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Genome wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits

Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, United States of America.
PLoS Genetics (Impact Factor: 8.17). 03/2011; 7(3):e1001324. DOI: 10.1371/journal.pgen.1001324
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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.

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Available from: Ruben Hernaez, Aug 27, 2015
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    • "Moreover, several genome-wide association studies have shown that a variant in NCAN (rs2228603), whose locus is near TM6SF2, is strongly associated with NAFLD [7] [8]. "
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    ABSTRACT: Table 1. Association between TM6SF2 rs58542926 variant and NAFLD.
    Journal of Hepatology 02/2015; 46(6). DOI:10.1016/j.jhep.2015.01.040 · 10.40 Impact Factor
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    • "Several SNPs, including rs2645424, rs343062, SNP rs1227756, rs6591182, rs887304, rs2499604, rs6487679, rs1421201, and rs2710833, have been associated with NAFLD, NASH, or serum aminotransferase elevation (Chalasani et al. 2010; Kilpelainen et al. 2011). Significant associations with histologic NAFLD have also been identified at variants in or near NCAN, GCKR, and LYPLAL1 (Speliotes et al. 2011). The roles of these SNPs in fatty liver diseases and lipid metabolism require further investigation. "
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    ABSTRACT: Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent form of human hepatic disease and feeding mice a High-Fat, High-Caloric (HFHC) diet is a standard model of NAFLD. To better understand the genetic basis of NAFLD, we conducted an expression quantitative trait locus (eQTL) analysis of mice fed a HFHC diet. 265 (A/J × C57BL/6J) F2 male mice were fed a HFHC diet for 8 weeks. eQTL analysis was utilized to identify genomic regions that regulate hepatic gene expression of Xbp1s and Socs3. We identified two overlapping loci for Xbp1s and Socs3 on Chr 1 (164.0-185.4 Mb and 174.4-190.5 Mb, respectively) and Chr 11 (41.1-73.1 Mb and 44.0-68.6 Mb, respectively), and an additional locus for Socs3 on Chr 12 (109.9-117.4 Mb). C57BL/6J-Chr 11(A/J)/ NaJ mice fed a HFHC diet manifested the A/J phenotype of increased Xbp1s and Socs3 gene expression (P < 0.05), while C57BL/6J-Chr 1(A/J)/ NaJ mice retained the C57BL/6J phenotype. In addition, we replicated the eQTLs on Chr 1 and 12 (LOD scores ≥ 3.5) using mice from the BXD murine reference panel challenged with CCl4 to induce chronic liver injury and fibrosis. We have identified overlapping eQTLs for Xbp1 and Socs3 on Chr 1 and 11, and consomic mice confirmed that replacing the C57BL/6J Chr 11 with the A/J Chr 11 resulted in an A/J phenotype for Xbp1 and Socs3 gene expression. Identification of the genes for these eQTLs will lead to a better understanding of the genetic factors responsible for NAFLD and potentially other hepatic diseases. Copyright © 2015 Author et al.
    G3-Genes Genomes Genetics 01/2015; 5(4). DOI:10.1534/g3.115.016626 · 2.51 Impact Factor
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    • "The common variant-common disease hypothesis has successfully been investigated using genome-wide association studies (GWAS) with unrelated individuals in the past decade (Saxena et al., 2007; Seng and Seng, 2008; Manolio, 2010; Speliotes et al., 2011). With the advent of high-throughput sequencing technologies, generally termed next generation sequencing (NGS) (Metzker, 2010), it has now become possible to efficiently study the rare variant-common disease hypothesis, even on the genome-wide level (Campbell and Manolio, 2007; Bodmer and Bonilla, 2008). "
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    ABSTRACT: The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.
    Frontiers in Genetics 09/2014; 5:323. DOI:10.3389/fgene.2014.00323
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