B.
Afiliación
  • Namjou B; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA. bahram.namjou@cchmc.org.
  • Lingren T; College of Medicine, University of Cincinnati, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA. bahram.namjou@cchmc.org.
  • Huang Y; College of Medicine, University of Cincinnati, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
  • Parameswaran S; Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
  • Cobb BL; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.
  • Stanaway IB; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.
  • Connolly JJ; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA.
  • Mentch FD; Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, WA, USA.
  • Benoit B; Center for Applied Genomics, Children's Hospital of Philadelphia, Bethesda, MD, USA.
  • Niu X; Center for Applied Genomics, Children's Hospital of Philadelphia, Bethesda, MD, USA.
  • Wei WQ; Research IS and Computing, Partners HealthCare, Harvard University, Somerville, MA, USA.
  • Carroll RJ; Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, USA.
  • Pacheco JA; Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, USA.
  • Harley ITW; Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, TN, USA.
  • Divanovic S; Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Carrell DS; Division of Immunobiology, Department of Pediatrics, Cincinnati Children's Hospital Research Foundation and the University of Cincinnati College of Medicine, Cincinnati, OH, USA.
  • Larson EB; Division of Immunobiology, Department of Pediatrics, Cincinnati Children's Hospital Research Foundation and the University of Cincinnati College of Medicine, Cincinnati, OH, USA.
  • Carey DJ; Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, WA, USA.
  • Verma S; Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, WA, USA.
  • Ritchie MD; Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA.
  • Gharavi AG; Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
  • Murphy S; Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA.
  • Williams MS; Department of Medicine, Columbia University, New York City, NY, USA.
  • Crosslin DR; Research Information Science and Computing, Partners HealthCare, Boston, MA, USA.
  • Jarvik GP; Genomic Medicine Institute (M.S.W.), Geisinger, Danville, PA, USA.
  • Kullo IJ; Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, WA, USA.
  • Hakonarson H; Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington Medical Center, Seattle, WA, USA.
  • Li R; Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA.
  • Xanthakos SA; Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
  • Harley JB; National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
BMC Med ; 17(1): 135, 2019 07 17.
Article en En | MEDLINE | ID: mdl-31311600
ABSTRACT

BACKGROUND:

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition.

METHODS:

First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI).

RESULTS:

Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p = 1.70 × 10- 20). This effect was consistent in both pediatric (p = 9.92 × 10- 6) and adult (p = 9.73 × 10- 15) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) (p = 3.94 × 10- 8, beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout (p = 1.09 × 10- 4). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p = 3.80 × 10- 8), and another near ZFP90-CDH1 for fibrosis (rs698718, p = 2.74 × 10- 11). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses.

CONCLUSIONS:

In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Enfermedad del Hígado Graso no Alcohólico Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: BMC Med Asunto de la revista: MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Enfermedad del Hígado Graso no Alcohólico Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: BMC Med Asunto de la revista: MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos