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Skeletal Muscle Expression of Phosphofructokinase is Influenced by Genetic Variation and Associated with Insulin Sensitivity
Sarah Keildson,1* Joao Fadista,2* Claes Ladenvall,2 Åsa K. Hedman,1 Targ Elgzyri,2 Kerrin S. Small,3,4 Elin Grundberg,3.4 Alexandra C. Nica,5 Daniel Glass,3 J. Brent Richards,3,6 Amy Barrett,7 James Nisbet,4 Hou Feng Zheng,6 Tina Rönn,2 Kristoffer Ström,2,8 Karl Fredrik Eriksson,2 Inga Prokopenko,1 MAGIC consortium, DIAGRAM consortium, MuTHER consortium, Timothy D Spector,3 Emmanouil T. Dermitzakis,5 Panos Deloukas,4 Mark I McCarthy,1,7,10 Johan Rung,9 Leif Groop,2 Paul W. Franks,11 Cecilia M. Lindgren,1,12* Ola Hansson,2*
1. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, United Kingdom 2. Lund University Diabetes Center, Department of Clinical Sciences, Diabetes and Endocrinology, Skåne University Hospital Malmö, Lund University, Malmö 20502, Sweden 3. Department of Twin Research and Genetic Epidemiology, King’s College London, London, SE1 7EH, United Kingdom 4. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA, United Kingdom 5. Department of Genetic Medicine and Development, University of Geneva, Medical School, 1211 Geneva 4, Switzerland 6. Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, H3T 1E2, Canada 7. Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, OX3 7LJ, United Kingdom 8. Swedish Winter Sports Research Centre, Department of Health Sciences, Mid Sweden University, SE 83125 Östersund, Sweden 9. EMBL EBI, European Molecular Biology Laboratory European Bioinformatics Institute, Cambridge, CB10 1SD, United Kingdom 10. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, OX3 7LE, United Kingdom 11. Lund University Diabetes Center, Department of Clinical Sciences, Genetic and Molecular Epidemiology, Skåne University Hospital Malmö, Lund University, Malmö 20502, Sweden 12. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. *. These authors contributed equally to this work
Corresponding author: Dr. Ola Hansson, E mail: [email protected]; Tel: +46 40 391228; Fax: +46 40 391222
Running title: eQTL screen of human skeletal muscle
Word count: 4005, Tables: 4, Figures: 3
1
Diabetes Publish Ahead of Print, published online December 4, 2013 Page 3 of 64 Diabetes
Abstract
Using an integrative approach where genetic variation, gene expression and clinical
phenotypes are assessed in relevant tissues may help functionally characterize the
genetic contribution to disease susceptibility. We sought to identify genetic variation
influencing skeletal muscle gene expression (eQTL), as well as expression associated
with measures of insulin sensitivity. We investigated associations of 3799401 genetic
variants with gene expression from >7000 genes from three cohorts (n=104). We
identified 287 genes with cis acting eQTLs (FDR<5%; P<1.96x10 5) and 49
expression insulin sensitivity phenotype associations (i.e. fasting insulin, HOMA IR
and BMI) (FDR<5%; P=1.34x10 4). One of these associations, fasting
insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Further, the expression
of PFKM, a rate limiting enzyme in glycolysis, was nominally associated with
glucose uptake in skeletal muscle (P=0.026, n=42) and over expressed (PBF
corrected=0.03) in skeletal muscle in T2D patients (n=102) compared with
6 normoglycemic controls (n=87). The PFKM eQTL (rs4547172; PeQTL=7.69x10 )
was nominally associated with glucose uptake, glucose oxidation rate, intramuscular
triglyceride content, and metabolic flexibility (P=0.016 0.048, n=178). We explored
eQTL results using published GWAS data (from DIAGRAM and MAGIC) and a
proxy for the PFKM eQTL (rs11168327, r2=0.75), was nominally associated with
3 T2D (PDIAGRAM=2.7x10 ). Taken together, our analysis highlights PFKM as a
potential regulator of skeletal muscle insulin sensitivity.
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Introduction
Although genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with traits and diseases, the molecular mechanisms underlying these associations remain largely unknown.
Changes in gene expression can affect phenotypic variation and consequently, understanding genetic regulation of gene expression through the identification of expression quantitative trait loci (eQTLs) could elucidate the mechanisms underlying genotype phenotype associations. While cis eQTL analyses have been carried out in a number of human tissues, their discovery in human skeletal muscle has been limited.
A large skeletal muscle eQTL study (n= 225) was carried out in healthy Pima Indians to assess the causes and prevalence of bimodal gene expression and suggest that bimodality may be an underlying factor in disease development (1). In a more recent study, cis eQTL effects were compared between blood and four non blood tissues
(including liver, subcutaneous and visceral adipose tissue and skeletal muscle) which provided new insights into the mechanisms by which genetic variants mediate tissue dependent gene expression (2).
Insulin resistance is a physiological condition where insulin mediated glucose disposal in skeletal muscle is inhibited (3) and it plays a key role in the pathogenesis of type 2 diabetes (T2D) (4). The molecular mechanisms underlying insulin resistance are largely unknown, but mitochondrial dysfunction coupled with metabolic inflexibility has been implicated as a key contributor (reviewed in (5)). One proposed hypothesis for the etiology of insulin resistance is a redistribution of lipid stores from adipose to non adipose tissues (e.g. skeletal muscle, liver and the insulin producing pancreatic β cells) resulting in accumulation of intracellular fatty acids. Accumulation
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of intracellular fatty acids in turn inhibits insulin stimulated glucose transport by
stimulating phosphorylation of serine sites on insulin receptor substrate 1 (6). The aim
of the current study was to identify skeletal muscle eQTLs, as well as expression
associated with measures of insulin sensitivity to elucidate genetic contribution to
skeletal muscle expression and insulin sensitivity.
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Materials and Methods
Cohort descriptions
Malmö exercise intervention (MEI)
The Malmö Exercise Intervention cohort consists of 50 male subjects from southern
Sweden. They all have European ancestry and 25 have and 25 do not have with or without a first degree family member with T2D (7). All participants had normal glucose tolerance and VO2MAX of 32.0±5.0ml/kg/min. All participants also completed a 6 months aerobic training period, aiming at 3 group training sessions per week (~60 min training / session, supervised by members of the research group).
Information from the baseline pre intervention screening visit was used for work presented in this manuscript.
Malmö men (MM)
The Malmö men cohort is a subset of 203 non obese Swedish men from the Malmö
Prospective Project (MPP) that were asked to participate in a training intervention (8;
9). The MPP was initiated in 1974 as an intervention project to prevent Type 2 diabetes in men born between 1926 and 1935. Upon inclusion in the MPP, all participants in MM had normal glucose tolerance, but at the baseline screening for the
MM intervention, some of them had developed impaired glucose tolerance or Type 2 diabetes. Information from the baseline pre intervention screening visit was used for work presented in this manuscript.
Multiple Tissue Human Expression Resource (MuTHER)
Multiple Tissue Human Expression Resource study consists of 856 female individuals of European descent (336 monozygotic and 520 dizygotic twins), recruited from the
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TwinsUK Adult twin registry (10). Thirty nine MuTHER participants had both
muscle tissue expression profiles and genome wide genotypes available. The age at
inclusion ranged from 40 to 87 years, with a median age of 62 years. Metabolic
phenotypes were measured at the same time point as the biopsies were collected. Due
to the twin structure of the data, the minimum effective sample size for the MuTHER
skeletal muscle samples was calculated and used in the analysis. Since there were 3
monozygotic twins, sharing 100% of their genetic material, 11 dizygotic twins sharing
50% of their genetic material and 11 singletons, we calculated the minimum effective
sample size to be 3 + (11+5.5) + 11 = 30.5.
Phenotype selection
Fasting insulin and HOMA IR were selected as measurements of peripheral insulin
sensitivity that were consistently measured in all three studies and where large scale
GWAS meta analyses have been performed. These measures will not exclusively
capture skeletal muscle insulin sensitivity, but also hepatic insulin sensitivity. Because
of the strong association between BMI and T2D we also included BMI as a third
phenotype. From the MuTHER cohort, 2 non fasted and/or diabetic individuals were
excluded from phenotype association analyses carried out on insulin and HOMA IR.
Similarly, 12 individuals were excluded from the MM cohort.
cis-eQTL analysis
A cis eQTL analysis was performed within each cohort on the 7006 genes common to
all three studies. The analysis was restricted to non diabetic individuals, leaving 26,
39 and 39 individuals from the MEI, MM and MuTHER cohorts for analysis. We
investigated associations between expression levels and all SNPs within 1MB up or
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downstream from the transcription start site of each of these genes. The Malmö studies (MEI and MM) were analyzed using a linear model adjusting for age as implemented in the R Matrix eQTL package (11). In MuTHER, the analysis was performed using a linear mixed effect model implemented in R by the lmer() function in the lme4 package. The model was adjusted for age and experimental batch (fixed effects), as well as family relationship (twin pairing) and zygosity (random effects).
A meta analysis of the cis eQTL results from each study was carried out using
METAL (12). Because gene expression levels were measured on different platforms in each study, the effect estimates of the cis eQTLs are not comparable across studies.
Thus, the “samplesize” scheme in METAL, which uses p values and direction of effect, weighted by sample size, was used for the meta analysis. Heterogeneity was assessed using the I2 statistic. We used a false discovery rate (FDR) < 5% threshold for the cis eQTL meta analysis, computed using the QVALUE package in R (13). To calculate the number of independent cis eQTLs, we looked for the SNP that was most strongly associated with each gene (top SNP per gene) amongst all significant cis eQTLs.
Gene expression-phenotype association
Associations between gene expression levels and the selected phenotypes were investigated separately within each study. All participants who were diabetic were excluded from the analysis and values within each study were inverse normal transformed (Blom) prior to analysis. A secondary analysis, adjustments for BMI, was also performed to test for associations independent of the effect of overall obesity.
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The same models as used in the cis eQTL analyses were used to test for these
associations.
In order to be able to perform fixed and random effect meta analyses on expression
phenotype associations, gene expression levels were inverse normal transformed
before analysis. Meta analyses were performed using Metal for all phenotypes and a
combined FDR <5% threshold was applied. We then looked for significant cis
eQTLs, where the gene was also significantly associated with a phenotype to
investigate evidence for genetic control of phenotypic variation through gene
expression.
Further information on quality control of genotyping, imputation and gene expression
can be found in the supplemental information section.
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Results
We combined genome wide SNP, gene expression and phenotype data from three independent cohorts of European origin: the Malmo Men study (MM; N = 26) (8), the
Malmo Exercise Intervention study (MEI; N = 39) (7) and the Multiple Tissue Human
Expression Resource consortium (MuTHER; N = 39) (14). An outline of the methods used here is summarized in Figure S1 and the sample size and characteristics for each study are shown in Table 1. For the gene expression analysis we used a gene centric approach by only selecting probes in each study that mapped (using NCBI build 37) to genes common to all three cohorts. This limited our analysis to the overlap of genes present on all the different arrays used (Ngenes = 7,006) that were analysed for association with 3,799,401 genetic variants. Further, we only used uniquely mapping probes with no mismatches and without common SNPs (MAF > 5%) to limit false positive findings in the analysis.
287 eQTLs were identified in human skeletal muscle
First, we investigated the associations between genetic variation (SNPs) and gene expression within an arbitrary ±1 Mb window around every gene. The summary statistics for skeletal muscle eQTLs from the three cohorts (effective N = 95.5; see
Materials and Methods for how this is calculated) were meta analyzed. We identify
287 genes with at least one significant eQTL (top eQTL SNP per gene; FDR < 5%, p value < 1.96x10 5, Table S1). The most significant eQTL was observed for mu crystallin (CRYM), a nicotinamide adenine dinucleotide phosphate (NADPH) regulated thyroid hormone binding protein, previously reported to be regulated by blood glucose concentrations (15) (Figure 1a). Amongst the other significant eQTLs were signal transducer and activator of transcription 3 (STAT3), endoplasmic
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reticulum aminopeptidase 2 (ERAP2) and muscle phosphofructokinase (PFKM)
(Figure 1b,c).
To describe the distribution of eQTL p values in relation to gene proximity, we
plotted the distribution of distances between the SNP with the lowest p value and the
transcription start site (TSS) for each gene for the combined analysis (Figure 2). We
found an enrichment of low p values closer to TSSs (approximately ± 250 kb),
showing that within this distance cis eQTLs are more likely to be found (binomial
test, p value = 9.71x10 6). Moreover, within a 250 kb distance, there is a higher
likelihood that it is the nearest gene that is influenced in cis by a SNP (binomial test,
p value= 2.93x10 6).
To assess the potential functional significance of the eQTLs, gene annotation was
performed for the top eQTL SNP per gene, and compared to all SNPs tested in the
dataset. As shown in Figure 3, most of the eQTL SNPs are located in intronic regions
(62.4%), and interestingly, we observe an enrichment for non coding regions
upstream/downstream and in untranslated regions (UTRs) but a depletion for
intergenic and coding regions (test of equal proportions, p value=0.0041, Bonferroni
corrected). To further quantify the potential regulatory significance of the non coding
eQTL SNPs, RegulomeDB, a database derived from the ENCODE project, was used
to annotate the variants (16). By using scores of functional regional significance, we
found that eQTL SNPs were enriched in the score category “likely to affect
transcription factor binding and linked to expression of a gene target” (test of equal
proportions, p value=0.013, Bonferroni corrected). A gene set enrichment analysis of
the eQTL genes (FDR < 1%) performed in DAVID (17) showed nominal enrichment
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in gene ontologies related to post transcriptional regulation of gene expression (Fisher exact test, p value = 0.009).
Further support for the identified muscle eQTLs was obtained by probing databases containing published eQTLs. Specifically, searches were made on the Phenotype
Genotype Integrator (https://www.ncbi.nlm.nih.gov/gap/PheGenI), Pritchard's lab eQTL browser (http://eqtl.uchicago.edu/cgi bin/gbrowse/eqtl/), Genevar (18), and also on a skeletal muscle eQTL study performed in Pima Indians (1). We found that
19% of our eQTLs were detected in at least one of these resources (Table S1). The observed discrepancy might be partly explained by the use of different genotyping and expression platforms, the known high degree of tissue specificity of eQTLs (2) and the fact that only one of those databases contains eQTL data from skeletal muscle.
Associations of significant eQTL SNPs in GWAS data from MAGIC and DIAGRAM
To explore the results of the eQTL analysis further we performed a lookup of our 287 significant (FDR < 5%, p value < 1.96x10 5) eQTLs in GWAS data from the
DIAGRAM consortium for T2D (19), and the MAGIC consortium for HOMA IR
(20), 2 hour glucose, 2 hour glucose adjusted for BMI, fasting insulin, fasting insulin adjusted for BMI, fasting glucose (FG) and fasting glucose adjusted for BMI (21). To investigate whether eQTLs are overrepresented in associations found in the published
DIAGRAM and MAGIC GWAS results, we calculated the enrichment of nominally significant (P ≤ 0.05) GWAS associations amongst our significant eQTLs using a binomial test (Table 2). The eQTL SNPs were enriched (P ≤ 0.05, uncorrected) in
GWAS signals for six out of the eight investigated phenotypes, i.e. for T2D, HOMA
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IR, fasting insulin adjusted for BMI, fasting glucose, fasting glucose adjusted for BMI
and 2 hour glucose. Three persisted after Bonferroni correction. One of the eQTL
SNPs (rs1019503 significantly associated with the expression level of ERAP2, PeQTL=
10 9 7.17x10 ) is also significantly associated with 2 hour glucose (PMAGIC=8.87x10 )
9 and 2 hour glucose adjusted for BMI (PMAGIC= 5.10x10 ) in the MAGIC consortium
6 dataset. Another eQTL (rs4547172 associated with PFKM, PeQTL=7.69x10 ) has a
proxy (rs11168327, r2=0.75), which was nominally associated with T2D
3 (PDIAGRAM=2.70x10 ).
Forty-nine skeletal muscle expression – phenotype associations were identified
We meta analyzed the association test statistics for gene expression (standardized
units) with fasting plasma insulin, HOMA IR and BMI in each study. Significant
(FDR < 5%, p value < 1.34x10 4) associations are presented in Table S2. The
expression of 18 and 11 genes were associated with fasting plasma insulin and
HOMA IR, respectively, and the expression levels of eight of these genes were
associated with both traits (Table S2). The expression levels for 20 genes were
associated with BMI and none overlapped with the fasting plasma insulin or HOMA
IR associated genes. Association of expression levels with fasting insulin and HOMA
IR adjusted for BMI were also investigated to control for BMI (Table S3) and putative
confounding factors correlated with BMI (e.g. diet and physical activity level). With
adjustment for BMI, the expression levels of eight and three genes were associated
with fasting plasma insulin and HOMA IR respectively. Several of the genes for
which expression levels were associated with measures of insulin sensitivity have
previously been implicated in T2D and related traits, e.g. Calsequestrin 1 (CASQ1)
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(22), solute carrier family 30, member 10 (SLC30A10) (23) and growth arrest specific
6 (GAS6) (24).
To identify genetic variations influencing clinical phenotypes through gene expression, we integrated significant results from the eQTL analysis (FDR < 5%, p value < 1.96x10 5) and gene expression phenotype associations (FDR < 5%, p value <
1.34x10 4), highlighting PFKM as the only gene associated in both these analyses.
PFKM (eQTL lead SNP = rs4547172, p value = 7.69x10 6, Figure 1c) gene expression was associated with fasting plasma insulin levels (p value =1.34x10 4) in our cohorts. This suggests that genetic variation at this locus could be involved in the interplay between fasting plasma insulin levels and PFKM expression.
Extended phenotype association of PFKM in the Malmo Men study
To extend the analysis of insulin sensitivity, we tested both the PFKM eQTL SNP
(rs4547172) and PFKM expression for association with M value (i.e. glucose uptake) using the euglycemic hyperinsulinemic clamp technique (the gold standard for characterizing insulin sensitivity in vivo) (25) in the MM study. Both the eQTL SNP
(rs4547172, beta = 0.15 [0.03, 0.27] (sqr (mg * min 1 * kg 1), p = 0.016, n = 178) and the transcription levels of PFKM (beta = 0.000295 [ 0.001, 0.000037] (sqr (mg * min 1 * kg 1)), p = 0.026, n = 42) were nominally associated with M value.
Given that PFKM catalyzes a rate limiting step in the glycolysis pathway, we investigated if the eQTL SNP (rs4547172) was associated with measures of oxidative fuel partitioning, i.e. metabolic flexibility measured as delta respiratory quotient (RQ)
(26). We examined the difference in RQ during the euglycemic hyperinsulinemic
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clamp, between the non insulin stimulated (basal) and the insulin stimulated (clamp)
state. The rs4547172 SNP was nominally associated with delta RQ (beta = 0.011
[0.001, 0.02] (AU), p = 0.030, n = 173). Also, insulin stimulated glucose oxidation
rate in the insulin stimulated state was nominally associated with the same SNP (beta
= 0.19 [0.025, 0.36] (mg * body weight 1 * min 1), per allele, p = 0.025, n = 178). We
also investigated if rs4547172 was associated with skeletal muscle energy stores, i.e.
intra muscular triglycerides (IMTG) and glycogen. The rs4547172 SNP was
nominally associated with IMTG (beta = 8.69 [0.26, 17.12] (AU), p = 0.043, n = 167,
but not with glycogen (p=0.213). These results and descriptive data are summarized
in Table S4 S5.
Replication of the expression - phenotype associations using publicly available data
The 29 expression phenotype associations (corresponding to 21 genes) (Table S2)
with fasting plasma insulin levels and/or HOMA IR were investigated for differential
expression between T2D patients (n=102) and normoglycemic insulin sensitive
controls (NGT, n=87). This was done using publicly available microarray skeletal
muscle expression data from three independent studies (27 29) that we retrieved from
ArrayExpress (http://www.ebi.ac.uk/arrayexpress/) and subsequently meta analyzed.
We found four genes, one of which was PFKM (CASQ1, DBNDD1, DHRS7 and
PFKM) that were associated with increased expression in muscle from T2D patients
versus NGT controls after Bonferroni correction (Table 3). This supports our initial
expression phenotype associations (Table S2). Further, skeletal muscle expression
raw data from seven independent studies (30 36) with available BMI data (n=185)
were used to replicate our 20 expression BMI associations (Table S2). The expression
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of two genes, i.e. MSTN and RCAN2, was positively and negatively associated respectively with BMI after Bonferroni correction (Table 4).
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Discussion
Here we have integrated genetic variation, skeletal muscle gene expression and
clinical phenotype data from 104 individuals in order to investigate the genetic
contribution to gene expression in skeletal muscle and insulin sensitivity. We
identified 287 muscle eQTLs and 49 associations between gene expression and
measures related to insulin sensitivity.
We find an equivalent number of cis eQTL in our analysis as previous studies (1), (2).
Our eQTL data suggest that significant associations between genetic variants and
gene expression are more likely to be found within a 250 kb region of a gene.
Previous studies have shown a similar enrichment of significant eQTLs in this region
(2), (37; 38). Proximal genes within this region are also more likely to be influenced
by a polymorphism in cis, than genes located further away. Furthermore, when
categorizing the known function of the genes with eQTLs, we found an enrichment of
genes annotated as being involved in post translational regulation of gene expression.
This suggests that the eQTL SNPs are not only affecting the expression of the nearest
gene, but may also regulate other genes and thereby form trans regulatory cascades.
When interpreting GWAS data, it is often difficult to determine which
genes/pathways are influenced by genetic variation. Utilizing eQTL data as an
intermediate phenotype is one possible way to address this problem. To this end, we
investigated our significant muscle eQTL SNPs in GWAS data from MAGIC, thereby
identifying an eQTL SNP for ERAP2 as being significantly associated with 2 hour
glucose adjusted and unadjusted for BMI. ERAP2 is an endoplasmatic reticulum
aminopeptidase that functions as an antigen trimming peptide that has been implicated
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as a regulator of blood pressure and angiogenesis (39). The rs1019503 SNP also influences the expression of ERAP2 in other tissues, such as in human pancreatic islets (unpublished data), lymphoblastoid cell lines, primary fibroblasts, T cells, skin and adipose tissue (14; 40 42). Our results suggest that differential expression in skeletal muscle of ERAP2 may be part of the molecular mechanism underlying this genome wide significant association with 2 hour glucose levels, but we cannot, at this point, exclude effects in other tissues. Nevertheless, the ERAP2 findings exemplify the efficacy of analyzing gene expression in disease relevant tissues to disentangle the molecular mechanisms underlying GWAS results.
Several of the genes we found to be associated with measures of insulin sensitivity have previously been implicated in T2D and related traits, e.g. CASQ1, a skeletal muscle protein expressed in the sarcoplasmic reticulum important for the regulation of calcium channel activity. We found CASQ1 expression to be both positively associated with fasting plasma insulin and having higher expression in individuals with T2D compared to normoglycemic individuals. SLC30A10 expression was negatively associated with both fasting insulin and HOMA IR. SLC30A10 is a zinc transporter and a SNP (rs4846567) proximal to this gene has previously been associated with waist hip ratio (43) and measures of insulin sensitivity, e.g. fasting plasma insulin, HOMA IR and ISI Matsuda index (23). This SNP (rs4846567) was, however, not identified as a skeletal muscle eQTL for SLC30A10 in the present study.
Three genes, i.e. GAS6, ALDH1A2 and CALML4 were found to be associated with fasting insulin with or without correcting for BMI. The relatively large influence of
BMI on the associations between expression and measures of insulin sensitivity suggests that for many genes, either BMI directly affects the expression of these genes
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and consequently impacts skeletal muscle insulin sensitivity or that other BMI
correlated factors like diet and physical activity level confound associations between
expression levels and the clinical phenotypes.
By cross referencing the significant eQTL results with data from the gene expression–
phenotype analysis, we found that SNP rs4547172 regulates the expression of PFKM,
a gene whose expression was also positively associated with fasting plasma insulin.
Since PFKM encodes for PFK1, the muscle isoform of phosphofructokinase and is a
key regulator of glycolysis (44), this gene represents a strong candidate for skeletal
muscle gene expression associated with glycemic traits. Mutations in PFKM have
been shown to cause glycogen storage disease VII (Tarui disease), an autosomal
recessive metabolic disorder characterized clinically by exercise intolerance, muscle
cramping, myopathy, and compensated hemolysis. To more in detail determine the
role of PFKM in the regulation of insulin sensitivity we extended these findings with
additional phenotypes related to insulin sensitivity and skeletal muscle metabolism,
e.g. with a euglycemic hyperinsulinemic clamp data like glucose uptake (M value)
(25). Both the A allele (displaying increased expression of PFKM) and increased
expression of PFKM were associated with reduced glucose uptake. As PFK1 regulates
a key step in glycolysis by fuelling the mitochondria with carbohydrates we examined
the influence on metabolic flexibility and found that the A allele was associated with
reduced flexibility, possibly by promoting an elevation of the glucose oxidation rate
in the fasted state. One of the first observations that instigated the concept of
metabolic flexibility was the observation that administration of fat emulsions during
an oral glucose tolerance test leads to increased glucose intolerance (45). Since then,
metabolic flexibility has been defined as the ability to switch from high rates of fatty
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acid uptake and lipid oxidation to suppression of lipid metabolism with a parallel increase in glucose uptake, storage and oxidation, e.g. in response to feeding or high intensity exercise (46). Both in patients with manifest T2D, as well as in prediabetic individuals, a reduced metabolic flexibility has been described (47).
The finding that increased expression of PFKM is associated with T2D and insulin resistance is somewhat paradoxical given that the encoded protein, PFK1, is rate limiting to glycolysis. We can only speculate about the reasons for this increased expression, which we consider secondary to, rather than a cause of, insulin resistance.
First, even in an insulin resistant state glycolysis might be insulin sensitive enough to the small amount of insulin required to maintain a normal flux, as the ED50 (the median effective dose) for stimulation of glycolysis/glucose oxidation is half that of what is required to stimulate glycogen synthesis (48). Therefore, under insulin resistant conditions it is possible that glucose entering the cell can still be shunted through glycolysis to ensure sufficient production of energy in the citric acid cycle.
Second, if the insulin resistant state is associated with, or caused by, excess oxidation of FFA, there will be a feedback inhibition of glycolysis, including PFK1, by accumulated citrate and Acetyl CoA. It is possible that the amount of PFKM transcript is increased in an attempt to overcome the allosteric inhibition of PFK1. It should be acknowledged, however, that the current study was not designed to address these issues, which will require flux measurements using isotopes as well as measurement of enzyme activities. However, increased PFKM activity has been associated with increased muscle fat deposition as measured by computed tomography (49) and increased glycolytic capacity in T2D has been suggested (49
54). These observations could possibly be attributed to fiber type composition with a
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shift towards increased glycolytic type IIx fibers in T2D (53). Our hypothesis that
PFKM is involved in T2D pathogenesis is strengthened by the observation that PFKM
is over expressed in diabetic muscle compared to muscle from non diabetic,
normoglycemic individuals, and a proxy SNP (r2 = 0.75) for rs4547172 has a
nominally significant association with T2D (none of the top eQTL SNPs for PFKM
were represented in DIAGRAM).
In conclusion, the identified association of PFKM with skeletal muscle insulin
sensitivity demonstrates how an integrative approach, combining different levels of
genomic data with clinical phenotype data from a disease relevant tissue, may help to
functionally characterize the genetic contribution to disease susceptibility.
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Supplemental Data Description
Supplemental information
Supplemental Figure S1
Supplemental Table S1
Supplemental Table S2
Supplemental Table S3
Supplemental Table S4
Supplemental Table S5
Supplemental Table S6
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Acknowledgements
The authors would like to thank the excellent technical assistance of Maria
Sterner, Gabriella Gremsperger, Esa Laurila and Tina Rönn at Lund University. The
MuTHER Study was supported by a program grant from the Wellcome Trust
(081917/Z/07/Z) and by core funding for the Wellcome Trust Centre for Human
Genetics (090532). Additional support came from the Swedish Research Council
Linnaeus grant: Lund University Diabetes Centre (Dnr 349 2006 237), European
Research Council (ERC) – Advanced Researcher Grant (GA 269045), the Wallenberg
Foundation, the Påhlsson foundation, the European Community's Seventh Framework
Programme (FP7/2007 2013), ENGAGE project and grant agreement (HEALTH F4
2007 2014139), the Swiss National Science Foundation and the NCCR Frontiers in
Genetics, the Louis Jeantet Foundation and a US National Institutes of Health–NIMH
grant (GTEx project). C.M.L. is a Wellcome Trust Research Career Development
Fellow (086596/Z/08/Z). O.H. 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. Author contributions: wrote the paper (SK, JF,
CML, LG, OH), carried out imputation (CL, HFZ, JBR), performed analysis (SK, JF,
JR, IP, OH), phenotyping/genotyping (BJR, AKH, TE, KSS, EG, ACN, DG, TR, AB,
JN, KS, KFE), study design (TDS, ETD, PD, MIM, JR, LG, PWF, CML, OH),
supervised study (LG, CML, OH).
No potential conflicts of interest relevant to this article were reported.
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Stumvoll M, Tonjes A, Viikari J, Balkau B, Ben-Shlomo Y, Bergman RN, Boeing H, Smith GD, Ebrahim S, Froguel P, Hansen T, Hengstenberg C, Hveem K, Isomaa B, Jorgensen T, Karpe F, Khaw KT, Laakso M, Lawlor DA, Marre M, Meitinger T, Metspalu A, Midthjell K, Pedersen O, Salomaa V, Schwarz PE, Tuomi T, Tuomilehto J, Valle TT, Wareham NJ, Arnold AM, Beckmann JS, Bergmann S, Boerwinkle E, Boomsma DI, Caulfield MJ, Collins FS, Eiriksdottir G, Gudnason V, Gyllensten U, Hamsten A, Hattersley AT, Hofman A, Hu FB, Illig T, Iribarren C, Jarvelin MR, Kao WH, Kaprio J, Launer LJ, Munroe PB, Oostra B, Penninx BW, Pramstaller PP, Psaty BM, Quertermous T, Rissanen A, Rudan I, Shuldiner AR, Soranzo N, Spector TD, Syvanen AC, Uda M, Uitterlinden A, Volzke H, Vollenweider P, Wilson JF, Witteman JC, Wright AF, Abecasis GR, Boehnke M, Borecki IB, Deloukas P, Frayling TM, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, North KE, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, Hirschhorn JN, Assimes TL, Wichmann HE, Thorsteinsdottir U, van Duijn CM, Stefansson K, Cupples LA, Loos RJ, Barroso I, McCarthy MI, Fox CS, Mohlke KL, Lindgren CM: Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 2010;42:949-960 44. Nakajima H, Noguchi T, Yamasaki T, Kono N, Tanaka T, Tarui S: Cloning of human muscle phosphofructokinase cDNA. FEBS Lett 1987;223:113-116 45. Felber JP, Vannotti A: Effects of Fat Infusion on Glucose Tolerance and Insulin Plasma Levels. Med Exp Int J Exp Med 1964;10:153-156 46. Kelley DE, Mandarino LJ: Fuel selection in human skeletal muscle in insulin resistance: a reexamination. Diabetes 2000;49:677-683 47. Russell RD, Kraemer RR, Nelson AG: Metabolic dysfunction in diabetic offspring: deviations in metabolic flexibility. Med Sci Sports Exerc 2013;45:8-15 48. Groop LC, Saloranta C, Shank M, Bonadonna RC, Ferrannini E, DeFronzo RA: The role of free fatty acid metabolism in the pathogenesis of insulin resistance in obesity and noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab 1991;72:96-107 49. Simoneau JA, Colberg SR, Thaete FL, Kelley DE: Skeletal muscle glycolytic and oxidative enzyme capacities are determinants of insulin sensitivity and muscle composition in obese women. FASEB J 1995;9:273-278 50. Bass A, Vondra K, Rath R, Vítek V, Havránek T: Metabolic changes in the quadriceps femoris muscle of obese people. Enzyme activity patterns of energy- supplying metabolism. Pflugers Arch 1975;359:325-334 51. Simoneau JA, Kelley DE: Altered glycolytic and oxidative capacities of skeletal muscle contribute to insulin resistance in NIDDM. J Appl Physiol 1997;83:166- 171 52. Lithell H, Lindgärde F, Hellsing K, Lundqvist G, Nygaard E, Vessby B, Saltin B: Body weight, skeletal muscle morphology, and enzyme activities in relation to fasting serum insulin concentration and glucose tolerance in 48-year-old men. Diabetes 1981;30:19-25 53. Oberbach A, Bossenz Y, Lehmann S, Niebauer J, Adams V, Paschke R, Schön MR, Blüher M, Punkt K: Altered fiber distribution and fiber-specific glycolytic and oxidative enzyme activity in skeletal muscle of patients with type 2 diabetes. Diabetes Care 2006;29:895-900 54. Krotkiewski M, Bylund-Fallenius AC, Holm J, Björntorp P, Grimby G, Mandroukas K: Relationship between muscle morphology and metabolism in
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Figure Legends
Figure 1. LocusZoom plots of three selected eQTL regional association results for the genes (A) CRYM, (B) ERAP2 and (C) PFKM.
Figure 2. Distance distribution from each gene's most strongly associated SNP to its TSS.
Figure 3. Functional annotation of eQTL SNPs. The bar plot indicates the percentage of eQTL SNPs per gene functional unit and its enrichment or depletion relative to all SNPs tested in this study. The SNPs were annotated with snpEff. Downstream and upstream regions are defined as being within 5kb distance from a gene.
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Tables
Table 1. Characteristics of the 104 individuals (effective N =95.5), separated by study cohort.
N MM MEI MuTHER Sex 26 39 39 Age (yr) Male Male Female BMI (kg/m2) 65.96 ± 1.58 37.83 ± 4.32 62.16 ± 7.51 HOMA IR 25.42 ± 3.97 28.54 ± 3.07 27.40 + 5.41 Fasting plasma insulin* 1.91 ± 1.59 1.39 ± 0.65 10.55 + 7.14 Fasting glucose (mmol/liter) 8.69 ± 6.59 7.37 ± 3.29 44.76 + 26.37 4.78 ± 0.48 4.25 ± 0.51 5.04 + 0.66 Data are represented as mean ± SD. HOMA IR, homeostasis model assessment of insulin resistance. * Units for fasting plasma insulin are measured in U/ml in MM and MEI and pmol/l in MuTHER. Phenotypes were inverse normalized in each study in the meta analysis.
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Table 2. Binomial test for enrichment (exact match) of GWAS signals for T2D, HOMA IR, 2 hour glucose, 2 hour glucose adjusted for BMI, fasting glucose, fasting glucose adjusted for BMI, fasting insulin and fasting insulin adjusted for BMI amongst our significant (FDR<5%) cis eQTL SNPs.
GWAS phenotype No. of Total no. Enrichment Ref SNPs <0.05 of SNPs P-value T2D 20 183 5.50x10-4* (19) HOMA IR 15 200 0.03 (20) 2 hour glucose 14 170 0.02 (21) 2 hour glucose adjusted for BMI 12 170 0.06 (21) Fasting glucose 29 199 1.90x10-7* (21) Fasting glucose adjusted for BMI 18 199 6.00x10-3* (21) Fasting insulin 11 198 0.11 (21) Fasting insulin adjusted for BMI 14 198 0.05 (21)
* p<6.3x10 3, survives Bonferroni correction (=0.05/8)
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Table 3. Differential expression in skeletal muscle between type 2 diabetic patients (T2D) and normoglycemic insulin sensitive individuals (NGT) of the 21 genes with association to at least one of the insulin sensitivity related phenotypes (FI and/or HOMA IR).
Meta-analysis of publicly available skeletal muscle expression Phenotype- T2D vs. NGT Gene expression (zScore) (P-value) (P -value) association adj CASQ1 +++ 4.13 3.56x10 5 7.11x10-4 DBNDD1 + + 3.45 5.69x10 4 0.01 DHRS7 +++ 3.34 8.52x10 4 0.02 PFKM +++ 3.18 1.45x10 3 0.03 IMPA2 +++ 2.55 0.01 0.21 GZMH +++ 2.38 0.02 0.35 ALDH1A2 + + 2.22 0.03 0.53 ARHGEF10L +++ 1.58 0.11 1.00 SLC30A10 1.51 0.13 1.00 G3BP2 +++ 1.28 0.20 1.00 RNF111 + + 1.17 0.24 1.00 C17orf101 +++ 1.12 0.26 1.00 EIF4E2 +++ 0.77 0.44 1.00 GAS6 0.74 0.46 1.00 BCL7A 0.73 0.46 1.00 CALML4 +++ 0.60 0.55 1.00 TMED2 +++ 0.51 0.61 1.00 SPOCK1 0.44 0.66 1.00 DAK ++ 0.29 0.77 1.00 UGT8 0.14 0.89 1.00 ATP6V0C ++ N.A N.A N.A
Positive zScore = higher expression in T2D compared to NGT, Padj value was Bonferroni corrected for 20 genes associated with fasting insulin and/or HOMA IR (as ATP6V0C was not represented on the arrays), N.A = not analyzed, no probe sets represented.
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Table 4. Replication of the skeletal muscle expression and BMI associations using publicly available data.
Meta-analysis of publicly available skeletal muscle expression
BMI- T2D vs. NGT Gene expression association (BetaBMI) (P-value) (Padj-value)
MSTN +++ 0.07 7.81x10 5 1.48x10-3
RCAN2 + 0.03 1.92x10 3 0.04
BCKDHB 0.01 0.01 0.25
FHL2 ++ 0.02 0.02 0.44
RBBP6 0.01 0.03 0.61
TRIO 4.39x10 3 0.11 1.00
TMOD1 +++ 0.01 0.17 1.00
ENPEP +++ 0.01 0.18 1.00
CA2 +++ 0.01 0.31 1.00
ARID1A +++ 2.97x10 3 0.33 1.00
PITPNM1 2.96x10 3 0.47 1.00
PDIA4 +++ 2.09x10 3 0.51 1.00
RAF1 +++ 2.06x10 3 0.54 1.00
CTSF + 1.60x10 3 0.70 1.00
SPR 1.14x10 3 0.78 1.00
AGXT2L1 1.65x10 3 0.81 1.00
WSB2 + 8.74x10 4 0.86 1.00
SH3GLB2 +++ 8.56x10 5 0.98 1.00
CD46 +++ 7.08x10 5 0.99 1.00
ARHGAP17 +++ N.A N.A N.A
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Padj value was Bonferroni corrected for 19 genes associated with fasting insulin and/or HOMA IR (as ARHGAP17 was not represented on the arrays), N.A = not analyzed, no probe sets represented.
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Figure1. LocusZoom plots of three selected eQTL regional association results for the genes (A) CRYM, (B) ERAP2 and (C) PFKM. 117x242mm (300 x 300 DPI)
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Figure 2. Distance distribution from each gene's most strongly associated SNP to its TSS. 220x183mm (72 x 72 DPI)
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Figure 3. Functional annotation of eQTL SNPs. The bar plot indicates the percentage of eQTL SNPs per gene functional unit and its enrichment or depletion relative to all SNPs tested in this study. The SNPs were annotated with snpEff. Downstream and upstream regions are defined as being within 5kb distance from a gene. 254x190mm (300 x 300 DPI)
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Supplemental information
Genotyping Genotyping of the Malmö Men and Malmö Training Intervention studies was done simultaneously at Lund University on HumanOmniExpress 12v1 C chips (Illumina, California, USA) and genotype calling was done with the Illumina Genome studio software. A total of 103403 and 81424 SNPs from the Malmö Men and Malmö Training Intervention studies failed to pass the QC. Details on exclusion criteria and a more detailed decomposition of reasons for failure are available in the supplement. Genotyping of the MuTHER samples was completed as part of the larger TwinsUK dataset (N~6000) and was done with a combination of Illumina arrays (HumanHap300, HumanHap610Q, 1M Duo and 1.2MDuo 1M). Intensity data for each of the three arrays were pooled separately (with 1M Duo and 1.2MDuo 1M pooled together), and genotypes were called with the Illuminus26 calling algorithm.
Genotyping quality control The following exclusion criteria were applied to samples from all three cohorts: (i) sample call rate <98%; (ii) evidence of non European ancestry as assessed by principal component analysis or multidimensionality scaling comparisons with HapMap populations; (iii) genome wide heterozygosity outliers detected on a plot of missingness vs. heterozygosity at genome wide level. In the Malmö cohorts, we also checked for (iv) gender mismatches using standard settings in PLINK; (v) relatedness between individuals using the IBD test as implemented in PLINK, excluding individuals from pairs with pi_hat ≥0.2 or outliers based upon average pi_hat. In the MuTHER dataset, the (v) IBD test was used to identify samples suggestive of sample identity errors. Exclusion criteria for SNPs were: (i) SNP call rate < 98% (Malmö) or SNP call rate < 97% (SNPs with MAF ≥ 5%) or SNP call rate <99% (SNPs with 1% ≤ MAF < 5%) (MuTHER); (ii) Hardy Weinberg equilibrium test performed in a set of unrelated samples, p values < 5.7*10 7 (SNPs with MAF ≥ 5%) or p values < 0.0001 (SNPs with 1% ≤ MAF < 5%) (Malmö) or Hardy Weinberg equilibrium test p values < 5.7*10 7 (MuTHER); (iii) MAF < 1% assessed in a set of unrelated samples.
Imputation In each study, we imputed individual genotypes to 1000 Genomes data, to provide a common set of SNPs for analysis. Imputation was carried out using IMPUTE2 (https://mathgen.stats.ox.ac.uk/impute/impute_v2) and the June 2011 release of the 1000 Genomes Phase I panel was used as a reference panel (http://mathgen.stats.ox.ac.uk/impute/data_download_1000G_phase1_interim). The MuTHER samples were imputed as part of the TwinsUK dataset (N~6000) and pre phasing of this data was carried out using IMPUTE2. The two Malmö cohorts were pre phased using SHAPEIT (http://www.shapeit.fr). For all analyses, probabilistic genotypes were used for the subsequent analyses. After imputation, SNPs were filtered using a minor allele frequency (MAF) > 5% and an IMPUTE2 info value of >0.8.
Gene expression Due to differences in gene expression arrays used across studies, we adopted a gene centric approach to our analyses, selecting probes in each study that mapped to the 7006 genes common to all three. All probes were mapped to NCBI build 37 of the Genome Browser and only uniquely mapping probes, with no mismatches and either Diabetes Page 42 of 64
an Ensembl or RefSeq ID, were kept for analysis. Probes encompassing a common SNP (MAF>5%) were excluded from all studies. Expression profiling of the MuTHER samples was performed using the Illumina Human HT 12 V3 BeadChips (Illumina Inc.), including more than 48000 probes. 200ng of total RNA was processed according to the protocol supplied by Illumina. All samples were randomized before array hybridization, and technical replicates were always hybridized on different BeadChips. Log2 transformed expression signals were normalized with quantile normalization of the replicates of each individual, followed by quantile normalization across all individuals. Post–quality control expression profiles were obtained for 42 individuals. The Illumina probe annotations were mapped to build 37 using the R package (illuminaHumanv3.db). After applying the probe inclusion criteria above, 9633 probes were left in the analysis. Gene expression analysis in the Malmo Men cohort was assayed using the Affymetrix HG U133a platform, while the Affymetrix custom array NUGOhs1a520180 was used for the Malmo Intervention cohort (Affymetrix Inc.). All probes were mapped to NCBI build 37 and annotated with R packages hgu133a.db and nugohs1a520180.db. Log2 transformed expression signals of both Malmo cohorts were normalized using RMA (Robust Multichip Average) expression measure (16).
Annotation and enrichment analysis The program SnpEff (1) was used to annotate and predict the effects of both our eQTL SNPs and all cis SNPs tested in our study. We assed the enrichment of low p values closer to TSSs using the Exact Binomial Test implemented in the R statistical environment. Specifically, we performed an exact test of a null hypothesis about the probability of success in a Bernoulli experiment, in which the number of successes corresponds to the number of eQTL SNPs where the associated gene is the closest. We only tested eQTL SNPs where the eQTL gene and the nearest gene were probed by our meta analysis (within 250kb of TSS). The Test of Equal or Given Proportions, implemented in R, was used to test the null hypothesis that the proportions (probabilities of success) are the same, or that they are equal certain given values. Specifically, this test was performed to test eQTL SNP enrichment for each gene functional unit and for functional RegulomeDB categories in comparison with the background of all cis SNPs tested.
Gene expression analysis of publicly available data sets Raw expression data for ten public microarray studies were retrieved from ArrayExpress (http://www.ebi.ac.uk/arrayexpress/), all based on the Affymetrix HG U133+2 array platform (Table S5). Data normalization was carried out with Robust Probabilistic Averaging (RPA) (2) with probesets mapped to Ensembl gene identifiers using the R/Bioconductor package customCDF v.1.2.1 (3), and quality controlled using the arrayQualityMetrics v3.14.0 (4). None of the selected samples failed quality control. Differential expression between subjects with type 2 diabetes (T2D) and normoglycemic (NGT) individuals was estimated by meta analysis of three datasets with full annotation for these groups: E GEOD 18732, E GEOD 19420, and E GEOD 25462, including 102 T2D and 87 NGT samples. The datasets were individually normalized with RPA and meta analysed using the geneMeta R/Bioconductor package [http://www.bioconductor.org/packages/release/bioc/html/GeneMeta.html]. Association to BMI was calculated by retrieving all samples within the seven selected datasets, with annotation for BMI (Table S3), from the HG U133+2 arrays and Page 43 of 64 Diabetes
normalized as a single dataset using RPA, followed by a linear regression for BMI adjusted for sex.
Integration of genome-wide association data from MAGIC and DIAGRAM To investigate whether our significant eQTL are enriched for associations to T2D and glycaemic traits, we did a lookup of our significant eQTLs SNPs in GWAS data from the DIAGRAM consortium for T2D (5), and from the MAGIC consortium for HOMA IR (6), 2 hour glucose, 2 hour glucose adjusted for BMI, fasting insulin, fasting insulin adjusted for BMI, fasting glucose and fasting glucose adjusted for BMI (7). We did a binomial sign test to quantify the extent of the enrichment in each of the GWAS studies, using only independent eQTL SNPs, by calculating the probability of observing the number of GWAS SNPs we found with a p value < 0.05 amongst our eQTL SNPs. We also noted any eQTL SNP that was associated with any of the DIAGRAM and MAGIC phenotypes with genome wide significance.
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Kao WH, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC, Barroso I: New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010;42:105-116 7. Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J, Magi R, Strawbridge RJ, Rehnberg E, Gustafsson S, Kanoni S, Rasmussen-Torvik LJ, Yengo L, Lecoeur C, Shungin D, Sanna S, Sidore C, Johnson PC, Jukema JW, Johnson T, Mahajan A, Verweij N, Thorleifsson G, Hottenga JJ, Shah S, Smith AV, Sennblad B, Gieger C, Salo P, Perola M, Timpson NJ, Evans DM, Pourcain BS, Wu Y, Andrews JS, Hui J, Bielak LF, Zhao W, Horikoshi M, Navarro P, Isaacs A, O'Connell JR, Stirrups K, Vitart V, Hayward C, Esko T, Mihailov E, Fraser RM, Fall T, Voight BF, Raychaudhuri S, Chen H, Lindgren CM, Morris AP, Rayner NW, Robertson N, Rybin D, Liu CT, Beckmann JS, Willems SM, Chines PS, Jackson AU, Kang HM, Stringham HM, Song K, Tanaka T, Peden JF, Goel A, Hicks AA, An P, Muller- Nurasyid M, Franco-Cereceda A, Folkersen L, Marullo L, Jansen H, Oldehinkel AJ, Bruinenberg M, Pankow JS, North KE, Forouhi NG, Loos RJ, Edkins S, Varga TV, Hallmans G, Oksa H, Antonella M, Nagaraja R, Trompet S, Ford I, Bakker SJ, Kong A, Kumari M, Gigante B, Herder C, Munroe PB, Caulfield M, Antti J, Mangino M, Small K, Miljkovic I, Liu Y, Atalay M, Kiess W, James AL, Rivadeneira F, Uitterlinden AG, Palmer CN, Doney AS, Willemsen G, Smit JH, Campbell S, Polasek O, Bonnycastle LL, Hercberg S, Dimitriou M, Bolton JL, Fowkes GR, Kovacs P, Lindstrom J, Zemunik T, Bandinelli S, Wild SH, Basart HV, Rathmann W, Grallert H, Maerz W, Kleber ME, Boehm BO, Peters A, Pramstaller PP, Province MA, Borecki IB, Hastie ND, Rudan I, Campbell H, Watkins H, Farrall M, Stumvoll M, Ferrucci L, Waterworth DM, Bergman RN, Collins FS, Tuomilehto J, Watanabe RM, de Geus EJ, Penninx BW, Hofman A, Oostra BA, Psaty BM, Vollenweider P, Wilson JF, Wright AF, Hovingh GK, Metspalu A, Uusitupa M, Magnusson PK, Kyvik KO, Kaprio J, Price JF, Dedoussis GV, Deloukas P, Meneton P, Lind L, Boehnke M, Shuldiner AR, van Duijn CM, Morris AD, Toenjes A, Peyser PA, Beilby JP, Korner A, Kuusisto J, Laakso M, Bornstein SR, Schwarz PE, Lakka TA, Rauramaa R, Adair LS, Smith GD, Spector TD, Illig T, de Faire U, Hamsten A, Gudnason V, Kivimaki M, Hingorani A, Keinanen-Kiukaanniemi SM, Saaristo TE, Boomsma DI, Stefansson K, van der Harst P, Dupuis J, Pedersen NL, Sattar N, Harris TB, Cucca F, Ripatti S, Salomaa V, Mohlke KL, Balkau B, Froguel P, Pouta A, Jarvelin MR, Wareham NJ, Bouatia-Naji N, McCarthy MI, Franks PW, Meigs JB, Teslovich TM, Florez JC, Langenberg C, Ingelsson E, Prokopenko I, Barroso I: Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 2012;44:991-1005
MuTHER consortium
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Members of the MuTHER (Multiple Tissue Human Expression Resource) Consortium Kourosh R. Ahmadi1, Chrysanthi Ainali2, Amy Barrett3, Veronique Bataille1, Jordana T. Bell1,4, Alfonso Buil5, Panos Deloukas6, Emmanouil T. Dermitzakis5, Antigone S. Dimas4,5, Richard Durbin6, Daniel Glass1, Elin Grundberg1,6, Neelam Hassanali3, Åsa K. Hedman4, Catherine Ingle6, Sarah Keildson4, David Knowles7, Maria Krestyaninova8, Cecilia M. Lindgren4, Christopher E. Lowe9,10, Mark I. McCarthy3,4,11, Eshwar Meduri1,6, Paola di Meglio12, Josine L. Min4, Stephen B. Montgomery5, Frank O. Nestle12, Alexandra C. Nica5, James Nisbet6, Stephen O’Rahilly9,10, Leopold Parts6, Simon Potter6, Magdalena Sekowska6, So Youn Shin6, Kerrin S, Small1, 6, Nicole Soranzo6, 1, Tim D. Spector1, Gabriela Surdulescu1, Mary E. Travers3, Loukia Tsaprouni6, Sophia Tsoka2, Alicja Wilk6, Tsun Po Yang6, Krina T. Zondervan4
Affiliations 1.Department of Twin Research and Genetic Epidemiology, King's College London, London, UK 2. Department of Informatics, School of Natural and Mathematical Sciences, King’s College London, Strand, London WC2R 2LS. 3. Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Churchill Hospital, Oxford, UK 4. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK. 5. Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland 6.Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK 7. University of Cambridge, Cambridge, UK 8. European Bioinformatics Institute, Hinxton, UK 9. University of Cambridge Metabolic Research Labs, Institute of Metabolic Science Addenbrooke’s Hospital Cambridge, UK 10. Cambridge NIHR Biomedical Research Centre, Addenbrooke’s Hospital, Cambridge, UK 11. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK 12. St. John's Institute of Dermatology, King's College London, London, UK
Supplementary figures and tables
Figure S1. Overview of methods
Table S1. Top SNP per gene for all significant (FDR<5%, p value=1.96E 05) eQTLs identified in the meta analysis. Information on bimodality of gene expression, distance of eQTL SNPs to transcription start sites (TSS), functional annotation of eQTL SNPs and previously reported eQTLs are also detailed.
Table S2. Significant (FDR < 5%, p value = 1.34E 04) associations identified between gene expression (standardized units) and fasting plasma insulin, HOMA IR and BMI through meta analysis.
Table S3. Significant (FDR < 5%, p value = 7.14E 05) associations identified between gene expression (standardized units) and fasting plasma insulin adjusted for BMI, HOMA IR adjusted for BMI and BMI through meta analysis.
Table S4. Extended analysis in the MM study of the influence of the PFKM eQTL SNP (rs4547172) on skeletal muscle metabolic phenotypes.
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Table S5. Public microarray gene expression data used.
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Figure S1. Descripion of data analysis 254x190mm (300 x 300 DPI)
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Table S1. Annotation of the 287 SNP-gene expression associations
gene Bimodal SNP relative Regulo SNP within in our SNP_pos eQTL seen SNP (dbSNP137) Gene Allele1 Allele2 Weight Zscore Pvalue Direction Het_I 2 Het_ChiSq Het_Df Het_Pval FDR gene transcript meDB which gene gene - TSS before expression location score list
rs2238479 CRYM a g 95,5 -11,309 1,18E-29 --- 52 8,337 2 0,01548 2,46E-22 CRYM yes 13 134 intron 6
rs2296805 PEX6 t g 95,5 9,065 1,24E-19 +++ 86,3 29,248 2 4,46E-07 2,90E-13 GNMT - -2 852 intron 1f yes
rs7810193 SEC61G t c 95,5 -8,896 5,78E-19 --- 74,3 15,569 2 0,0004162 4,15E-13 - - -68 068 intergenic 6
rs251851 ERAP2 a t 95,5 8,434 3,35E-17 +++ 74,8 15,875 2 0,0003571 6,65E-12 LNPEP yes 113 375 intron 6 yes
rs138616686 NQO2 a g 95,5 -8,233 1,83E-16 --- -67,6 2,387 2 0,3032 2,90E-11 NQO2 yes 3 904 intron yes
rs12483950 DDT c g 95,5 8,09 5,96E-16 +++ 77,5 17,814 2 0,0001354 9,39E-11 - - -17 378 downstream 5 yes
rs10073049 PFDN1 a c 95,5 -8,089 6,03E-16 --- 34,4 6,099 2 0,04739 9,44E-11 CYSTM1 - -18 515 intron 6
downstream, rs80317390 NUDT2 a g 95,5 7,988 1,37E-15 +++ 0 1,049 2 0,5918 1,25E-10 - - 49 182 5 yes upstream downstream, rs58655904 LDHC t c 95,5 7,792 6,60E-15 +++ 68,5 12,691 2 0,001755 3,97E-10 yes - - -1 662 4 upstream
rs1955657 SERPINA5 a g 95,5 7,718 1,19E-14 +++ 0 0,642 2 0,7252 6,69E-10 SERPINA3 - 24 918 intron 6
rs7503161 EIF5A a c 95,5 -7,418 1,19E-13 --- 70 13,347 2 0,001264 5,62E-09 - - -2 353 upstream yes
rs2278022 ATMIN t c 95,5 7,357 1,89E-13 +++ 55 8,895 2 0,01171 8,74E-09 ATMIN yes 6 035 missense
IFNGR2, rs2409494 TMEM50B t c 95,5 -6,828 8,61E-12 --- 71,9 14,23 2 0,000813 2,57E-07 yes 3 616 intron yes TMEM50B
rs3863496 TIMM22 t c 95,5 6,756 1,42E-11 +++ 70,4 13,527 2 0,001155 4,02E-07 yes TIMM22 yes 2 807 intron yes
rs2268177 CDC42 a t 95,5 6,624 3,49E-11 +-+ 93,4 60,627 2 6,84E-14 9,17E-07 CDC42 yes 36 291 intron 5 yes
rs9859086 LRRFIP2 a c 95,5 6,593 4,30E-11 +++ 0 0,548 2 0,7605 1,11E-06 LRRFIP2 yes 52 075 intron yes
rs4374997 PIWIL2 a c 95,5 6,544 6,00E-11 +++ 0 0,469 2 0,7909 1,44E-06 yes PIWIL2 yes 36 517 intron 5 yes
rs1055138 FAM149A c g 95,5 6,488 8,69E-11 +++ 28,8 5,62 2 0,06021 2,05E-06 CYP4V2 - 47 405 missense 4
rs8076632 SPATA20 c g 95,5 -6,475 9,46E-11 --- 18,1 4,885 2 0,08696 2,22E-06 SPATA20 yes 1 500 missense 1f yes
rs7008207 EPHX2 a c 95,5 6,356 2,07E-10 +++ -52,9 2,616 2 0,2704 4,50E-06 EPHX2 yes 42 547 intron 6
rs11089856 H1F0 a c 95,5 -6,26 3,85E-10 --- 42,4 6,939 2 0,03114 8,11E-06 GCAT - 4 671 intron 5
rs178092 SNAP29 a c 95,5 6,25 4,11E-10 +++ 37,7 6,419 2 0,04038 8,51E-06 - - 45 001 intergenic yes
rs403908 CARKD a g 95,5 -6,101 1,05E-09 --- 12 4,547 2 0,103 1,88E-05 CARKD yes 17 752 intron 5 yes
rs10872251 FABP7 a g 95,5 6,097 1,08E-09 +-+ 87,3 31,5 2 1,45E-07 1,91E-05 yes FABP7 yes 3 449 intron 6
rs10861347 KIAA1033 t c 95,5 6,091 1,12E-09 +++ 78 18,148 2 0,0001146 1,98E-05 ALDH1L2 - -24 081 intron 3a
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rs2491020 KIAA1279 a g 95,5 -6,085 1,17E-09 --- 37,2 6,368 2 0,04143 2,05E-05 - - 30 090 downstream 4 yes
rs1131017 RPS26 c g 95,5 -5,999 1,98E-09 --- 80,2 20,194 2 4,12E-05 3,28E-05 RPS26 yes 244 5´UTR 4 yes
rs4443587 KLHDC10 t g 95,5 5,95 2,67E-09 +++ 78,2 18,332 2 0,0001045 4,34E-05 - - 68 170 downstream 6
rs1271970 RPP40 a g 95,5 -5,886 3,95E-09 --- 42,9 7,004 2 0,03014 6,22E-05 RPP40 yes -207 downstream 6 yes
rs34270592 AMFR a c 95,5 5,884 3,99E-09 +++ 42,9 7,009 2 0,03006 6,27E-05 AMFR yes 36 315 intron 6
rs17137288 TYW1 a g 95,5 -5,849 4,94E-09 --- 46,2 7,437 2 0,02427 7,65E-05 TYW1 yes 120 179 intron 6 yes
rs11779069 LY96 t c 95,5 -5,804 6,49E-09 --- 44 7,14 2 0,02815 9,88E-05 UBE2W yes -134 803 intron 6
rs8070763 STAT3 t c 95,5 -5,784 7,29E-09 --- 67,9 12,467 2 0,001963 0,00010992 STAT3 yes 71 054 intron
rs12433361 ARG2 a t 95,5 5,761 8,38E-09 +++ 32,4 5,92 2 0,05182 0,00012319 ARG2 yes 8 488 intron yes
rs141761164 PSMG1 t c 95,5 -5,732 9,90E-09 --- 0 1,483 2 0,4764 0,00014325 - - -18 253 intergenic
rs7646106 ARL8B t c 95,5 5,694 1,24E-08 +++ 0 0,088 2 0,9572 0,00016938 EDEM1 yes 94 042 3´UTR
rs3762815 PDCD6IP a g 95,5 5,646 1,64E-08 +++ 70 13,34 2 0,001269 0,00021789 PDCD6IP yes 23 658 intron 5
rs17354693 HYDIN a g 95,5 5,595 2,21E-08 +++ 28,8 5,621 2 0,06019 0,0002602 MARVELD3 - 822 416 intron 5 yes
rs7487568 CHD4 t c 95,5 5,593 2,24E-08 +++ 0 1,781 2 0,4105 0,00026276 ANO2 - -686 186 intron
rs3816864 LPPR2 t g 95,5 -5,582 2,37E-08 --- -12,9 3,542 2 0,1702 0,00027352 RAB3D - -19 541 intron 5 yes
rs6537035 ZNF330 a g 95,5 5,581 2,39E-08 +++ 51,6 8,262 2 0,01607 0,00027491 RNF150 - -20 349 intergenic
rs11131799 AGA a g 95,5 -5,56 2,69E-08 --- -8,7 3,678 2 0,1589 0,00028666 yes AGA yes 11 450 intron 4 yes rs113671109 MKRN2 t c 95,5 5,553 2,81E-08 +++ 0 1,01 2 0,6034 0,00029275 yes MKRN2 yes 22 292 intron 6 yes DEAF1, downstream, rs10902201 TMEM80 a g 95,5 -5,527 3,25E-08 --- 0 0,803 2 0,6693 0,0003311 EPS8L2, yes 8 968 5 upstream TMEM80 rs118129429 PCDH8 a g 95,5 -5,526 3,29E-08 --- 0 1,128 2 0,5688 0,0003311 LECT1 yes -136 623 intron 6 rs11237443 GAB2 a c 95,5 -5,519 3,41E-08 --- 0 1,832 2 0,4 0,00034023 GAB2 yes 78 754 intron yes
rs4737751 ARMC1 a g 95,5 5,515 3,49E-08 +++ 57 9,298 2 0,009572 0,00034776 MTFR1 yes 85 543 intron
rs10816774 CTNNAL1 a g 95,5 -5,475 4,36E-08 --- 40,2 6,688 2 0,0353 0,00042674 EPB41L4B yes 232 151 intron 5
rs4747339 CACNB2 t c 95,5 -5,453 4,95E-08 --- -15,2 3,473 2 0,1761 0,00045885 CACNB2 yes 161 818 intron rs35962983 NR1D2 t c 95,5 -5,399 6,70E-08 --- 0 1,292 2 0,524 0,00059798 NR1D2 yes 24 432 intron
rs6781329 HRG a g 95,5 -5,386 7,20E-08 --- 0 1,443 2 0,486 0,0006349 - - -203 576 intergenic 6
rs9428015 CCBL2 a g 95,5 -5,353 8,65E-08 --- 0 1,046 2 0,5928 0,00074335 PKN2 - -132 115 intron 5 yes
rs114624166 PRR16 t g 95,5 -5,316 1,06E-07 --- 41,2 6,799 2 0,03339 0,00086608 - - -35 365 intergenic 6 yes
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rs2433611 GATM a c 95,5 -5,274 1,34E-07 --- 0 0,583 2 0,747 0,00102064 GATM yes 12 332 intron
rs62092421 TCF4 t g 95,5 5,243 1,58E-07 +++ 54,6 8,809 2 0,01222 0,00113307 - - -23 468 intergenic 5
rs11668620 TMEM161A c g 95,5 -5,242 1,59E-07 --- -98,2 2,018 2 0,3646 0,00114013 SLC25A42 yes -11 939 intron 5 yes
rs12048758 CNIH4 a g 95,5 5,235 1,65E-07 +++ 0 0,137 2 0,934 0,00117581 WDR26 yes 34 454 intron
rs111584059 DOPEY2 a g 95,5 5,229 1,70E-07 +++ -59,3 2,51 2 0,285 0,00120534 DOPEY2 yes 25 465 intron 6 yes
rs150478765 WDR41 a g 95,5 5,217 1,82E-07 +++ 29,3 5,659 2 0,05906 0,00126536 WDR41 yes 70 691 intron
rs62580498 CDK5RAP2 a g 95,5 5,192 2,08E-07 +++ 56,7 9,237 2 0,00987 0,00141436 CDK5RAP2 yes 8 349 intron 5 yes
rs6689008 NUCKS1 t c 95,5 -5,188 2,13E-07 --- 0 1,969 2 0,3735 0,00144529 yes SLC45A3 - -32 773 intron 4
rs9819419 FYCO1 a g 95,5 -5,182 2,20E-07 --- 41,3 6,811 2 0,03319 0,00147624 - - 177 969 intergenic 5
rs7289655 GSTT1 a c 95,5 -5,168 2,37E-07 --- 82,2 22,445 2 1,34E-05 0,00158174 - - -40 910 intergenic 5 yes
rs9296398 PRPH2 t c 95,5 5,144 2,69E-07 +++ -29,2 3,095 2 0,2128 0,00177154 PRPH2 yes 9 346 intron yes
rs6572656 ATP5S a c 95,5 5,116 3,13E-07 +++ 19,8 4,987 2 0,08263 0,00201754 L2HGDH - -3 727 intron 1b yes
rs246771 KCNIP1 c g 95,5 -5,113 3,18E-07 --- 16,4 4,783 2 0,09148 0,00204533 DOCK2 yes -658 847 intron 6
rs2490273 ZNF593 t g 95,5 5,112 3,19E-07 +-+ 78,3 18,405 2 0,0001008 0,00204985 - - -6 629 downstream 5
rs3120065 OSGEP t c 95,5 -5,09 3,58E-07 --- 19,5 4,971 2 0,08329 0,00225477 OSGEP yes 4 506 intron yes
rs2359614 EIF2B2 a t 95,5 -5,059 4,22E-07 -+- 66,1 11,786 2 0,002758 0,00253322 TMED10 - 160 173 intron 5 yes
rs7657455 MFAP3L c g 95,5 5,048 4,46E-07 +++ 80,6 20,608 2 3,35E-05 0,00266691 - - 51 096 intergenic 5 yes
rs17818245 COX4I1 a g 95,5 5,042 4,60E-07 +++ -0,1 3,995 2 0,1357 0,00272888 yes - - 241 929 intergenic
rs59633892 CDKAL1 t c 95,5 -5,016 5,28E-07 --- 60,3 10,068 2 0,006514 0,00309946 - - 698 658 downstream yes
rs10823398 TSPAN15 a c 95,5 -5,003 5,64E-07 --- 0 0,028 2 0,9862 0,00329182 - - 59 138 downstream
rs72940355 FECH a g 95,5 -4,984 6,22E-07 --- 30,9 5,793 2 0,05523 0,00358652 FECH yes 20 685 intron 6
LRRC37A2, 17-44619118 NSF a g 95,5 4,98 6,35E-07 +++ 0 0,575 2 0,7502 0,00364945 - -48 916 intron ARL17A
rs35030877 COG7 a g 95,5 -4,977 6,46E-07 --- -7,5 3,722 2 0,1555 0,00370233 - - -4 785 downstream 6
rs75000818 SGCB a g 95,5 4,973 6,59E-07 +++ 0 1,685 2 0,4306 0,00377028 - - 196 337 intergenic
rs1899288 CPNE3 t c 95,5 4,967 6,82E-07 +++ 71,9 14,226 2 0,0008143 0,00388201 CPNE3 yes 38 671 intron
rs148654891 FIBP a g 95,5 -4,952 7,34E-07 --- -19,1 3,359 2 0,1865 0,00410278 - - -75 947 intergenic
rs10200594 WIPF1 a g 95,5 -4,95 7,43E-07 --- -72,9 2,313 2 0,3146 0,00410278 - - -12 812 upstream 4
rs8007801 EML1 c g 95,5 4,942 7,72E-07 +++ -10 3,638 2 0,1622 0,00422405 - - 393 986 intergenic 5
Diabetes Page 52 of 64
rs78701930 HMBOX1 t c 95,5 4,935 8,00E-07 +++ 57,3 9,358 2 0,009289 0,00436969 KIF13B yes 215 862 intron
rs2520180 C17orf48 t c 95,5 4,931 8,17E-07 +++ -12,3 3,561 2 0,1685 0,00443719 SCO1 - -6 597 intron yes
rs12086750 PPIE c g 95,5 4,924 8,48E-07 +++ 70,5 13,546 2 0,001144 0,0045865 PPIE yes 5 952 intron 5 yes rs11204702 ARNT a c 95,5 4,924 8,49E-07 +++ 80,3 20,316 2 3,88E-05 0,00459016 GOLPH3L yes -118 673 intron
rs61752479 AMPD1 a g 95,5 -4,922 8,57E-07 --- 64,8 11,369 2 0,003398 0,00460719 AMPD1 yes 15 535 missense
rs35044129 PDLIM5 a t 95,5 4,917 8,79E-07 +++ 27,8 5,538 2 0,06273 0,00469205 PDLIM5 yes 139 807 intron 6
rs73181350 PCK1 c g 95,5 4,913 8,97E-07 ++- 90,8 43,446 2 3,68E-10 0,00476031 - - 512 190 intergenic
11-36166998 TRAF6 a g 95,5 -4,905 9,34E-07 --- 60 10,009 2 0,006708 0,00493152 LDLRAD3 - -338 318 intron
rs13267051 CCDC25 a g 95,5 4,896 9,76E-07 +++ 0 1,28 2 0,5273 0,00511079 CCDC25 yes 22 484 intron 5
rs935074 SPSB1 a g 95,5 4,876 1,08E-06 +++ 0 0,071 2 0,9652 0,00558444 CTNNBIP1 yes 577 027 intron 5
rs2297811 CYP4B1 t c 95,5 4,876 1,08E-06 +++ 53,5 8,603 2 0,01355 0,00559199 CYP4B1 yes 15 120 intron 6 yes rs56007403 LGMN t c 95,5 -4,872 1,11E-06 --- 0 0,469 2 0,791 0,00568303 yes SLC24A4 - -247 484 intron 5
rs1218556 LENEP t g 95,5 -4,861 1,17E-06 --- -1 3,96 2 0,1381 0,00594316 KCNN3 yes -144 949 intron 5
rs72670883 ZCCHC11 t c 95,5 -4,85 1,23E-06 --- 0 0,529 2 0,7675 0,006232 SLC1A7 - 709 213 intron
rs76611793 CPVL t c 95,5 -4,836 1,32E-06 --- -5,2 3,802 2 0,1494 0,00661326 CPVL, CHN2 yes 146 872 intron 5
rs2543519 MPST a g 95,5 -4,833 1,35E-06 --- 0 1,268 2 0,5305 0,00671862 TMPRSS6 yes 64 568 intron 5
rs147990233 RPS16 a t 95,5 4,823 1,41E-06 +++ 54,2 8,728 2 0,01273 0,00698077 SUPT5H yes 28 629 intron yes
rs11119673 KCNH1 a t 95,5 -4,812 1,49E-06 --- -65,7 2,414 2 0,2992 0,00729092 KCNH1 yes 381 442 intron 5
rs686618 PAMR1 t c 95,5 -4,811 1,50E-06 --- 0 1,812 2 0,4042 0,00730873 - - 113 019 intergenic rs56019197 TMEM110 t c 95,5 -4,81 1,51E-06 --- 0 0,264 2 0,8764 0,0073618 TMEM110 yes 3 982 intron
rs2032890 ERAP1 a c 95,5 4,797 1,61E-06 +++ 65,3 11,523 2 0,003146 0,00778284 ERAP1 yes 10 965 intron yes
rs1033181 MYCT1 a g 95,5 -4,791 1,66E-06 --- 0 1,768 2 0,4131 0,00795101 ESR1 yes -824 208 intron
rs2517145 PDGFRL a c 95,5 -4,769 1,85E-06 --- 0 0,94 2 0,6251 0,00875921 PDGFRL yes 11 892 intron 6 rs11057389 ABCB9 t c 95,5 -4,769 1,85E-06 --- 14,9 4,7 2 0,09537 0,00877216 DNAH10 - 968 605 intron
rs6739468 CRIPT t g 95,5 4,761 1,93E-06 +++ 0 1,336 2 0,5127 0,00907339 CRIPT yes 5 056 intron yes
rs113265714 ALAS1 a g 95,5 4,752 2,02E-06 +++ 0 1,976 2 0,3724 0,00941876 WDR82 - 77 071 intron
rs7503584 PNPO a g 95,5 -4,751 2,03E-06 --- 0 1,26 2 0,5327 0,00945275 CDK5RAP3 - 36 148 intron 1f
rs2194762 ABI2 t c 95,5 -4,743 2,11E-06 --- 44,3 7,181 2 0,02758 0,00980376 - - 841 720 intergenic yes
Page 53 of 64 Diabetes
rs11169368 CSRNP2 t c 95,5 4,74 2,14E-06 +++ -1,8 3,928 2 0,1403 0,00993279 - - -755 947 intergenic
rs2114308 MMP20 a g 95,5 4,731 2,23E-06 +++ 0 1,267 2 0,5307 0,01029151 YAP1 yes -399 232 intron 6
rs8010851 WARS t c 95,5 4,73 2,25E-06 +++ 18,9 4,931 2 0,08495 0,0103516 WARS yes 24 516 intron 6
rs6443851 MCCC1 a g 95,5 4,726 2,29E-06 +++ 17,1 4,823 2 0,08968 0,01051029 MCCC1 yes 86 044 upstream 6
rs4268913 TMBIM1 a g 95,5 -4,719 2,37E-06 --- 0 1,809 2 0,4047 0,01082444 PNKD - 39 959 intron 6
rs35273276 ZBTB16 t g 95,5 4,718 2,39E-06 +++ 0 0,419 2 0,8109 0,01086709 yes - - 936 909 intergenic
rs10829610 MGMT a g 95,5 4,716 2,40E-06 +++ -55,2 2,577 2 0,2756 0,01093736 MGMT yes 165 615 intron 6 yes
rs200680626 B3GALNT1 a g 95,5 -4,713 2,44E-06 --- -63,5 2,447 2 0,2942 0,01109586 yes - - 848 718 intergenic
rs12510251 ING2 a g 95,5 -4,693 2,70E-06 --- -48,6 2,691 2 0,2604 0,01191568 ODZ3 yes -811 635 intron 6
rs11265398 S100A9 a g 95,5 -4,69 2,73E-06 --- 89,1 36,632 2 1,11E-08 0,01203476 UBAP2L yes 905 328 intron 5
rs7534861 MOSC1 a g 95,5 -4,689 2,75E-06 --- 0 1,138 2 0,566 0,01211015 MARC1 - 25 862 intron 3a
rs75701904 KLHL2 t c 95,5 -4,687 2,77E-06 --- -19,5 3,346 2 0,1877 0,01218174 - - 318 830 intergenic
rs13275128 MSRA c g 95,5 -4,685 2,80E-06 --- 0 1,129 2 0,5687 0,01233604 MSRA yes 170 804 intron 6 yes
rs7725534 MRPL22 t c 95,5 -4,68 2,87E-06 --- 0 0,802 2 0,6698 0,01259078 - - 403 490 intergenic
rs10509573 IFIT2 t c 95,5 -4,674 2,96E-06 --- 0 1,034 2 0,5963 0,01284854 - - 127 516 downstream 6
rs4383259 ZNF415 a g 95,5 -4,667 3,06E-06 --- -22,1 3,275 2 0,1945 0,01323687 ZNF347 - 50 206 intron 5
rs140929099 COL4A2 t c 95,5 4,66 3,17E-06 +++ -33 3,007 2 0,2224 0,01364047 COL4A1 - -54 157 intron
rs3903399 RBBP5 t c 95,5 4,657 3,20E-06 +++ 64 11,101 2 0,003886 0,01376838 CNTN2 yes -13 727 intron 5
rs79079466 UNC5B t c 95,5 -4,656 3,23E-06 --- 0 1,909 2 0,385 0,01384725 CDH23 - 423 042 intron 4
rs79028889 CNGA1 a g 95,5 4,65 3,32E-06 +++ 0 0,282 2 0,8684 0,01408365 CORIN yes -319 732 intron 6
rs12978924 C19orf57 t c 95,5 4,645 3,41E-06 +++ 0 1,794 2 0,4077 0,01436931 CACNA1A yes -454 970 intron
rs12206195 SMPDL3A a c 95,5 -4,641 3,47E-06 --- 0 1,75 2 0,4169 0,01452832 - - 73 794 intergenic 6
rs10194503 DHX57 c g 95,5 -4,632 3,63E-06 --- 0 0,203 2 0,9034 0,01495591 GALM - -104 393 intron 6
rs79294885 NOP10 a c 95,5 -4,628 3,69E-06 --- -33,8 2,989 2 0,2244 0,01518109 SLC12A6 yes -4 046 intron 4
rs36094466 FOXI1 a g 95,5 4,628 3,70E-06 +++ 0 0,67 2 0,7154 0,01521513 - - 37 769 intergenic
rs2216704 PPWD1 a g 95,5 4,623 3,79E-06 +++ 0 0,139 2 0,9329 0,01543906 - - -921 047 intergenic 6 yes
rs78291594 PRPSAP2 a g 95,5 4,62 3,85E-06 +++ 0 0,428 2 0,8075 0,01562557 FAM18B1 - -69 161 intron 5
rs10751259 UVRAG t c 95,5 4,619 3,86E-06 +++ 0,2 4,006 2 0,1349 0,01568339 LRRC32 yes 730 661 intron 5
Diabetes Page 54 of 64
rs114026170 PPP2R2D c g 95,5 4,616 3,92E-06 +++ 66,6 11,968 2 0,002519 0,01585021 PPP2R2D yes 18 892 intron 5
rs62179167 AGPS t c 95,5 -4,616 3,92E-06 --- -80,8 2,213 2 0,3307 0,01585117 OSBPL6 - 902 043 intron 6
rs7534670 USP1 a g 95,5 -4,613 3,97E-06 --- 0 0,969 2 0,616 0,01602956 ALG6 yes 963 636 intron 6 rs12653735 COMMD10 a c 95,5 -4,605 4,13E-06 --- 0 0,648 2 0,7234 0,01646018 COMMD10 yes 301 808 intron 5
rs8062453 DEXI t g 95,5 -4,603 4,16E-06 --- 0 0,247 2 0,8839 0,01650584 yes LITAF yes 651 635 intron
rs4740353 C9orf78 t c 95,5 -4,602 4,18E-06 --- -7,6 3,718 2 0,1558 0,01650584 HMCN2 - 658 805 intron 5
rs62223782 DYRK1A t c 95,5 4,6 4,24E-06 +++ -94,1 2,061 2 0,3569 0,01655952 HLCS yes -573 641 intron
-1 012 rs8063558 ABAT t c 95,5 -4,596 4,30E-06 --- -15,5 3,463 2 0,177 0,01676633 - - intergenic 6 732
rs1887353 TPP2 a g 95,5 -4,594 4,34E-06 --- -25,4 3,19 2 0,2029 0,01687961 - - -12 082 intergenic 5
rs56039808 NFKBIB t c 95,5 4,592 4,39E-06 +++ 0 1,832 2 0,4001 0,01704252 yes SIPA1L3 yes -827 407 intron 5
rs184005941 THAP7 a c 95,5 -4,592 4,39E-06 --- 0 0,439 2 0,8031 0,0170478 MAPK1 yes 774 756 intron
rs1036951 SLC22A3 c g 95,5 4,591 4,41E-06 +++ 0 0,456 2 0,7963 0,01712683 IGF2R yes -309 342 intron yes
rs7334264 MPHOSPH8 c g 95,5 4,591 4,42E-06 +++ 0 1,483 2 0,4765 0,01715148 - - 641 645 intergenic
rs2332313 COX11 t g 95,5 -4,588 4,48E-06 --- 0 1,67 2 0,4339 0,01735707 TOM1L1 - -10 433 intron 5
rs2811555 GLUL a g 95,5 -4,587 4,49E-06 --- -83,6 2,178 2 0,3365 0,01738867 NMNAT2 yes 958 681 intron 6
rs9955053 WDR7 a t 95,5 -4,587 4,51E-06 --- -21,9 3,281 2 0,1939 0,0174325 - - 382 825 downstream 6
rs74646715 MRS2 a g 95,5 4,585 4,54E-06 +++ -59,5 2,507 2 0,2855 0,01755546 MRS2 yes 18 587 intron 6
rs2232669 POLR1D t c 95,5 -4,579 4,66E-06 --- -62 2,469 2 0,291 0,01796695 POLR1D yes -420 intron 4
rs12171327 EXOSC9 a g 95,5 4,577 4,71E-06 +++ 0 1,981 2 0,3715 0,01811298 - - -70 791 intergenic 6
rs7851652 LHX3 t c 95,5 -4,575 4,76E-06 --- -85,4 2,157 2 0,34 0,01826053 - - -574 982 downstream 6
rs61946623 FAM48A a g 95,5 -4,574 4,78E-06 --- 9 4,397 2 0,111 0,01832717 - - 530 953 intergenic 5
rs9535526 RNASEH2B t c 95,5 -4,573 4,82E-06 --- 0 0,784 2 0,6756 0,01843265 RNASEH2B yes 632 intron 1b yes
rs4938447 PHLDB1 a c 95,5 4,569 4,90E-06 +++ 0 1,372 2 0,5037 0,01869764 - - -720 862 intergenic rs78358857 DLGAP1 t c 95,5 -4,568 4,92E-06 --- 0 1,612 2 0,4467 0,0187572 DLGAP1 yes 571 525 intron 6
rs7782374 CHN2 t c 95,5 -4,565 5,00E-06 --- -6,7 3,75 2 0,1534 0,01902723 CHN2 yes -26 428 intron 5
rs6069006 BCAS1 a g 95,5 4,564 5,03E-06 +++ 0 0,313 2 0,8551 0,01912073 - - 900 116 intergenic 5
PPP1CB, rs4254466 PPP1CB a g 95,5 -4,558 5,16E-06 --- 50,2 8,027 2 0,01807 0,01952426 yes 34 436 intron SPDYA rs2922932 DLX5 a g 95,5 -4,555 5,23E-06 --- -14,7 3,488 2 0,1748 0,01973571 - - -254 891 intergenic 5
Page 55 of 64 Diabetes
rs11168700 C12orf41 a c 95,5 -4,552 5,31E-06 --- -21,8 3,284 2 0,1936 0,01991291 CCNT1 yes 34 204 intron 2b
rs72844739 CD151 c g 95,5 -4,548 5,41E-06 --- 44,8 7,24 2 0,02678 0,02026703 DEAF1 yes -175 399 intron 5
4-7837218 KIAA0232 a g 95,5 4,543 5,54E-06 +++ 0 1,699 2 0,4275 0,02070535 AFAP1 - 1 052 760 intron
rs2890662 KLHL5 a g 95,5 -4,543 5,55E-06 --- 0 1,44 2 0,4867 0,02075025 KLHL5 yes 53 929 intron 6
rs7920643 LARP4B a t 95,5 -4,538 5,69E-06 --- 0 0,749 2 0,6875 0,02114456 - - 999 011 intergenic 5
rs36073297 OPA3 t c 95,5 4,538 5,69E-06 +++ 0 1,122 2 0,5706 0,02114822 MARK4 yes -292 775 intron 5
rs143844677 CDC14B t c 95,5 -4,535 5,76E-06 --- 0 1,275 2 0,5285 0,02131638 - - -401 822 intergenic
rs71455663 FLJ13224 a t 95,5 4,533 5,82E-06 +++ -73,5 2,306 2 0,3157 0,02148959 DENND5B - 82 812 intron 6
rs8003968 ESRRB a c 95,5 -4,532 5,85E-06 --- 0 1,265 2 0,5313 0,02158124 ESRRB yes -45 606 intron 6
rs6065703 JPH2 t c 95,5 -4,527 5,99E-06 --- -24 3,225 2 0,1994 0,02202319 - - -3 388 downstream 4
rs9525501 C13orf15 c g 95,5 4,525 6,05E-06 +++ 0 1,059 2 0,5889 0,02221089 - - 25 148 intergenic
rs115929263 KIF1A c g 95,5 -4,524 6,06E-06 --- 0 0,2 2 0,9047 0,02221089 NDUFA10 yes -797 962 intron 6
rs74676427 MRPL42 a g 95,5 4,522 6,13E-06 +++ 0 0,968 2 0,6164 0,02243002 - - 625 267 intergenic 5
rs58809197 ELAC2 a g 95,5 -4,52 6,18E-06 --- 0 1,739 2 0,4192 0,02254977 ELAC2 yes 25 332 intron 6 yes
rs10271869 GIMAP4 t c 95,5 4,517 6,26E-06 +++ 0 1,779 2 0,4109 0,02278997 - - -211 469 intergenic 4
rs116939934 ZNF550 a g 95,5 -4,514 6,37E-06 --- -40,3 2,851 2 0,2404 0,02313126 - - -263 286 upstream 5
rs581508, OPN3 c g 95,5 4,513 6,39E-06 +++ -26,8 3,154 2 0,2066 0,02320349 OPN3 yes 44 950 intron 4 rs60228684
rs12711940 RALB a t 95,5 4,509 6,51E-06 +++ 39,1 6,563 2 0,03757 0,02357759 RALB yes 25 973 intron 6 yes
rs4351362 FLNC c g 95,5 4,505 6,64E-06 +++ 30,2 5,732 2 0,05691 0,02388425 - - 937 884 downstream 6
rs2810715 MIPEP c g 95,5 -4,504 6,67E-06 --- 62,8 10,743 2 0,004647 0,0239468 - - 201 789 intron
MTMR3, rs73164728 MTMR3 a g 95,5 -4,504 6,68E-06 --- 27,1 5,489 2 0,06428 0,02398218 yes 256 412 intron 6 HORMAD2
rs1358085 TMOD3 a g 95,5 -4,503 6,70E-06 --- 0 0,9 2 0,6377 0,02402141 - - 702 239 upstream
rs10503718 BIN3 t c 95,5 -4,503 6,71E-06 --- 0 0,058 2 0,9712 0,0240506 PEBP4 - 134 650 intron 4
rs246228 ABCC1 a c 95,5 -4,502 6,72E-06 --+ 71,7 14,155 2 0,0008441 0,0240572 ABCC1 yes 92 209 intron 2b
rs12456603 OSBPL1A c g 95,5 -4,495 6,96E-06 --- 77,3 17,654 2 0,0001467 0,02463214 OSBPL1A yes 23 409 intron 5 yes
rs9997795 ANK2 a g 95,5 4,49 7,14E-06 +++ 0 0,044 2 0,978 0,02463511 - - -351 220 intergenic
rs60208411 PIK3CD t c 95,5 4,479 7,50E-06 +++ 0 1,605 2 0,4482 0,02507555 UBE4B yes 434 591 intron
rs5050 COG2 t g 95,5 4,477 7,59E-06 +++ -62,9 2,455 2 0,293 0,02531486 AGT yes 71 685 5´UTR 1a
Diabetes Page 56 of 64
rs76826033 FHOD3 a g 95,5 -4,475 7,64E-06 --- -24 3,226 2 0,1993 0,02544942 yes FHOD3 yes 114 632 intron
rs4547172 PFKM a g 95,5 -4,474 7,69E-06 --- 0 1,272 2 0,5294 0,02554614 - - -161 233 intergenic 5
rs2854181 FTSJ3 t c 95,5 4,474 7,69E-06 +++ 63,5 10,974 2 0,00414 0,02554871 - - 72 495 downstream rs10920310 TIMM17A c g 95,5 4,473 7,71E-06 +++ 0 0,553 2 0,7585 0,02561107 - - 73 323 intergenic 5 yes
CBLN3, 5´UTR, rs2273630 KHNYN t c 95,5 4,47 7,81E-06 +++ -31,1 3,05 2 0,2176 0,02574839 yes 205 2b KHNYN upstream
rs2239325 ABCC6 a c 95,5 -4,469 7,85E-06 --- -11,4 3,589 2 0,1662 0,02579527 ABCC6 yes 26 710 intron 6
rs4839353 CSDE1 t g 95,5 4,464 8,03E-06 +++ -0,2 3,991 2 0,136 0,02627248 yes - - -658 256 intergenic rs72669230 MEAF6 a g 95,5 4,461 8,16E-06 +++ 0 1,297 2 0,5227 0,02667234 - - 975 076 intergenic
rs66708929 RPL39L a c 95,5 4,46 8,20E-06 +++ -72,3 2,322 2 0,3132 0,02675358 DGKG - -977 076 downstream 5
rs6982560 ANXA13 c g 95,5 -4,458 8,27E-06 --- -33,1 3,004 2 0,2227 0,02687986 - - 734 981 intergenic
rs62310596 CLOCK a g 95,5 4,456 8,35E-06 +++ 0 1,104 2 0,5759 0,02704948 KIAA1211 - 816 257 intron
GML, rs4736320 LY6D a t 95,5 4,455 8,38E-06 +++ -6,2 3,765 2 0,1522 0,02713658 yes 128 064 intron 5 CYP11B2 rs10948137 RANBP9 t c 95,5 4,452 8,51E-06 +++ 0 0,263 2 0,8769 0,02737097 - - -961 440 intergenic 6
rs12363722 UNC93B1 a c 95,5 4,444 8,84E-06 +++ 0 0,37 2 0,8312 0,02804576 PPP6R3 - 477 717 intron 5
rs5805912, CLDN10 a t 95,5 -4,441 8,96E-06 --- 0 0,234 2 0,8895 0,0283433 ABCC4 yes -194 420 intron 6 rs62941261 rs2690923 CUX1 a g 95,5 4,436 9,17E-06 +++ 0 0,13 2 0,9371 0,02878093 yes - - -24 313 intergenic
rs28422906 SLC25A22 t c 95,5 -4,436 9,17E-06 --- 0 1,344 2 0,5106 0,02878093 AP2A2 yes 205 161 intron 5
rs654533 CD3E t c 95,5 -4,434 9,25E-06 --- 0 0,375 2 0,8292 0,02898132 - - 412 514 intergenic 5
C7orf50, rs12702047 GPER a g 95,5 4,432 9,35E-06 +++ 0 0,682 2 0,7109 0,02926389 yes yes 6 278 3´UTR, intron 4 GPER rs10752954 IVNS1ABP a g 95,5 -4,425 9,64E-06 --- 43 7,02 2 0,0299 0,02982545 - - 146 861 intergenic 5
rs4781128 LITAF a g 95,5 4,425 9,65E-06 +++ -64,5 2,431 2 0,2965 0,0298506 LITAF yes 65 994 intron 2b
rs7616890 ABHD5 a g 95,5 -4,421 9,82E-06 --- 0 1,149 2 0,5629 0,02992474 ANO10 yes -124 878 intron
rs13195767 CDYL t c 95,5 4,421 9,85E-06 +++ -6,5 3,757 2 0,1528 0,02994204 - - -374 735 intergenic 5 yes
rs4351859 OAS1 a c 95,5 4,419 9,94E-06 +++ -55,5 2,572 2 0,2764 0,03019668 - - 707 146 intergenic 6
rs112042189 OXCT1 t c 95,5 4,413 1,02E-05 +++ 0 0,136 2 0,934 0,03075482 - - 171 231 upstream
rs2249888 FN3KRP a g 95,5 4,412 1,03E-05 +++ 58,9 9,741 2 0,007668 0,03089098 FN3KRP yes 1 157 intron 1f yes
rs677991 TULP3 t c 95,5 -4,404 1,06E-05 --- 38,3 6,488 2 0,03901 0,03178429 TSPAN9 - 358 751 intron 5 rs145785014 FUS t c 95,5 4,404 1,06E-05 +++ -6,4 3,761 2 0,1525 0,03180505 - - 398 896 intergenic
Page 57 of 64 Diabetes
rs10249186 INSIG1 a t 95,5 4,401 1,08E-05 +++ 0 0,825 2 0,6618 0,03215427 DPP6 yes -916 802 intron 6
rs9301945 ABCC4 t c 95,5 4,4 1,08E-05 +++ 0 0,415 2 0,8126 0,03230546 GPC6 - -796 774 intron 6
rs28372678 BAIAP3 a g 95,5 -4,394 1,11E-05 --- -53,2 2,611 2 0,2711 0,03274606 SPSB3 yes 449 010 upstream 2a
rs139621578 KIAA0922 a g 95,5 4,391 1,13E-05 +++ 27,3 5,499 2 0,06395 0,03305217 - - -478 849 intergenic
rs1391102 ATP2C2 c g 95,5 -4,389 1,14E-05 --- -85,9 2,151 2 0,3411 0,03328447 CRISPLD2 yes 541 168 downstream
rs658065 FAM8A1 a t 95,5 -4,384 1,17E-05 --- -78,6 2,239 2 0,3264 0,03391939 - - -808 814 intergenic 6
rs9907526 ITGB4 c g 95,5 4,381 1,18E-05 +++ 0 0,779 2 0,6774 0,03420253 PRPSAP1 yes 597 662 intron
rs10792434 MEN1 a g 95,5 4,38 1,19E-05 +++ -27,8 3,13 2 0,2091 0,03432872 AP003774.4 - -352 575 3´UTR
rs118122922 POLDIP3 t g 95,5 -4,378 1,20E-05 --- -6,9 3,741 2 0,1541 0,03445416 MPPED1 - 921 316 intron 5
rs4461 TOM1 a g 95,5 4,378 1,20E-05 +++ 0 0,289 2 0,8656 0,03453109 TOM1 yes 6 intron 4
rs7474813 ADO t c 95,5 -4,374 1,22E-05 --- 0 0,437 2 0,8038 0,03496645 ZNF365 yes -175 568 intron
rs2300371 IL10RB t c 95,5 -4,373 1,23E-05 -+- 70 13,32 2 0,001281 0,03513308 IFNAR2 - 11 228 intron 6
rs7521247 CD244 a g 95,5 4,371 1,24E-05 +++ 0 0,425 2 0,8087 0,03530857 CD84 yes -259 389 intron 6
rs882354 CAMK2A a g 95,5 -4,37 1,24E-05 --- -59,7 2,505 2 0,2858 0,03541133 SLC6A7 yes -1 893 downstream 5
rs247902 TAF1C t c 95,5 4,369 1,25E-05 +++ 47 7,543 2 0,02301 0,03550585 ATP2C2 yes 262 145 intron 6
rs12216486 IGF2R a g 95,5 -4,369 1,25E-05 --- -10,4 3,624 2 0,1633 0,0355864 - - -60 514 downstream 6
rs4695739 SCRG1 a c 95,5 4,368 1,25E-05 +++ 0 1,428 2 0,4896 0,03566205 - - 274 873 intergenic 6
rs7210280 EVI2A c g 95,5 4,368 1,25E-05 +++ 56,1 9,105 2 0,01054 0,03568076 SUZ12P - -564 657 intron
rs13053864 CRYBB1 t c 95,5 -4,36 1,30E-05 --- -59,4 2,51 2 0,2851 0,03669693 - - 877 102 intergenic 5
rs9481169 LAMA4 t g 95,5 -4,36 1,30E-05 --- -82,2 2,195 2 0,3337 0,03676676 - - -499 271 upstream 5
rs4458132 PDE4C a t 95,5 4,358 1,31E-05 +++ 0 0,495 2 0,7806 0,03700917 PDE4C yes 17 590 intron 5
rs431691 BLM a g 95,5 4,354 1,34E-05 +++ 0 0,829 2 0,6607 0,03759998 - - 99 177 downstream 5
rs1858790 KCNQ1DN a t 95,5 4,351 1,36E-05 +++ 0 0,778 2 0,6776 0,03808836 ZNF195 yes 474 308 intron 6
rs2345055 EIF2AK1 a c 95,5 -4,348 1,37E-05 --- 0 1,018 2 0,6011 0,03841638 AIMP2 yes -1 259 intron 4
rs35755011 HOXB2 t c 95,5 4,348 1,37E-05 +++ 0 0,104 2 0,9492 0,03843405 - - -57 563 intergenic 4
rs146608062 SLC30A1 a g 95,5 -4,348 1,38E-05 --- 0 1,637 2 0,4412 0,03850772 - - -340 285 intergenic
rs9318072 C13orf34 t c 95,5 4,34 1,43E-05 +++ 0 1,827 2 0,401 0,03959639 - - -309 604 intergenic
rs2276419 HYOU1 t c 95,5 -4,338 1,44E-05 --- 0 0,763 2 0,6827 0,03973891 UBE4A yes -684 536 5´UTR 4
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rs4919639 PSD c g 95,5 4,336 1,45E-05 +++ 41,6 6,853 2 0,03251 0,04011794 - - 54 484 upstream 5
rs4863686 TBC1D9 c g 95,5 4,336 1,45E-05 +++ -4,4 3,833 2 0,1471 0,04017954 MAML3 yes -863 769 intron 5
rs62449081 GGCT t c 95,5 -4,32 1,56E-05 --- 0 0,806 2 0,6684 0,04224068 yes FAM188B - 350 034 intron 5
rs4982255 PSMA6 a g 95,5 -4,317 1,58E-05 --- 0 1,591 2 0,4514 0,04270324 - - 45 196 intergenic
RAD21L1, rs56982547 SNPH a g 95,5 -4,317 1,58E-05 --- 0 0,552 2 0,7589 0,04275174 yes 16 742 intron 5 SNPH
rs1790469 GRIK4 a g 95,5 4,317 1,59E-05 +++ 31,4 5,832 2 0,05414 0,04282169 - - -504 575 intergenic 5
rs75845312 ANKZF1 t c 95,5 4,316 1,59E-05 +++ 0 0,012 2 0,9939 0,04287016 - - 815 546 intergenic rs35787376 RPL23A c g 95,5 -4,313 1,61E-05 --- 5,2 4,219 2 0,1213 0,04325673 - - -721 322 intergenic
rs4326952 ELF1 t c 95,5 -4,311 1,62E-05 --- -31,5 3,043 2 0,2184 0,0434435 - - 481 791 intergenic
rs34302476 HBQ1 a g 95,5 4,31 1,63E-05 +++ 0 1,82 2 0,4025 0,04364955 - - 640 329 intergenic
rs8078610 EVI2B t c 95,5 -4,309 1,64E-05 --- 0 0,226 2 0,8932 0,04385082 - - 285 593 intergenic 6
rs3818508 CELSR2 t c 95,5 4,308 1,65E-05 +++ -78,1 2,246 2 0,3254 0,04399117 KIAA1324 - -52 167 intron 5
rs3796242 TSC22D2 a t 95,5 4,308 1,65E-05 +++ 0 1,805 2 0,4055 0,04401221 yes CLRN1 - 563 652 synonymous 5
rs3784330 CIB2 t c 95,5 4,308 1,65E-05 +++ 23 5,195 2 0,07445 0,04401632 CIB2 yes 23 321 intron 4 yes
rs3008011 RPS6KA2 t c 95,5 -4,307 1,65E-05 --- -9,5 3,652 2 0,161 0,04406808 PDE10A yes -809 093 intron 5
rs1044522 TMEM134 a g 95,5 -4,302 1,69E-05 --- 0 0,819 2 0,6641 0,04478612 CTSF yes -895 986 synonymous 4
rs11582371 SH3GLB1 t c 95,5 4,302 1,69E-05 +++ 16,1 4,769 2 0,09213 0,04480688 - - -180 447 intergenic
rs339985 RORA a t 95,5 4,298 1,72E-05 +++ 0 0,478 2 0,7872 0,04545507 RORA yes 137 621 intron 6
rs217401 OGDH a c 95,5 -4,298 1,73E-05 --- 3,4 4,139 2 0,1263 0,04554332 - - -54 843 intergenic 6 rs35334347 NIN a g 95,5 -4,296 1,74E-05 --- -90 2,105 2 0,3491 0,04576713 yes NIN yes 31 502 intron
rs11895231 DGKD t c 95,5 -4,295 1,74E-05 --- 13,2 4,609 2 0,09979 0,0458613 DGKD yes -2 250 intron
rs12731262 NECAP2 t c 95,5 4,294 1,76E-05 +++ 0 1,198 2 0,5493 0,04612456 - - -686 224 upstream 6
rs77604319 RPS6KC1 a c 95,5 -4,292 1,77E-05 --- 0 1,049 2 0,592 0,04648575 - - -536 591 intergenic 5 rs114111458 TRAF5 a g 95,5 4,288 1,80E-05 +++ -50,6 2,655 2 0,2651 0,04710087 HHAT - -901 611 intron 5
rs2600560 DYNC1I1 a g 95,5 -4,286 1,82E-05 --- -11,8 3,579 2 0,167 0,04754005 DYNC1I1 yes 89 946 intron
rs12438010 PDE8A t g 95,5 4,285 1,82E-05 +++ 0 0,528 2 0,7678 0,0475803 - - 329 662 intergenic 5
rs72665339 EPHB2 a g 95,5 -4,285 1,83E-05 --- 0 0,297 2 0,862 0,04759446 - - -606 410 intergenic 3a
rs4512352 MATN2 a g 95,5 -4,285 1,83E-05 --- 0 1,937 2 0,3797 0,04760861 MATN2 yes 58 928 intron 6
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rs533494 IL19 t c 95,5 -4,284 1,83E-05 --- 0 1,524 2 0,4667 0,04776932 PIGR yes 107 570 intron
rs74444868 ARFGAP3 t c 95,5 -4,283 1,85E-05 --- 0 0,527 2 0,7682 0,04798377 - - -7 277 intergenic
rs61921903 USP15 t c 95,5 -4,281 1,86E-05 --- 32,8 5,949 2 0,05108 0,04826345 - - 194 750 intergenic
rs13003555 SP110 a g 95,5 -4,28 1,87E-05 --- -67,2 2,392 2 0,3024 0,04849208 SP110 yes -55 795 intron 5
rs12584413 SACS t g 95,5 4,277 1,90E-05 +++ 53,2 8,555 2 0,01387 0,04893561 SACS yes 15 074 intron
rs11622788 ADAM20 t c 95,5 4,275 1,91E-05 +-+ 53,7 8,648 2 0,01325 0,04926932 - - 298 423 intergenic 6
rs62011588 NR2F2 a c 95,5 4,274 1,92E-05 +++ 0 0,121 2 0,9411 0,04928237 NR2F2-AS1 - -191 252 intron
rs13278111 TNFRSF10D t c 95,5 4,273 1,93E-05 +++ 0 1,322 2 0,5164 0,04928237 - - -432 912 intergenic 5
rs12144062 PHGDH t c 95,5 4,271 1,95E-05 +++ 0 1,341 2 0,5113 0,04954813 - - -130 428 intergenic
rs2949950 HELZ t c 95,5 -4,27 1,96E-05 --- 0 0,463 2 0,7934 0,04969809 yes PITPNC1 yes 386 347 intron 6
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TableS2. 49 gene expression-phenotype associations Het P- Gene Phenotype P-value Direction value RCAN2 BMI 2,21E-08 -+- 0,015 SH3GLB2 BMI 3,56E-08 +++ 0,508 RBBP6 BMI 8,50E-08 --- 0,600 ALDH1A2 HOMA-IR 9,22E-08 +-+ 0,009 DBNDD1 HOMA-IR 2,11E-07 +-+ 0,015 DBNDD1 insulin 3,34E-07 +-+ 0,006 ARHGEF10L insulin 2,41E-06 +++ 0,221 PITPNM1 BMI 3,44E-06 --- 0,018 WSB2 BMI 9,29E-06 -+- 0,002 GZMH insulin 9,89E-06 +++ 0,951 BCL7A HOMA-IR 1,03E-05 --- 0,176 BCL7A insulin 1,15E-05 --- 0,186 ENPEP BMI 1,22E-05 +++ 0,440 C17orf101 insulin 1,23E-05 +++ 0,229 UGT8 insulin 1,45E-05 --- 0,112 FHL2 BMI 1,53E-05 -++ 0,010 CA2 BMI 1,55E-05 +++ 0,595 MSTN BMI 1,82E-05 +++ 0,088 DHRS7 insulin 1,84E-05 +++ 0,762 IMPA2 insulin 2,39E-05 +++ 0,063 ARID1A BMI 2,52E-05 +++ 0,560 TRIO BMI 2,91E-05 --- 0,774 RAF1 BMI 3,11E-05 +++ 0,490 SPR BMI 3,36E-05 --- 0,011 SPOCK1 HOMA-IR 3,52E-05 --- 0,175 TMOD1 BMI 3,92E-05 +++ 0,036 SLC30A10 insulin 4,00E-05 --- 0,817 ARHGEF10L HOMA-IR 4,02E-05 +++ 0,211 UGT8 HOMA-IR 4,36E-05 --- 0,041 ALDH1A2 insulin 4,85E-05 +-+ 0,017 C17orf101 HOMA-IR 5,15E-05 +++ 0,300 DAK insulin 5,83E-05 -++ 0,000 BCKDHB BMI 6,16E-05 --- 0,881 EIF4E2 insulin 6,41E-05 +++ 0,173 ARHGAP17 BMI 6,51E-05 +++ 0,083 ATP6V0C HOMA-IR 6,66E-05 -++ 0,073 GZMH HOMA-IR 6,82E-05 +++ 0,988 PDIA4 BMI 7,14E-05 +++ 0,797 G3BP2 insulin 7,87E-05 +++ 0,371 AGXT2L1 BMI 8,42E-05 --- 0,372 CASQ1 insulin 8,54E-05 +++ 0,191 SLC30A10 HOMA-IR 9,04E-05 --- 0,764 TMED2 insulin 9,08E-05 +++ 0,077 Page 61 of 64 Diabetes
CD46 BMI 9,93E-05 +++ 0,781 RNF111 HOMA-IR 1,07E-04 +-+ 0,024 GAS6 insulin 1,20E-04 --- 0,367 CTSF BMI 1,28E-04 -+- 0,001 CALML4 insulin 1,33E-04 +++ 0,403 PFKM insulin 1,34E-04 +++ 0,044
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Table S3. 27 gene expression-phenotype associations, insulin and HOMA-IR were adjusted for BMI Het P- Gene Phenotype P-value Direction value RCAN2 BMI 2,21E-08 -+- 0,015 SH3GLB2 BMI 3,56E-08 +++ 0,508 RBBP6 BMI 8,50E-08 --- 0,600 UNG insulin(BMI) 1,08E-06 +++ 0,329 CKAP5 insulin(BMI) 1,11E-06 --- 0,433 PITPNM1 BMI 3,44E-06 --- 0,018 TBC1D1 insulin(BMI) 4,20E-06 +++ 0,196 CALML4 insulin(BMI) 9,05E-06 +++ 0,192 WSB2 BMI 9,29E-06 -+- 0,002 ENPEP BMI 1,22E-05 +++ 0,440 FHL2 BMI 1,53E-05 -++ 0,010 CA2 BMI 1,55E-05 +++ 0,595 MSTN BMI 1,82E-05 +++ 0,088 ALDH1A2 HOMA-IR(BMI) 1,95E-05 +-+ 0,048 PRRX1 insulin(BMI) 2,32E-05 -+- 0,003 ARID1A BMI 2,52E-05 +++ 0,560 TRIO BMI 2,91E-05 --- 0,774 RAF1 BMI 3,11E-05 +++ 0,490 NRXN3 insulin(BMI) 3,25E-05 -+- 0,008 SPR BMI 3,36E-05 --- 0,011 GAS6 insulin(BMI) 3,63E-05 --- 0,724 TMOD1 BMI 3,92E-05 +++ 0,036 BCKDHB BMI 6,16E-05 --- 0,881 ING2 HOMA-IR(BMI) 6,28E-05 -++ 0,014 ARHGAP17 BMI 6,51E-05 +++ 0,083 ATP1B1 HOMA-IR(BMI) 6,85E-05 +++ 0,534 PDIA4 BMI 7,14E-05 +++ 0,797
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Table S4. Extended phenotype association analysis in the MM cohort of the PFKM eQTL SNP rs4547172 Non-T2D T2D All beta IQR p-value n beta IQR p-value n beta IQR p-value n
M-value 0,27 0,22 0.044-0.39 0,015 71 0,15 0.029-0.27 0,016 178
Delta RQ 0,017 0.003-0.031 0,016 102 0,4 0,011 0.00012-0.022 0,048 173
Glycogen 0,56 50,1 4.6-95.6 0,032 63 0,21
IMTG 15,7 5.20-26.2 0,004 103 0,88 8,69 0.26-17.12 0,043 167
CGOXBW 0,22 0,25 0.022-0.48 0,032 71 0,19 0.025-0.36 0,025 178
Respiratory quotient (RQ), intra muscular triglycerides (IMTG), whole body glucose oxidation rate in the insulin stimulated state during clamp divided by body weight (CGOXBW). Delta RQ = RQclamp – RQbasal. All analysis was adjusted for age and BMI (except for M-value and CGOXBW), Delta RQ was in addition adjusted for RQbasal. M-value was square root transformed.
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Table S5. Descriptive data for the extended phenotype association analysis in the MM cohort All T2D Non-T2D T2D vs. Non-T2D
Mean SD n Mean SD n Mean SD n p-value*
M-value 4.41 2.27 178 3.35 1.88 71 5.12 2.24 107 1.42 x 10-7 (mg / kg / min) Delta RQ 0.071 0.049 173 0.054 0.047 71 0.082 0.047 102 1.73 x 10-4 (AU) Glycogen 373 101 166 371 117 63 374 90 103 0.88 (mmol / kg) IMTG 59.3 37.8 167 60.4 39.1 64 58.6 37.2 103 0.76 (mmol / kg) CGOXBW 1.96 0.74 178 1.75 0.65 71 2.10 0.77 107 1.99 x 10-3 (mg / kg / min) *analysed using student-t test, unadjusted. SD = standard deviation
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Table S6. Publicly available expression data sets used Study N (T2D) N (NGT) N (BMI) E-GEOD-18832 - - 21 E-GEOD-22435 - - 17 E-GEOD-28998 - - 14 E-GEOD-5109 - - 6 E-GEOD-6798 - - 29 E-GEOD-8157 - - 13 E-MEXP-2559 - - 35 E-GEOD-18732 66 40 - E-GEOD-19420 27 10 - E-GEOD-25462 9 37 50 TOTAL 102 87 185