Diabetes Page 2 of 58
A PPARA Polymorphism Influences the Cardiovascular Benefit of Fenofibrate in Type 2 Diabetes: Findings from ACCORD Lipid Short title: Genetics of cardiovascular response to fibrates
Authors: Mario Luca Morieri M.D.1,2,3, Hetal S. Shah M.D., M.P.H. 1,2, Jennifer Sjaarda, B.S.4, Petra A. Lenzini, M.S.5, Hannah Campbell, M.P.H5,6, Alison A. Motsinger-Reif Ph.D.7, He Gao Ph.D.1,2, Laura Lovato M.S.8, Sabrina Prudente Ph.D.9, Assunta Pandolfi Ph.D.10, Marcus G. Pezzolesi Ph.D., M.P.H.11, Ronald J. Sigal M.D., M.P.H.12, Guillaume Paré M.D., M.Sc.4, Santica M. Marcovina Ph.D., D.Sc.13, Daniel M. Rotroff Ph.D.14, Elisabetta Patorno M.D., Dr.P.H.15, Luana Mercuri Ph.D.9, Vincenzo Trischitta M.D.9,16, Emily Y. Chew M.D.17, Peter Kraft Ph.D.18, John B. Buse M.D. Ph.D.19, Michael J. Wagner Ph.D.20, Sharon Cresci, M.D. 5,6, Hertzel C. Gerstein M.D. M.Sc.4, Henry N. Ginsberg, M.D.21, Josyf C. Mychaleckyj M.A., D.Phil.22, Alessandro Doria M.D., Ph.D., M.P.H..1,2
1: Research Division, Joslin Diabetes Center, Boston, MA 2: Department of Medicine, Harvard Medical School, Boston, MA 3: Department of Medicine, University of Padova, Padova, Italy 4: McMaster University and the Population Health Research Institute, Hamilton, Ontario, Canada 5: Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 6: Department of Medicine, Washington University School of Medicine, St. Louis, Missouri 7: Biostatistics and Computational Biology Branch National Institute of Environmental Health Sciences Durham, NC 8: Wake Forest School of Medicine, Winston Salem, NC 9: Research Unit of Metabolic and Cardiovascular Diseases, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy 10: Department of Medical, Oral and Biotechnological Sciences, University "G. d'Annunzio", Chieti, Italy; 11: Division of Nephrology and Hypertension and Diabetes and Metabolism Center, University of Utah, Salt Lake City, UT 12: Departments of Medicine, Cardiac Sciences, and Community Health Sciences, Cumming School of Medicine, Faculties of Medicine and Kinesiology, University of Calgary, Calgary, Alberta, Canada 13: Department of Medicine, University of Washington, and Northwest Lipid Metabolism and Diabetes Research Laboratories, Seattle, WA 14: Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 15: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 16: Department of Experimental Medicine, "Sapienza" University, Rome, Italy 17: Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
Diabetes Publish Ahead of Print, published online January 23, 2020 Page 3 of 58 Diabetes
18: Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 19: Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 20: Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, NC 21: Columbia University College of Physicians and Surgeons, Irving Institute for Clinical and Translational Research, Columbia University, New York, NY 22: Center for Public Health Genomics, University of Virginia, Charlottesville, VA
Words count: 4,699; Tables and Figures = 8;
Corresponding Author: Dr. Alessandro Doria Section on Genetics and Epidemiology, Joslin Diabetes Center, One Joslin Place, Boston, MA 02215 Telephone: 617-309-2406, Fax: 617-309-2667 E-mail address: [email protected]
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A common genetic variant in PPARA can identify patients with type 2 diabetes who experience a cardiovascular benefit from fenofibrate + statins (see figure 3) Diabetes Page 4 of 58
Abstract
The cardiovascular benefits of fibrates have been shown to be heterogeneous and to depend on the
presence of atherogenic dyslipidemia. We investigated whether genetic variability in the PPARA gene, coding for the pharmacological target of fibrates (PPAR-alpha), could be used to improve the selection of patients with type 2 diabetes who may derive cardiovascular benefit from addition of this treatment to statins. We identified a common variant at the PPARA locus (rs6008845, C/T)
displaying a study-wide-significant influence on the effect of fenofibrate on major cardiovascular
events (MACE) among 3,065 self-reported White subjects treated with simvastatin and randomized
to fenofibrate or placebo in the Action-to-Control-Cardiovascular-Risk-in-Diabetes (ACCORD)
Lipid Trial. T/T homozygotes (36% of participants) experienced a 51% MACE reduction in
response to fenofibrate (HR=0.49; 95%C.I. 0.34-0.72) whereas no benefit was observed for other
genotypes (p for interaction=3.7x10-4). The “rs6008845-by-fenofibrate” interaction on MACE was
replicated in African-Americans from ACCORD (N=585, p=0.02) and in external cohorts
(ACCORD-Blood-Pressure, ORIGIN, and TRIUMPH, total N=3059, p=0.005). Remarkably,
rs6008845 T/T homozygotes experienced a cardiovascular benefit from fibrate even in the absence
of atherogenic dyslipidemia. Among these individuals, but not among carriers of other genotypes,
fenofibrate treatment was associated with lower circulating levels of CCL11 - a pro-inflammatory
and atherogenic chemokine also known as eotaxin (p for rs6008845-by-fenofibrate
interaction=0.003). The Genotype-Tissue Expression (GTEx) dataset revealed regulatory functions
of rs6008845 on PPARA expression in many tissues. In summary, we have found a common
PPARA regulatory variant that influences the cardiovascular effects of fenofibrate and that could be
used to identify T2D patients who would derive benefit from fenofibrate treatment, in addition to
those with atherogenic dyslipidemia. Page 5 of 58 Diabetes
INTRODUCTION
Cardiovascular events due to accelerated atherogenesis are major determinants of
morbidity and mortality in patients with type 2 diabetes mellitus (1). The causes of increased
atherogenesis in type 2 diabetes are complex and include, in addition to exposure to
hyperglycemia, the presence of other cardiovascular risk factors that frequently accompany type
2 diabetes such as dyslipidemia and hypertension (2). The current recommendations to prevent
major adverse cardiovascular events (MACE) in patients with type 2 diabetes include lifestyle
modifications, improvement of glycemic control, treatment of hypertension, and use of
cholesterol-lowering therapies (1).
Treatment with fibrates as an additional intervention to further improve cardiovascular
outcomes in type 2 diabetes has been studied several times in the last decades (3-7). Fibrates are
agonists of peroxisome proliferator–activated receptor-alpha (PPAR-α) - a transcription factor
that functions as a master regulator of lipid homeostasis, cardiac energy metabolism, vascular
inflammation and cell differentiation. PPAR-α activation reduces serum triglycerides, raises
plasma high-density lipoprotein (HDL) cholesterol levels, and reduces systemic inflammation
(8,9). However, despite such beneficial effects, clinical trials of fibrates have shown inconsistent
benefit of this treatment in preventing MACE (4,5,10), including among subjects with type 2
diabetes (4,5). At the same time, analyses of these trials have consistently shown that fibrates
might have a beneficial effect among subjects with atherogenic dyslipidemia (defined by low
HDL-cholesterol and high triglycerides levels) (11-14). For these reasons, fibrates are not
currently recommended as a standard treatment to prevent MACE in type 2 diabetes, but may be
considered as second or third line treatments in patients with atherogenic dyslipidemia (1,15,16). Diabetes Page 6 of 58
The lipid and inflammatory responses to fibrates vary in the population, in part due to genetic factors (17,18). Thus, one can hypothesize that the inconclusive results from clinical trials may be partly due to an underlying genetic heterogeneity in the cardiovascular response to fibrates. A corollary of this hypothesis is that it may be possible to develop genetic tests that can help distinguish individuals who would benefit from fibrates from those who would not. We have tested these postulates in the Action to Control Cardiovascular Risk in Diabetes
(ACCORD) Lipid study (4) – the largest randomized clinical trial (RCT) to date on fibrate treatment as add-on to statin therapy in type 2 diabetes. We specifically directed our attention to
PPARA - the gene that codes for the molecule (PPAR- α) through which fibrates are believed to exert their pharmacological effects.
Research Design and Methods
Study Populations
Accord-Lipid trial
ACCORD-Lipid was part of the ACCORD trial, an NHLBI-funded clinical trial that tested the effectiveness of intensive versus standard glycemic control in preventing MACE (a composite of non-fatal myocardial infarction, non-fatal stroke and cardiovascular death) among
10,251 subjects with type 2 diabetes at high risk of atherosclerotic cardiovascular disease (19).
The trial had a double, 2x2 factorial design, with 4,733 patients additionally randomized to a blood pressure trial (ACCORD-BP) (20) and 5,518 to a lipid trial (ACCORD-Lipid)(4). Subjects were specifically enrolled in the ACCORD lipid trial if they had HDL cholesterol <55 mg/dl deciliter for women and Blacks or <50 mg/dl for all other groups, LDL cholesterol level between
60 and 180 mg/dl and fasting triglycerides <750 mg/dl if not on triglyceride-lowering treatment or < 400 mg/dl if they were receiving triglyceride-lowering treatment. ACCORD-Lipid Page 7 of 58 Diabetes
investigated whether fenofibrate, given in addition to statin, was more effective than statin alone
in preventing MACE. This trial showed a modest, non-significant trend towards a benefit of
fenofibrate (HR=0.92, 95% CI 0.79 – 1.08) (4). Genetic data were available for 4,414 ACCORD
Lipid participants (80% of the total), who had provided consent for genetic studies. To avoid
race/ethnic confounding, genetic analyses were initially restricted to self-reported non-Hispanic
Whites (n=3,065) and then extended to self-reported African-Americans (n=585). Other
racial/ethnic groups were too sparse to be considered individually.
Accord-Blood-Pressure, ORIGIN, and TRIUMPH cohorts
ACCORD Blood Pressure (20) investigated whether reducing systolic blood pressure to
less than 120 mmHg was more effective than standard treatment (target < 140 mmHg) in
preventing MACE among 4,733 subjects with type 2 diabetes at high cardiovascular risk. After a
median follow-up time of 4.7 years the study reported lack of significant cardiovascular effect of
this treatment.
The Outcome-Reduction with an Initial Glargine Intervention trial (ORIGIN) (21)
investigated, in a 2x2 factorial design, the effect of titrated basal insulin versus standard care and
of n–3 fatty acid supplements versus placebo on MACE occurrence among 12,537 subjects with
impaired fasting glucose, impaired glucose tolerance or type 2 diabetes, and high cardiovascular
risk. After a median follow-up time of 6.2 years the study reported lack of significant
cardiovascular effect of these two treatments.
The Translational Research Investigating Underlying disparities in acute Myocardial
infarction Patients’ Health status (TRIUMPH) study is a large, prospective, observational cohort
study of 4,340 consecutive patients, (31% with type 2 diabetes), designed to examine the Diabetes Page 8 of 58
complex interactions between genetic and environmental determinants of post-myocardial infarction outcomes (22).
The current study included 1,407, 1,244, and 408 self-reported White participants from
ACCORD-BP, ORIGIN and TRIUMPH, respectively, who had type 2 diabetes or dysglycemia and were on concomitant statin + fibrate or statin alone therapies before the occurrence of cardiovascular events or before being censored, and for whom genetic data were available.
ACCORD-MIND Study
The ACCORD-MIND study, included 2,977 participants from the overall ACCORD trial, and 562 of these participants, with available serum samples, participated in an ancillary biomarker study (23). This study included 133 self-reported White subjects from this ancillary study (24), who were also included in the ACCORD Lipid trial and for whom genetic data were available.
Outcomes:
In this post-hoc study, the primary outcome was a 3-point MACE (a composite of non- fatal myocardial infarction, non-fatal stroke and cardiovascular death), defined accordingly to the pre-specified primary end-point definitions in ACCORD (19) and ORIGIN (21) clinical trials. In
TRIUMPH study, the primary outcome was mortality after acute myocardial infarction.
Data Analysis
PPARA SNP x fenofibrate treatment interaction on MACE risk
To identify common variants in or around the candidate gene PPARA that modulated the effect of fenofibrate on the ACCORD primary outcome (MACE), genotype data for 360 genotyped or imputed SNPs having minor allele frequencies (MAF) >5% and spanning the entire
PPARA gene plus 40Kb on either side (GRCh37/hg19 bp coordinates of chromosome 22: Page 9 of 58 Diabetes
46,506,499 – 46,679,653) were extracted from the ACCORD genetic dataset. Detailed DNA
extraction, genotyping, quality control (QC) methods and imputation can be found in the
Supplemental Material of the paper in which this dataset was first reported (25). Separate
analyses were conducted in the two genotyping subsets that compose the ACCORD genetic
dataset (‘ANYSET’, including patients who gave consent to genetic studies by any investigator,
and ‘ACCSET’, including patients who gave consent only to genetic studies by ACCORD
investigators) and results meta-analyzed as previously described (25). When the two subsets
were analyzed together, an indicator variable for the genotyping platform was used as covariate.
The SNPs were analyzed according to an additive genetic model. The interaction between
fenofibrate treatment and each of the 360 SNPs on MACE risk was assessed by means of Cox
proportional hazards models, each including the SNP minor allele dosage, fenofibrate
assignment (yes/no), and a SNP x fenofibrate interaction term along with assignment to intensive
or standard glycemic control group, clinical center network, presence of CVD at baseline, age,
and sex as covariates. The number of independent tests that were conducted by analyzing these
360 SNPs was estimated by means of the simpleM method (26), which considers the correlation,
or linkage disequilibrium (LD), among variants. Based on the results of this analysis (81
independent comparisons), the Bonferroni-adjusted threshold for significance was set to
p=6.2x10-4 (α=0.05/81) (Supplemental Figure 1).
All self-reported Whites (N=3,065) and African-Americans (N=585) participants
randomized to fenofibrate or placebo for whom genetic data were available were included in the
study. All 360 SNPs were analyzed for their interaction with fenofibrate in Whites. SNPs found
to have a significant effect in Whites were then analyzed in African-Americans. A summary Diabetes Page 10 of 58
estimate of the interaction effect across the two races was obtained by means of a fixed-effects meta-analysis using an inverse-variance approach.
The number of patients who need to be treated to prevent one additional MACE event over 5 years (number needed to treat – NNT) with fenofibrate + statin as compared to statin alone treatments was calculated as previously described (27).
Replication of the rs6008845 x fenofibrate treatment interaction
The SNP showing a significant interaction with fenofibrate in ACCORD-Lipid
(rs6008845) was further investigated in the ACCORD-BP trial (20), by contrasting 87 participants who were on concomitant fibrate + statin therapy with 1,320 participants who were only on concomitant statin, in the ORIGIN trial (21), by contrasting 82 participants who were on concomitant fibrate + statin therapy with 1,162 participants who were only on concomitant statin, and in the TRIUMPH study (22) , by contrasting 21 participants who were on concomitant fibrate + statin therapy with 387 participants who were only on concomitant statin. The SNP x fenofibrate interaction on the primary outcome was evaluated by Cox proportional hazards models including treatment arms, age, sex, and history of CVD as covariates (in the TRIUMPH study, since all subjects were in secondary prevention by study design, history of CVD was not included in the analyses). Results were meta-analyzed with a fixed-effect inverse variance approach. rs6008845 x fenofibrate treatment interaction on other clinical features
The association between rs6008845 and baseline characteristics was evaluated by means of ANCOVA or logistic regression models. Presence of atherogenic dyslipidemia was defined by the same previously used cut-off (4) of having both low-HDL-cholesterol (≤34 mg/dl, i.e. the first tertile of HDL-cholesterol distribution) and high triglycerides levels (≥ 204 mg/dl, i.e. in the Page 11 of 58 Diabetes
third tertile of TG distribution). The influence of rs6008845 on the effect of fenofibrate (SNP x
fenofibrate interaction) on change from baseline to the average on-trial value of plasma lipids
was tested by ANCOVA with the baseline biomarker level included as a covariate along with the
predictors included in the Cox regression models.
rs6008845 x fenofibrate treatment interaction on chemokines levels.
Data on serum levels of seven chemokines (CCL2, CCL3, CCL4, CCL11, CXCL8,
CXC10, and CXC3CR1) were available from the ACCORD-MIND ancillary study, in which
biomarkers were measured in a subset of ACCORD participants by means of multiplexing kits
from Millipore and Luminex using a single lot of reagent and quality control material. Baseline
and 12-months levels were log-transformed, and the influence of rs6008845 on the effect of
fenofibrate (SNP x fenofibrate interaction) on 12-month levels was tested by ANCOVA with the
baseline chemokine level included as a covariate along with the same predictors included in the
Cox regression models.
Expression studies and functional annotation
The association between rs6008845 and PPARA expression was tested using RNA-seq
data from 44 tissues collected from up to 449 donors as part of the Genotype-Tissue Expression
(GTEx) project (Release Version V6p) (28). In single-tissue eQTL analysis, the SNP effect size
(beta) was estimated as the slope of the linear regression of normalized expression data versus
the three genotype categories coded as 0, 1, and 2 (http://www.gtexportal.org/home/
documentationPage). Trans-eQTL analyses of the influence of rs6008845 on PPAR-α target
genes were performed using the same approach. Results across tissues were summarized, as in
the GTEx portal, by means of Han and Eskin's Random Effects model (RE2) with METASOFT
(29). Additional functional annotations were derived using two web-based tools integrating data Diabetes Page 12 of 58
from ENCODE, RegulomeDB (http://regulomedb.org/index) and the Roadmap Epigenomics
Project (HaploReg), http://archive.broadinstitute.org/mammals/haploreg/haploreg.php) .
Additional information on the association between the top significant SNPs and other phenotypes
of interest was obtained by browsing the GWAS catalog (https://www.ebi.ac.uk/gwas/).
Statistical analyses were performed using SAS v9.4 (SAS Institute, Cary, NC). Graphs
were edited with GraphPad Prism (Version 7.02).
RESULTS
A PPARA variant modulating the fenofibrate effect on MACE in Whites
Among 3,065 self-reported White subjects from ACCORD-Lipid (Supplemental Table
1), fenofibrate treatment was associated with a non-significant reduction of MACE risk during a
median follow-up of 4.7 years (HR=0.82; 95%C.I. 0.66-1.02). In this population, a total of 360
SNPs in the PPARA gene region were tested for a modulatory influence on the effect of fenofibrate treatment on MACE risk. Results are shown in Figure 1 as a function of the SNP positions along the genome. Evidence of interaction with fenofibrate meeting study-wide significance (p<6.2x10-4 based on a Bonferroni adjustment for the 81 independent comparisons that were made by analyzing those 360 SNPs) was observed for SNP rs6008845 - a T to C substitution placed about 25 Kb upstream of the PPARA transcription start site (p=3.7x10-4)
(Table 1). The interaction was such that the T allele conferred protection among subjects treated with statin plus fenofibrate (HR=0.75; 95%CI 0.60-0.95) whereas it was associated with a higher risk of MACE among those randomized to statin alone (HR=1.27; 95% CI 1.01-1.60) (Figure 2, top section).
Trans-ethnic and external validation of the gene x fenofibrate interaction Page 13 of 58 Diabetes
The interaction between rs6008845 and fenofibrate treatment was internally validated
among 585 self-reported African-Americans enrolled in ACCORD-Lipid. Despite the lower
frequency of the T allele in this racial group (0.21 vs. 0.60 in Whites, Supplemental Figure 2),
the interaction was in the same direction as in Whites, with the T allele being associated with
MACE prevention in subjects randomized to fenofibrate + statin (HR=0.31; 95% CI 0.11 - 0.90),
but not among those randomized to statin + placebo (OR=1.37; 95% CI 0.74 - 2.53, P for
rs6008845 x fenofibrate interaction = 0.02; Figure 2, section B). Meta-analyses of results from
self-reported Whites and African-Americans led to a P value for rs6008845 x fenofibrate
interaction of 6x10-5 (Suppl. Table 2).
A similar synergism between rs6008845 T allele and fenofibrate was observed in an
observational setting by analyzing data on concomitant medications among self-reported Whites
from the ACCORD-BP and ORIGIN trials and from the TRIUMPH cohort (Figure 2, section C).
In a combined analysis of the three cohorts (see baseline clinical characteristics in Supplemental
Table 3), the T allele was associated with a significantly lower risk of events among subjects on
concomitant fibrate + statin therapy (HR=0.45; 95%CI 0.25-0.79) whereas no association was
present among participants on statin alone (HR=1.03; 95%CI 0.89-1.20). The summary P value
for interaction was 0.0046. The meta-analysis of results from Accord Lipid (Whites and
African-American) combined with those from observational studies, yielded a P value for
interaction of 1 x 10-6 (Figure 2, bottom section).
Fenofibrate effect on MACE according to rs6008845 genotype and lipid profile
Figure 3 (panel A) shows the interaction described above from a different perspective,
that is, as the effect of fenofibrate on MACE risk reduction across genotypes, which is more
meaningful from a clinical viewpoint. In Whites from ACCORD Lipid, T/T homozygotes (about Diabetes Page 14 of 58
one third of the cohort) experienced a 51% reduction in MACE risk when randomized to fenofibrate (HR=0.49; 95%CI 0.34-0.72), while no beneficial response was observed among heterozygotes (HR=0.98; 95%CI 0.72-1.34) or C/C homozygotes (HR=1.38; 95% CI 0.79-2.48).
As shown in Figure 3 central and bottom panels, this interaction was only evident among participants without overt atherogenic dyslipidemia, resulting in a beneficial effect of fenofibrate among T/T homozygotes even in the absence of the combination of both low HDL-c and high triglycerides. Among participants with atherogenic dyslipidemia, the known beneficial effect of fenofibrate on MACE risk reduction was confirmed with no significant modulation by rs6008845 genotypes. Notably, in the group of participants without overt atherogenic dyslipidemia and with rs6008845 T/T genotype, the hazard ratio of fenofibrate and the number needed-to-treat (NNT) to prevent one MACE over 5 years were similar to those for subjects with atherogenic dyslipidemia, for whom fibrates are currently indicated (Table 2). As shown in Supplemental
Table 4, results were similar using an alternative definition of atherogenic dyslipidemia (HDL-c
<50.2 mg/dl or 1.3 mmol/l for women and <38.7 mg/dl or 1.0 mmol/l for men combined with triglycerides>203.7 mg/dl or 2.3 mmol/l, regardless of sex (16)). rs6008845 effect on lipid and chemokine response to fenofibrate
As shown in Table 3, the larger cardiovascular benefit of fenofibrate among T/T homozygotes was not paralleled by differences in clinical characteristics at baseline, the only nominally significant difference being in the age of onset of diabetes. The lipid response to fenofibrate treatment, in terms of increase in HDL cholesterol and decrease in triglycerides and total cholesterol, was also equivalent in the three genotypes (Figure 4). Consistent with the lack of association with lipid profile in ACCORD, rs6008845 was not in linkage disequilibrium (LD) with any of the PPARA variants previously reported to be associated with lipid profile at GWAS Page 15 of 58 Diabetes
levels (30). Rather, a recent genome-wide association study reported an association between
genetic variants in the PPARA region, including rs6008845, and serum levels of a pro-
inflammatory chemokine (CCL27) (31). Thus, we evaluated the “rs6008845 by fenofibrate”
interaction on circulating levels of seven chemokines available for 133 subjects from ACCORD
Lipid included in the ACCORD-MIND ancillary study (24). This subset had slightly different
clinical characteristics as compared to the whole ACCORD Lipid cohort; in particular they were
characterized by shorter duration of diabetes, lower blood pressure, HbA1c and LDL-cholesterol
levels and lower prevalence of CVD at baseline (Supplemental Table 5). As shown in Figure 6
and Supplemental Table 6, we found a significant interaction between rs6008845 and fenofibrate
in the circulating levels of CCL11 (also known as eotaxin), in the sense that fenofibrate was
associated with lower levels of CCL11 levels (P=0.01) among T/T homozygotes, but not among
T/C or C/C subjects. Though not reaching statistical significance, a similar pattern of interaction
was observed for CLL3 and CXCL8 (Supplemental Table 7).
rs6008845 regulatory function
In an analysis of RNA-seq data from the GTEx Project, the rs6008845 T allele was
significantly associated with lower PPARA expression in skin (P=6x10-17), whole blood
(P=9x10-3), skeletal muscle (P=2x10-2), vagina (P=3x10-3), and esophageal mucosa (P=4x10-4).
The association also approached significance in liver (P=7x10-2) despite the smaller sample size.
In a meta-analysis across all 44 tissues available in GTEx, rs6008845 was significantly
associated with PPARA mRNA levels (P=3x10-21 – Supplemental Figure 3), although these
results should be interpreted with caution due to the correlation between expression
measurements in different tissues obtained from the same donors. Of note, consistent with its
influence on PPARA mRNA levels, rs6008845 was also associated in the above tissues with the Diabetes Page 16 of 58
expression of multiple PPAR-α targets (24 genes yielding P<0.05 out of 98 tested, binomial P
value=6x10-11; Supplemental Table 8). The regulatory role of rs6008845 was also supported by data from Encode and Roadmap Epigenomics Projects (Supplemental Figure 4 and Supplemental
Table 9), indicating that SNP rs6008845 is placed in a DNAse I hypersensitivity cluster in the 5’ flanking region of the PPARA gene where chromatin immunoprecipitation experiments have
shown binding of several transcription factors in different cell types (mainly white blood cells
derived). Histone modification ChIP-seq peaks confirmed the occurrence of rs6008845 in a
regulatory locus in multiple cell lines as this variants was found to be placed inside epigenetic
peaks for Histone 3 (H3K4Me1, H3K4Me1 and H3K27Ac - as shown in suppl. Figure 5), which
suggests an active enhancer region.
DISCUSSION
Clinical trials assessing the effect of fibrates on cardiovascular risk among patients with
type 2 diabetes (4,5) have demonstrated small, if any, cardiovascular benefits of these drugs (10).
These studies, however, have shown a highly variable response to these agents (3-7,11,12),
suggesting the possibility of designing precision medicine algorithms to identify patients who
have a higher probability of deriving cardiovascular benefit from fibrates (32). In this study, we
have identified a genetic variant near the gene coding for the pharmacological target of
fenofibrate (PPAR-α) that could be used for this purpose. Among homozygotes for the major allele of this variant (about one third of ACCORD-Lipid participants), randomization to fenofibrate + statin rather than statin alone yielded a 50% reduction in MACE – much larger than in the overall study population. Importantly, this benefit was present in the absence of overt atherogenic dyslipidemia - the only condition that today represents an indication for the addition Page 17 of 58 Diabetes
of fenofibrate to statins for cardiovascular prevention (16). If the results of this study were
brought to the clinic, they would translate into more than a doubling in the number of patients
with type 2 diabetes who would benefit from treatment with fenofibrate as an add-on to statins.
Several aspects of these findings make them especially robust. First, the genetic effect is
linked to the gene that codes for the main pharmacological target of fibrates and as such had a
very high prior probability of being involved in the modulation of fenofibrate effects. Second,
the SNP x fenofibrate interaction was observed in the rigorous setting of a double-blind,
randomized, placebo controlled clinical trial characterized by excellent adherence to the study
protocol. Third, the statistical significance of the interaction was robust to adjustment for the
number of independent polymorphisms that were tested at the PPARA locus. Fourth, the
interaction was validated through trans-ethnic replication in African-Americans from ACCORD-
Lipid and also by using concomitant medication data from three well-characterized cohorts
(ACCORD BP, ORIGIN, and TRIUMPH), through which we were able to reproduce the same
exact exposures as in ACCORD-Lipid (fibrate+statin vs. statin alone) - an essential factor for a
meaningful replication of genetic findings (33). The fact that we could observe the same
rs6008845-by-fibrate interaction in these cohorts as in ACCORD-Lipid is quite remarkable,
considering the different clinical characteristics and settings (i.e. observational and
interventional) of study populations.
Another critical element in support of the robustness of our findings is the association
observed in multiple tissues between the SNP interacting with fenofibrate and mRNA levels of
PPARA and PPAR-α targets. The decrease in PPARA expression associated with the SNP
suggests that the latter is functional, providing experimental confirmation of the in silico
predictions based on Encode and Roadmap Epigenomic Projects data. The association with the Diabetes Page 18 of 58
expression of PPAR-α targets indicates that the effect of the rs6008845 on PPARA expression
translates into allelic differences in PPAR-α activity that propagate downstream and influence
cellular functions. As PPAR-α expression is reduced in rs6008845 T/T homozygotes, one can
speculate that carriers of this genotype derive benefit from fibrate treatment because they start
from lower PPAR-α activity whereas C allele carriers derive no benefit because their PPAR-α
activity is already optimal. Consistent with this interpretation was the tendency of the T allele to
be associated with increased risk of MACE among subjects treated with statins alone, which was
reversed to significant protection from MACE by the addition of fenofibrate.
The PPARA variant does not appear to act by influencing the effect of fenofibrate on circulating lipids – a finding hardly surprising, considering that changes in plasma lipid profile have been shown to explain less than 25% of the cardiovascular benefit of fibrates (34). Rather, our finding suggests that the variant exerts its modulatory effects by enhancing the ability of fenofibrate to dampen pro-inflammatory chemokines such as CCL11. The circulating levels of this chemokine were unaffected by fenofibrate in the overall ACCORD study population.
However, T/T subjects, i.e., those experiencing the cardiovascular benefit of fenofibrate, had
significantly lower levels of CCL11 when treated with fenofibrate. CCL11, also known as
eotaxin, is a chemokine expressed in multiple tissues, which, in addition to its chemotactic
activity on eosinophils, basophils, and Th2 lymphocytes (35,36), has been consistently identified,
with its receptor CCR3, as a player in vascular inflammatory processes(37-39). Moreover several
epidemiological studies have found a significant association between higher CCL11 levels and
increased CV risk (40-42), with RCTs showing that cardio-protective therapies such as
metformin and atorvastatin reduce circulating CCL11 (43,44). There are no reports in the
literature, beside the present one, describing a similar effect of fenofibrate in humans. However, Page 19 of 58 Diabetes
such an effect is supported by a study in a mouse model, in which up-regulation of PPAR-alpha
activity decreased skin expression of CCL11 and other chemokines (45). Such actions may relate
to the inhibitory effect of PPAR-α activation on the NF-B pathway (e.g., by inducing the NF-
B inhibitor IB) (45-47), which is known to regulate the expression of CCL11 and its receptor
CCR3(48,49). Altogether, our findings provide support for a complex mechanism of action of
fibrates on cardiovascular risk, consistent with the pleiotropic effects that activation of PPAR-α
has in multiple cell types relevant to atherogenesis including monocytes/macrophages, smooth
muscle cells, endothelial cells, platelet, and fibroblasts (9,50,51).
Some limitations of our study must be acknowledged. First, this was a post-hoc analysis
that included only 80% of the subjects in the ACCORD-Lipid trial (i.e., those for whom DNA
was available). As such, this analysis deviates from an intention-to-treat approach. On the other
hand, the lack of differences in baseline clinical characteristics between treatment arms in the
subset included in the study, and the external validation in three different cohorts, attenuates the
importance of this limitation. While these other cohorts were from observational studies, in
which fibrate and statin treatments were not randomized and were based on self-report, the lack
of differences in clinical characteristics among rs6008845 genotypes and the fact that results
were similar across the three cohorts and consistent with the findings from ACCORD Lipid
provide reassurance about the validity of these data. Second, since ACCORD-Lipid investigated
fenofibrate given in combination with a statin and was specifically directed to subjects with type
2 diabetes at high cardiovascular risk (as were the ORIGIN, ACCORD-BP trials and TRIUMPH
cohorts), caution should be exercised in generalizing these findings to treatment with fenofibrate
alone or to subjects having different characteristics. This aspect deserves special attention given
the known cross-talk between fibrates and statins (52,53). Importantly, although we were able to Diabetes Page 20 of 58
replicate our findings among African-Americans, further evidence should be gathered before extending conclusions to other races or ethnic groups. Third, since only mortality data were available for the TRIUMPH cohort, we could not analyze the effect of the variant on MACE in this study, as was done in the other two cohorts. However, all TRIUMPH participants were enrolled in that study after an acute myocardial infarction; thus, the majority of deaths in this cohort are likely to have had a cardiovascular cause. Fourth, due to the relatively small sample size, our analysis was limited to common polymorphisms (MAF>5%) and we cannot therefore exclude that other, less common variants may exist in the PPARA gene region that also modulate the CV responsiveness to fenofibrate. Finally, given the small number of participants with chemokine data, the finding of association between fenofibrate treatment and reduced CCL11 levels in T/T homozygotes should be interpreted with caution and should be considered at this point as merely hypothesis generating.
Our findings have promising implications for the treatment of diabetic patients at high cardiovascular risk. Due to the lack of clear cardiovascular benefit, in 2016 the FDA removed the indication for cardiovascular prevention of the addition of fibrates to statins for cardiovascular prevention (54), and current guidelines do not recommended this treatment for that purpose (1,15). The only exceptions are patients with atherogenic dyslipidemia, i.e., with low HDL-cholesterol and high triglycerides, for whom this treatment may be considered due to the consistent evidence of benefit in this small subgroup (12-14). Our findings confirm the established benefit of fenofibrate in the presence of atherogenic dyslipidemia, but importantly suggest that rs6008845 could be used as a marker to identify an additional group of subjects (i.e., those with T/T genotype) for whom therapy with a fibrate as an add-on to statins could be indicated for cardiovascular disease prevention even in the absence of atherogenic dyslipidemia. Page 21 of 58 Diabetes
The use of this marker would at least double the proportion of patients eligible for fenofibrate
therapy, with obvious public health implications. However, before this approach can be brought
into clinical practice, our findings will require validation through specifically designed
pharmacogenetics clinical trials, in which randomization to fibrate or placebo is stratified by
PPARA genotype. In the case of fenofibrate, the low cost and the well-documented safety profile
of this drug may facilitate this goal by making pragmatic clinical trials possible. Our findings
may also prompt ancillary studies of ongoing cardiovascular clinical trials of new fibrates, such
as the PROMINENT clinical trial of pemafibrate (ClinicalTrials.gov: NCT03071692).
In conclusion, we have identified a genetic variant at the PPARA locus that modulates the
cardiovascular response to fenofibrate in patients with type 2 diabetes. These findings suggest a
precision medicine approach to prescribe fenofibrate optimally, rescuing a drug that would be
otherwise dismissed as ineffective and offering a cardio-protective drug to those patients that are
most likely to experience a robust benefit from this medication. Diabetes Page 22 of 58
Acknowledgements: The authors wish to thank the investigators, staff and participants of the
ACCORD study for their support and contributions, and in giving us access to this rich dataset.
Members of the ACCORD DSMB included: Antonio M. Gotto. Jr. (chair), Kent Bailey, Dorothy
Gohdes, Steven Haffner, Roland Hiss, Kenneth Jamerson, Kerry Lee, David Nathan, James
Sowers, LeRoy Walters. The following companies provided study medications, equipment, or supplies: Abbott Laboratories (Abbott Park, IL); Amylin Pharmaceutical (San Diego, CA);
AstraZeneca Pharmaceuticals LP (Wilmington, DE); Bayer HealthCare LLC (Tarrytown, NY);
Closer Healthcare Inc. (Tequesta, FL); GlaxoSmithKline Pharmaceuticals (Philadelphia, PA);
King Pharmaceuticals, Inc. (Bristol, TN); Merck & Co., Inc. (Whitehouse Station, NJ); Novartis
Pharmaceuticals, Inc. (East Hanover, NJ); Novo Nordisk, Inc. (Princeton, NJ); Omron
Healthcare, Inc. (Schaumburg, IL); Sanofi-Aventis U.S. (Bridgewater, NJ); Schering-Plough
Corporation (Kenilworth, NJ); Takeda Pharmaceuticals (Deerfield, IL). None of these companies had an interest or bearing on the genome-wide analysis of the ACCORD data.
Author Contributions: M.L.M. designed the study, acquired, analyzed and interpreted the data, and wrote the manuscript; H.S.S. designed the study, acquired, analyzed and interpreted the data, and reviewed the manuscript; A.A.M., H.G. acquired and interpreted data and reviewed the manuscript; L.L., J.S., G.P., P.A.L., H.C. acquired and analyzed data and reviewed the manuscript; S.P., A.P., M.P., L.M., V.T., D.M.R., E.P., interpreted the data and reviewed the manuscript; R.J.S., E.Y.C. designed the study and reviewed the manuscript; S.C., H.C.G. designed the study, acquired and interpreted data, and reviewed the manuscript S.M., J.B.B.,
M.J.W. acquired and interpreted data, and reviewed the manuscript; P.K. designed the study, interpreted the data, and reviewed the manuscript; H.N.G. designed the study, acquired and Page 23 of 58 Diabetes
interpreted the data and reviewed the manuscript; J.C.M. designed the study, acquired, analyzed,
and interpreted the data, and reviewed the manuscript; A.D. designed the study, acquired,
analyzed, and interpreted the data, and wrote the manuscript. A.D. is the guarantor of this work
and, as such, had full access to all the data in the study and takes responsibility for the integrity
of the data and the accuracy of the data analysis.
FUNDING SUPPORT
The ACCORD genome-wide association analysis was supported by grants HL110400 (to
A.D.), HL110380 (to J.B.B.), and DK36836 (Advanced Genomics and Genetics Core of the
Diabetes Research Center at the Joslin Diabetes Center) from the National Institutes of Health
(NIH). The project described was also supported by the National Center for Advancing
Translational Sciences (NCATS), NIH, through Grant Award Number UL1TR001111. H.N.G.
was also supported by NHLBI grant HL110418 and J.B.B. by the NCATS, NIH, through Grant
Award Number UL1TR001111. M.L.M. was supported by a William Randolph Hearst
Fellowship provided by the Hearst Foundation and by a Research Fellowship provided by
Fondazione SISA. R.J.S. was supported by a Health Senior Scholar award from Alberta
Innovates-Health Solutions. S.P was supported by the Italian Ministry of Health (Ricerca
Corrente 2018-2020). V.T was supported by the Italian Ministry of Health (Ricerca Corrente
2015 and 2016), by the Italian Ministry of University and Research (PRIN 2015) and by
Fondazione Roma (Biomedical Research: Non-Communicable Diseases 2013" grant). H.C.G. is
supported by the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and
Care. A.M.R. is supported by the Intramural Research Program of the NIH, National Institute of
Environmental Health Sciences. S.C., H.C. and P.A.L. efforts were in part supported by National
Institutes of Health (Cresci R01 NR013396). TRIUMPH was sponsored by the National Diabetes Page 24 of 58
Institutes of Health: Washington University School of Medicine SCCOR Grant P50
HL077113.The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or other funders.
The ACCORD study (ClinicalTrials.gov Identifier: NCT00000620) was supported by
National Heart Lung and Blood Institute contracts N01-HC-95178, N01-HC-95179, N01-HC-
95180, N01-HC-95181, N01-HC-95182, N01-HC-95183, N01-HC-95184, and IAA #Y1-HC-
9035 and IAA #Y1-HC-1010. Other components of the National Institutes of Health, including the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute on
Aging, and the National Eye Institute, contributed funding. The Center for Disease Control funded substudies within ACCORD on cost-effectiveness and health-related quality of life.
General Clinical Research Centers and Clinical and Translational Science Awards provided support at many sites.
The ORIGIN study (ClinicalTrials.gov Identifier: NCT00069784) was funded by Sanofi.
The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund
(http://commonfund.nih.gov/GTEx/index) of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from: the GTEx Portal on 03/03/2017 version V6.
Data on coronary artery disease / myocardial infarction have been contributed by the
CARDIoGRAMplusC4D and UK Biobank CardioMetabolic Consortium CHD working group who used the UK Biobank Resource (application number 9922). Data have been downloaded from www.CARDIOGRAMPLUSC4D.ORG' Page 25 of 58 Diabetes
Conflicts of Interest: M.L.M., H.S.S., A.A.M., H.G., L.L., J.S., S.C., H.C., P.A.L., S.P.,
A.P., M.P., R.J.S., S.M., D.M.R,L.M., V.T., E.Y.C, P.K., M.J.W., J.C.M. have no conflicts of
interest to disclose. G.P. received research funding from Sanofi. E.P. received research funding
from GSK and Boehringer-Ingelheim (outside the submitted work). HCG has received research
grant support from Sanofi, Lilly, AstraZeneca and Merck, honoraria for speaking from Sanofi,
Novo Nordisk, AstraZeneca, and Boehringer Ingelheim and consulting fees from Sanofi, Lilly,
AstraZeneca, Merck, Novo Nordisk, Abbot, Amgen, and Boehringer Ingelheim. JBB’s
contracted consulting fees and travel support for contracted activities are paid to the University
of North Carolina by Adocia, AstraZeneca, Dance Biopharm, Eli Lilly, MannKind, NovaTarg,
Novo Nordisk, Senseonics, vTv Therapeutics, and Zafgen as well as grant support from Novo
Nordisk, Sanofi, Tolerion and vTv Therapeutics. He is also a consultant to Cirius Therapeutics
Inc, CSL Behring, Mellitus Health, Neurimmune AG, Pendulum Therapeutics, and Stability
Health. He holds stock/options in Mellitus Health, Pendulum Therapeutics, PhaseBio, and
Stability Health. He is supported by a grant from the National Institutes of Health
(UL1TR002489, U01DK098246, UC4DK108612, U54DK118612), PCORI and ADA. H.N.G. is
a consultant to Kowa and member of the Prominent Trial steering committee. A.D. received
research funding from Sanofi (outside the submitted work).
Data Sharing: The ACCORD database is available upon request from the NHLBI
Biologic Specimen and Data Repository (https://biolincc.nhlbi.nih.gov/studies/accord/). The
ACCORD genetic data are deposited in dbGAP, accession: phs0001411.
IRB Approval: Institutional review board or ethics committee at each center approved
the study prior to data collection. Diabetes Page 26 of 58
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Table 1: Characteristics of the top SNPs modulating the fenofibrate effectiveness in ACCORD among the self-reported non-Hispanic Whites -3 (with P value for interaction<5x10 ). Legend: Effect RGE = beta for SNP by fenofibrate interaction; SE = Standard Error of the beta; LD = linkage disequilibrium; In/Del = insertion / deletion; GEN = genotyped SNP; IMP = imputed SNP. The highlighted SNP is the SNP passing the study-wide significant threshold of P = 6x10-4 (rs6008845, IMPUTE2-info score > 0.95).
Minor Ref. Effect Imputed - LD with SNP Position MAF P Value S.E. Allele Allele RGE Genotyped rs6008845 rs6008845 46525357 C T 40% 3.7E-04 0.59 0.17 IMP ref. rs6007904 46521999 G A 42% 6.4E-04 0.56 0.16 IMP 0.78 rs135570 46532781 G A 46% 9.0E-04 0.54 0.16 IMP 0.72 rs135557 46541227 G A 44% 9.3E-04 0.54 0.16 IMP 0.61 rs9306519 46516140 G A 39% 1.2E-03 0.54 0.17 IMP 0.76 rs2105914 46527955 G A 48% 1.9E-03 0.50 0.16 GEN 0.67 rs135577 46526617 A G 28% 2.2E-03 0.56 0.18 IMP 0.58 rs9615264 46632589 A G 8% 2.6E-03 0.95 0.32 IMP 0.008 rs6008801 46518278 G C 28% 3.1E-03 0.54 0.18 IMP 0.57 rs6008799 46518189 C T 28% 3.2E-03 0.54 0.18 IMP 0.57 rs6008798 46518082 C T 27% 3.9E-03 0.52 0.18 GEN 0.55 rs552533545 46601416 In/del In/del 14% 4.9E-03 -0.74 0.26 IMP 0.02 rs6008800 46518260 G T 27% 4.9E-03 0.51 0.18 IMP 0.55 Diabetes Page 32 of 58
Table 2: Number of subjects needed to be treated with fenofibrate to prevent one MACE in 5 years in different subgroups. (Analyses conducted on 3050 self-reported white subjects with data on presence or absence of dyslipidemia at baseline).
Number of subjects in Hazard Ratio Number Needed to Treat Subgroups each group (95% CI) (95% CI) By Dyslipidemia:
Presence of Atherogenic Dyslipidemia N= 611/3050 (20.0 %) 0.49 (0.31 to 0.77) 12 (9 to 28)
Absence of Atherogenic Dyslipidemia N= 2439/3050 (80.0 %) 0.96 (0.75 to 1.24) 265 (40 to -44) No Benefit
By rs6008845: T/T Genotype N= 873/3050 (28.6 %) 0.51 (0.33 to 0.79) 15 (11 to 36)
T/C Genotype N= 1184/3050 (38.8 %) 1.20 (0.84 to 1.73) -58 (72 to -16) No Benefit
C/C Genotype N= 382/3050 (16.6 %) 1.65 (0.83 to 3.25) -22 (80 to -7) No Benefit
Note: Atherogenic dyslipidemia at baseline is defined by cut off previously used [HDL-c ≤ 34 mg/dL (0.88 mmol/l) and triglycerides ≥ 204 mg/dL (2.1 mmol/L)]. Page 33 of 58 Diabetes
Table 3: Baseline characteristics according to rs6008845 genotype in self-reported Whites.
TT TC CC P (1100) (1488) (477) Female (%) 319 (29.0%) 433 (29.1%) 138 (28.9%) 0.9 Age (year) 62.8 ± 6.4 62.7 ± 6.5 62.9 ± 6.8 0.8 Years of Diabetes 10.1 ± 6.8 10.9 ± 7.7 11.0 ± 7.5 0.006 Previous CVD 404 (36.7%) 551 (37.0%) 176 (36.9%) 0.9 Hb A1c (%) 8.2 ± 0.9 8.2 ± 0.9 8.1 ± 0.9 0.3 Fasting Plasma Glucose (mg/dl) 179.4 ± 50.4 176.8 ± 49.7 179.5 ± 51.9 0.7 BMI (kg/m2) 33.2 ± 5.1 33.0 ± 5.2 33.0 ± 5.0 0.7 Atherogenic dyslipidemia (%) 223 (20.3%) 296 (20.0%) 92 (19.4%) 0.6 Triglycerides (mg/dl) 177 (125-244) 174 (125-239) 171 (120-249) 0.7 Tot-cholesterol (mg/dl) 177.4 ± 36.8 175.2 ± 36.9 174.6 ± 37.0 0.07 HDL-chol (mg/dl) 37.6 ± 7.5 37.3 ± 7.4 37.6 ± 7.6 0.6 LDL-chol (mg/dl) 100.9 ± 30.0 99.2 ± 30.0 98.6 ± 31.1 0.08 Systolic BP (mmHg) 133.3 ± 17.1 132.7 ± 17.2 132.6 ± 16.6 0.3 Diastolic BP (mmHg) 73.7 ± 9.9 72.8 ± 10.3 73.3 ± 10.6 0.2 Serum Creatinine (mg/dl) 0.91 ± 0.22 0.92 ± 0.22 0.92 ± 0.21 0.3
* For representation purposes, subjects were considered as major allele homozygotes if the minor allele dosage was <0.5, heterozygotes if the minor allele dosage was ≥0.5 and <1.5, and minor allele homozygotes if the minor allele dosage was ≥1.5. Atherogenic dyslipidemia was defined as HDL-cholesterol ≤ 34 mg/dl and triglycerides ≥204 mg/dl. To convert cholesterol values to millimoles per liter, multiply by 0.02586. To convert triglyceride values to millimoles per liter, multiply by 0.01129. Diabetes Page 34 of 58
Figure Legends:
Figure 1:
Regional plot of the PPARA gene region. Each point represents one SNP. The base pairs position on chr22
(from 46.5 to 46.7 Mb) is on the X axis, the negative log transformation of the P value for interaction between each SNP and fenofibrate on the primary outcome is on the Y axis. The significance threshold
(P=6.2*10-4) after adjustment for multiple comparisons is indicated by the dashed line. Colors indicate the amount of linkage disequilibrium between plotted SNPs and rs6008845, specific information of top SNPs can be found in Supplemental Table 2. Plots were generated using LocusZoom v1.1 (Abecasis Lab,
University of Michigan School of Public Health).
Figure 2:
rs6008845 association with primary outcome stratified by fenofibrate treatment in discovery and validation cohorts of subjects with type 2 diabetes and high cardiovascular risk.
Note: the association between rs6008845 and the primary outcome is depicted as the effect per each T allele copy
Figure 3:
Fenofibrate cardiovascular effectiveness according to rs6008845 genotypes. Top panel: among all self- reported white subjects randomized to fenofibrate or placebo in the ACCORD Lipid study. Middle and bottom panels: in the same population according to absence or presence of atherogenic dyslipidemia at baseline (a few subjects, N=15, were not included due to missing data on lipid profile at baseline). Page 35 of 58 Diabetes
Figure 4:
Effects of fenofibrate on changes in lipid levels among self-reported Whites in Accord Lipid, stratified by
rs6008845 genotypes. Error bars represent standard errors. One Standard Deviation (S.D.) equal to 5.4
mg/dl for HDL-cholesterol, 87.1 mg/dl for Triglycerides, 35.2 mg/dl for total cholesterol, 29.8 mg/dl for
LDL-cholesterol. To convert cholesterol values to millimoles per liter, multiply by 0.02586. To convert the
values for triglycerides to millimoles per liter, multiply by 0.01129.
Figure 5:
Fenofibrate effects on chemokine levels according to rs6008845 genotypes among self-reported Whites
from the ACCORD-MIND biomarker study. Error bars represent standard errors. Diabetes Page 36 of 58
Regional plot of the PPARA gene region. Each point represents one SNP. The position on chr22 (from 46.5 to 46.7 Mb) is on the X-axis, the negative log transformation of the P-value for interaction between each SNP and fenofibrate on the primary outcome is on the Y-axis. The significance threshold (P=6.2*10-4) after adjustment for multiple comparisons is indicated by the dashed line. Colors indicate the amount of linkage disequilibrium between plotted SNPs and rs6008845, specific information of top SNPs can be found in Supplemental Table 2. Plots were generated using LocusZoom v1.1 (Abecasis Lab, University of Michigan School of Public Health).
180x119mm (300 x 300 DPI) Page 37 of 58 Diabetes
rs6008845 association with the primary outcome stratified by fenofibrate treatment in discovery and validation cohorts of subjects with type 2 diabetes and high cardiovascular risk. Note: the association between rs6008845 and primary outcome is depicted as the effect per each T allele copy.
180x193mm (300 x 300 DPI) Diabetes Page 38 of 58
Fenofibrate cardiovascular effectiveness according to rs6008845 genotypes. Top panel: among all self- reported white subjects randomized to fenofibrate or placebo in the ACCORD Lipid study. Middle and bottom panels: in the same population according to absence or presence of atherogenic dyslipidemia at baseline (a few subjects, N=15, were not included due to missing data on lipid profile at baseline).
180x170mm (300 x 300 DPI) Page 39 of 58 Diabetes
Effects of fenofibrate on changes in lipid levels among self-reported Whites in Accord Lipid, stratified by rs6008845 genotypes. Error bars represent standard errors. One Standard Deviation (S.D.) equal to 5.4 mg/dl for HDL-cholesterol, 87.1 mg/dl for Triglycerides, 35.2 mg/dl for total cholesterol, 29.8 mg/dl for LDL- cholesterol. To convert cholesterol values to millimoles per liter, multiply by 0.02586. To convert the values for triglycerides to millimoles per liter, multiply by 0.01129.
180x119mm (300 x 300 DPI) Diabetes Page 40 of 58
Fenofibrate effects on chemokine levels according to rs6008845 genotypes among self-reported Whites from the ACCORD-MIND biomarker study. Error bars represent standard errors.
180x119mm (300 x 300 DPI) Page 41 of 58 Diabetes
Online Data Supplements
Supplemental Table 1: Characteristics of the Patients at Baseline in ACCORD-Lipid.
Characteristics Whites (N=3065) African-Americans (N=585)
Statin Statin + Statin Statin + Fenofibrate Fenofibrate (N=1496) (N=1569) (N=306) (N=279) Female sex – no. (%) 441 (29.5%) 449 (28.6%) 119 (38.9%) 116 (41.6%) Age – yr 62.7 ± 6.5 62.9 ± 6.5 62.0 ± 6.8 61.6 ± 6.4 BMI – kg/m2 33.3 ± 5.2 32.9 ± 5.0 32.4 ± 5.1 32.6 ± 5.0 Waist circumference – cm 110.4 ± 12.8 109.8 ± 12.5 105.6 ± 13.0 105.8 ± 12.5 Systolic BP – mmHg 132.7 ± 17.1 133.2 ± 17.0 136.7 ± 18.1 135.7 ± 18.0 Diastolic BP – mmHg 73.1 ± 10.3 73.4 ± 10.1 75.9 ± 11.0 75.5 ± 10.6 Baseline CVD – no. (%) 547 (36.6%) 584 (37.2%) 100 (32.7%) 93 (33.3%) Years of Diabetes 10.6 ± 7.4 10.6 ± 7.3 11.4 ± 7.6 10.5 ± 7.4 Glycated hemoglobin – % 8.2 ± 0.9 8.2 ± 0.9 8.5 ± 1.1 8.5 ± 1.1 Triglycerides – mg/dl 198.0 ± 106.9 198.8 ± 105.7 131.5 ± 75.8 140.1 ± 85.4 Total cholesterol – mg/dl 177.0 ± 37.4 174.9 ± 36.4 170.6 ± 32.6 171.6 ± 36.1 HDL-cholesterol – mg/dl 37.5 ± 7.5 37.4 ± 7.4 41.2 ± 8.2 40.4 ± 8.2 LDL-cholesterol – mg/dl 100.6 ± 30.6 98.8 ± 29.8 103.5 ± 27.6 104.0 ± 30.6 Atherogenic dyslipidemia – no. (%) 298 (19.9%) 316 (20.1%) 14 (4.6%) 26 (9.3%) Serum Creatinine – mg/dl 0.92 ± 0.21 0.92 ± 0.22 0.98 ± 0.22 1.01 ± 0.24
Values are means ± SD or N (%). Atherogenic dyslipidemia is defined by cut off previously used of HDL-c ≤ 34 mg/dL and triglycerides ≥ 204 mg/dL. There were no differences between the groups except for the BMI in Whites (P=0.04). To convert cholesterol values to millimoles per liter, multiply by 0.02586. To convert the values for triglycerides to millimoles per liter, multiply by 0.01129. Diabetes Page 42 of 58
Supplemental Table 2: Top SNPs (with Meta-Analysis P < 1x10-2) modulating fenofibrate effectiveness in self-reported Whites and African- Americans from ACCORD-Lipid and in a meta-analysis of the two racial groups.
Whites African-American Meta-Analysis Conditional Hg19 bp Allele Allele SNP analysis position 1 2 Effect Allele1 Effect Allele1 Effect Heterog. P Value P Value P value P value ± S.E. Freq ± S.E. Freq ± S.E. P value rs6008845 46525357 c t 3.7E-04 0.59±0.17 0.40 2.4E-02 1.39±0.62 0.79 5.6E-05 0.65±0.16 0.46 ref rs135557 46541227 g a 9.3E-04 0.54±0.16 0.44 6.8E-03 1.43±0.53 0.72 7.3E-05 0.62±0.16 0.13 3.0E-01 rs135570 46532781 g a 9.0E-04 0.54±0.16 0.46 5.3E-03 2.16±0.77 0.83 1.3E-04 0.61±0.16 0.22 5.4E-01 rs6007904 46521999 g a 6.4E-04 0.56±0.16 0.42 1.9E-01 0.71±0.54 0.73 2.6E-04 0.57±0.16 0.80 9.4E-01 rs2105914 46527955 g a 1.9E-03 0.50±0.16 0.48 2.7E-02 1.74±0.79 0.83 5.0E-04 0.55±0.16 0.48 6.8E-01 rs135577 46526617 a g 2.2E-03 0.56±0.18 0.28 1.1E-01 0.70±0.44 0.57 5.8E-04 0.58±0.17 0.44 6.4E-01 rs11090910 46507670 c t 8.1E-03 0.49±0.18 0.27 3.5E-02 0.89±0.42 0.41 1.1E-03 0.55±0.17 0.84 4.2E-02 rs135562 46539636 t c 1.0E-02 0.47±0.18 0.29 2.3E-02 0.93±0.41 0.60 1.1E-03 0.54±0.17 0.62 5.2E-01 rs135556 46543485 c t 6.8E-03 0.49±0.18 0.28 5.1E-02 0.83±0.42 0.51 1.1E-03 0.55±0.17 0.50 3.7E-01 rs135550 46553234 c t 1.2E-02 0.46±0.18 0.28 2.6E-02 0.88±0.40 0.56 1.3E-03 0.53±0.17 0.34 4.6E-01 rs135542 46556037 c t 5.8E-03 0.51±0.18 0.28 1.1E-01 0.72±0.46 0.28 1.6E-03 0.54±0.17 0.44 3.4E-01 rs135551 46553021 a g 1.5E-02 0.45±0.18 0.28 2.8E-02 0.87±0.40 0.53 1.7E-03 0.52±0.17 0.34 4.6E-01 rs4823551 46525916 g a 8.7E-03 0.44±0.17 0.38 6.2E-02 0.90±0.48 0.75 2.0E-03 0.49±0.16 0.74 3.7E-01 rs9615904 46664741 c t 1.2E-02 0.46±0.18 0.29 2.9E-02 1.15±0.52 0.81 2.1E-03 0.53±0.17 0.61 1.3E-02 rs6007919 46525794 c t 9.5E-03 0.43±0.17 0.38 6.3E-02 0.90±0.48 0.75 2.2E-03 0.48±0.16 0.75 3.4E-01 rs4508712 46652959 g a 1.6E-02 0.43±0.18 0.29 2.1E-02 1.18±0.51 0.81 2.4E-03 0.51±0.17 0.56 1.2E-02 rs135553 46552743 c t 1.9E-02 0.38±0.16 0.43 5.9E-03 1.48±0.54 0.75 2.4E-03 0.47±0.16 0.09 6.9E-01 rs5767213 46526528 a g 1.8E-02 0.40±0.17 0.36 1.9E-02 1.12±0.48 0.68 2.5E-03 0.48±0.16 0.28 2.2E-01 rs4823547 46523710 g a 9.8E-03 0.43±0.17 0.38 8.7E-02 0.78±0.45 0.69 2.6E-03 0.47±0.16 0.53 5.7E-01 rs135549 46553308 c t 1.3E-02 0.40±0.16 0.43 5.5E-02 0.85±0.44 0.63 2.8E-03 0.46±0.15 0.13 8.2E-01 rs2024929 46515210 c t 3.4E-02 0.45±0.21 0.15 6.5E-03 1.49±0.55 0.74 3.0E-03 0.59±0.20 0.12 4.5E-01 rs9615264 46632589 a g 2.6E-03 0.95±0.32 0.09 9.6E-01 0.08±1.59 0.02 3.1E-03 0.91±0.31 0.51 2.3E-03 Page 43 of 58 Diabetes
rs135563 46536017 c t 1.7E-02 0.39±0.16 0.48 3.8E-03 2.21±0.76 0.83 3.4E-03 0.47±0.16 0.12 5.3E-01 rs135560 46539655 c t 2.2E-02 0.41±0.18 0.30 3.6E-02 0.85±0.41 0.60 3.4E-03 0.48±0.16 0.52 7.0E-01 rs9306519 46516140 g a 1.2E-03 0.54±0.17 0.39 7.7E-01 -0.12±0.42 0.51 3.6E-03 0.45±0.15 0.16 4.5E-01 rs135561 46539639 a g 2.2E-02 0.41±0.18 0.30 4.4E-02 0.82±0.41 0.60 3.7E-03 0.47±0.16 0.48 7.2E-01 rs135552 46552815 c t 7.1E-03 0.50±0.18 0.28 2.9E-01 0.49±0.47 0.31 3.9E-03 0.50±0.17 0.99 6.3E-01 rs135539 46559267 c a 1.3E-02 0.41±0.16 0.44 1.3E-01 0.65±0.43 0.67 4.0E-03 0.44±0.15 0.32 8.5E-01 rs135569 46534187 a t 1.5E-02 0.39±0.16 0.48 2.9E-02 1.49±0.68 0.80 4.1E-03 0.45±0.16 0.15 4.9E-01 rs5768769 46521225 c t 1.6E-02 0.40±0.17 0.38 8.1E-02 0.84±0.48 0.75 4.5E-03 0.45±0.16 0.78 3.4E-01 rs135538 46564628 g c 4.4E-02 0.32±0.16 0.42 9.2E-04 1.88±0.57 0.77 4.5E-03 0.44±0.15 0.04 9.1E-01 rs135567 46534634 g a 2.4E-02 0.41±0.18 0.30 5.7E-02 0.83±0.44 0.63 4.8E-03 0.47±0.17 0.07 8.9E-01 rs135568 46534601 a c 2.0E-02 0.42±0.18 0.30 1.1E-01 0.65±0.41 0.58 5.8E-03 0.46±0.17 0.12 8.6E-01 rs58863484 46542393 g a 9.5E-02 0.39±0.24 0.14 1.1E-02 1.11±0.44 0.34 7.5E-03 0.55±0.21 0.27 3.1E-01 rs135548 46553503 g a 6.4E-03 0.50±0.18 0.28 7.7E-01 0.15±0.53 0.20 7.6E-03 0.46±0.17 0.63 7.0E-01 rs16994874 46515716 c t 2.8E-02 0.48±0.22 0.16 1.2E-01 0.78±0.50 0.22 8.3E-03 0.53±0.20 0.21 2.2E-01 rs135547 46553650 g c 5.0E-02 0.35±0.18 0.31 4.6E-02 0.83±0.42 0.58 9.7E-03 0.42±0.16 0.56 8.9E-01 rs135554 46552719 t a 8.1E-03 0.49±0.18 0.28 7.7E-01 0.14±0.50 0.25 9.8E-03 0.45±0.17 0.67 8.0E-01
Note: The conditional analysis P value is the P value for the SNP by fenofibrate interaction in the entire study population (Whites+African- Americans) after adding the “rs6008845 by fenofibrate” and rs6008845 variables to the model. Diabetes Page 44 of 58
Supplemental Table 3: Characteristics at baseline of self-reported white participants in ACCORD Blood Pressure, ORIGIN and TRIUMPH cohorts on concomitant treatment with statin alone or statin + fibrate.
Characteristics ACCORD Blood Pressure ORIGIN Triumph Statin Statin + Fibrate Statin Statin + Fibrate Statin Statin + Fibrate P P P (N=1320) (N=87) (N=1162) (N=82) (N=387) (N=21) Female sex 546 (41.4%) 15 (17.2%) <0.001 339 (29.2%) 17 (20.7%) 0.1 132 (34.1%) 6 (28.6%) 0.6 Age – yr 63.2 ± 6.5 61.5 ± 6.6 0.02 63.0 ± 7.7 60.3 ± 7.2 0.002 61.9 ± 11.1 64.1 ± 8.9 0.4 BMI – kg/m2 32.7 ± 5.2 33.5 ± 4.8 0.2 30.7 ± 5.1 31.2 ± 4.1 0.4 32.4 ± 7.6 34.1 ± 7.5 0.3 Systolic BP – mmHg 137.1 ± 15.0 136.8 ± 13.6 0.9 143.9 ± 21.3 139.0 ± 21.2 0.05 120.5 ± 20.3 126.9 ± 17.1 0.2 Diastolic BP – mmHg 74.5 ± 10.0 73.6 ± 10.2 0.4 83.3 ± 11.8 80.2 ± 13.3 0.02 67.1 ± 11.2 67.8 ± 10.5 0.8 Baseline CVD 504 (38.2%) 53 (60.9%) <0.001 831 (71.5%) 63 (76.8%) 0.4 100 100 --- Years of Diabetes 11.0 ± 7.6 10.9 ± 7.5 0.9 4.2 ± 4.9 4.5 ± 4.1 0.1 N/A N/A --- Glycated hemoglobin (%) 8.2 ± 0.9 8.1 ± 1.0 0.7 6.4 ± 0.9 6.6 ± 1.0 0.6 8.4 ± 3.4 6.8 ± 1.3 0.003 Triglycerides – mg/dl 197 ± 125 277 ± 166 <0.001 163.6 ± 97.7 217.2 ± 139.4 <0.001 187 ± 150 287 ± 283 0.1 Total cholesterol – mg/dl 182.3 ± 37.5 187.6 ± 40.1 0.2 183.0 ± 47.8 189.6 ± 53.9 0.2 170.2 ± 51.6 155.5 ± 47.5 0.2 HDL-cholesterol – mg/dl 44.4 ± 11.7 35.3 ± 8.6 <0.001 45.9 ± 12.3 40.9 ± 9.8 <0.001 37.7 ± 10.5 36.2 ± 11.6 0.5 LDL-cholesterol – mg/dl 99.6 ± 29.8 99.4 ± 33.3 0.9 105.0 ± 41.6 109.8 ± 41.9 0.3 96.8 ± 42.0 75.0 ± 35.2 0.04 Atherogenic dyslipidemia 176 (13.3%) 35 (40.2%) <0.001 50 (4.3%) 8 (9.9%) 0.04 57 (14.7%) 5 (23.8 %) 0.3 Serum Creatinine – mg/dl 0.90 ± 0.22 1.00 ± 0.24 <0.001 1.00 ± 0.24 1.07 ± 0.29 0.009 1.26 ± 0.91 1.35 ± 0.58 0.5
Values are means ± SD or N (%). Atherogenic dyslipidemia was defined as HDL-c ≤ 34 mg/dL and triglycerides ≥ 204 mg/dL. To convert cholesterol values to millimoles per liter, multiply by 0.02586; to convert triglyceride values to millimoles per liter, multiply by 0.01129. In ORIGIN, concomitant medications were evaluated at baseline. In ACCORD BP, and TRIUMPH, concomitant medications were evaluated at baseline and every 12 months (subjects were considered to be on a medication if they were reported to be on that medication at baseline and at least once during follow-up, prior to the event or censoring; subjects with follow-up shorter than 1 year were considered to be on a medication only on the basis of the baseline information). Page 45 of 58 Diabetes
Supplemental table 4: Effectiveness of fenofibrate on MACE risk reduction according to presence or absence of ahterogenic dyslipidemia (defined by previously defined cut-off (16)) and rs6008845 genotypes.
rs6008845 HR for fibrate on MACE rs6008845 by fenofibrate Group N/Events Genotype (95% C.I.) Interaction ALL 1021/117 0.62 (0.43-0.90) Female: HDL<50.3 mg/dl and TG >203.7 mg/dl TT 381/47 0.42 (0.22-0.78) or TC 486/53 0.78 (0.45-1.34) P=0.238 Male: HDL<38.7 mg/dl and TG >203.7 mg/dl CC 154/17 0.54 (0.19-1.47)
ALL 2029/205 0.95 (0.72-1.25) TT 715/66 0.52 (0.31-0.86) All others TC 994/105 1.10 (0.65-1.62) P=0.004 CC 320/34 1.68 (0.80-3.53) Diabetes Page 46 of 58
Supplemental Table 5: Characteristics at baseline of self-reported white participants included or not in the MIND-biomarker study.
Not included in MIND Included in MIND P biomarker study biomarker study (N=2932) (N=133) Female sex – no. (%) 844 (28.8%) 46 (34.6%) 0.15 Age – yr 62.7 ± 6.5 64.0 ± 6.4 0.03 BMI – kg/m2 33.1 ± 5.2 33.0 ± 4.5 0.96 Waist circumference – cm 110.2 ± 12.7 109.1 ± 10.9 0.27 Systolic BP – mmHg 133.1 ± 17.1 128.1 ± 16.6 0.001 Diastolic BP – mmHg 73.3 ± 10.3 71.5 ± 9.4 0.06 Baseline CVD – no. (%) 1100 (37.5%) 31 (23.3%) 0.001 Years of Diabetes 10.7 ± 7.4 9.3 ± 6.5 0.05 Glycated hemoglobin – % 8.2 ± 0.9 8.0 ± 0.9 0.02 Triglycerides – mg/dl 198.2 ± 106.2 203.8 ± 107.5 0.55 Total cholesterol – mg/dl 176.1 ± 36.9 171.1 ± 35.9 0.12 HDL-cholesterol – mg/dl 37.4 ± 7.4 38.6 ± 8.1 0.07 LDL-cholesterol – mg/dl 100.0 ± 30.2 93.0 ± 29.9 0.01 Atherogenic dyslipidemia – no. (%) 586 (20.1%) 25 (18.8%) 0.72 Serum Creatinine – mg/dl 0.92 ± 0.22 0.91 ± 0.19 0.44 Page 47 of 58 Diabetes
Supplemental table 6: For each chemokine, we report the mean ± standard deviation values at baseline and follow-up (log transformed), and the estimated treatment difference (ETD) for fenofibrate use with its standard error (SE), along with the respective p-values. The last column reports the estimate and P value of fenofibrate by rs6008845 interaction, testing whether the fenofibrate effect on chemokine changes across rs6008845 genotypes.
Chemokines Fenofibrate Fenofibrate by rs6008845 P Placebo (N=64) Fenofibrate (N=69) P effect (SE) interaction Baseline Follow-up Baseline Follow-up CCL2 6.25 ± 0.55 6.23 ± 0.50 6.12 ± 0.65 6.10 ± 0.57 -0.02 (0.05) 0.60 0.03 (0.07) 0.667 CCL3 3.66 ± 1.24 3.77 ± 1.42 4.06 ± 1.12 4.07 ± 1.07 -0.08 (0.13) 0.53 0.19 (0.21) 0.363 CCL4 4.28 ± 0.95 4.30 ± 0.87 4.58 ± 0.98 4.52 ± 0.77 -0.01 (0.09) 0.92 -0.14 (0.14) 0.300 CCL11 4.72 ± 0.56 4.76 ± 0.53 4.66 ± 0.58 4.71 ± 0.58 -0.01 (0.05) 0.85 0.22 (0.07) 0.003 CXCL8 3.12 ± 0.97 3.20 ± 1.01 3.47 ± 1.10 3.47 ± 1.12 -0.06 (0.12) 0.64 0.30 (0.18) 0.110 CXCL10 4.87 ± 0.56 4.85 ± 0.56 4.84 ± 0.68 4.81 ± 0.68 0.01 (0.07) 0.93 -0.01 (0.10) 0.927 CXC3CR1 4.04 ± 1.92 3.91 ± 1.91 3.85 ± 1.62 3.92 ± 1.73 0.10 (0.18) 0.60 -0.20 (0.29) 0.485 Diabetes Page 48 of 58
Supplemental table 7: For CCL11, we report the mean ± standard deviation log transformed values at baseline and follow-up, and the estimated treatment difference (ETD) for fenofibrate use with its standard error (SE), along with the respective p-values.
Fenofibrate rs6008845 Fenofibrate by Placebo (N=64) Fenofibrate (N=69) P P Genotype effect (SE) rs6008845 interaction Baseline Follow-up Baseline Follow-up
TT (n=39) 4.65 ± 0.33 4.85 ± 0.41 4.63 ± 0.54 4.63 ± 0.59 -0.20 (0.07) 0.01 CCL11 TC (n=73) 4.67 ± 0.45 4.71 ± 0.45 4.61 ± 0.62 4.69 ± 0.59 0.00 (0.06) 0.99 0.22 (0.07) 0.003 CC (n=21) 4.92 ± 0.92 4.77 ± 0.80 5.01 ± 0.40 5.07 ± 0.40 0.34 (0.19) 0.10
TT (n=39) 3.28 ± 0.97 3.43 ± 1.07 3.96 ± 1.01 3.87 ± 1.09 -0.22 (0.19) 0.27 CCL3 TC (n=73) 3.54 ± 1.16 3.69 ± 1.37 4.12 ± 1.23 4.12 ± 1.10 -0.17 (0.14) 0.24 0.19 (0.21) 0.363 CC (n=21) 4.39 ± 1.46 4.36 ± 1.78 4.04 ± 0.94 4.40 ± 0.75 0.70 (0.52) 0.21
TT (n=39) 2.88 ± 0.65 3.03 ± 0.69 3.39 ± 0.92 3.31 ± 0.93 -0.23 (0.26) 0.38 CXCL8 TC (n=73) 2.99 ± 0.95 3.20 ± 1.06 3.50 ± 1.25 3.53 ± 1.27 -0.13 (0.18) 0.47 0.30 (0.18) 0.110 CC (n=21) 3.71 ± 1.14 3.42 ± 1.20 3.53 ± 0.85 3.72 ± 0.75 0.76 (0.41) 0.09 Page 49 of 58 Diabetes
Supplemental Table 8: Association between rs6008845 (effect of the T allele) and PPAR-alpha target gene in Skin (Sun exposed and not sun exposed), Skeletal Muscle, Whole blood, Esophagus Mucosae, Vagina and Liver from GtEx. Meta Analysis across tissue done with RE2 random-effect models. Highlighted genes are those with meta-analysis P value surviving a False Discovery Rate of 5%.
Skin* Muscle Blood Esophagus Vagina Liver Meta-Analysis Heterogeneity N=498 N=361 N=338 N=241 N=79 N=97 N=1614 genes β (P value) β (P value) β (P value) β (P value) β (P value) β (P value) (P value) I2 (pval) PPARA -0.46 (1E-29) -0.09 (2E-02) -0.16 (9E-03) -0.17 (4E-04) -0.24 (3E-03) -0.23 (7E-02) SLC25A20 -0.21 (1E-03) -0.12 (2E-02) -0.04 (5E-01) -0.19 (2E-02) 0.20 (1E-01) -0.08 (5E-01) 4.5E-05 0.59 (2E-02) HSD17B4 -0.13 (6E-04) -0.05 (3E-01) -0.10 (1E-02) -0.06 (2E-01) -0.04 (8E-01) -0.03 (7E-01) 4.6E-05 0.00 (7E-01) ETFDH -0.22 (9E-08) -0.05 (2E-01) 0.03 (5E-01) -0.01 (8E-01) 0.11 (3E-01) -0.09 (3E-01) 5.2E-05 0.73 (1E-03) HMGCS2 -0.25 (1E-03) -0.03 (7E-01) -0.07 (3E-01) -0.06 (4E-01) -0.05 (8E-01) 0.02 (8E-01) 5.3E-04 0.53 (5E-02) ACADVL -0.13 (5E-04) -0.03 (5E-01) -0.07 (1E-01) -0.04 (5E-01) -0.11 (4E-01) -0.02 (8E-01) 6.5E-04 0.00 (7E-01) FABP3 -0.04 (5E-01) -0.16 (6E-04) 0.01 (9E-01) -0.03 (7E-01) -0.02 (8E-01) -0.41 (9E-04) 7.6E-04 0.63 (1E-02) PRKCA 0.10 (4E-02) 0.03 (6E-01) 0.09 (8E-02) 0.08 (3E-01) 0.05 (3E-01) 0.10 (5E-01) 8.0E-04 0.00 (8E-01) MLYCD -0.04 (2E-01) -0.15 (1E-02) -0.06 (1E-01) -0.07 (3E-01) 0.11 (3E-01) -0.28 (2E-02) 2.7E-03 0.37 (1E-01) ACAA2 -0.07 (7E-02) -0.11 (2E-02) 0.05 (4E-01) -0.18 (1E-02) 0.11 (4E-01) -0.14 (2E-01) 4.9E-03 0.43 (1E-01) ACADS -0.11 (1E-02) -0.04 (3E-01) -0.01 (8E-01) -0.01 (8E-01) 0.14 (2E-01) -0.02 (9E-01) 5.9E-03 0.47 (8E-02) HADHA -0.11 (3E-03) -0.10 (3E-02) 0.01 (8E-01) -0.06 (3E-01) 0.15 (2E-01) -0.07 (5E-01) 7.0E-03 0.38 (1E-01) CPT2 -0.09 (2E-02) -0.05 (1E-01) -0.02 (5E-01) -0.05 (4E-01) -0.01 (9E-01) -0.13 (1E-01) 7.0E-03 0.00 (8E-01) GALNT2 0.03 (7E-01) 0.06 (2E-01) -0.06 (1E-02) 0.01 (9E-01) 0.04 (6E-01) 0.08 (2E-01) 1.0E-02 0.69 (3E-03) CYP4A11 -0.05 (5E-01) 0.05 (5E-01) -0.10 (7E-02) N/A -0.32 (2E-02) -0.16 (7E-02) 1.7E-02 0.42 (1E-01) CYP4X1 -0.08 (5E-02) 0.01 (9E-01) -0.04 (5E-01) -0.08 (2E-01) -0.21 (1E-01) -0.11 (3E-01) 1.8E-02 0.00 (7E-01) ACAA1 -0.02 (7E-01) -0.08 (2E-02) -0.01 (8E-01) -0.11 (2E-02) 0.06 (4E-01) -0.02 (8E-01) 2.3E-02 0.20 (3E-01) F3 0.02 (8E-01) 0.06 (3E-01) 0.13 (4E-02) 0.02 (7E-01) 0.21 (4E-02) 0.07 (4E-01) 2.4E-02 0.23 (3E-01) ECH1 -0.13 (2E-03) -0.03 (5E-01) -0.01 (8E-01) -0.07 (3E-01) 0.14 (3E-01) -0.12 (3E-01) 3.3E-02 0.30 (2E-01) ACSL5 -0.08 (3E-01) -0.10 (8E-02) -0.04 (4E-01) 0.00 (1E+00) 0.08 (5E-01) 0.06 (6E-01) 4.3E-02 0.33 (2E-01) CETP 0.17 (1E-02) 0.08 (3E-01) 0.04 (4E-01) -0.03 (7E-01) -0.24 (7E-02) 0.17 (5E-02) 4.5E-02 0.45 (9E-02) LIPC -0.14 (5E-02) N/A -0.02 (8E-01) 0.00 (1E+00) -0.02 (9E-01) 0.02 (9E-01) 4.6E-02 0.11 (3E-01) ANGPTL4 0.06 (2E-01) -0.04 (5E-01) 0.07 (1E-01) 0.07 (2E-01) 0.14 (2E-01) 0.04 (7E-01) 4.6E-02 0.00 (7E-01) Diabetes Page 50 of 58
CPT1A -0.10 (4E-02) -0.08 (2E-01) -0.02 (8E-01) -0.09 (3E-01) 0.10 (4E-01) -0.04 (8E-01) 4.6E-02 0.00 (8E-01) SCARB1 -0.10 (3E-02) 0.09 (9E-02) -0.12 (2E-02) -0.02 (8E-01) 0.07 (5E-01) 0.26 (3E-02) 4.7E-02 0.66 (7E-03) ETFA -0.05 (3E-01) -0.05 (2E-01) -0.05 (2E-01) 0.01 (9E-01) 0.03 (7E-01) -0.10 (2E-01) 6.5E-02 0.00 (8E-01) SLC16A1 0.06 (2E-01) 0.01 (7E-01) 0.07 (1E-01) 0.03 (4E-01) -0.07 (5E-01) 0.00 (1E+00) 7.1E-02 0.00 (7E-01) HNF4A N/A N/A -0.04 (6E-01) N/A N/A -0.27 (2E-02) 9.1E-02 0.70 (7E-02) KLF10 0.02 (7E-01) -0.02 (8E-01) -0.07 (1E-01) -0.12 (6E-02) -0.09 (5E-01) -0.23 (1E-01) 1.1E-01 0.05 (4E-01) ABCC3 -0.05 (5E-01) -0.10 (1E-01) -0.01 (8E-01) -0.08 (2E-01) 0.00 (1E+00) 0.01 (1E+00) 1.2E-01 0.00 (7E-01) ABCD3 0.01 (8E-01) -0.02 (5E-01) -0.05 (9E-02) -0.01 (8E-01) -0.19 (6E-02) -0.09 (4E-01) 1.2E-01 0.29 (2E-01) VCAM1 0.05 (4E-01) 0.05 (4E-01) -0.02 (8E-01) 0.08 (1E-01) 0.01 (9E-01) -0.02 (9E-01) 1.2E-01 0.00 (6E-01) KLF11 -0.07 (1E-01) 0.06 (3E-01) 0.01 (7E-01) -0.16 (2E-02) -0.07 (5E-01) -0.14 (2E-01) 1.3E-01 0.36 (2E-01) NOS3 -0.15 (1E-03) 0.07 (2E-01) 0.05 (4E-01) -0.04 (5E-01) 0.01 (1E+00) 0.10 (3E-01) 1.3E-01 0.56 (3E-02) ELOVL3 0.05 (3E-01) 0.04 (6E-01) 0.09 (7E-02) 0.02 (8E-01) -0.29 (4E-02) N/A 1.4E-01 0.31 (2E-01) PNPLA2 -0.04 (3E-01) -0.08 (2E-01) 0.00 (9E-01) 0.03 (5E-01) -0.15 (5E-02) -0.11 (4E-01) 1.5E-01 0.12 (3E-01) APOA1 0.04 (6E-01) 0.02 (8E-01) -0.11 (8E-02) -0.10 (3E-01) -0.20 (2E-01) 0.26 (7E-03) 1.6E-01 0.59 (2E-02) ETFB -0.01 (6E-01) -0.07 (3E-02) 0.02 (5E-01) -0.07 (2E-01) 0.00 (1E+00) -0.02 (8E-01) 1.7E-01 0.00 (6E-01) CPT1B 0.00 (9E-01) -0.12 (1E-02) -0.02 (7E-01) 0.00 (1E+00) -0.06 (5E-01) 0.10 (3E-01) 2.0E-01 0.00 (4E-01) LIPA 0.07 (6E-02) -0.09 (1E-01) -0.09 (4E-02) 0.06 (2E-01) 0.05 (6E-01) -0.06 (5E-01) 2.0E-01 0.52 (5E-02) ABCA1 0.01 (8E-01) 0.07 (2E-01) -0.01 (9E-01) 0.02 (8E-01) 0.13 (3E-01) 0.10 (3E-01) 2.2E-01 0.00 (8E-01) HADHB -0.08 (6E-02) -0.04 (4E-01) 0.00 (1E+00) -0.01 (8E-01) 0.09 (5E-01) -0.02 (9E-01) 2.5E-01 0.00 (7E-01) PEMT -0.06 (5E-01) -0.03 (4E-01) 0.00 (9E-01) -0.04 (4E-01) -0.12 (3E-01) 0.02 (8E-01) 2.5E-01 0.00 (4E-01) SLC27A2 -0.08 (5E-01) N/A 0.02 (7E-01) 0.01 (9E-01) -0.04 (7E-01) -0.19 (6E-02) 2.6E-01 0.49 (8E-02) AGPAT2 -0.03 (5E-01) 0.01 (8E-01) 0.05 (3E-02) -0.03 (5E-01) 0.06 (6E-01) -0.07 (5E-01) 2.7E-01 0.00 (5E-01) CD36 0.05 (3E-01) -0.01 (9E-01) 0.02 (6E-01) -0.02 (8E-01) 0.26 (5E-02) 0.13 (3E-01) 2.7E-01 0.00 (4E-01) AQP3 0.05 (5E-01) -0.01 (9E-01) 0.04 (3E-01) 0.06 (4E-01) -0.12 (2E-01) 0.15 (3E-01) 2.7E-01 0.20 (3E-01) ALAS1 0.01 (8E-01) 0.00 (1E+00) -0.04 (3E-01) -0.02 (6E-01) -0.11 (2E-01) -0.24 (7E-02) 2.9E-01 0.00 (5E-01) FGF21 N/A -0.08 (2E-01) -0.01 (9E-01) N/A N/A -0.09 (5E-01) 3.1E-01 0.00 (7E-01) G0S2 -0.04 (4E-01) 0.01 (7E-01) -0.07 (8E-02) 0.03 (6E-01) -0.08 (5E-01) 0.04 (7E-01) 3.2E-01 0.00 (6E-01) GPAM 0.00 (1E+00) 0.03 (5E-01) -0.02 (7E-01) -0.08 (1E-01) -0.09 (3E-01) -0.10 (2E-01) 3.2E-01 0.00 (6E-01) ABCD2 -0.04 (5E-01) -0.04 (5E-01) 0.06 (1E-01) -0.19 (2E-02) 0.02 (8E-01) N/A 3.3E-01 0.45 (1E-01) Page 51 of 58 Diabetes
FBP1 0.01 (9E-01) 0.03 (6E-01) 0.06 (2E-01) 0.00 (9E-01) -0.01 (9E-01) 0.07 (6E-01) 3.7E-01 0.00 (8E-01) CES3 0.08 (1E-01) 0.00 (1E+00) -0.04 (6E-01) 0.04 (6E-01) 0.06 (6E-01) 0.03 (8E-01) 3.9E-01 0.00 (9E-01) PLTP 0.05 (3E-01) 0.00 (1E+00) -0.10 (3E-02) -0.09 (2E-01) 0.07 (4E-01) 0.15 (1E-01) 3.9E-01 0.44 (1E-01) SREBF1 0.04 (4E-01) 0.06 (3E-01) -0.01 (9E-01) 0.02 (7E-01) -0.04 (7E-01) 0.09 (5E-01) 4.1E-01 0.00 (9E-01) GK -0.02 (7E-01) -0.03 (6E-01) -0.04 (1E-01) 0.09 (7E-02) -0.13 (3E-01) -0.09 (4E-01) 4.2E-01 0.12 (3E-01) ACACB -0.05 (8E-02) 0.00 (9E-01) -0.02 (8E-01) 0.02 (6E-01) 0.01 (8E-01) -0.05 (6E-01) 4.3E-01 0.00 (8E-01) LPCAT3 -0.04 (3E-01) -0.12 (4E-02) 0.01 (7E-01) -0.01 (9E-01) 0.04 (7E-01) -0.02 (9E-01) 4.3E-01 0.00 (5E-01) SLC22A5 -0.01 (9E-01) -0.02 (5E-01) 0.03 (6E-01) -0.05 (3E-01) -0.06 (6E-01) -0.06 (6E-01) 4.3E-01 0.00 (9E-01) NFKB1 -0.04 (2E-01) -0.02 (6E-01) 0.01 (9E-01) 0.03 (4E-01) -0.20 (2E-02) -0.05 (5E-01) 4.4E-01 0.27 (2E-01) ABCG2 -0.10 (1E-01) 0.03 (7E-01) 0.02 (7E-01) -0.02 (8E-01) -0.06 (4E-01) -0.03 (7E-01) 4.6E-01 0.00 (6E-01) VNN1 -0.04 (5E-01) -0.01 (9E-01) 0.04 (4E-01) 0.09 (2E-01) -0.03 (8E-01) 0.16 (2E-01) 4.6E-01 0.00 (7E-01) ACSL1 -0.04 (3E-01) -0.03 (5E-01) 0.01 (7E-01) -0.01 (8E-01) -0.04 (8E-01) -0.13 (2E-01) 4.8E-01 0.00 (8E-01) PLIN2 0.05 (1E-01) -0.06 (2E-01) 0.04 (2E-01) -0.03 (6E-01) 0.14 (2E-01) -0.16 (3E-01) 4.9E-01 0.35 (2E-01) ACOT7 -0.01 (9E-01) 0.01 (9E-01) 0.06 (4E-02) -0.03 (5E-01) -0.08 (5E-01) -0.13 (2E-01) 4.9E-01 0.08 (4E-01) APOA5 N/A N/A -0.07 (3E-01) N/A N/A 0.08 (5E-01) 5.0E-01 0.15 (3E-01) ME1 -0.03 (7E-01) -0.03 (6E-01) 0.06 (3E-01) -0.04 (3E-01) 0.00 (1E+00) -0.10 (4E-01) 5.1E-01 0.28 (2E-01) PEX11A 0.04 (3E-01) -0.07 (1E-01) -0.01 (9E-01) -0.04 (4E-01) 0.00 (1E+00) -0.04 (8E-01) 5.3E-01 0.00 (8E-01) NR1H3 0.01 (7E-01) 0.01 (9E-01) -0.01 (9E-01) 0.03 (6E-01) 0.03 (8E-01) 0.04 (6E-01) 5.5E-01 0.00 (1E+00) PCK1 0.01 (9E-01) 0.07 (3E-01) -0.02 (8E-01) 0.01 (9E-01) 0.09 (4E-01) 0.12 (3E-01) 5.6E-01 0.00 (6E-01) ABCB4 -0.02 (6E-01) -0.03 (3E-01) 0.09 (1E-01) -0.07 (4E-01) -0.16 (2E-01) 0.04 (8E-01) 5.7E-01 0.08 (4E-01) ACAD8 0.04 (5E-01) -0.04 (2E-01) -0.05 (4E-01) -0.02 (8E-01) 0.04 (8E-01) -0.09 (5E-01) 5.8E-01 0.00 (5E-01) KDR 0.02 (5E-01) -0.03 (7E-01) -0.05 (4E-01) -0.14 (5E-02) -0.06 (4E-01) 0.05 (5E-01) 5.8E-01 0.10 (4E-01) LPL -0.01 (9E-01) -0.04 (3E-01) 0.03 (7E-01) -0.07 (4E-01) 0.02 (8E-01) 0.07 (7E-01) 5.8E-01 0.00 (9E-01) ALDH9A1 -0.01 (7E-01) 0.02 (7E-01) 0.03 (5E-01) 0.01 (7E-01) 0.03 (8E-01) 0.02 (8E-01) 6.2E-01 0.00 (1E+00) ACOX1 -0.06 (2E-01) -0.04 (3E-01) 0.02 (4E-01) -0.01 (7E-01) 0.02 (7E-01) -0.05 (6E-01) 6.2E-01 0.00 (4E-01) APOC3 0.05 (5E-01) 0.06 (5E-01) -0.10 (5E-02) 0.03 (7E-01) -0.13 (4E-01) 0.07 (4E-01) 6.6E-01 0.07 (4E-01) ACADM 0.05 (2E-01) 0.00 (9E-01) 0.00 (9E-01) -0.02 (7E-01) 0.02 (9E-01) 0.03 (8E-01) 6.6E-01 0.00 (9E-01) CREB3L3 N/A N/A -0.08 (2E-01) 0.01 (1E+00) N/A 0.04 (6E-01) 7.0E-01 0.00 (5E-01) PDK4 0.01 (9E-01) -0.07 (9E-02) 0.04 (4E-01) 0.05 (5E-01) 0.16 (1E-01) 0.18 (1E-01) 7.0E-01 0.33 (2E-01) Diabetes Page 52 of 58
TXNIP 0.02 (5E-01) 0.01 (8E-01) -0.02 (6E-01) 0.01 (8E-01) -0.01 (1E+00) 0.09 (5E-01) 7.1E-01 0.00 (9E-01) RETSAT -0.09 (3E-02) 0.03 (5E-01) 0.04 (5E-01) 0.01 (8E-01) 0.02 (8E-01) -0.08 (4E-01) 7.1E-01 0.08 (4E-01) SLC27A4 -0.04 (8E-02) 0.03 (2E-01) 0.04 (3E-01) -0.04 (4E-01) 0.00 (1E+00) -0.01 (9E-01) 7.3E-01 0.12 (3E-01) CIDEC 0.01 (9E-01) 0.01 (9E-01) -0.09 (2E-01) -0.02 (9E-01) 0.06 (5E-01) 0.17 (9E-02) 7.5E-01 0.00 (5E-01) ACSL3 -0.07 (2E-01) 0.07 (2E-01) 0.01 (7E-01) 0.04 (5E-01) 0.05 (5E-01) -0.04 (7E-01) 7.6E-01 0.03 (4E-01) PEX19 0.00 (9E-01) 0.03 (3E-01) -0.06 (2E-01) 0.06 (3E-01) 0.08 (3E-01) -0.12 (2E-01) 7.9E-01 0.18 (3E-01) PLIN4 0.04 (4E-01) -0.08 (4E-02) 0.03 (4E-01) 0.00 (1E+00) -0.01 (9E-01) -0.02 (9E-01) 7.9E-01 0.00 (5E-01) FADS1 0.06 (1E-01) 0.00 (1E+00) -0.09 (1E-01) -0.07 (3E-01) 0.02 (8E-01) -0.10 (5E-01) 8.1E-01 0.07 (4E-01) SCD 0.04 (5E-01) 0.09 (2E-01) -0.03 (5E-01) 0.06 (4E-01) -0.06 (6E-01) -0.21 (1E-01) 8.3E-01 0.15 (3E-01) HSD17B10 0.03 (4E-01) -0.04 (3E-01) 0.01 (6E-01) -0.06 (3E-01) 0.04 (6E-01) 0.02 (8E-01) 8.4E-01 0.00 (7E-01) FABP1 0.00 (9E-01) 0.05 (5E-01) -0.01 (8E-01) 0.01 (9E-01) 0.09 (6E-01) -0.22 (6E-02) 8.6E-01 0.00 (7E-01) G6PC N/A N/A -0.01 (9E-01) N/A N/A 0.00 (1E+00) 9.2E-01 0.00 (9E-01) PLIN1 0.00 (9E-01) 0.04 (6E-01) -0.10 (2E-01) 0.03 (8E-01) 0.07 (5E-01) 0.09 (5E-01) 9.4E-01 0.00 (8E-01) APOA2 0.05 (5E-01) 0.10 (2E-01) -0.07 (2E-01) 0.06 (6E-01) -0.15 (4E-01) 0.03 (7E-01) 9.4E-01 0.00 (6E-01) SLC25A42 0.02 (5E-01) -0.01 (8E-01) 0.00 (1E+00) 0.01 (9E-01) -0.08 (2E-01) -0.02 (8E-01) 9.9E-01 0.00 (9E-01) LIPG 0.00 (9E-01) -0.02 (8E-01) 0.11 (1E-01) -0.01 (8E-01) 0.01 (9E-01) -0.22 (7E-02) 9.9E-01 0.13 (3E-01) PCTP -0.01 (8E-01) -0.05 (4E-01) 0.00 (1E+00) 0.08 (2E-01) 0.10 (3E-01) -0.15 (1E-01) 9.9E-01 0.23 (3E-01) VLDLR -0.05 (3E-01) -0.01 (8E-01) 0.09 (3E-01) 0.06 (3E-01) -0.01 (9E-01) -0.06 (6E-01) 1.0E+00 0.00 (6E-01)
Note: Nominally significant associations are in bold. eQTL effect size (beta) represents the slope of the linear regression of normalized expression data versus the three genotype categories using single-tissue eQTL analysis. The normalized expression values are based on quantile normalization within each tissue, followed by inverse quantile normalization for each gene across samples. P-values were generated for each variant-gene pair by testing the alternative hypothesis that the slope of a linear regression model between genotype and expression deviates from 0. *Skin is the random effect meta-analysis of N=302 samples from Skin-Sun Exposed (lower leg) and N=196 samples from Skin-Not Sun Exposed (Suprapubic). Page 53 of 58 Diabetes
Supplemental table 9: Annotation of regulatory function of rs6008845, from RegulomeDB[1] and HaploReg[2], summarizing Encode and Roadmap Epigenomics projects data.
SNP Position Promoter Histone Marks Enhancer Histone Marks DNase Protein Motif Bound1 Altered1 rs6008845 Intergenic Primary monocytes, B Cells H1 Derived Mesenchymal Stem Primary monocyte, B Cells GABPB1, VDR*-CAR- Allele T/C 22kb 5’ of and neutrophils from Cells, Primary B cells from cord from peripheral blood, PAX5*, PXR PPARA peripheral blood, Primary blood, Primary hematopoietic Primary T cells from cord POLR2A*, ; hematopoietic stem cells G- stem cells, Primary Natural Killer blood, Primary GABPA, BCL, CSF-mobilized Male, cells from peripheral blood, hematopoietic stem cells ELF1*, ERalpha-a, Foreskin Melanocyte Primary hematopoietic G-CSF-mobilized Female, FOXP2, EWSR1- Primary Cells, Liver, Colonic stem cells G-CSF-mobilized Fetal Adrenal Gland, Fetal SPI1* FLI1, and Rectal Mucosa, Rectal Female, Muscle Satellite Cultured Heart, Fetal Kidney, Fetal ; KAP1, Smooth Muscle, GM12878 Cells, Foreskin Fibroblast Primary Lung, Fetal Muscle Trunk, ELF1*, NRSF, Lymphoblastoid Cells, Cells, Adipose Nuclei, Duodenum Fetal Muscle Leg, PAX5C20*, VDR* HepG2 Hepatocellular Mucosa, Esophagus, Fetal Adrenal Placenta, Fetal Stomach, PU1*, Carcinoma Cell Line, Gland, Fetal Muscle Trunk, Fetal Ovary, Pancreas, Psoas POL2* Monocytes-CD14+ RO01746 Muscle Leg, Placenta, Fetal Muscle, GM12878 Primary Cells Stomach, Fetal Thymus, Left Lymphoblastoid Cells, Ventricle, Lung, Ovary, Pancreas, HepG2 Hepatocellular Rectal Mucosa, Right Atrium, Carcinoma Cell Line, Right Ventricle, Skeletal Muscle, Monocytes-CD14+ Stomach Smooth Muscle, Spleen, RO01746 Primary Cells HSMM Skeletal Muscle Myoblasts Cells, NHDF-Ad Adult Dermal Fibroblast Primary Cells Note: 1Data on protein bound and motif altered are separated by semicolons, before are from RegulomeDb and after from HaploReg database. Asterisk marks repeated sites at both datasets. Diabetes Page 54 of 58
Supplementary Figures:
Supplemental Figure 1: Graphical representation of the Principal Component (PC) Analysis to estimate the number of Independent comparisons, that correspond to the number of PCs derived to explain 99.5% of trait variance among the 360 SNPs analyzed. Note: in post-imputation QC an IMPUTE- info threshold of 0.8 was used to filter out poorly imputed SNPs[3]. Page 55 of 58 Diabetes
Supplemental Figure 2: Distribution of rs6008845 C allele dosage in whites (A) and African-Americans (B) in ACCORD-Lipid. According to this distribution, for graphical and representation purposes, subjects were considered as major allele homozygotes if the minor allele dosage was <0.5, heterozygotes if the minor allele dosage was ≥0.5 and <1.5, and minor allele homozygotes if the minor allele dosage was ≥ 1.5. Diabetes Page 56 of 58
Supplemental Figure 3: eQTL association of rs6008845 with PPAR-alpha expression in 44 different tissues from GtEx. Box plot show the normalized PPARA gene expression levels according to rs6008845 genotypes in Skin (lower leg) - figures adapted from http://www.gtexportal.org/home/snp/rs6008845
Note: As described in http://www.gtexportal.org/home/documentationPage meta-analysis of all tissues was computed by means of Han and Eskin's Random Effects model (RE2). eQTL effect size (beta) represent, for each tissue with at least 70 sample genotyped and with RNA-seq data available, the slope of the linear regression of normalized expression data versus the three genotype categories using single-tissue eQTL analysis (effect size was referred to the T allele). The normalized expression values are based on quantile normalization within each tissue, followed by inverse quantile normalization for each gene across samples. Page 57 of 58 Diabetes
Supplemental figure 4: RegulomeDB output showing the transcription factors whose binding is likely to be affected by rs6008845 in different cell types. Diabetes Page 58 of 58
Supplemental figure 5: PPARA region with detail on rs6008845 (yellow-line). rs6008845 is located inside epigenetic peaks for Histone 3 (H3K4Me1, H3K4Me1 and H3K27Ac). Figure generated with freely available bionformatic resources from University of Santa Cruz (UCSC) genome browser gateway http://genome.ucsc.edu [4] (based on assembly GRCh37/hg19)..
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References
1. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, Karczewski KJ, Park J, Hitz BC, Weng S, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790-7. 2. Ward LD and Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930-4. 3. Southam L, Panoutsopoulou K, Rayner NW, Chapman K, Durrant C, Ferreira T, Arden N, Carr A, Deloukas P, Doherty M, et al. The effect of genome-wide association scan quality control on imputation outcome for common variants. Eur J Hum Genet. 2011;19:610-4. 4. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM and Haussler D. The human genome browser at UCSC. Genome Res. 2002;12:996-1006.