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      Genome-Wide Association Analysis Identifies Variants Associated with Nonalcoholic Fatty Liver Disease That Have Distinct Effects on Metabolic Traits

      1 , 2 , 3 , 4 , * , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 12 , 10 , 10 , 14 , 7 , 8 , 15 , 5 , 5 , 16 , 1 , 2 , 11 , 17 , 18 , 19 , 20 , 21 , 21 , 22 , 20 , 21 , 23 , 19 , 24 , 25 , 26 , 27 , 26 , NASH CRN, GIANT Consortium, MAGIC Investigators, 4 , 28 , 6 , 10 , 21 , 23 , 12 , 7 , 15 , 4 , 11 , 29 , 6 , * , GOLD Consortium

      PLoS Genetics

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%–27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels ( p<5×10 −8) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT–assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits.

          Author Summary

          NAFLD is a spectrum of disease that ranges from steatosis to steatohepatitis (nonalcoholic steatohepatitis or NASH: inflammation around the fat) to fibrosis/cirrhosis. Hepatic steatosis can be measured non-invasively using computed tomography (CT) whereas NASH/fibrosis is assessed histologically. The genetic underpinnings of NAFLD remain to be determined. Here we estimate that 26%–27% of the variation in CT measured hepatic steatosis is heritable or genetic. We identify three variants near PNPLAL3, NCAN, and PPP1R3B that associate with CT hepatic steatosis and show that variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B, associate with histologic lobular inflammation/fibrosis. Variants in or near NCAN, GCKR, and PPP1R3B associate with altered serum lipid levels, whereas those in or near LYPLAL1 and PNPLA3 do not. Variants near GCKR and PPP1R3B also affect glycemic traits. Thus, we show that NAFLD is genetically influenced and expand the number of common genetic variants that associate with this trait. Our findings suggest that development of hepatic steatosis, NASH/fibrosis, or abnormalities in metabolic traits are probably influenced by different metabolic pathways that may represent distinct therapeutic targets.

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          Most cited references 34

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          Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

          By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
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            Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease

            Nonalcoholic fatty liver disease (NAFLD) is a burgeoning health problem of unknown etiology that varies in prevalence among ethnic groups. To identify genetic variants contributing to differences in hepatic fat content, we performed a genome-wide association scan of nonsynonymous sequence variations (n=9,229) in a multiethnic population. An allele in PNPLA3 (rs738409; I148M) was strongly associated with increased hepatic fat levels (P=5.9×10−10) and with hepatic inflammation (P=3.7×10−4). The allele was most common in Hispanics, the group most susceptible to NAFLD; hepatic fat content was > 2-fold higher in PNPLA3-148M homozygotes than in noncarriers. Resequencing revealed another allele associated with lower hepatic fat content in African-Americans, the group at lowest risk of NAFLD. Thus, variation in PNPLA3 contributes to ethnic and inter-individual differences in hepatic fat content and susceptibility to NAFLD.
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              Newly identified loci that influence lipid concentrations and risk of coronary artery disease.

              To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                March 2011
                March 2011
                10 March 2011
                : 7
                : 3
                Affiliations
                [1 ]Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, United States of America
                [2 ]Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
                [3 ]Division of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [4 ]Broad Institute, Cambridge, Massachusetts, United States of America
                [5 ]Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
                [6 ]Division of Statistical Genomics, Department of Genetics, Washington University, Saint Louis, Missouri, United States of America
                [7 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [8 ]Division of General Internal Medicine, The Johns Hopkins Hospital, Baltimore, Maryland, United States of America
                [9 ]Department of Internal Medicine, Washington Hospital Center, Washington D.C., United States of America
                [10 ]Laboratory of Epidemiology, Demography, and Biometry, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
                [11 ]Divisions of Endocrinology and Genetics and Program in Genomics, Children's Hospital, Boston, Massachusetts, United States of America
                [12 ]Icelandic Heart Association, Kopavogur, Iceland
                [13 ]University of Iceland, Reykjavik, Iceland
                [14 ]Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
                [15 ]Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland, United States of America
                [16 ]Geriatric Research and Education Clinical Center (GRECC), Veterans Administration Medical Center, Baltimore, Maryland, United States of America
                [17 ]Cardiovascular Epidemiology and Genetics, Institut Municipal d'Investigació Mèdica, Barcelona, Spain
                [18 ]CIBER Epidemiología y Salud Pública, Barcelona, Spain
                [19 ]Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [20 ]Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
                [21 ]Framingham Heart Study, National Heart, Lung, and Blood Institute (NHLBI), Framingham, Massachusetts, United States of America
                [22 ]Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America
                [23 ]Division of Intramural Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, United States of America
                [24 ]Chronic Disease Epidemiology Unit, Department of Health Promotion and Chronic Disease Prevention, National Public Health Institute, Helsinki, Finland
                [25 ]Pacific Biosciences, Menlo Park, California, United States of America
                [26 ]Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington, United States of America
                [27 ]Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
                [28 ]Departments of Radiologic Sciences, Internal Medicine-Cardiology, and Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
                [29 ]Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
                University of Oxford, United Kingdom
                Author notes

                Conceived and designed the experiments: EKS CJO CSF WHLK JNH IBB. Performed the experiments: EKS LMYA JW RH JLB MFF. Analyzed the data: EKS LMYA JW RH CDP GE MEG LJL MAN SJH JMM BFV AVS. Contributed reagents/materials/analysis tools: EKS VG JMC BDM ARS MT UH JMM CJO DVS VS EES SMS DSS NASH CRN GIANT Consortium MAGIC Investigators JJC TBH CSF WHLK JNH IBB. Wrote the paper: EKS LMYA JW RH LJK CSF WHLK JNH IBB GOLD Consortium.

                ¶ For membership information, please see Acknowledgments.

                Article
                10-PLGE-RA-NV-3374R3
                10.1371/journal.pgen.1001324
                3053321
                21423719
                This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
                Page count
                Pages: 14
                Categories
                Research Article
                Diabetes and Endocrinology/Obesity
                Gastroenterology and Hepatology/Hepatology
                Genetics and Genomics/Complex Traits

                Genetics

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