AMP-activated protein kinase (AMPK) is a key molecular regulator of cellular metabolism, and its activity is induced by both metformin and thiazolidinedione antidiabetic medications. It has therefore been proposed both as a putative agent in the pathophysiology of type 2 diabetes and as a valid target for therapeutic intervention. Thus, the genes that encode the various AMPK subunits are intriguing candidates for the inherited basis of type 2 diabetes. We therefore set out to test for the association of common variants in the genes that encode three selected AMPK subunits with type 2 diabetes and related phenotypes. Of the seven genes that encode AMPK isoforms, we initially chose PRKAA2, PRKAB1, and PRKAB2 because of their higher prior probability of association with type 2 diabetes, based on previous reports of genetic linkage, functional molecular studies, expression patterns, and pharmacological evidence. We determined their haplotype structure, selected a subset of tag single nucleotide polymorphisms that comprehensively capture the extent of common genetic variation in these genes, and genotyped them in family-based and case/control samples comprising 4,206 individuals. Analysis of single-marker and multi-marker tests revealed no association with type 2 diabetes, fasting plasma glucose, or insulin sensitivity. Several nominal associations of variants in PRKAA2 and PRKAB1 with BMI appear to be consistent with statistical noise.

Type 2 diabetes arises from the complex interplay of various pathophysiologic mechanisms involving peripheral insulin resistance and relative insulin insufficiency. The final expression of the diabetic phenotype is strongly influenced by inheritance; however, with the exception of rare monogenic forms of diabetes, common type 2 diabetes is thought to have a polygenic architecture (1). The two most widely reproduced associations of common variants with type 2 diabetes, the P12A polymorphism in the peroxisome proliferator–activated receptor (PPAR)-γ (encoded by PPARG) and the E23K polymorphism in the islet ATP-activated potassium channel Kir6.2 (encoded by KCNJ11), occur in genes that are targets for antidiabetic medications (2).

AMP-activated protein kinase (AMPK), a central cellular energy regulator, has recently emerged as a primary candidate for a role in metabolic dysfunction (3). This highly conserved heterotrimer consists of an α catalytic subunit and β and γ regulatory subunits, each of which has more than one isoform. The activation of AMPK by the depletion in cellular energy levels results in a host of metabolic effects ranging from mitochondrial biogenesis to improved insulin sensitivity (46), leading to increases in glucose availability and fatty acid oxidation (7,8). Enhanced AMPK activity in low-energy states is thought to be due to both transcriptional and post-translational mechanisms. AMPK activity is induced in human muscle as a result of moderate- to high-intensity exercise, with corresponding increases in phosphorylation and deactivation of acetyl-CoA carboxylase-β (a key enzyme in fatty acid synthesis) and nuclear translocation of the catalytic α2 subunit (911). Interestingly, acute exercise, which normalizes GLUT4 translocation and glucose uptake in patients with type 2 diabetes, causes a normal (approximately threefold) increase in α2 activity in these patients (12).

The α2 catalytic subunit, encoded by PRKAA2 in chromosome 1p31, is phosphorylated under stimulation by the widely used antidiabetic drug metformin (1316). Although this relationship is well established, the precise mechanism of activation has yet to be elucidated. It does not appear to act through ATP depletion, nor through direct phosphorylation of the upstream kinase LKB1 (17,18). PRKAA2 is located under a type 2 diabetes linkage peak detected in a diabetic Japanese population (logarithm of odds [LOD] score 1.24, nominal P = 0.014) (19). Furthermore, in contrast to mice devoid of the α1 subunit, α2 subunit knockout mice display hyperglycemia and insulin resistance (20). Finally, muscle contraction and moderate-intensity exercise have consistently resulted in increased α2 activity (21).

PRKAB1, the gene that encodes the β1 regulatory subunit, is located in chromosome 12q24.1, under the 12q24.31 type 2 diabetes linkage peak found in several populations (2224). Additionally, it has been shown that mutations in phosphorylation and post-translational modification sites in β1 affect levels of AMPK activity and/or its nuclear distribution (25). The β1 subunit also binds either glycogen or glycogen-debranching enzyme, resulting in changes to overall AMPK complex activity (26,27).

PRKAB2, the gene that encodes the β2 regulatory subunit, is located in 1q21.2, near the type 2 diabetes linkage peak found in Pima Indians (28), which is supported by studies in additional populations (2931). The β2 subunit is the predominant isoform found in human skeletal muscle, and it associates primarily with the α2 isoform (32,33). Expression studies conducted by the Genomics Institute of the Novartis Research Foundation also provide evidence of high expression of β2 in human liver (online database, http://symatlas.gnf.org/SymAtlas/) (34).

Taken together, these various lines of evidence establish PRKAA2, PRKAB1, and PRKAB2 as attractive candidate genes for increasing type 2 diabetes risk and influencing related physiological parameters. We therefore set out to characterize their haplotype structures and assess a comprehensive set of common variants in these genes for association with type 2 diabetes and intermediate phenotypes.

Haplotype structure.

The haplotype structures of the α2, β1, and β2 genes were determined by selecting single nucleotide polymorphisms (SNPs) from the publicly available dbSNP database at regularly spaced intervals, and genotyping these in the HapMap reference panel from the CEPH (Centre d’Etude du Polymorphisme Humain) consisting of 30 parent-offspring trios from Utah with European ancestry (CEU). This dataset was supplemented with genotypes available from the International HapMap Project (www.hapmap.org) for this same reference panel. For all genes, we extended our investigation of the haplotype structure both upstream and downstream of the gene until linkage disequilibrium (LD) decayed on both flanks, as indicated by the termination of a haplotype block according to the definition of Gabriel et al. (35). Accordingly, the region examined for PRKAA2 extends for 100 kb, from 27 kb upstream of the transcription start site to 12 kb downstream of the 3′ untranslated region; likewise for PRKAB1 and PRKAB2, the regions analyzed cover 156 kb (from −84 to +74 kb) and 93 kb (from −38 to +35 kb), respectively. SNPs of <5% frequency, those that failed genotyping due to technical errors, or those that failed to meet Hardy-Weinberg equilibrium (P < 0.01) were removed from the haplotype structure and subsequent analyses.

Genotyping.

Genotyping was performed as previously described, using primer extension of multiplex products with detection by MALDI-TOF (matrix-assisted laser desorption ionization–time of flight mass spectroscopy) on the Sequenom platform (35,36). Average genotyping success was 99.7% in the reference panel and 94% in the disease panel; our concordance rate, based on 1,281 duplicate comparisons, was 99.77%. Genotype counts for the various samples tested in this study are shown in online appendix Table 1 (available from http://diabetes.diabetesjournals.org as well as on our website http://genetics.mgh.harvard.edu/AltshulerWeb/publicationdata/Sun_AMPK.html).

Tagging methodology.

Single- and multi-marker tests were identified using the software Tagger (37) (http://www.broad.mit.edu/tagger), such that these tests collectively capture all SNPs ≥5% frequency at a minimum r2 of 0.8. We used the aggressive (multi-marker) tagging mode and constrained tag SNPs that appear in multi-marker tests to be in strong LD (LOD score >2.0). Phasing of chromosomes was performed by the weighted expectation-maximization algorithm incorporated into the software Haploview (38) (http://www.broad.mit.edu/mpg/haploview/) when possible and as previously described (39) for the discordant sibpairs.

Clinical samples.

The diabetic samples are presented in Table 1 and have been described elsewhere (40,41). Briefly, they comprise 321 Scandinavian trios; 1,189 Scandinavian siblings discordant for type 2 diabetes; a Scandinavian case-control sample totaling 942 subjects individually matched for age, BMI, and geographic region; a case/control sample from Sweden totaling 1,028 subjects who were individually matched for sex, age, and BMI; and an individually matched case/control sample totaling 254 subjects from the Saguenay Lac–St. Jean region in Quebec, Canada. In the Scandinavian samples, case subjects included those with type 2 diabetes or severe impaired glucose tolerance, defined as a 2-h blood glucose of 8.5 to <10.0 mmol/l during an oral glucose tolerance test (OGTT). These samples have been validated by the replication of the two most widely reproduced associations in type 2 diabetes, PPARG P12A (40) and KCNJ11 E23K (42), and by the overlap with other groups’ findings in the promoter region of the hepatocyte nuclear factor 4α (41).

Statistical analysis.

Power calculations were performed using the program of Purcell et al. (43), available at http://pngu.mgh.harvard.edu/∼purcell/gpc/. To examine the association of SNPs and haplotypes with type 2 diabetes, we used simple χ2 analysis in the case/control samples, the transmission disequilibrium test (44) in the diabetic trios, and the discordant allele test (45) in the sibpairs; the first two have been implemented in Haploview (http://www.broad.mit.edu/mpg/haploview/) for both single- and multi-marker association testing (38). In Haploview, possible ambiguity in haplotype assignments is accounted for by incorporating haplotype probabilities in the tests of disease association. The probability estimate of each multi-marker haplotype test was compared individually against all others; only haplotypes with frequencies >5% in the reference panel were examined. Results from the various samples were combined by Mantel-Haenszel meta-analysis of the odds ratios (ORs) (46); all P values are two-tailed. Homogeneity of ORs among study samples was tested using a Pearson χ2 goodness-of-fit test, as previously described (46).

Quantitative trait comparisons.

Plasma glucose was measured by a glucose oxidase method on a glucose analyzer (Beckman Instruments, Fullerton, CA). Insulin was measured by radioimmunoassay. A 75-g OGTT was performed in a subset of the control Scandinavian subjects (n = 756, 363 female). The whole-body insulin sensitivity index (ISI) was calculated as in Matsuda and DeFronzo (47). Nondiabetic individuals were sorted by their diploid genotypes at each locus; each most-likely inferred multi-marker haplotype test was compared against all other possibilities at the corresponding loci. Mean fasting plasma glucose, ISI, and BMI (the latter two after log transformation for non-normality) were compared by ANOVA across the three genotypic groups for each marker.

We initially selected 73 SNPs from the dbSNP database and genotyped them in the HapMap CEU panel. Of these SNPs, 37 passed our criteria for inclusion, including genotyping percentage >75%, Hardy-Weinberg equilibrium, and minor allele frequency (MAF) >5%. These SNPs were combined with genotypic data downloaded from the HapMap (CEU) to determine the structure of variation in PRKAA2, PRKAB1, and PRKAB2. Respectively, 40, 49, and 29 SNPs were used to characterize the haplotype structures of PRKAA2, PRKAB1, and PRKAB2, with an average spacing of one SNP every 2.5–3.3 kb; strong LD was noted for all three genes, with almost all variants contained within haplotype blocks as defined by Gabriel et al. (35). Detailed figures of each gene’s haplotype structure can be found in the online appendix (supplementary Fig. 1AC) as well as on our website (http://genetics.mgh.harvard.edu/AltshulerWeb/publicationdata/Sun_AMPK.html).

Our tagging procedure resulted in the selection of 22 tag SNPs in total, which collectively specify 22 single-marker and 18 multi-marker tests (9/6 for PRKAA2, 8/8 for PRKAB1, and 5/4 for PRKAB2). Thus, these 40 tests constitute the tests of association to the trait (both of themselves and of the other variants captured by them), which were performed in the disease samples (online appendix Fig. 1 and online appendix Table 2).

Assuming a type 2 diabetes prevalence of 8% and a multiplicative model, our power calculations demonstrated that our sample of 1,112 case/control pairs, 321 trios, and 1,189 discordant sibs had >90% power (at P < 0.05) to detect an association with type 2 diabetes for alleles or haplotypes of frequency ≥20% if the genotype relative risk (GRR) was 1.2 or higher and for alleles or haplotypes of frequency ≥10% if GRR was 1.3 or higher. For a GRR of 1.2, power was >70% for allele or haplotype frequencies of 10% and dropped to ∼45% for allele or haplotype frequencies of 5%.

A meta-analysis of the association studies for the SNPs and multi-marker haplotypes in each of the genes is presented in Table 2. No heterogeneity was detected among subsamples. We observed no significant association with type 2 diabetes for any of these tests. Genotype counts for each subsample is available in the online appendix (supplementary Table 1) and on our website (http://genetics.mgh.harvard.edu/AltshulerWeb/publicationdata/Sun_AMPK.html). After correcting for having tested multiple hypotheses by permutation, the nominal P values of 0.05 for rs2393550 in PRKAB1 and 0.04 for test 38 in PRKAB2 no longer reached statistical significance.

Because our case/control samples were matched for BMI, it is possible that overmatching in our case/control panels may have prevented us from detecting a true effect on risk of type 2 diabetes, if this effect was mediated through BMI. We therefore assessed whether BMI was associated with any of the 40 genotypic tests in our control sample. BMI comparisons across genotypic groups showed nominal P values <0.05 for several tests in PRKAA2 and PRKAB1, although after correction by permutation testing, the best result did not retain empirical statistical significance (Table 3).

Given the role played by AMPK activation in glucose uptake and insulin resistance (6,48), we analyzed common variants in these genes for association with differences in fasting plasma glucose and in the ISI in the 756 control subjects for whom we had OGTT data. We found no significant differences in fasting plasma glucose for any of the 40 genotypic tests; the nominally significant differences in ISI observed for SNPs rs894467 and rs1890039 in PRKAB2 were due to genotypic groups that only had two observations and therefore do not represent reliable findings (online appendix Table 3, also available at our website at http://genetics.mgh.harvard.edu/AltshulerWeb/publicationdata/Sun_AMPK.html).

We set out to test the hypothesis that genetic variation in the AMPK enzyme may contribute to the risk of type 2 diabetes. Based on the higher prior probability we ascribed to the α2, β1, and β2 subunits, we initially selected these three genes for investigation. In a sample comprising both case/control and family-based panels totaling 4,206 individuals, we were unable to document a significant association of a comprehensive set of common variants in the AMPK genes PRKAA2, PRKAB1, and PRKAB2 with type 2 diabetes or two related intermediate traits.

Our negative results can have several explanations. First, genetic variation in AMPK may not contribute to the risk of type 2 diabetes. If functional variation is present, epigenetic factors (rather than common genetic variation) in AMPK may be responsible for its observed functional role in ameliorating derangements of glucose homeostasis in humans. It is also possible that genetic variants in AMPK, while influencing intermediate phenotypes, may not have an effect that is strong enough to impact diabetes risk; our inability to detect a major effect of AMPK variants on two related phenotypes (fasting plasma glucose and a measure of insulin sensitivity) argues against the latter explanation.

Second, our findings may represent false negative results. Although the power of a meta-analysis of five smaller subsamples is not equivalent to a well-designed association study of the same size, we note that no heterogeneity was detected among our subsamples and that the present design takes advantage of two family-based panels that are robust to population stratification. In addition, these same samples were adequate to detect the most commonly reproduced genetic associations with type 2 diabetes (40,42), both of which have a fairly modest effect on risk.

Third, genetic variation in AMPK genes that affects the risk of type 2 diabetes may be due to rare variants not detected by our LD-based methods. We deliberately evaluated SNPs with MAF >5% based on our power calculations, which were designed to detect variants that confer a modest effect on risk in a sample of the size and characteristics available to us. While we believe our tagging methodology to be adequate to capture all common genetic variation (MAF >5%) and our sample size to provide enough power for genotypic relative risks of 1.2 and above, rarer variants or those that have more modest influence on risk may have been missed. In addition, because our tagging tests were selected in the HapMap CEU panel and tested in Caucasian samples, we may have missed variants that are specific to non-European populations. Nevertheless, we can conclude that the effect of common variants in the AMPK genes PRKAA2, PRKAB1, and PRKAB2 on type 2 diabetes, if present, appears to be negligible in Caucasians.

Fourth, overmatching in our samples may have prevented us from detecting a genetic effect of these three AMPK genes on type 2 diabetes if this was mediated through BMI. We explored this possibility by evaluating the effect of the same variants on BMI in our control subjects. None of the tests seemed to have a statistically significant effect on BMI in our sample; this suggests that the impact of common genetic variation in the AMPK genes PRKAA2, PRKAB1, and PRKAB2 on BMI is small or absent and may therefore require larger sample sizes for detection.

And finally, common genetic variants in other AMPK genes may still be associated with type 2 diabetes. Given our current negative results with PRKAA2, PRKAB1, and PRKAB2, we have begun characterizing common genetic variation in the remaining four genes for future association studies, although the available haplotype structure from one of the four genes (PRKAG2) indicates that this particular gene may not be efficiently evaluated with current LD approaches. In addition, several planned large whole-genome association scans in conjunction with the phase II release of the HapMap (49) may significantly alter the prioritization of individual candidate gene studies.

The AMPK variants that showed nominal associations with BMI in our Scandinavian samples were genotyped in an additional obese/lean case/control sample totalling 2,873 Caucasian individuals from the U.S. and Poland, provided by Genomics Collaborative. We were unable to replicate any association with BMI, lending further support to the notion that the nominally significant BMI results reported here are due to statistical fluctuation (H.N. Lyon, T. Bersaglieri, K.G. Ardlie, J.N.H., unpublished observations).

TABLE 1

Clinical characteristics of patient samples

Sex (M/F)Age (years)BMI (kg/m2)Fasting plasma glucose (mmol/l)HbA1c (%)* or plasma glucose at 2-h OGTT (mmol/l)†
Scandinavian trios      
    Affected probands 168/153 39 ± 9 27 ± 5 7.2 ± 2.6 8.5 ± 2.9† 
    Parents 236/236     
Sibships      
    Diabetes/severe IGT sib 280/329 65 ± 10 29 ± 5 9.3 ± 3.3 14.3 ± 5.6† 
    NGT sib 275/305 62 ± 10 26 ± 3 5.4 ± 0.4 6.0 ± 1.1† 
Scandinavian C/C      
    Diabetes/severe IGT 252/219 60 ± 10 28 ± 5 9.8 ± 3.4 15.0 ± 5.3† 
    NGT 254/217 60 ± 10 27 ± 4 6.2 ± 1.8 6.8 ± 2.8† 
Swedish C/C      
    Diabetes/severe IGT 267/247 66 ± 12 28 ± 4 9.6 ± 2.9 6.5 ± 1.5* 
    NGT 267/247 66 ± 12 28 ± 4 5.5 ± 0.7 ND 
Canadian C/C      
    Diabetes 70/57 53 ± 8 29 ± 5 6.4 ± 1.8 12.8 ± 2.1† 
    NGT 70/57 52 ± 8 29 ± 4 5.1 ± 0.6 6.1 ± 1.1† 
Sex (M/F)Age (years)BMI (kg/m2)Fasting plasma glucose (mmol/l)HbA1c (%)* or plasma glucose at 2-h OGTT (mmol/l)†
Scandinavian trios      
    Affected probands 168/153 39 ± 9 27 ± 5 7.2 ± 2.6 8.5 ± 2.9† 
    Parents 236/236     
Sibships      
    Diabetes/severe IGT sib 280/329 65 ± 10 29 ± 5 9.3 ± 3.3 14.3 ± 5.6† 
    NGT sib 275/305 62 ± 10 26 ± 3 5.4 ± 0.4 6.0 ± 1.1† 
Scandinavian C/C      
    Diabetes/severe IGT 252/219 60 ± 10 28 ± 5 9.8 ± 3.4 15.0 ± 5.3† 
    NGT 254/217 60 ± 10 27 ± 4 6.2 ± 1.8 6.8 ± 2.8† 
Swedish C/C      
    Diabetes/severe IGT 267/247 66 ± 12 28 ± 4 9.6 ± 2.9 6.5 ± 1.5* 
    NGT 267/247 66 ± 12 28 ± 4 5.5 ± 0.7 ND 
Canadian C/C      
    Diabetes 70/57 53 ± 8 29 ± 5 6.4 ± 1.8 12.8 ± 2.1† 
    NGT 70/57 52 ± 8 29 ± 4 5.1 ± 0.6 6.1 ± 1.1† 

Data are means ± SD. Plasma glucose was measured at baseline (fasting) and 2 h after an OGTT. C/C, case/control; IGT, impaired glucose tolerance; ND, not determined; NGT, normal glucose tolerance. Severe IGT was defined as a 2-h OGTT blood glucose ≥8.5 but <10.0 mmol/l.

TABLE 2

Association of variants in PRKAA2, PRKAB1, and PRKAB2 with type 2 diabetes

GeneTestSNPPositionAllelesMAFOR95% CIP
PRKAA2 rs2404987 56800626 T/C 0.48 1.05 0.95–1.17 0.31 
 rs6588640 56820444 G/A 0.07 0.92 0.74–1.14 0.43 
 rs2746343 56847835 C/T 0.16 0.99 0.86–1.15 0.93 
 rs857156 56875275 A/T 0.43 1.00 0.90–1.10 0.96 
 rs1342382 56889409 A/T 0.05 1.09 0.85–1.39 0.50 
 rs3738567 56890947 A/C 0.30 0.93 0.83–1.05 0.23 
 rs857142 56896486 C/A 0.32 0.96 0.86–1.07 0.45 
 rs10489617 56899028 C/G 0.12 1.00 0.82–1.22 0.97 
 rs2275732 56901814 T/C 0.05 1.20 0.89–1.61 0.23 
  Multi-marker tests
 

 
Hap
 
Freq
 
OR
 
95% CI
 
P
 
 10 rs6588640, rs2746343  A, T 0.05 1.08 0.87–1.34 0.47 
 11 rs2404987, rs6588640, rs2746343  C, G, C 0.42 0.95 0.86–1.05 0.35 
 12 rs2404987, rs2746343, rs3738567  T, T, C 0.12 1.06 0.90–1.25 0.50 
 13 rs2746343, rs857156, rs1342382  C, T, A 0.44 0.99 0.89–1.11 0.93 
 14 rs857156, rs1342382  A, A 0.38 1.01 0.91–1.12 0.83 
 15 rs857142, rs10489617  C, C 0.57 0.95 0.85–1.05 0.30 
GeneTestSNPPositionAllelesMAFOR95% CIP
PRKAA2 rs2404987 56800626 T/C 0.48 1.05 0.95–1.17 0.31 
 rs6588640 56820444 G/A 0.07 0.92 0.74–1.14 0.43 
 rs2746343 56847835 C/T 0.16 0.99 0.86–1.15 0.93 
 rs857156 56875275 A/T 0.43 1.00 0.90–1.10 0.96 
 rs1342382 56889409 A/T 0.05 1.09 0.85–1.39 0.50 
 rs3738567 56890947 A/C 0.30 0.93 0.83–1.05 0.23 
 rs857142 56896486 C/A 0.32 0.96 0.86–1.07 0.45 
 rs10489617 56899028 C/G 0.12 1.00 0.82–1.22 0.97 
 rs2275732 56901814 T/C 0.05 1.20 0.89–1.61 0.23 
  Multi-marker tests
 

 
Hap
 
Freq
 
OR
 
95% CI
 
P
 
 10 rs6588640, rs2746343  A, T 0.05 1.08 0.87–1.34 0.47 
 11 rs2404987, rs6588640, rs2746343  C, G, C 0.42 0.95 0.86–1.05 0.35 
 12 rs2404987, rs2746343, rs3738567  T, T, C 0.12 1.06 0.90–1.25 0.50 
 13 rs2746343, rs857156, rs1342382  C, T, A 0.44 0.99 0.89–1.11 0.93 
 14 rs857156, rs1342382  A, A 0.38 1.01 0.91–1.12 0.83 
 15 rs857142, rs10489617  C, C 0.57 0.95 0.85–1.05 0.30 
GeneTestSNPPositionAllelesMAFOR95% CIP
PRKAB1 16 rs2015795 118499750 C/T 0.45 1.01 0.91–1.12 0.92 
 17 rs1541345 118546518 G/A 0.05 0.88 0.68–1.15 0.34 
 18 rs2393550 118560728 G/A 0.20 0.88 0.78–1.00 0.05 
 19 rs278143 118571734 A/G 0.35 1.03 0.91–1.16 0.64 
 20 rs278123 118587298 G/A 0.34 1.03 0.91–1.17 0.65 
 21 rs278124 118592151 A/G 0.19 0.92 0.79–1.07 0.29 
 22 rs2285595 118619128 A/G 0.50 0.92 0.83–1.02 0.10 
 23 rs278109 118634715 C/T 0.23 0.91 0.80–1.03 0.13 
  Multi-marker tests
 

 
Hap
 
Freq
 
OR
 
95% CI
 
P
 
 24 rs2015795, rs278123  C, G 0.23 1.11 0.99–1.24 0.07 
 25 rs2015795, rs1541345, rs278123  T, G, G 0.42 0.99 0.87–1.12 0.88 
 26 rs2015795, rs278143  C, A 0.23 1.05 0.94–1.17 0.43 
 27 rs2393550, rs278124  A, G 0.19 1.07 0.93–1.22 0.36 
 28 rs278124, rs278109  G, T 0.19 1.10 0.94–1.28 0.22 
 29 rs2393550, rs278123  G, A 0.31 0.91 0.80–1.02 0.11 
 30 rs278123, rs2285595, rs278109  A, G, C 0.27 0.94 0.80–1.11 0.48 
 31 rs2285595, rs278109  G, C 0.28 0.99 0.88–1.13 0.93 
GeneTestSNPPositionAllelesMAFOR95% CIP
PRKAB1 16 rs2015795 118499750 C/T 0.45 1.01 0.91–1.12 0.92 
 17 rs1541345 118546518 G/A 0.05 0.88 0.68–1.15 0.34 
 18 rs2393550 118560728 G/A 0.20 0.88 0.78–1.00 0.05 
 19 rs278143 118571734 A/G 0.35 1.03 0.91–1.16 0.64 
 20 rs278123 118587298 G/A 0.34 1.03 0.91–1.17 0.65 
 21 rs278124 118592151 A/G 0.19 0.92 0.79–1.07 0.29 
 22 rs2285595 118619128 A/G 0.50 0.92 0.83–1.02 0.10 
 23 rs278109 118634715 C/T 0.23 0.91 0.80–1.03 0.13 
  Multi-marker tests
 

 
Hap
 
Freq
 
OR
 
95% CI
 
P
 
 24 rs2015795, rs278123  C, G 0.23 1.11 0.99–1.24 0.07 
 25 rs2015795, rs1541345, rs278123  T, G, G 0.42 0.99 0.87–1.12 0.88 
 26 rs2015795, rs278143  C, A 0.23 1.05 0.94–1.17 0.43 
 27 rs2393550, rs278124  A, G 0.19 1.07 0.93–1.22 0.36 
 28 rs278124, rs278109  G, T 0.19 1.10 0.94–1.28 0.22 
 29 rs2393550, rs278123  G, A 0.31 0.91 0.80–1.02 0.11 
 30 rs278123, rs2285595, rs278109  A, G, C 0.27 0.94 0.80–1.11 0.48 
 31 rs2285595, rs278109  G, C 0.28 0.99 0.88–1.13 0.93 
GeneTestSNPPositionAllelesMAFOR95% CIP
PRKAB2 32 rs750467 143814574 C/T 0.21 0.96 0.85–1.10 0.58 
 33 rs2883434 143873900 A/C 0.26 1.11 0.99–1.25 0.07 
 34 rs1816802 143874874 T/C 0.11 0.97 0.85–1.11 0.70 
 35 rs894467 143886834 T/C 0.05 0.93 0.71–1.21 0.59 
 36 rs1890039 143906767 C/T 0.11 0.86 0.68–1.08 0.19 
  Multi-marker tests
 

 
Hap
 
Freq
 
OR
 
95% CI
 
P
 
 37 rs750467, rs2883434  C, A 0.55 1.09 0.98–1.20 0.12 
 38 rs750467, rs2883434  C, C 0.24 0.88 0.79–0.99 0.04 
 39 rs750467, rs1890039  T, C 0.15 1.08 0.93–1.24 0.32 
 40 rs2883434, rs1816802  A, T 0.62 1.09 0.99–1.21 0.09 
GeneTestSNPPositionAllelesMAFOR95% CIP
PRKAB2 32 rs750467 143814574 C/T 0.21 0.96 0.85–1.10 0.58 
 33 rs2883434 143873900 A/C 0.26 1.11 0.99–1.25 0.07 
 34 rs1816802 143874874 T/C 0.11 0.97 0.85–1.11 0.70 
 35 rs894467 143886834 T/C 0.05 0.93 0.71–1.21 0.59 
 36 rs1890039 143906767 C/T 0.11 0.86 0.68–1.08 0.19 
  Multi-marker tests
 

 
Hap
 
Freq
 
OR
 
95% CI
 
P
 
 37 rs750467, rs2883434  C, A 0.55 1.09 0.98–1.20 0.12 
 38 rs750467, rs2883434  C, C 0.24 0.88 0.79–0.99 0.04 
 39 rs750467, rs1890039  T, C 0.15 1.08 0.93–1.24 0.32 
 40 rs2883434, rs1816802  A, T 0.62 1.09 0.99–1.21 0.09 

Twenty two single-marker tests and 18 multi-marker haplotype tests were defined based on 22 tag SNPs (9 and 6 for PRKAA2, 8 and 8 for PRKAB1, and 5 and 4 for PRKAB2) and tested for association with type 2 diabetes in our samples. Results from the various samples were combined by Mantel-Haenszel meta-analysis of the ORs. All P values are two-tailed. Chromosomal position is according to the NCBI build 35 release; alleles and OR of individual SNPs are reported as major vs. minor allele.

TABLE 3

Association of variants in PRKAA2, PRKAB1, and PRKAB2 with BMI

PRKAA2
BMI (kg/m2)
TestSingle-markerM/MM/mmmP
rs2404987 26.8 ± 4.3 26.2 ± 4.9 26.4 ± 4.4 0.26 
rs6588640 26.4 ± 4.6 26.3 ± 5.1 24.4 ± 2.8 0.21 
rs2746343 26.3 ± 4.5 26.6 ± 4.6 26.4 ± 6.4 0.18 
rs857156 26.2 ± 5.0 26.2 ± 4.6 26.7 ± 4.5 0.25 
rs1342382 26.3 ± 4.4 26.4 ± 7.0 33.3 ± 7.4 0.004* 
rs3738567 26.2 ± 4.5 26.4 ± 4.9 26.2 ± 5.0 0.56 
rs857142 26.4 ± 4.1 26.3 ± 5.1 26.4 ± 4.8 0.60 
rs10489617 26.4 ± 4.7 26.3 ± 4.4 23.7 ± 3.8 0.14 
rs2275732 26.3 ± 4.7 26.6 ± 4.2 24.5 ± 1.0 0.58 
      
 Multi-marker
 
11
 
12
 
22
 

 
10 2, 3 24.4 ± 2.8 26.9 ± 3.8 26.6 ± 3.7 0.16 
11 1, 2, 3 26.5 ± 3.7 26.5 ± 3.6 27.1 ± 3.9 0.07 
12 1, 3, 6 25.2 ± 2.8 27.0 ± 3.4 26.6 ± 3.8 0.07 
13 3, 4, 5 27.1 ± 4.0 26.6 ± 3.6 26.6 ± 3.7 0.27 
14 4, 5 26.5 ± 3.6 26.5 ± 3.6 27.1 ± 4.0 0.04 
15 7, 8 26.5 ± 3.5 26.8 ± 3.9 26.4 ± 3.5 0.35 
PRKAA2
BMI (kg/m2)
TestSingle-markerM/MM/mmmP
rs2404987 26.8 ± 4.3 26.2 ± 4.9 26.4 ± 4.4 0.26 
rs6588640 26.4 ± 4.6 26.3 ± 5.1 24.4 ± 2.8 0.21 
rs2746343 26.3 ± 4.5 26.6 ± 4.6 26.4 ± 6.4 0.18 
rs857156 26.2 ± 5.0 26.2 ± 4.6 26.7 ± 4.5 0.25 
rs1342382 26.3 ± 4.4 26.4 ± 7.0 33.3 ± 7.4 0.004* 
rs3738567 26.2 ± 4.5 26.4 ± 4.9 26.2 ± 5.0 0.56 
rs857142 26.4 ± 4.1 26.3 ± 5.1 26.4 ± 4.8 0.60 
rs10489617 26.4 ± 4.7 26.3 ± 4.4 23.7 ± 3.8 0.14 
rs2275732 26.3 ± 4.7 26.6 ± 4.2 24.5 ± 1.0 0.58 
      
 Multi-marker
 
11
 
12
 
22
 

 
10 2, 3 24.4 ± 2.8 26.9 ± 3.8 26.6 ± 3.7 0.16 
11 1, 2, 3 26.5 ± 3.7 26.5 ± 3.6 27.1 ± 3.9 0.07 
12 1, 3, 6 25.2 ± 2.8 27.0 ± 3.4 26.6 ± 3.8 0.07 
13 3, 4, 5 27.1 ± 4.0 26.6 ± 3.6 26.6 ± 3.7 0.27 
14 4, 5 26.5 ± 3.6 26.5 ± 3.6 27.1 ± 4.0 0.04 
15 7, 8 26.5 ± 3.5 26.8 ± 3.9 26.4 ± 3.5 0.35 
PRKAB1
TestSingle-markerM/MM/mm/m
16 rs2015795 26.2 ± 3.5 26.8 ± 3.8 26.9 ± 4.0 0.02 
17 rs1541345 26.7 ± 3.7 26.4 ± 4.0 28.0 ± 3.2 0.48 
18 rs2393550 26.7 ± 3.8 26.7 ± 3.7 26.1 ± 3.3 0.50 
19 rs278143 26.8 ± 3.9 26.5 ± 3.5 26.6 ± 3.7 0.58 
20 rs278123 26.8 ± 3.9 26.4 ± 3.4 26.5 ± 3.8 0.25 
21 rs278124 26.7 ± 3.7 26.6 ± 3.7 25.7 ± 2.9 0.33 
22 rs2285595 27.0 ± 4.1 26.5 ± 3.6 26.5 ± 3.6 0.15 
23 rs278109 26.8 ± 3.8 26.4 ± 3.6 26.5 ± 3.4 0.43 
 Multi-marker
 
11
 
12
 
22
 

 
24 16, 20 26.1 ± 3.5 26.7 ± 3.7 26.7 ± 3.7 0.26 
25 16, 17, 20 26.9 ± 3.9 26.9 ± 3.7 26.2 ± 3.5 0.01 
26 16, 19 26.1 ± 3.4 26.6 ± 3.8 26.8 ± 3.7 0.21 
27 18, 21 26.0 ± 3.3 26.5 ± 3.6 26.7 ± 3.8 0.42 
28 21, 23 26.0 ± 3.4 26.5 ± 3.6 26.8 ± 3.7 0.34 
29 18, 20 26.5 ± 3.9 26.4 ± 3.4 26.8 ± 3.9 0.22 
30 20, 22, 23 27.0 ± 4.1 26.3 ± 3.4 26.8 ± 3.8 0.09 
31 22, 23 27.1 ± 4.1 26.3 ± 3.4 26.8 ± 3.8 0.04 
PRKAB1
TestSingle-markerM/MM/mm/m
16 rs2015795 26.2 ± 3.5 26.8 ± 3.8 26.9 ± 4.0 0.02 
17 rs1541345 26.7 ± 3.7 26.4 ± 4.0 28.0 ± 3.2 0.48 
18 rs2393550 26.7 ± 3.8 26.7 ± 3.7 26.1 ± 3.3 0.50 
19 rs278143 26.8 ± 3.9 26.5 ± 3.5 26.6 ± 3.7 0.58 
20 rs278123 26.8 ± 3.9 26.4 ± 3.4 26.5 ± 3.8 0.25 
21 rs278124 26.7 ± 3.7 26.6 ± 3.7 25.7 ± 2.9 0.33 
22 rs2285595 27.0 ± 4.1 26.5 ± 3.6 26.5 ± 3.6 0.15 
23 rs278109 26.8 ± 3.8 26.4 ± 3.6 26.5 ± 3.4 0.43 
 Multi-marker
 
11
 
12
 
22
 

 
24 16, 20 26.1 ± 3.5 26.7 ± 3.7 26.7 ± 3.7 0.26 
25 16, 17, 20 26.9 ± 3.9 26.9 ± 3.7 26.2 ± 3.5 0.01 
26 16, 19 26.1 ± 3.4 26.6 ± 3.8 26.8 ± 3.7 0.21 
27 18, 21 26.0 ± 3.3 26.5 ± 3.6 26.7 ± 3.8 0.42 
28 21, 23 26.0 ± 3.4 26.5 ± 3.6 26.8 ± 3.7 0.34 
29 18, 20 26.5 ± 3.9 26.4 ± 3.4 26.8 ± 3.9 0.22 
30 20, 22, 23 27.0 ± 4.1 26.3 ± 3.4 26.8 ± 3.8 0.09 
31 22, 23 27.1 ± 4.1 26.3 ± 3.4 26.8 ± 3.8 0.04 
PRKAB2
TestSingle-markerM/MM/mm/m
32 rs750467 26.6 ± 3.6 26.6 ± 4.0 27.1 ± 3.4 0.67 
33 rs2883434 26.5 ± 3.7 26.7 ± 3.8 27.0 ± 3.7 0.31 
34 rs1816802 26.6 ± 3.7 26.7 ± 3.9 26.9 ± 3.4 0.90 
35 rs894467 26.7 ± 3.7 26.1 ± 3.6 25.8 ± 0.9 0.43 
36 rs1890039 26.7 ± 3.7 26.5 ± 3.8 25.4 ± 1.5 0.78 
 Multi-marker
 
11
 
12
 
22
 

 
37 32, 33 26.5 ± 3.6 26.7 ± 3.7 26.9 ± 3.8 0.44 
38 32, 33 27.0 ± 3.7 26.7 ± 3.7 26.6 ± 3.7 0.51 
39 32, 36 27.0 ± 3.5 26.3 ± 4.0 26.7 ± 3.6 0.26 
40 33, 34 26.5 ± 3.6 26.7 ± 3.7 26.9 ± 3.8 0.54 
PRKAB2
TestSingle-markerM/MM/mm/m
32 rs750467 26.6 ± 3.6 26.6 ± 4.0 27.1 ± 3.4 0.67 
33 rs2883434 26.5 ± 3.7 26.7 ± 3.8 27.0 ± 3.7 0.31 
34 rs1816802 26.6 ± 3.7 26.7 ± 3.9 26.9 ± 3.4 0.90 
35 rs894467 26.7 ± 3.7 26.1 ± 3.6 25.8 ± 0.9 0.43 
36 rs1890039 26.7 ± 3.7 26.5 ± 3.8 25.4 ± 1.5 0.78 
 Multi-marker
 
11
 
12
 
22
 

 
37 32, 33 26.5 ± 3.6 26.7 ± 3.7 26.9 ± 3.8 0.44 
38 32, 33 27.0 ± 3.7 26.7 ± 3.7 26.6 ± 3.7 0.51 
39 32, 36 27.0 ± 3.5 26.3 ± 4.0 26.7 ± 3.6 0.26 
40 33, 34 26.5 ± 3.6 26.7 ± 3.7 26.9 ± 3.8 0.54 

BMI (kg/m2) was determined in our control subjects and logarithmically transformed; untransformed values are presented as mean ± SD. Log-transformed values were compared by ANOVA depending on each of the specified genotypic tests. The SNPs that define each multi-marker test (bottom panel for each gene) are numbered as in the top panel and correspond to those in Table 2. M/M, homozygotes for the major allele; M/m, heterozygotes; m/m, homozygotes for the minor allele. 1 1, two copies of the multi-marker haplotype; 1 2, one copy of the multi-marker haplotype; 2 2, zero copies of the multi-marker haplotype.

*

The best nominally significant P value (P = 0.004 for rs1342382) was not statistically significant after 1,000 permutations (P = 0.107).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

M.W.S. is currently affiliated with the University of California-Davis School of Medicine, Sacramento, California. J.Y.L. is currently affiliated with the Department of Systems Biology, Harvard Medical School and Division of Signal Transduction, Beth Israel Deaconess Medical Center, Boston, Massachusetts.

This work was supported, in part, by a Pilot and Feasibility Grant Award from the Boston Area Diabetes Endocrinology Research Center (BADERC) to J.C.F. and by European Community project EXGENESIS 005272 to L.G. T.T. is a Research Fellow at the Academy of Finland. D.A. is a Charles E. Culpeper Scholar of the Rockefeller Brothers Fund and a Burroughs Wellcome Fund Clinical Scholar in Translational Research. D.A., M.J.D., and J.N.H. are recipients of The Richard and Susan Smith Family Foundation/ADA Pinnacle Program Project Award. L.G., T.T., and the Botnia Study are principally supported by the Sigrid Juselius Foundation, the Academy of Finland, the Finnish Diabetes Research Foundation, The Folkhalsan Research Foundation, the European Community (BM4-CT95-0662, GIFT), the Swedish Medical Research Council, the Juvenile Diabetes Foundation Wallenberg Foundation, and the Novo Nordisk Foundation. J.C.F. is supported by National Institutes of Health Research Career Award 1 K23 DK65978-02.

We thank the members of the Altshuler, Hirschhorn, Daly, and Groop labs for helpful discussions.

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