Genome-Wide Association Analysis Identifies Variants
Associated with Nonalcoholic Fatty Liver Disease That
Have Distinct Effects on Metabolic Traits
Elizabeth K. Speliotes1,2,3,4*., Laura M. Yerges-Armstrong5., Jun Wu6., Ruben Hernaez7,8,9., Lauren J.
Kim10., Cameron D. Palmer11, Vilmundur Gudnason12,13, Gudny Eiriksdottir12, Melissa E. Garcia10,
Lenore J. Launer10, Michael A. Nalls14, Jeanne M. Clark7,8,15, Braxton D. Mitchell5, Alan R. Shuldiner5,16,
Johannah L. Butler1,2,11, Marta Tomas17,18, Udo Hoffmann19,20, Shih-Jen Hwang21, Joseph M.
Massaro21,22, Christopher J. O’Donnell20,21,23, Dushyant V. Sahani19, Veikko Salomaa24, Eric E. Schadt25,
Stephen M. Schwartz26,27, David S. Siscovick26, NASH CRN", GIANT Consortium", MAGIC
Investigators", Benjamin F. Voight4, J. Jeffrey Carr28, Mary F. Feitosa6, Tamara B. Harris10, Caroline S.
Fox21,23, Albert V. Smith12., W. H. Linda Kao7,15., Joel N. Hirschhorn4,11,29., Ingrid B. Borecki6*., GOLD
1Department of Internal Medicine, Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, United States of America, 2Center for Computational
Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America, 3Division of Gastroenterology, Massachusetts General Hospital,
Boston, Massachusetts, United States of America, 4Broad Institute, Cambridge, Massachusetts, United States of America, 5Division of Endocrinology, Diabetes, and
Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, 6Division of Statistical Genomics,
Department of Genetics, Washington University, Saint Louis, Missouri, United States of America, 7Department of Epidemiology, Johns Hopkins Bloomberg School of
Public Health, Baltimore, Maryland, United States of America, 8Division of General Internal Medicine, The Johns Hopkins Hospital, Baltimore, Maryland, United States of
America, 9Department of Internal Medicine, Washington Hospital Center, Washington D.C., United States of America, 10Laboratory of Epidemiology, Demography, and
Biometry, Intramural Research Program, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America, 11Divisions of
Endocrinology and Genetics and Program in Genomics, Children’s Hospital, Boston, Massachusetts, United States of America, 12Icelandic Heart Association, Kopavogur,
Iceland, 13University of Iceland, Reykjavik, Iceland, 14Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United
States of America, 15Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland, United States of America, 16Geriatric Research and
Education Clinical Center (GRECC), Veterans Administration Medical Center, Baltimore, Maryland, United States of America, 17Cardiovascular Epidemiology and Genetics,
Institut Municipal d’Investigacio ´ Me `dica, Barcelona, Spain, 18CIBER Epidemiologı ´a y Salud Pu ´blica, Barcelona, Spain, 19Department of Radiology, Massachusetts General
Hospital, Boston, Massachusetts, United States of America, 20Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America,
21Framingham Heart Study, National Heart, Lung, and Blood Institute (NHLBI), Framingham, Massachusetts, United States of America, 22Department of Biostatistics,
Boston University School of Public Health, Boston, Massachusetts, United States of America, 23Division of Intramural Research, National Heart, Lung, and Blood Institute,
Bethesda, Maryland, United States of America, 24Chronic Disease Epidemiology Unit, Department of Health Promotion and Chronic Disease Prevention, National Public
Health Institute, Helsinki, Finland, 25Pacific Biosciences, Menlo Park, California, United States of America, 26Cardiovascular Health Research Unit, Departments of
Medicine and Epidemiology, University of Washington, Seattle, Washington, United States of America, 27Department of Epidemiology, University of Washington, Seattle,
Washington, United States of America, 28Departments of Radiologic Sciences, Internal Medicine-Cardiology, and Public Health Sciences, Wake Forest University School of
Medicine, Winston-Salem, North Carolina, United States of America, 29Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
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,561028) 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.
PLoS Genetics | www.plosgenetics.org1March 2011 | Volume 7 | Issue 3 | e1001324
Citation: Speliotes EK, Yerges-Armstrong LM, Wu J, Hernaez R, Kim LJ, et al. (2011) Genome-Wide Association Analysis Identifies Variants Associated with
Nonalcoholic Fatty Liver Disease That Have Distinct Effects on Metabolic Traits. PLoS Genet 7(3): e1001324. doi:10.1371/journal.pgen.1001324
Editor: Mark I. McCarthy, University of Oxford, United Kingdom
Received June 18, 2010; Accepted February 2, 2011; Published March 10, 2011
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.
Funding: This work was partially supported by NIH grants T32 DK07191-32 to Daniel Podolsky (for EKS), F32 DK079466-01 to EKS, NIH K23DK080145-01 to EKS,
and R01DK075787 to JNH. The AGES-Reykjavik Study is funded by NIH contract N01-AG-12100, the NIA Intramural Research Program, Hjartavernd (the Icelandic
Heart Association), and the Althingi (the Icelandic Parliament). The Old Order Amish Study was supported by NIH research grants K01 DK067207, R01 AG18728,
R01HL088119, U01 HL72515, and U01 HL084756. Partial funding was also provided by the Diabetes Research and Training Center of Maryland (P60 DK079637)
and the Nutrition and Obesity Research Center of Maryland (P30DK072488). LMY-A was supported by NIH training grants T32AG000262 and F32AR059469. RH
was supported by the American Diabetes Association Mentor-Based Postdoctoral Fellowship Program (7-07-MN-08). The NHLBI Family Heart Study was supported
by NIDDK R01DK075681 (to IBB) and NHLBI R01HL087700. This research was conducted in part using data and resources from the Framingham Heart Study of the
National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and
resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was
partially supported by the National Heart, Lung, and Blood Institute’s Framingham Heart Study (Contract No. N01-HC-25195) and its contract with Affymetrix for
genotyping services (Contract No. N02-HL-6-4278). The Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) is supported by the National Institute
of Diabetes and Digestive and Kidney Diseases (NIDDK) (grants U01DK061718, U01DK061728, U01DK061731, U01DK061732, U01DK061734, U01DK061737,
U01DK061738, U01DK061730, U01DK061713). Several clinical centers use support from General Clinical Research Centers or Clinical and Translational Science
Awards in conduct of NASH CRN Studies (grants UL1RR024989, M01RR000750, M01RR00188, ULRR02413101, M01RR000827, UL1RR02501401, M01RR000065,
M01RR020359). The MIGen study was funded by the US National Institutes of Health (NIH) and National Heart, Lung, and Blood Institute’s STAMPEED genomics
research program. MT was supported by the Beatriu de Pino ´s postdoctoral fellowship, Generalitat de Catalunya (2007BP-B100068, MT), and Red HERACLES, ISCIII
(RD06/0009). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Eric E. Schadt is an employee of Pacific Biosciences.
* E-mail: email@example.com (EKS); firstname.lastname@example.org (IBB)
. These authors contributed equally to this work.
" For membership information, please see Acknowledgments.
NAFLD includes a spectrum of disease ranging from fatty
infiltration of the liver (steatosis) to histologic evidence of
inflammation (nonalcoholic steatohepatitis or NASH), to fibrosis
or cirrhosis, without a history of excessive alcohol ingestion [1,2].
NAFLD can lead to liver failure and is accompanied by substantial
morbidity and mortality, with few known effective treatments .
Obesity is a primary risk factor for NAFLD, but not all obese
individuals are affected . Familial clustering of the disease has
been identified [5–7], suggesting that NAFLD may be influenced
by genetic variants. However, thus far only one genetic locus has
been found to reproducibly associate with magnetic resonance
measured steatosis [8,9].
Liver attenuation measured using computed tomography (CT)
is a quantitative measure that is inversely related to the amount of
fat in the liver [10–12]. It is highly correlated (r=0.92) with the
macrovesicular hepatic steatosis and thus is a non invasive
measure of NAFLD . The purpose of the present study was
to determine the heritability of CT measured hepatic steatosis and
to search for associated genetic variants in a meta-analysis of 7,176
individuals of European descent from the Framingham Heart
Study (FRAM), the Old Order Amish Study (Amish), the Family
Heart Study (FamHS), and the Age, Gene/Environment Suscep-
tibility-Reykjavik study (AGES), which together comprise the
GOLD (Genetics of Obesity-related Liver Disease) consortium
(See Table S1). To validate top associating variants for risk of
histologically verified NAFLD, we utilized cases from the NASH
Clinical Research Network (NASH CRN) that were genetically
matched to healthy controls from the Myocardial Genetics
Consortium (MIGen) consortium(See Table S1). We then further
tested genome wide significant or replicating SNPs for associations
with histologic NAFLD using the same cases from the NASH
Clinical Research Network (NASH CRN) versus a different set of
controls from the Illumina Control Database (iCONT) (See Table
S1). Further, we report the association of these SNPs with other
metabolic traits using data from the Global Lipids Genetics ,
GIANT , DIAGRAM , and MAGIC  Consortia, as
well as investigate cis gene expression variation (eQTLs) in liver,
subcutaneous and visceral fat from bariatric surgery patients from
Massachusetts General Hospital (Figure 1).
We estimated the heritability of CT hepatic steatosis in three
family-based cohorts. We found that the heritability of CT hepatic
steatosis was 0.27 (standard error, SE 0.08), 0.27 (SE=0.04), and
0.26 (SE 0.04) in the Amish, FamHS, and FRAM cohorts
respectively (n=880–3,070) (See Materials and Methods and
Table 1). These data suggest that CT hepatic steatosis, like other
measures of fat has a genetic basis and that a search for influential
genetic variants is warranted.
To identify specific genetic loci associated with CT hepatic
steatosis, genome-wide association analyses were carried out in
each of the four studies (See Materials and Methods and Tables
S2, S3) and the results combined using a fixed effects meta-analysis
(N=7,176 in total). Variants at three loci emerged as being
associated with CT hepatic steatosis at genome-wide significance
levels (p,561028; Table 2, Figure 2A). These included rs738409
in PNPLA3 (p=4.3610234), a locus previously reported as
associated with magnetic resonance spectroscopy measured
steatosis,  and two additional novel loci: rs4240624 near
PPP1R3B (rs4240624, p=3.68610218) and rs2228603 near NCAN
(rs2228603, p=1.22610218). The alleles associated with increas-
ing CT hepatic steatosis ranged in frequency from 0.07 to 0.92
and together account for 4.4% of the variance in hepatic steatosis
(Table 2; range 0.79–2.41%). After removing these genome-wide
significant loci, a quantile-quantile plot of the results demonstrated
an excess of low p-values compared to expectations under the null
(Figure 2B), suggesting that additional variants among those with
moderately low p-values may also be associated with this trait.
Except for variants near PNPLA3, we did not observe any variants
in the region of any of the previously reported liver function test
associated regions . We could not assess whether the recently
reported NAFLD associated variants near APOC3  associate
with CT hepatic steatosis as they were not genotyped on the
Five GWAS Loci Associate with NAFLD
PLoS Genetics | www.plosgenetics.org2 March 2011 | Volume 7 | Issue 3 | e1001324
Affymetrix or Illumina platforms used by our studies and these
variants do not have proxies that we could use in HapMap to
To determine whether SNPs with evidence of association with
CT hepatic steatosis are also associated with histologic NAFLD,
we genotyped 46 SNPs (independent SNPs with p,5610-3, with
independence defined as pairwise r2,0.1; See Table S4 for SNP
details in GOLD and each cohort) in 592 subjects with biopsy-
proven NAFLD from the NASH CRN (See Table S1). Using
ancestry-informative genetic markers , we had previously
matched these cases to 1,405 healthy controls  from the
MIGen study  that had undergone GWAS genotyping and
imputation (See Table S1). Forty-five of the 46 SNPs passed
genotyping and imputation quality control in the NASH CRN and
MIGen data sets respectively (See Table S3) and were tested for
association with histologic NAFLD in this sample. Two of the
three variants with genome-wide significant associations to CT
hepatic steatosis were also significantly associated with histologic
NAFLD (corresponding to a false discovery rate (FDR) p,0.001):
rs738409 in PNPLA3 (OR=3.26, p=3.6610243) as we and others
have recently reported [21,23] and rs2228603 in NCAN
(OR=1.65, p=5.2961025) which is a novel finding (Table 2;
See Table S5). The rs4240624 variant near PPP1R3B was not
associated with histologic NAFLD in this sample (OR=0.93,
p=0.29). Of the 43 remaining SNPs showing suggestive
association with CT hepatic steatosis, rs780094 in GCKR
(OR=1.45, p=2.5961028) and rs12137855 near LYPLAL1
(OR=1.37, p=4.1261025) were also significantly associated with
histologic NAFLD (Table 2; See Table S5).
To confirm that the effects on histologic NAFLD observed in
the NASH CRN/MIGen analyses were not due to the
characteristics of the controls, we performed a separate analysis
of the NASH CRN cases with an alternate set of controls from the
Illumina Control database (iCONT; http://www.illumina.com/
science/icontroldb.ilmn). We found that the effects and p values of
rs738409 in PNPLA3 (OR=3.24, p=2.16610264), rs2228603 in
NCAN (OR=1.90, p=6.82610210), rs4240624 near PPP1R3B
(OR=0.86, p=0.15), rs780094 in GCKR (OR=1.18, p=0.01),
and rs12137855 near LYPLAL1 (OR=1.21, p=0.03) were similar
to the effects seen in MIGen establishing that these results are not
dependent on the choice of control sample (See Table S6).
Furthermore, assessment of imputation accuracy with the SNPs in
these control sets indicates that imputed genotypes at the
associated SNPs are likely to be highly accurate (see Tables S7,
The variants with the lowest p-values of association with CT
hepatic steatosis at the PNPLA3(rs738408), NCAN (rs2228603), and
GCKR (rs780094) loci are in high LD with or are themselves non-
synonymous variants in PNPLA3 (rs738409; I148M, R2=1),
NCAN (rs2228603; P91S, same as hepatic steatosis SNP), and
GCKR (rs1260326; P446L; R2=0.93) (Figure 3). The variants with
Figure 1. Study design. Meta-analysis of genome-wide association data was performed in Stage 1 across the cohorts shown. SNPs representing the
best associating loci were genotyped in histology based NAFLD samples (Stage 2) from the NASH CRN matched to genome wide genotyped and
imputed MIGen controls. The effects of the five NAFLD associated SNPs on NASH CRN/iCONT, metabolic phenotypes and eQTLs in liver and adipose
tissue were then performed (Stage 3).
NAFLD isaspectrumofdiseasethatrangesfrom steatosisto
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 histo-
logically. 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 inflam-
mation/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
GCKRandPPP1R3B alsoaffectglycemic traits. Thus, weshow
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.
Five GWAS Loci Associate with NAFLD
PLoS Genetics | www.plosgenetics.org3 March 2011 | Volume 7 | Issue 3 | e1001324
the lowest p-values of association with CT hepatic steatosis at
LYPLAL1 and PPP1R3B lie downstream and upstream of the
coding regions of these genes (Figure 3).
In epidemiologic studies NAFLD is associated with increased
central obesity, higher low density lipoprotein (LDL)- cholesterol
and lower high density lipoprotein (HDL)-cholesterol levels,
impaired fasting glucose, increased risk of diabetes and increased
insulin resistance. In addition, variants in or near GCKR,
NCAN, and PPP1R3B have been previously associated with lipid
levels, GCKR with glycemic traits and LYPLAL1 with abdominal
obesity [16,25–29]. Therefore, we examined the associations of
each of the CT hepatic steatosis-associated variants with serum
LDL-cholesterol, HDL-cholesterol, triglycerides (TG), 2 hour
glucose levels, 2 hour glucose levels controlled for body mass
index (BMI), fasting glucose, homeostatic model for beta call
function (HOMA-B), homeostatic model for insulin resistance
(HOMA-IR), fasting insulin, BMI, waist to hip ratio (WHR)
controlled for BMI, and diabetes in the largest analyses of these
traits available from the Global Lipids Genetics , GIANT
, DIAGRAM , and MAGIC  Consortia (see Table 2,
Table S9) Interestingly, we observed several distinct patterns of
association. The allele associated with increasing CT hepatic
steatosis at NCAN was associated with lower triglycerides and
plasma LDL-cholesterol levels. By contrast, the hepatic steatosis-
increasing allele at GCKR was associated with higher levels of
plasma LDL-cholesterol and triglycerides, lower fasting glucose,
lower fasting insulin, lower HOMA-IR, but increased 2 hour
glucose, increased 2 hour glucose controlled for BMI, and WHR
controlled for BMI. The hepatic steatosis increasing allele at
PPP1R3B was associated with increased HDL- and LDL-
Figure 4). The variants near PNPLA3 and LYPLAL1 were not
associated with any of the traits tested (See Table 2, Table S9 and
For PNPLA3 (rs738408), NCAN (rs2228603), and GCKR
(rs780094) the variants with the lowest p-values of association
with CT hepatic steatosis are either themselves missense SNPs or
in high LD with missense SNPs. Thus, the most parsimonious
model of how they may act is by directly affecting protein structure
or function. However, the variants with the lowest p-values of
association with CT hepatic steatosis near LYPLAL1 and PPP1R3B
fall in non-coding regions and thus for these (as well as the other
three loci above) we tested whether they have effects on the
expression of nearby genes in liver and adipose tissue from a
sample of bariatric surgery patients  (See Table S10). We
found that that the hepatic steatosis increasing variant (rs4240624)
at the PPP1R3B locus increased liver mRNA expression of
PPP1R3B and AW673036_RC and decreased expression of
AK055863. The hepatic steatosis increasing variant (rs780094)
at the GCKR locus increased expression of C2orf16 mRNA in liver.
In these cases the eQTL with the lowest p-value of affecting these
transcripts in the region was the same or highly correlated with the
allele that had the lowest p-value of association with CT hepatic
steatosis consistent with the possibility that these SNPs may
function by affecting expression of nearby genes. For all other
cases, the eQTL with the lowest p-value of affecting transcript
expression at the locus was not eliminated by controlling for the
variant that had the lowest p- value of association with CT hepatic
steatosis and thus in these cases, the data do not support an
expression effect as mediating the association with steatosis.
Because alteration of PPP1R3B expression has been shown to
affect serum lipid levels  one possibility is that changes in
expression of this gene could mediate its effect on hepatic steatosis.
For GCKR, the variant with the lowest p-value of association with
CT hepatic steatosis is in high LD with a missense variant in
GCKR which has been shown to affect GCKR function .
Thus, at GCKR an alternate model of action of how the CT
hepatic steatosis associated variant affects hepatic steatosis is via
altering GCKR function rather than via altering expression of
C2orf16. Further functional work will be needed to prove that these
variants exert their effects on hepatic steatosis via these possible
We have identified variants in three novel loci (NCAN, GCKR,
and LYPLAL1) and one previously reported locus (PNPLA3) that
are associated with both increasing CT hepatic steatosis and
histologic NAFLD. PPP1R3B is associated with CT steatosis but
not histologic NAFLD that includes individuals mostly with
inflammation and fibrosis. These variants all have distinct patterns
of effects on NAFLD and metabolic traits.
We have shown that CT hepatic steatosis is heritable and that
GWA meta-analysis led to the identification of variants associated
not only with CT hepatic steatosis but, also, with more severe
NASH/fibrosis mostly present in the NASH CRN sample.
Because CT hepatic steatosis measurements can be obtained
noninvasively, much larger sample sizes can be accumulated,
thereby increasing power to identify variants that associate with
NAFLD compared with only studying individuals that have
histology diagnosed disease. Follow-up association testing in
samples with histologic phenotypes remains useful however. We
did observe one variant near PPP1R3B that was associated with
CT–assessed liver attenuation but not histology-proven NAFLD.
Possible reasons for why the variant near PPP1R3B is associated
with CT liver steatosis but not histology-proven NAFLD include 1.
It influences steatosis only, not progression to NASH/fibrosis: 2. its
association with CT fat may be a false positive: 3. the NASH
CRN/MIGen sample is underpowered to see an effect on
histologic NAFLD: or 4. the variant is associated with something
other than fat reflected in the CT scan (eg. glycogen content).
Further work is needed to differentiate among these possibilities.
Table 1. Characterization of family data for heritability estimation.
StudyN N familiesDesign Age range (years)Heritability SE
Amish8801 founder population participants link to a
single, 14-generation pedigree
Family Heart Study 2679 508 3-generational pedigrees32–83 0.27 0.04
Framingham Heart Study3070 7212-generational pedigrees 31–83 0.260.04
N: total number of individuals with fatty liver phenotype; SE: Standard error; For all studies, SOLAR software was used to estimate heritability .
Five GWAS Loci Associate with NAFLD
PLoS Genetics | www.plosgenetics.org4 March 2011 | Volume 7 | Issue 3 | e1001324
Table 2. Genome-wide significant or replicating variants from GOLD, NASH CRN/MIGen, and metabolic phenotype analyses.
NASH CRN/MIGen analysis
Metabolic Phenotypes Meta Analyses **
GOLD: Genetics of Obesity-related Liver Disease; NASH CRN: Nonalcoholic Steatohepatitis Clinical Research Network; MIGen: Myocardial Infarction Genetics Consortium;
**from The Global Lipids Genetics, GIANT, MAGIC, and DIAGRAM Consortia; Chr. Chromosome; Pos.: position, build 35; Mb: Megabase; EA: effect allele; EAF: Effect allele frequency; Effect: increase in inverse normalized fatty liver by
computed tomography SE: Standard Error;% Var- percentage of variance explained; GOLD P: p-value of association in GOLD;
EAFa: Frequency of the effect allele in cases from the NASH CRN study;
EAFb: Frequency of the effect allele in controls from the MIGen study; NAFLD: nonalcoholic fatty liver disease; ORNAFLD: odds ratio for the presence of NAFLD on pathology per effect allele; NAFLD P: False discovery rate p-value of
association for histologic NAFLD; LDL: low density lipoprotein cholesterol; HDL: high density lipoprotein cholesterol; TG: triglycerides; Glucose: fasting glucose; HOMA-IR: homeostatic model assessment of insulin resistance; P: p-value of association; Dir: direction of effect allele for significant associations in GOLD, NASH CRN/MIGen, LDL, HDL, TG, glucose, HOMA-IR analyses respectively; +/2 represents increasing/decreasing fatty liver in GOLD, and having a
higher/lower odds of having NAFLD in the NASH CRN/MIGen analyses and higher/lower LDL, HDL, TG, glucose, HOMA-IR respectively; N represents no significant effect; PNPLA3: patatin-like phospholipase domain-containing
protein 3 (HUGO Gene Nomenclature Committee, HGNC: 18590); NCAN: neurocan (HGNC: 2465); LYPLAL1: lysophospholipase-like 1 (HGNC: 20440); GCKR: glucokinase regulatory protein (HGNC: 4196); PPP1R3B: protein phosphatase 1, regulatory subunit 3b (HGNC: 14942).
Five GWAS Loci Associate with NAFLD
PLoS Genetics | www.plosgenetics.org5March 2011 | Volume 7 | Issue 3 | e1001324
We show that some of the variants that are associated with
increased CT hepatic steatosis have distinct patterns of effects on
metabolic traits that, when taken together, give us insight into their
functional clustering. For example, unlike the other three loci,
variants in or near PNPLA3 and LYPLAL1 do not affect any of the
other metabolic traits and interestingly PNPLA3 and LYPLAL1-
related proteins have been predicted to play a role in consecutive
steps in triglyceride breakdown [31,32]. Thus these could increase
hepatic steatosis by preventing breakdown of triglycerides, as
recently shown for PNPLA3(I148M) . The apparent discor-
dance between the strong effect on hepatic steatosis and modest, if
any, effect on serum lipid levels suggests that these genes, if they
are involved in lipid metabolism, exert their effects within the liver
in ways that are not well reflected in serum measurements. Thus,
similarities in the pattern of pleiotropic effects on other traits may
provide insights into the functional clustering of the genes that
these variants effect.
Unlike PNPLA3 and LYPLAL1, variants near NCAN(which
encodes for an adhesion molecule ), PPP1R3B (which encodes
for a protein that regulates glycogen breakdown ), and GCKR
(which, through inhibition of glucokinase, regulates glucose
storage/disposal and provides substrates for de novo lipogenesis
), are associated with distinct changes in serum and liver lipids
as well as glycemic traits. Indeed, these data may provide new
insights into how obesity can lead to metabolic complications in
some but not all individuals- some but not all of these individuals
carry variants that predispose them both to liver fat deposition and
to metabolic dysregulation. Further, our data show that the alleles
of SNPs that associate with increased liver steatosis are also
associated with a diverse pattern of metabolic phenotypes
including different combinations of increased or decreased serum
LDL-cholesterol, increased serum HDL-cholesterol, increased
serum TG, decreased serum fasting glucose and insulin, decreased
insulin resistance, and increased WHR adjusted for BMI. In
addition, some hepatic steatosis-associated variants are not
strongly associated with any of these metabolic traits (PNPLA3
and LYPLAL1). These results indicate that hepatic steatosis is likely
to be influenced by different metabolic pathways, based on these
various patterns of association. Thus it may be possible to resolve
genetic heterogeneity in the etiology of hepatic steatosis, which
may present unique opportunities for personalized therapies.
Compared with earlier efforts, this study is well-powered, using
more than 7,176 individuals for discovery of variants that affect
NAFLD. Thus, noninvasive measures of hepatic steatosis such as
Figure 2. Genome-wide association results for GOLD (Stage 1). A. Manhattan plot showing the significance of association of all SNPs in the
Stage 1 GOLD meta-analysis with CT hepatic steatosis. SNPs are plotted on the x-axis according to their position on each chromosome against
association with CT hepatic steatosis on the y-axis (shown as -log10 p-value). SNPs that also associate with histology based NAFLD are in red, those
that only associate with CT hepatic steatosis in blue. B. Quantile-quantile plot of SNPs after Stage 1 GOLD meta-analysis (black) and after removing
any SNPs within 500 kb of PNPLA3, PPP1R3B, and NCAN (red).
Five GWAS Loci Associate with NAFLD
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Five GWAS Loci Associate with NAFLD
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CT scanning can provide valuable information for use in
population- and family-based studies aimed at identifying genetic
risk factors for NAFLD. Although the identities of nearby genes
and effects on lipid levels provide important clues, functional
studies will be needed to further understand the mechanisms by
which these risk factors influence the development and progression
of NAFLD. Overall however, our work gives us new insights into
the biology and genetics of NAFLD and opens up avenues for
biological, diagnostic, and therapeutic research for this condition
Materials and Methods
All work done in this paper was approved by local institutional
review boards or equivalent committees.
GOLD studies and genetic analyses
Each of the participating studies had the overarching objective
of investigating cardiovascular disease and its risk factors. The
studies are population based and 3 of the 4 are family studies.
Genome-wide SNP data were available in each case, and the
platforms and quality control measures are described in Tables S2
Age Gene-Environment Susceptibility—Reykjavik Study
The AGES-Reykjavik Study is a single
center prospective population-based cohort nested in the original
Rykjavik Study, a cohort of 30,795 randomly sampled persons
living in Reykjavik, Iceland. The cohort included 19,381 men and
women born between 1907 and 1935. Re-examination of a sample
of surviving members of the Reykjavik Study was initiated in 2002
as the AGES-Reykjavik Study. This study included imaging by
computerized tomography, from which liver attenuation was
measured from a 1mm thick slice at the level of the L1/L2
vertebrae by calculating the average Hounsfield Unit in a region of
interest with a diameter of 1 cm located 10% of the distance from
where a tangent from the mid-anterior of the spinal canal bisected
a line between the second and third rib. Four thousand seven
hundred and seventy two individuals were assessed for hepatic
steatosis using CT scanning. Liver attenuation controlled for an
external phantom was inverse normally transformed and residuals
created from a linear regression model in Proable /R with
covariates of age, age2, gender and drinks along with the SNPs in
an additive genetic model (See Tables S2, S3).
Figure 4. Effects on traits. Direction of effect on CT fatty liver, histology NAFLD, lipid and glycemic traits of the best associating SNPs at the loci
shown. Direction is shown only for significant associations. CT: CT hepatic steatosis; LDL: low density lipoprotein cholesterol; HDL: high density
lipoprotein cholesterol; TG: triglycerides; HOMA-IR: homeostatic model of insulin resistance; PNPLA3: patatin-like phospholipase domain-containing
protein 3 (HGNC: 18590); NCAN: neurocan (HGNC: 2465); LYPLAL1: lysophospholipase-like 1 (HGNC: 20440); GCKR: glucokinase regulatory protein
(HGNC: 4196); PPP1R3B: protein phosphatase 1, regulatory subunit 3b (HGNC: 14942).
Figure 3. Regional plots of genome-wide significant or replicating loci of association in GOLD. SNPs are plotted by position on
chromosome against association with CT hepatic steatosis (–log10 p-value). The figures highlight the SNP taken into Stage 2 (diamond). The SNPs
surrounding the most significant SNP are color-coded to reflect their LD with this SNP as in the inset (taken from pairwise R2 values from the HapMap
CEU database, www.hapmap.org). Estimated recombination rates (from HapMap) are plotted in cyan to reflect the local LD structure. Genes and the
direction of transcription, are noted below the plots (data from UCSC genome browser, genome.ucsc.edu). Coding SNPs in high LD with the best SNP
are noted with rs number and protein change.
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cardiovascular health in the Old Order Amish community in
Lancaster County: the Amish Family Calcification Study (2001–
2006)  and the Amish Longevity Study (2000–2006) . In
total, 541 individuals had both genome-wide SNP data and CT-
assessed hepatic steatosis. Thoracic electron-beam computerized
tomography (EBCT) scans were obtained as part of the Amish
Family Calcification Study by an Imatron C-150 EBCT scanner.
Measurements from two regions-of-interest, the liver and spleen,
were obtained. The spleen measurements were used as an
attenuation standard. Accu View (Accuimage Corp.) software
was used to calculate the attenuation coefficient in Hounsfield
Units for each region-of-interest. Two 1.0-cm2region-of-interest
measurements were obtained from the liver and one was
obtained from the spleen. The average of the liver attenuation
measurements divided by the spleen attenuation measurement was
then calculated. The region-of-interest measurements were placed
in such a manner that minimized measurements from vessels, focal
lesions, areas of artifact or near the edges of the organs.
The liver attenuation/spleen attenuation ratio was inverse
normally transformed and association was tested with genotypes in
an additive genetic model controlling for age, age2, and gender
and relatedness; alcohol is generally not consumed in this
population. A (n-1)-degree-of-freedom t test was used to assess
the significance of the measured genotype. The polygenic
component was modeled using the relationship matrix derived
from the complete 14-generation pedigree structure, to properly
control for the relatedness of all subjects in the study.
The Family Heart Study.
The Family Heart Study (https://
dsgweb.wustl.edu/PROJECTS/MP1.html) recruited 1,200 families,
half randomly sampled, and half selected because of an excess of
coronary heart disease (CHD) or risk factor abnormalities as
compared with age- and sex-specific population rates  from
four population-based parent studies: the Framingham Heart Study,
the Utah Family Tree Study, and two Atherosclerosis Risk in
Communities centers (Minneapolis, and Forsyth County, NC). Study
participants belonging to the largest pedigrees were invited for a
second clinical exam, at which time coronary artery calcification was
assessed using computed tomography, which included imaging of the
liver. A total of 2,767 Caucasian subjects in 508 extended families
were examined; the heritability was estimated in this sample . A two-
stage design was adopted for the GWAS. In the first stage, 1,016
subjects were chosen, equally distributed between the highest and
lowest quartiles of age- and sex-adjusted values for coronary artery
calcification, assessed by CT scan. These subjects were chosen to be
largely unrelated with 200 subjects having 1 or more siblings selected
into the sample. We report association results based on 886 subjects
after excluding 130 subjects ascertained from the Framingham
Massachusetts to avoid any possible overlap with the Framingham
Heart Study participants.
Participants underwent a cardiac multidetector CT exam with
four detectors using a standardized protocol as described
previously . For participants weighing 100 kg (220 lbs) or
greater, the milliamperes were increased by 25%. Participants
received two sequential scans of the heart with ECG gating in late
diastole. A phantom with either 3 or 4 samples of calcium
hydroxylapatite was included in each participants scan. CT images
from all study centers were sent electronically to the central CT
reading center located at Wake Forest University Health Sciences,
Winston Salem, NC, USA.
CT images were analyzed using Medical Image Processing,
Analysis, and Visualization (MIPAV) software (McAuliffe 2009)
with custom programmed subroutines (a.k.a.‘‘plug-ins’’) coded at
Wake Forest University Health Sciences. CT images of the chest
Subjects were identified from 2 studies of
were used to measure liver attenuation corresponding to superior
aspects of the right and medial lobes or hepatic segments 4a, 7 and
8 using the Couinaud system. An external calcium standard was
used as a control for penetrance of the films.
The liver attenuation was regressed on age, age2, age3, field
center, phantom average, alcohol consumption and 10 genetic
principal components, by sex, using a stepwise procedure and
retaining terms significant at the 5% level. We then applied an
inverse normal rank transformation to the adjusted phenotype
within sex strata and association was assessed assuming an additive
model using PROC MIXED in SAS to account for the siblings.
The Framingham Heart Study.
Study recruited 5,209 residents in 1948 from the population in
Framingham, Massachusetts . These individuals have had
serial examinations and collection of respective data since. In
1971, 5,124 offspring from the original residents and their spouses
were recruited into the Offspring Study and have been followed
for four to eight years since . In 2002, 4,095 third generation
members and their spouses were enrolled .
Between 2002 and 2005, 1,400 individuals from the Offspring
Study and 2,011 individuals from third generation underwent
multidectector computed tomograms on which we evaluated liver
attenuation as previously described . Inclusion criteria
favored individuals who lived in the New England area and
included 755 families. Minimum age was 35 in men and 40 in
women. Women of childbearing age were screened and pregnant
women and individuals .160 kilograms were excluded from
scanning. Individuals with scans that could not be interpreted for
hepatic steatosis or did not attend offspring examination 7 as they
lacked covariate data were not used for analysis. The average of
the liver attenuation measures and a high density external
calcium control were used to create a liver/phantom ratio to
control for scan penetrance.
For GWAS analysis, inverse normally transformed liver
attenuation/phantom ratio was used in a mixed linear model
(controlling for relatedness) in R with covariates of age, age
squared, gender, and alcoholic drinks (4 oz =1 drink) with the first
ten principal components (as determined in Eigenstrat ) as
covariates. Principal components were first generated using an
unrelated sample of 718 and then projected to the rest of the
cohort. Individuals who deviated from the mean of the principal
components of more than six standard deviations were removed
prior to analysis (n=1).
Three of the four studies parti-
cipating in this consortium were family studies and the family
structure characteristics used for heritability are shown in Table 1.
Liver attenuation adjusted for scan penetrance and then inverse
normally transformed and corrected for age gender and number of
alcoholic drinks (drinks in FamHS, and FRAM only as the Amish
do not drink) was estimated in each of the studies and then
heritability assessed using a variance components method as
implemented in the software SOLAR . Despite the diverse
character of these family studies, there was remarkable consistency
in the estimates of the proportion of variance due to genetic effects,
and the magnitude of the heritabilities is comparable to many
complex quantitative traits and suggests that a search for
underlying genetic variants is warranted.
Meta-analysis and GWAS.
studies above were filtered for SNPs that had a minor allele
frequency .1% and for SNPs that had an imputation quality
score of .0.3. All files were GC corrected after filtering and before
meta-analysis. The inflation factor for the AGES study was 1.01,
for the Amish was 1.05, for the Family Heart Study was 1.03, for
the Framingham Heart Study was 1.02. Meta-analysis was
The Framingham Heart
Association data from the four
Five GWAS Loci Associate with NAFLD
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conducted using a fixed effects model with a beta and standard
error as implemented in METAL (http://www.sph.umich.edu/
csg/abecasis/metal/). After meta-analysis, SNPs present in fewer
than 3 studies were eliminated from analysis. The inflation factor
for the overall meta-analysis was 1.03. The meta-analysis was GC
corrected before the final p values were reported. The variation in
CT hepatic steatosis explained by the tested SNPs was estimated
from stage 2 analyses using 2f (1 – f) a2, where f is the frequency of
the variant and a is its additive effect in units of standard
deviations from the meta analysis .
Selection of SNPs for validation/replication with
To define independently associated SNPs, the LD was required
to be R2,0.10 and the SNPs located at least 1 megabase from
each other. From among these, the SNP with the strongest
association was chosen for follow up (P,0.0001). Two iPlex pools
consisting of 46 SNPs were designed and were successfully
genotyped in the NASH CRN samples. Of these, only 45 were
imputed well in MIGen, and only these SNPs were analyzed.
Variants with a false discovery rate of q ,0.05 were considered
associated with NAFLD.
NASH CRN samples
Study: The NASH CRN samples were collected from eight
different centers in the U.S. as previously described [2,49]. Adults
from both the Database and the PIVENS trial (Pioglitazone versus
Vitamin E versus Placebo for the Treatment of Nondiabetic Patients
with Nonalcoholic Steatohepatitis) were used for analysis. Briefly,
individuals from the Database were part of an observational study of
nonalcoholic fatty liver disease. Inclusion criteria included age .18,
histologic diagnosis for NAFLD, or histologic diagnosis for
cryptogenic cirrhosis or suspected NAFLD on the basis imaging
studies suggestive of NAFLD, or clinical evidence of cryptogenic
cirrhosis.Nosubjectsreportedregular excessive use of alcoholwithin
two years prior to the initial screening period. Exclusion criteria
included histologic evidence of liver disease besides nonalcoholic
liver disease, known HIV positivity, and conditions that would
interfere with study follow up. Individuals in the PIVENS database
were part of a multicenter placebo controlled study with three
parallel groups examining the effects of pioglitazone vs. vitamin E vs.
placebo on NAFLD. Inclusion and exclusion criteria were as
described previously [2,49]. For this analysis, we excluded
individuals who did not describe their race as being white and
non-Hispanic. There were 678 adults who matched these criteria.
Finally, individuals without histology available for central review
were excluded, leaving 592 adults for the current study.
Histology determination in NASH CRN
Histologic diagnoses were determined in the NASH CRN by
central review by NASH CRN hepatopathologists using previously
published criteria [2,49]. Predominantly macrovesicular steatosis
was scored from grade 0–3. Inflammation was graded from 0–3
and cytologic ballooning from 0–2. The fibrosis stage was assessed
from a Masson trichrome stain and classified from 0–4 according
to the NASH CRN criteria. Individuals could contribute to more
than one of these outcomes. The NASH CRN samples were
genotyped and analyzed as described in Tables S2 and S3.
Analysis in NASH CRN/MIGen samples
MIGen controls were matched to the NASH CRN samples for
genetic background. As previously described, the MIGen samples
were collected from various centers in the US and Europe by the
Myocardial Infarction Genetics Consortium (MIGen)  as
controls for individuals with early onset MI. The genetic ancestry
the MIGen samples was explored by using the program Eigenstrat
; the first principal component was the most significant and
correlated with the commonly observed Northwest- Southeast axis
within Europe  and genetic ancestry along this principal
component is correlated with reported country of origin in the
MIGen sample . From this analysis, 120 unlinked SNPs were
chosen from the MIGen genotype data that were most strongly
correlated with the first principal component. These SNPs were
genotyped in the NASH CRN samples to enable matching of
MIGen controls to the NASH CRN  cases for genetic
background. PLINK  was used to match individuals based on
identity by state (IBS) distance using a pairwise population
concordance test statistic of .161023for matching. The SNPs
selected for validation were tested in this case-control sample using
logistic regression controlling for age, age2, gender, and the first 5
principal components as covariates in PLINK . We report the
p-values, odds ratios and confidence intervals.
We obtained 3,294 population based control samples with
genotypes from Illumina (see http://www.illumina.com/science/
icontroldb.ilmn). These individuals were used as controls in
various case control analyses. Individuals were removed as
described in Table S4 and 3,212 individuals were then used as
controls for the NASH CRN/iCONT analyses.
Analysis in NASH CRN/iCONT samples
The 592 individuals from the NASH CRN described above
were used as cases and 3,212 individuals from the iCONT
database were used as controls. Genome wide significant or
replicating SNPs were tested in this case-control sample using
logistic regression controlling for gender in PLINK . We
report the p-values, odds ratios and confidence intervals.
Concordance analysis of imputed SNPs in MIGen and
iCONT with the HapMap3 TSI sample
To assess the concordance of imputed SNPs in the MIGen and
iCONT samples we obtained the genotyped SNPs from the
HapMap3 TSI (Tuscans from Italy) sample. Using only the SNPs
present on the Affymetrix 6.0 platform (used to genotype MIGen)
or only the SNPs present on the Illumina platform (used to
genotype iCONT samples) and the LD information from
HapMap2 we imputed the remainder of the SNPs using
MACH(1.0.16) and compared the imputed calls to the actual
genotypes stratified by imputation quality score (R2 hat).
Evaluation of effects on other metabolic traits
To obtain data on whether CT hepatic steatosis SNPs affect
other metabolic traits we obtained data from four consortia that
had the largest and most powered analyses of these traits.
Association results for HDL-, LDL- cholesterol levels and
triglycerides (TG) were obtained from publicly available data of
the GLOBAL Lipids Genetics Consortium (http://www.sph.
Association results for fasting insulin, glucose, 2 hr-glucose,
HOMA-IR and HOMA-B were obtained from the MAGIC
Investigators. Association results for risk of type 2 diabetes were
obtained from the DIAGRAM consortium .
Association results for risk of BMI and waist to hip ratio
controlled for BMI were obtained from the GIANT consortium
. We used a conservative nominal p,0.0008 corresponding to
et al.2010) .
Five GWAS Loci Associate with NAFLD
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a bonferroni correction of 12 phenotypes tested for 5 SNPs to
Expression QTL analyses
The expression QTL analyses in liver, subcutaneous and
omental fat tissue have been described in detail previously .
Tissue were obtained from patients who underwent bariatric
surgery, and RNA expression assessed using a custom Agilent
44,000 feature microarray composed of 39,280 oligonucleotide
probes targeting transcripts representing 34,266 known and
predicted genes. Patients were also genotyped on the Illumina
650Y SNP genotyping arrays. SNPs were tested for cis-associations
with transcripts within a 1 Mb region, assuming an additive effect
of the CT hepatic steatosis increasing allele adjusting for age, race,
gender, and surgery year using linear regression. Cis-associations
between each SNP and the adjusted gene expression data were
tested, and only associations with a nominal p-value ,3.561025
corresponding to a bonferroni correction for 284 gene transcripts x
5 SNPs tested are shown in Table S10. Conditional analyses were
performed by conditioning the CT hepatic steatosis associated
SNP on the most significant cis-associated SNP for that particular
gene transcript and vice versa.
not practiced in Amish culture and not measured this study.
GOLD: Genetics of Obesity-related Liver Disease; NASH CRN:
Nonalcoholic Steatohepatitis Clinical Research Network; MIGen:
Myocardial Infarction Genetics Consortium; iCONT: Illumina
Control Database; SD: standard deviation; P25, P75: 25th and
75th percentiles; Phantom LD or HD- low or high density external
hydroxyapetite CT control; Median raw liver measures in
Hounsfield units; steatosis .5% more than 5% steatosis on
histology; NASH: having histologic criteria for diagnosis of
nonalcoholic steatohepatitis (NASH); Fibrosis: having histologic
criteria for diagnosis of fibrosis.
Found at: doi:10.1371/journal.pgen.1001324.s001 (0.05 MB
Study sample characteristics. (*) Drinking alcohol is
Imputation; MAF: minor allele frequency; HWE: Hardy Wein-
berg Equilibrium; GOLD: Genetics of Obesity-related Liver
Disease; NASH CRN: Nonalcoholic Steatohepatitis Clinical
Research Network; MIGen: Myocardial Infarction Genetics
Consortium; iCONT: Illumina Control database.
Found at: doi:10.1371/journal.pgen.1001324.s002 (0.05 MB
Genotyping and association information. Imp’n:
percentage of successfully genotyped SNPs per sample. GOLD:
Genetics of Obesity-related Liver Disease; NASH CRN: Nonal-
coholic Steatohepatitis Clinical Research Network; MIGen:
Myocardial Infarction Genetics Consortium; iCONT: Illumina
Control database; IBD pi hat: value for identical by descent of
Found at: doi:10.1371/journal.pgen.1001324.s003 (0.05 MB
Quality control. * Sample genotyping success rate; i.e.
Family Heart Study, Framingham Heart Study. GOLD: Genetics
of Obesity-related Liver Disease; Chr.: Chromosome; Pos.:
position, build 35; EA: effect allele; OA: other allele; EAF:
Frequency of the effect allele in the analyses (weighted average in
GOLD); Effect: increase in inverse normalized fatty liver by
computed tomography SE: Standard Error; P: p-value of
Top genotyped hits from GOLD, AGES, AMISH,
association in the analyses; % Var: % variance explained; P het:
p-value for heterogeneity across studies; N: number of individuals
in the analyses.
Found at: doi:10.1371/journal.pgen.1001324.s004 (0.41 MB
NASH CRN: Nonalcoholic Steatohepatitis Clinical Research
Network; MIGen: Myocardial Infarction Genetics Consortium;
Chr. Chromosome; Pos.: position, build 35; EA: effect allele;
OA:other allele; EAFa: Frequency of the effect allele in cases from
the NASH CRN study; EAFb :Frequency of the effect allele in
controls from the MIGen study; Impb: Imputation quality score in
MIGen; NAFLD: nonalcoholic fatty liver disease; OR NAFLD:
odds ratio for the presence of NAFLD on pathology per effect
allele; P NAFLD: False discovery rate p-value of association for
Found at: doi:10.1371/journal.pgen.1001324.s005 (0.10 MB
Top genotyped hits in NASH CRN/MIGen analysis.
NASH CRN/iCONT analysis. NASH CRN: Nonalcoholic
Steatohepatitis Clinical Research Network; iCONT: Illumina
Control database; EA: effect allele; OA:other allele; EAFa:
Frequency of the effect allele in cases from the NASH-CRN
study; EAFb :Frequency of the effect allele in controls from
iCONT; Impb: Imputation quality score in iCONT; NAFLD:
nonalcoholic fatty liver disease; OR NAFLD: odds ratio for the
presence of NAFLD on pathology per effect allele; P NAFLD:
False discovery rate p-value of association for histologic NAFLD.
Found at: doi:10.1371/journal.pgen.1001324.s006 (0.03 MB
Genome-wide significant or replicating variants in
real genotypes in TSI individuals from HapMap 3. TSI: Toscans
in Italy; R2 hat: Imputation quality score from MACH; N SNPs:
number of SNPs used for concordance analysis; concordance:
average concordance amongst the SNPs assayed.
Found at: doi:10.1371/journal.pgen.1001324.s007 (0.01 MB
Imputation R2 hat measures versus concordance to
versus concordance to real genotypes in TSI individuals from
HapMap 3. Impa: imputation quality score in MIGen; Con-
cordancea: average concordance of SNPs in TSI given imputation
quality score in MIGen; Impb: imputation quality score in
iCONT; Concordanceb: average concordance of SNPs in TSI
given imputation quality score in iCONT.
Found at: doi:10.1371/journal.pgen.1001324.s008 (0.01 MB
Imputation R2 hat measures in MIGen and iCONT
variants on glucose, anthropometric and lipid traits. Association
results for high density lipoprotein (HDL)-, low density lipoprotein
(LDL)- Cholesterol levels and triglycerides (TG) were obtained
from publicly available data of the GLOBAL Lipids Genetics
Teslovich et al. 2010) Association results for fasting Insulin and
glucose, 2hr-glucose, HOMA-IR and HOMA-B were obtained
from the MAGIC Consortium (Dupuis et al. Nature Genetics
2010). Association results for risk of type 2 diabetes were obtained
from the DIAGRAM consortium (Voight et al. Nature Genetics
2010). Association results for risk of BMI and waist to hip ratio
controlled for BMI were obtained from the GIANT consortium
(Speliotes et al. Nature Genetics 2010). BMI: body mass index;
HOMA-IR: homeostasis model assessment insulin resistance;
HOMA-B: homeostasis model assessment beta cell function; EA:
Effect of genome-wide significant or replicating
Five GWAS Loci Associate with NAFLD
PLoS Genetics | www.plosgenetics.org11 March 2011 | Volume 7 | Issue 3 | e1001324
effect allele; OA: other allele; Effect: The change in the trait per
effect allele from the various studies; SE: standard error in the
effect from the various studies; P: p-value of association from the
various studies; N: number of individuals in the analyses; OR:
odds ratio for the effect allele on diabetes; U95% and L95%-
upper and lower 95% confidence levels for the OR.
Found at: doi:10.1371/journal.pgen.1001324.s009 (0.08 MB
icant or replicating SNPs and cis gene expression (cis -eQTLs) in
liver, omental fat and subcutaneous fat. SNP: the fatty liver
associating SNP from GWAS analysis. EA: effect allele (fatty liver
increasing allele from GWAS). Effecta: Direction of effect on the
gene transcript expression level for the effect allele. P: p-value of
association of the fatty liver SNP with change in gene expression.
Padjb :p-value for the fatty liver SNP after conditioning on the
most significant SNP for change in gene transcript. Peak SNPc:
SNP in the region that has the most significant eQTL p-value on
expression of the gene transcript Rsqd: the R squared correlation
between the fatty liver SNP and the peak SNP. Padje: p-value for
the peak SNP after conditioning on the fatty liver SNP for change
in gene transcript. NA: peak SNP is the same as the fatty liver
Found at: doi:10.1371/journal.pgen.1001324.s010 (0.04 MB
Significant associations between genome-wide signif-
We would like to thank Dr. Qiong Yang for use of her R program for
related analyses in The Framingham Heart Study and Dr. Monty Krieger
for critically reviewing the manuscript. We would like to thank Arun
Sanyal for serving as our liason to the NASH CRN. Samples were
provided by the NASH CRN for analyses in this paper.
Arthur McCullough, M.D.; Diane Bringman, R.N., B.S.N.; Srinivasan
Dasarathy, M.D.; Kevin Edwards, N.P.; Carol Hawkins, R.N.; Yao-Chang
Liu, M.D.; Nicholette Rogers, Ph.D.; P.A.-C.; Ruth Sargent, L.P.N.;
Margaret Stager, M.D.; Anna Mae Diehl, M.D.; Manal Abdelmalek,
M.D.; Marcia Gottfried, M.D.; Cynthia Guy, M.D.; Paul Killenberg,
M.D.; Samantha Kwan; Yi-Ping Pan; Dawn Piercy, F.N.P.; Melissa Smith;
Naga Chalasani, M.D.; Prajakta Bhimalli; Oscar W. Cummings, M.D.;
Ann Klipsch, RN; Lydia Lee; Jean Molleston, M.D.; Linda Ragozzino; Raj
Vuppalanchi, M.D.; Brent A. Neuschwander-Tetri, M.D.; Sarah Barlow,
M.D.; Jose Derdoy, M.D.; Joyce Hoffmann; Debra King, R.N.; Joan
Siegner, R.N.; Susan Stewart, R.N.; Judy Thompson, R.N.; Elizabeth
Brunt, M.D.; Joel E. Lavine, M.D., Ph.D.; Cynthia Behling, M.D.; Lisa
Clark; Janis Durelle; Tarek Hassanein, M.D.; Lita Petcharaporn; Jeffrey B.
Schwimmer, M.D.; Claude Sirlin, M.D.; Tanya Stein; Nathan M. Bass,
M.D., Ph.D.; Kiran Bambha, M.D.; Linda D. Ferrell, M.D.; Danuta
Filipowski; Raphael Merriman, M.D.; Mark Pabst; Monique Rosenthal;
Philip Rosenthal, M.D.; Tessa Steel; Arun J. Sanyal, M.D.; Sherry Boyett,
R.N.; Daphne Bryan, M.D.; Melissa J. Contos, M.D.; Michael Fuchs,
M.D.; Martin Graham, M.D.; Amy Jones; Velimir A.C. Luketic, M.D.;
Bimalijit Sandhu, M.D.; Carol Sargeant, R.N., M.P.H.; Kimberly Selph;
Melanie White, R.N.; Kris V. Kowdley, M.D.; Grace Gyurkey; Jody
Mooney, M.S.; James Nelson, Ph.D.; Sarah Roberts; Cheryl Saunders,
M.P.H.; Alice Stead; Chia Wang, M.D.; Matthew Yeh, M.D., Ph.D.;
David Kleiner, M.D., Ph.D.; Edward Doo, M.D.; Jay Everhart, M.D.,
M.P.H.; Jay H. Hoofnagle, M.D.; Patricia R. Robuck, Ph.D.; Leonard
Seeff, M.D.; James Tonascia, Ph.D.; Patricia Belt, B.S.; Fred Brancati,
M.D., M.H.S.; Jeanne Clark, M.D., M.P.H.; Ryan Colvin, M.P.H.;
Michele Donithan, M.H.S.; Mika Green, M.A.; Milana Isaacson; Wana
Kim; Laura Miriel; Alice Sternberg, Sc.M.; Aynur U¨nalp, M.D., Ph.D.;
Mark Van Natta, M.H.S.; Laura Wilson, Sc.M.; Katherine Yates, Sc.M.
Elizabeth K. Speliotes, Cristen J. Willer, Sonja I. Berndt, Keri L.
Monda, Gudmar Thorleifsson, Anne U. Jackson, Hana Lango Allen,
Cecilia M. Lindgren, Jian’an Luan, Reedik Ma ¨gi, Joshua C. Randall,
Sailaja Vedantam, Thomas W. Winkler, Lu Qi, Tsegaselassie Work-
alemahu, Iris M. Heid, Valgerdur Steinthorsdottir, Heather M. Stringham,
Michael N. Weedon, Eleanor Wheeler, Andrew R. Wood, Teresa Ferreira,
Robert J. Weyant, Ayellet V. Segre `, Karol Estrada, Liming Liang, James
Nemesh, Ju-Hyun Park, Stefan Gustafsson, Tuomas O. Kilpela ¨inen, Jian
Yang, Nabila Bouatia-Naji, To ˜nu Esko, Mary F. Feitosa, Zolta ´n Kutalik,
Massimo Mangino, Soumya Raychaudhuri, Andre Scherag, Albert
Vernon Smith, Ryan Welch, Jing Hua Zhao, Katja K. Aben, Devin M.
Absher, Najaf Amin, Anna L. Dixon, Eva Fisher, Nicole L. Glazer,
Michael E. Goddard, Nancy L. Heard-Costa, Volker Hoesel, Jouke-Jan
Hottenga, A˚sa Johansson, Toby Johnson, Shamika Ketkar, Claudia
Lamina, Shengxu Li, Miriam F. Moffatt, Richard H. Myers, Narisu
Narisu, John R.B. Perry, Marjolein J. Peters, Michael Preuss, Samuli
Ripatti, Fernando Rivadeneira, Camilla Sandholt, Laura J. Scott, Nicholas
J. Timpson, Jonathan P. Tyrer, Sophie van Wingerden, Richard M.
Watanabe, Charles C. White, Fredrik Wiklund, Christina Barlassina,
Daniel I. Chasman, Matthew N. Cooper, John-Olov Jansson, Robert W.
Lawrence, Niina Pellikka, Inga Prokopenko, Jianxin Shi, Elisabeth
Thiering, Helene Alavere, Maria T. S. Alibrandi, Peter Almgren, Alice
M. Arnold, Thor Aspelund, Larry D. Atwood, Beverley Balkau, Anthony J.
Balmforth, Amanda J. Bennett, Yoav Ben-Shlomo, Richard N. Bergman,
Sven Bergmann, Heike Biebermann, Alexandra I.F. Blakemore, Tanja
Boes, Lori L. Bonnycastle, Stefan R. Bornstein, Morris J. Brown, Thomas
A. Buchanan, Fabio Busonero, Harry Campbell, Francesco P. Cappuccio,
Christine Cavalcanti-Proenc ¸a, Yii-Der Ida Chen, Chih-Mei Chen, Peter S.
Chines, Robert Clarke, Lachlan Coin, John Connell, Ian N.M. Day,
Martin den Heijer, Jubao Duan, Shah Ebrahim, Paul Elliott, Roberto
Elosua, Gudny Eiriksdottir, Michael R. Erdos, Johan G. Eriksson,
Maurizio F. Facheris, Stephan B. Felix, Pamela Fischer-Posovszky, Aaron
R. Folsom, Nele Friedrich, Nelson B. Freimer, Mao Fu, Stefan Gaget,
Pablo V. Gejman, Eco J.C. Geus, Christian Gieger, Anette P. Gjesing,
Anuj Goel, Philippe Goyette, Harald Grallert, Ju ¨rgen Gra ¨bler, Danielle
?M. Greenawalt, Christopher J. Groves, Vilmundur Gudnason, Candace
Guiducci, Anna-Liisa Hartikainen, Neelam Hassanali, Alistair S. Hall, Aki
S. Havulinna, Caroline Hayward, Andrew C. Heath, Christian Hengsten-
berg, Andrew A. Hicks, Anke Hinney, Albert Hofman, Georg Homuth,
Jennie Hui, Wilmar Igl, Carlos Iribarren, Bo Isomaa, Kevin B. Jacobs,
Ivonne Jarick, Elizabeth Jewell, Ulrich John, Torben Jørgensen, Pekka
Jousilahti, Antti Jula, Marika Kaakinen, Eero Kajantie, Lee M. Kaplan,
Sekar Kathiresan, Johannes Kettunen, Leena Kinnunen, Joshua W.
Knowles, Ivana Kolcic, Inke R. Ko ¨nig, Seppo Koskinen, Peter Kovacs,
Johanna Kuusisto, Peter Kraft, Kirsti Kvaløy, Jaana Laitinen, Olivier
Lantieri, Chiara Lanzani, Lenore J. Launer, Cecile Lecoeur, Terho
Lehtima ¨ki, Guillaume Lettre, Jianjun Liu, Marja-Liisa Lokki, Mattias
Lorentzon, Robert N. Luben, Barbara Ludwig, MAGIC, Paolo Manunta,
Diana Marek, Michel Marre, Nicholas G. Martin, Wendy L. McArdle,
Anne McCarthy, Barbara McKnight, Thomas Meitinger, Olle Melander,
David Meyre, Kristian Midthjell, Grant W. Montgomery, Mario A.
Morken, Andrew P. Morris, Rosanda Mulic, Julius S. Ngwa, Mari Nelis,
Matt J. Neville, Dale R. Nyholt, Christopher J. O’Donnell, Stephen
O’Rahilly, Ken K. Ong, Ben Oostra, Guillaume Pare ´, Alex N. Parker,
Markus Perola, Irene Pichler, Kirsi H. Pietila ¨inen, Carl G.P. Platou, Ozren
Polasek, Anneli Pouta, Suzanne Rafelt, Olli Raitakari, Nigel W. Rayner,
Martin Ridderstra ˚le, Winfried Rief, Aimo Ruokonen, Neil R. Robertson,
Peter Rzehak, Veikko Salomaa, Alan R. Sanders, Manjinder S. Sandhu,
Serena Sanna, Jouko Saramies, Markku J. Savolainen, Susann Scherag,
Sabine Schipf, Stefan Schreiber, Heribert Schunkert, Kaisa Silander, Juha
Sinisalo, David S. Siscovick, Jan H. Smit, Nicole Soranzo, Ulla Sovio,
Jonathan Stephens, Ida Surakka, Amy J. Swift, Mari-Liis Tammesoo, Jean-
Claude Tardif, Maris Teder-Laving, Tanya M. Teslovich, John R.
Thompson, Brian Thomson, Anke To ¨njes, Tiinamaija Tuomi, Joyce B.J.
van Meurs, Gert-Jan van Ommen, Vincent Vatin, Jorma Viikari, Sophie
Visvikis-Siest, Veronique Vitart, Carla I. G. Vogel, Benjamin F. Voight,
Lindsay L. Waite, Henri Wallaschofski, G.Bragi Walters, Elisabeth Widen,
Susanna Wiegand, Sarah H. Wild, Gonneke Willemsen, Daniel R. Witte,
Jacqueline C. Witteman, Jianfeng Xu, Qunyuan Zhang, Lina Zgaga,
Andreas Ziegler, Paavo Zitting, John P. Beilby, I. Sadaf Farooqi, Johannes
Hebebrand, Heikki V. Huikuri, Alan L. James, Mika Ka ¨ho ¨nen, Douglas F.
Levinson, Fabio Macciardi, Markku S. Nieminen, Claes Ohlsson, Lyle J.
Palmer, Paul M. Ridker, Michael Stumvoll, Jacques S. Beckmann, Heiner
Boeing, Eric Boerwinkle, Dorret I. Boomsma, Mark J. Caulfield, Stephen J.
Chanock, Francis S. Collins, L. Adrienne Cupples, George Davey Smith,
Jeanette Erdmann, Philippe Froguel, Henrik Gro ¨nberg, Ulf Gyllensten, Per
Hall, Torben Hansen, Tamara B. Harris, Andrew T. Hattersley, Richard
Five GWAS Loci Associate with NAFLD
PLoS Genetics | www.plosgenetics.org12 March 2011 | Volume 7 | Issue 3 | e1001324
B. Hayes, Joachim Heinrich, Frank B. Hu, Kristian Hveem, Thomas Illig,
Marjo-Riitta Jarvelin, Jaakko Kaprio, Fredrik Karpe, Kay-Tee Khaw,
Lambertus A. Kiemeney, Heiko Krude, Markku Laakso, Debbie A.
Lawlor, Andres Metspalu, Patricia B. Munroe, Willem H. Ouwehand,
Oluf Pedersen, Brenda W. Penninx, Annette Peters, Peter P. Pramstaller,
Thomas Quertermous, Thomas Reinehr, Aila Rissanen, Igor Rudan,
Nilesh J. Samani, Peter E.H. Schwarz, Alan R. Shuldiner, Timothy D.
Spector, Jaakko Tuomilehto, Manuela Uda, Andre ´ Uitterlinden, Timo T.
Valle, Martin Wabitsch, Ge ´rard Waeber, Nicholas J. Wareham, Hugh
Watkins on behalf of Procardis Consortium, James F. Wilson, Alan F.
Wright, M.Carola Zillikens, Nilanjan Chatterjee, Steven A. McCarroll,
Shaun Purcell, Eric E. Schadt, Peter M. Visscher, Themistocles L. Assimes,
Ingrid B. Borecki, Panos Deloukas, Caroline S. Fox, Leif C. Groop, Talin
Haritunians, David J. Hunter, Robert C. Kaplan, Karen L. Mohlke,
Jeffrey R. O’Connell, Leena Peltonen, David Schlessinger, David P.
Strachan, Cornelia M. van Duijn, H.-Erich Wichmann, Timothy M.
Frayling, Unnur Thorsteinsdottir, Gonc ¸alo R. Abecasis, Ine ˆs Barroso,
Michael Boehnke, Kari Stefansson, Kari E. North, Mark I. McCarthy, Joel
N. Hirschhorn, Erik Ingelsson, Ruth J.F. Loos
Jose ´e Dupuis, Claudia Langenberg, Inga Prokopenko, Richa Saxena,
Nicole Soranzo, Anne U Jackson, Eleanor Wheeler, Nicole LGlazer,
Nabila Bouatia-Naji, Anna LGloyn, Cecilia MLindgren, Reedik Ma ¨gi,
Andrew P Morris, Joshua Randall, Toby Johnson, Paul Elliott, Denis
Rybin, Gudmar Thorleifsson, Valgerdur Steinthorsdottir, Peter Henne-
man, Harald Grallert, Abbas Dehghan, Jouke Jan Hottenga, Christopher S
Franklin, Pau Navarro, Kijoung Song, Anuj Goel, John R B Perry,
Josephine M Egan, Taina Lajunen, Niels Grarup, Thomas Sparsø, Alex
Doney, Benjamin F Voight, Heather M Stringham, Man Li, Stavroula
Kanoni, Peter Shrader, Christine Cavalcanti-Proenc ¸a, Meena Kumari, Lu
Qi, Nicholas J Timpson, Christian Gieger, Carina Zabena, Ghislain
Rocheleau, Erik Ingelsson, Ping An, Jeffrey O’Connell, Jian’an Luan,
Amanda Elliott, Steven A McCarroll, Felicity Payne, Rosa Maria
Roccasecca, Franc ¸ois Pattou, Praveen Sethupathy, Kristin Ardlie, Yavuz
Ariyurek, Beverley Balkau, Philip Barter, John P Beilby, Yoav Ben-
Shlomo, Rafn Benediktsson, Amanda J Bennett, Sven Bergmann, Murielle
Bochud, Eric Boerwinkle, Ame ´lie Bonnefond, Lori L Bonnycastle, Knut
Borch-Johnsen, Yvonne Bo ¨ttcher, Eric Brunner, Suzannah J Bumpstead,
Guillaume Charpentier, Yii-Der Ida Chen, Peter Chines, Robert Clarke,
Lachlan J M Coin, Matthew N Cooper, Marilyn Cornelis, Gabe Crawford,
Laura Crisponi, Ian N M Day, Eco J Cde Geus, Jerome Delplanque,
Christian Dina, Michael R Erdos, Annette C Fedson, Antje Fischer-
Rosinsky, Nita G Forouhi, Caroline S Fox, Rune Frants, Maria Grazia
Franzosi, Pilar Galan, Mark O Goodarzi, Ju ¨rgen Graessler, Christopher J
Groves, Scott Grundy, Rhian Gwilliam, Ulf Gyllensten, Samy Hadjadj,
Go ¨ran Hallmans, Naomi Hammond, Xijing Han, Anna-Liisa Hartikainen,
Neelam Hassanali, Caroline Hayward, Simon C Heath, Serge Hercberg,
Christian Herder, Andrew A Hicks, David R Hillman, Aroon DHingorani,
Albert Hofman, Jennie Hui, Joe Hung, Bo Isomaa, Paul R V Johnson,
Torben Jørgensen, Antti Jula, Marika Kaakinen, Jaakko Kaprio, Y Antero
Kesaniemi, Mika Kivimaki, Beatrice Knight, Seppo Koskinen, Peter
Kovacs, Kirsten Ohm Kyvik, GMark Lathrop, Debbie A Lawlor, Olivier
Le Bacquer, Ce ´cile Lecoeur, Yun Li, Valeriya Lyssenko, Robert Mahley,
Massimo Mangino, Alisa K Manning, Marı ´a Teresa Martı ´nez-Larrad,
Jarred B McAteer, Laura J McCulloch, Ruth McPherson, Christa
Meisinger, David Melzer, David Meyre, Braxton D Mitchell, Mario A
Morken, Sutapa Mukherjee, Silvia Naitza, Narisu Narisu, Matthew J
Neville, Ben A Oostra, Marco Orru `, Ruth Pakyz, Colin NA Palmer,
Giuseppe Paolisso, Cristian Pattaro, Daniel Pearson, John F Peden, Nancy
L Pedersen, Markus Perola, Andreas F H Pfeiffer, Irene Pichler, Ozren
Polasek, Danielle Posthuma, Simon C Potter, Anneli Pouta, Michael A
Province, Bruce M Psaty, Wolfgang Rathmann, Nigel WRayner, Kenneth
Rice, Samuli Ripatti, Fernando Rivadeneira, Michael Roden, Olov
Rolandsson, Annelli Sandbaek, Manjinder Sandhu, Serena Sanna, Avan
Aihie Sayer, Paul Scheet, Laura J Scott, Udo Seedorf, Stephen J Sharp,
Beverley Shields, Gunnar Sigurðsson, Eric J G Sijbrands, Angela Silveira,
Laila Simpson, Andrew Singleton, Nicholas L Smith, Ulla Sovio, Amy
Swift, Holly Syddall, Ann-Christine Syva ¨nen, Toshiko Tanaka, Barbara
Thorand, Jean Tichet, Anke To ¨njes, Tiinamaija Tuomi, Andre ´ GUitter-
linden, Ko Willems van Dijk, Mandy van Hoek, Dhiraj Varma, Sophie
Visvikis-Siest, Veronique Vitart, Nicole Vogelzangs, Ge ´rard Waeber, Peter
J Wagner, Andrew Walley, G Bragi Walters, Kim L Ward, Hugh Watkins,
Michael N Weedon, Sarah H Wild, Gonneke Willemsen, Jaqueline C M
Witteman, John W G Yarnell, Eleftheria Zeggini, Diana Zelenika, Bjo ¨rn
Zethelius, Guangju Zhai, Jing Hua Zhao, M Carola Zillikens, DIAGRAM
Consortium, GIANT Consortium, Global BPgen Consortium, Ingrid B
Borecki, Ruth J F Loos, Pierre Meneton, Patrik KEMagnusson, David M
Nathan, Gordon H Williams, Andrew T Hattersley, Kaisa Silander,
Veikko Salomaa, George Davey Smith, Stefan R Bornstein, Peter Schwarz,
Joachim Spranger, Fredrik Karpe, Alan R Shuldiner, Cyrus Cooper,
George V Dedoussis, Manuel Serrano-Rı ´os, Andrew D Morris, Lars Lind,
Lyle J Palmer, Frank B Hu, Paul W Franks, Shah Ebrahim, Michael
Marmot, W H Linda Kao, James S Pankow, Michael J Sampson, Johanna
Kuusisto, Markku Laakso, Torben Hansen, Oluf Pedersen, Peter Paul
Pramstaller, H Erich Wichmann, Thomas Illig, Igor Rudan, Alan F
Wright, Michael Stumvoll, Harry Campbell, James F Wilson, Anders
Hamsten on behalf of Procardis Consortium, Richard N Bergman,
Thomas A Buchanan, Francis S Collins, Karen L Mohlke, Jaakko
Tuomilehto, Timo T Valle, David Altshuler, Jerome I Rotter, David S
Siscovick, Brenda W J H Penninx, Dorret I Boomsma, Panos Deloukas,
Timothy D Spector, Timothy M Frayling, Luigi Ferrucci, Augustine Kong,
Unnur Thorsteinsdottir, Kari Stefansson, Cornelia Mvan Duijn, Yurii
SAulchenko, Antonio Cao, Angelo Scuteri, David Schlessinger, Manuela
Uda, Aimo Ruokonen, Marjo-Riitta Jarvelin, Dawn M Waterworth, Peter
Vollenweider, Leena Peltonen, Vincent Mooser, Goncalo R Abecasis,
Nicholas J Wareham, Robert Sladek, Philippe Froguel, Richard M
Watanabe, James B Meigs, Leif Groop, Michael Boehnke, Mark I
McCarthy, Jose C Florez & Ine ˆs Barroso for the MAGIC investigators.
Gudny Eiriksdottir, Melissa E. Garcia, Vilmundur Gudnason, Tamara
B. Harris, Lauren J. Kim, Lenore J. Launer, Michael A. Nalls, Albert V.
Smith,Jeanne M. Clark, Ruben Hernaez, W. H. Linda Kao, Braxton D.
Mitchell, Alan R. Shuldiner, Laura M. Yerges-Armstrong, Ingrid B.
Borecki, J. Jeffrey Carr, Mary F. Feitosa, Jun Wu, Johannah L. Butler,
Caroline S. Fox, Joel N. Hirschhorn, Udo Hoffmann, Shih-Jen Hwang,
Joseph M. Massaro, Christopher J. O’Donnell, Cameron D. Palmer,
Dushyant V. Sahani, Elizabeth K. Speliotes.
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.
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