New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk

Article (PDF Available)inNature Genetics 42(2):105-16 · February 2010with208 Reads
DOI: 10.1038/ng.520 · Source: PubMed
Abstract
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.
Nature GeNetics VOLUME 42 | NUMBER 2 | FEBRUARY 2010 105
A R T I C L E S
Impaired beta-cell function and insulin resistance are key determinants
of type 2 diabetes (T2D). Hyperglycemia in the fasting state is one of
the criteria that defines T2D
1
, it can predict definitive clinical end-
points in nondiabetic individuals
2,3
and, when corrected in subjects
with T2D, may help prevent microvascular
4,5
and long-term macro-
vascular
6,7
complications. To date, there are nearly 20 published loci
reproducibly associated with T2D
8
; most of these are also associated
with decreased insulin secretion
9
due to defective beta-cell function
or beta-cell mass. Association studies for diabetes-related quantitative
traits in participants without diabetes have also identified loci influ-
encing fasting glucose levels, whose effects appear to be mediated by
impairment of the glucose-sensing machinery in beta cells
10–17
.
We recently formed the Meta-Analyses of Glucose and Insulin-
related traits Consortium (MAGIC) to conduct large-scale meta-
analyses of genome-wide data for continuous diabetes-related traits
in participants without diabetes
15
. We aimed to identify additional
loci that influence glycemic traits in individuals free of diabetes and
investigate their impact on related metabolic phenotypes. We were
also interested in understanding variation in the physiological range
of glycemia and evaluating the extent to which the same variants
influence pathological fasting glucose variation and T2D risk. The
initial MAGIC collaboration identified the fasting glucose- and
T2D-associated locus in MTNR1B
15
, which was also reported by
others
16,17
; this finding demonstrated that studies of continuous gly-
cemic phenotypes in nondiabetic individuals can complement the
genetic analyses of diabetes as a dichotomous trait and can improve our
understanding of the mechanisms involved in beta-cell function and
glucose homeostasis. Here, we extend our previous approach by per-
forming meta-analyses of ~2.5 million directly genotyped or imputed
autosomal SNPs from 21 genome-wide association studies (GWAS).
These 21 cohorts include up to 46,186 nondiabetic participants of
European descent informative for fasting glucose and 20 GWAS includ-
ing up to 38,238 nondiabetic individuals informative for fasting insu-
lin, as well as the surrogate estimates of beta-cell function (HOMA-B)
and insulin resistance (HOMA-IR) derived from fasting variables by
homeostasis model assessment
18
. Follow-up of 25 lead SNPs in up to
76,558 additional individuals of European ancestry identified nine
new genome-wide significant associations (empirically determined as
P < 5 × 10
−8
)
19
with fasting glucose and one with fasting insulin and
HOMA-IR. Five of these loci also demonstrated genome-wide signifi-
cant evidence for association between the glucose-raising allele and
T2D risk in up to 40,655 cases and 87,022 nondiabetic controls.
The wealth of loci newly discovered to be associated with fasting
glucose and HOMA-B contrasts with the single new locus identified
for fasting insulin and HOMA-IR and suggests that there is a differ-
ent genetic architecture for beta-cell function and insulin resistance.
Furthermore, our data support the hypothesis that not all loci that
influence glycemia within the physiological range are also associated
with pathological levels of glucose and T2D risk.
RESULTS
Genome-wide association meta-analysis of glycemic traits
We conducted a two-stage association study in individuals of European
descent (Online Methods, Supplementary Fig. 1 and Supplementary
Table 1a,b). Because we sought to identify variants that influence fast-
ing glucose in the unaffected population, hyperglycemia in the diabetic
range exerts deleterious effects on beta-cell function
20,21
and treat-
ment can confound glucose and insulin measurements, we excluded
individuals with known diabetes, those on anti-diabetic treatment,
and those with fasting glucose 7 mmol/l. We combined data from
21 stage 1 discovery GWAS for fasting glucose (n = 46,186) and
20 GWAS for fasting insulin (n = 38,238), HOMA-B (n = 36,466)
New genetic loci implicated in fasting glucose
homeostasis and their impact on type 2 diabetes risk
*
A full list of authors and affiliations appears at the end of the paper.
Received 13 August 2009; accepted 14 October 2009; published online 17 January 2010; doi:10.1038/ng.520
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses  
of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) 
and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional 
subjects identified 16 loci associated with fasting glucoseand HOMA-B and two loci associated with fasting insulin and HOMA-IR. 
These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FAD S1, GLIS3, 
SLC2A2, PROX1and C2CD4B ) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association 
of ADCY5, PROX1, GCK, GCKRand DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes 
influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate 
that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are 
associated with a modest elevation in glucose levels but are not associated with overt diabetes.
© 2010 Nature America, Inc. All rights reserved.
106 VOLUME 42 | NUMBER 2 | FEBRUARY 2010 Nature GeNetics
A R T I C L E S
Replication studies and global meta-analysis for 25 loci
We carried forward to stage 2 all independent loci with association
to any of the four traits at P < 2 × 10
−5
; we did not include SNPs in
the known T2D genes TCF7L2 and SLC30A8, for which no further
validation was sought (Table 1 and Supplementary Table 2). We also
included the nominally associated top SNP from a likely biological
candidate (IRS1, P = 10
−4
for HOMA-IR) and a locus with P values
that approached genome-wide significance in several stage 1 discovery
cohorts (PLXDC2-NEBL), even though their overall stage 1 P values
were > 2 × 10
−5
(Table 1 and Supplementary Table 2). In total,
25 loci were chosen for replication.
We directly genotyped 25 variants in 26 additional stage 2 studies
with up to 63,850 nondiabetic participants of European ancestry for
fasting glucose and 25 studies and up to 52,892 participants for fasting
insulin, HOMA-IR and HOMA-B (Supplementary Table 1b and Online
Methods). We also obtained in silico replication data for 12,708 additional
individuals from seven studies for fasting glucose (9,372 participants and
five studies for fasting insulin, HOMA-IR and HOMA-B), for a total of
up to 76,558 individuals for fasting glucose and 62,264 for fasting insulin,
HOMA-IR and HOMA-B in stage 2 association analyses.
Our combined stage 1 and 2 meta-analysis, including a total of up
to 122,743 participants for fasting glucose (98,372 for fasting insu-
lin, HOMA-IR and HOMA-B), established genome-wide significant
associations for nine new loci for fasting glucose and/or HOMA-B (in
or near ADCY5, MADD, CRY2, ADRA2A, FADS1, PROX1, SLC2A2,
GLIS3 and C2CD4B) and one for fasting insulin and HOMA-IR
(upstream of IGF1) (Table 1 and Fig. 1aj). Here, we replicate the
recently reported associations of the loci DGKB-TMEM195 (with
fasting glucose)
24
and GCKR (with fasting glucose, fasting insulin
and HOMA-IR)
11,12,25
at levels that exceed the threshold for genome-
wide significance. Loci that had previously achieved genome-wide
significant associations with fasting glucose (G6PC2, MTNR1B and
GCK) were also confirmed (Table 1).
and HOMA-IR (n = 37,037) and analyzed associations for ~2.5
million autosomal SNPs directly genotyped and imputed
22,23
from
HapMap CEU sample data, assuming an additive genetic effect for
each of the 4 traits.
Inverse variance-weighted meta-analyses revealed 12 inde-
pendent loci associated with fasting glucose and/or HOMA-B at
genome-wide significance levels (Table 1, Supplementary Table 2
and Supplementary Fig. 2a,b). These included five newly discov-
ered associations for loci in or near ADCY5, MADD, ADRA2A,
CRY2 and FADS1 (Table 1 and Fig. 1aj), four previously reported
fasting glucose-associated loci in or near GCK, GCKR, G6PC2 and
MTNR1B, the recently reported
24
locus in DGKB-TMEM195,
and two loci in the T2D susceptibility genes TCF7L2 (rs4506565,
r
2
= 0.92 with the previously reported SNP rs7903146) and
SLC30A8 (rs11558471, r
2
= 0.96 with the previously reported SNP
rs13266634). Seven additional loci had reproducible evidence for
association with fasting glucose and/or HOMA-B across stud-
ies at the arbitrary summary threshold of P < 2 × 10
−5
, chosen
to prioritize SNPs for follow-up (Table 1 and Supplementary
Table 2). After excluding SNPs within the four previously discov-
ered genome-wide significant fasting glucose loci in GCK, GCKR,
G6PC2 and MTNR1B, we still observed an excess of small P values
compared to the distribution expected under the null hypothesis
(Fig. 2a,b), suggesting that some of these additional loci are likely
to represent new fasting glucose and/or HOMA-Bassociated loci
that merit additional investigation.
Stage 1 analyses of fasting insulin and HOMA-IR revealed no loci
that reached genome-wide significance, but there were six loci with
consistent evidence for association across study samples at P < 2 ×
10
−5
(Table 1, Supplementary Table 2 and Supplementary Fig. 2c,d).
Comparison of the observed P values with the distribution expected
under the null hypothesis showed an excess of small P values that
warrant further investigation (Fig. 2c,d).
Table 1 SNPs associated with fasting glucose-related or insulin-related traits at genome-wide significance levels
Glucose/HOMA-B selected SNPs Fasting glucose HOMA-B
SNP Nearest gene(s)
Alleles
(effect/other) Freq Discovery P
I
2
estimate
(P ) Global P
Joint
analysis n Discovery P
I
2
estimate
(P) Global P
Joint
analysis n
rs560887 G6PC2 C/T 0.70 4.4 × 10
–75
0.31 (0.18) 8.7 × 10
–218
119,169 2.0 × 10
–28
0.54 (0.01) 1.5 × 10
–66
94,839
rs10830963 MTNR1B G/C 0.30 1.2 × 10
–68
0.00 (1.00) 5.8 × 10
–175
112,844 1.8 × 10
–22
0.45 (0.03) 2.7 × 10
–43
90,364
rs4607517 GCK A/G 0.16 4.5 × 10
–36
0.19 (0.46) 6.5 × 10
–92
118,500 7.5 × 10
–8
0.36 (0.12) 1.8 × 10
–16
94,112
rs2191349 DGKB-TMEM195 T/G 0.52 7.8 × 10
–17
0.10 (0.68) 3.0 × 10
–44
122,743 5.4 × 10
–11
0.09 (0.71) 2.8 × 10
–17
98,372
rs780094 GCKR C/T 0.62 2.5 × 10
–12
0.00 (1.00) 5.6 × 10
–38
118,032 0.25 0.32 (0.18) 3.2 × 10
–4
93,990
rs11708067 ADCY5 A/G 0.78 8.7 × 10
–9
0.04 (0.89) 7.1 × 10
–22
118,475 2.2 × 10
–4
0.37 (0.10) 2.5 × 10
–12
94,212
rs7944584 MADD A/T 0.75 1.5 × 10
–9
0.00 (1.00) 2.0 × 10
–18
118,741 1.1 × 10
–4
0.16 (0.51) 3.5 × 10
–5
94,408
rs10885122 ADRA2A G/T 0.87 8.4 × 10
–11
0.00 (1.00) 2.9 × 10
–16
118,410 3.7 × 10
–6
0.11 (0.66) 2.0 × 10
–6
94,128
rs174550 FADS1 T/C 0.64 1.5 × 10
–8
0.00 (1.00) 1.7 × 10
–15
118,908 4.5 × 10
–5
0.01 (0.99) 5.2 × 10
–13
94,536
rs11605924 CRY2 A/C 0.49 1.5 × 10
–9
0.00 (1.00) 1.0 × 10
–14
116,479 5.2 × 10
–6
0.03 (0.94) 3.2 × 10
–5
92,326
rs11920090 SLC2A2 T/A 0.87 1.9 × 10
–6
0.00 (1.00) 8.1 × 10
–13
119,024 1.4 × 10
–4
0.36 (0.11) 4.5 × 10
–6
94,629
rs7034200 GLIS3 A/C 0.49 1.2 × 10
–4
0.00 (1.00) 1.0 × 10
–12
106,250 1.9 × 10
–6
0.19 (0.46) 1.2 × 10
–13
83,759
rs340874 PROX1 C/T 0.52 7.1 × 10
–8
0.00 (1.00) 6.6 × 10
–12
116,882 3.7 × 10
–5
0.00 (1.00) 5.3 × 10
–6
92,942
rs11071657 C2CD4B A/G 0.63 2.8 × 10
–7
0.00 (1.00) 3.6 × 10
–8
114,454 0.23 0.08 (0.73) 0.002 90,675
rs11558471 SLC30A8 A/G 0.68 2.6 × 10
–11
45,996 1.4 × 10
–6
36,283
rs4506565 TCF7L2 T/A 0.31 1.2 × 10
–8
46,181 1.4 × 10
–6
36,461
Insulin/HOMA-IR selected SNPs Fasting insulin HOMA-IR
rs780094 GCKR C/T 0.62 1.1 × 10
–4
0.14 (0.57) 3.6 × 10
–20
96,126 9.9 × 10
–7
0.25 (0.32) 3.0 × 10
–24
94,636
rs35767 IGF1 G/A 0.85 1.0 × 10
–7
0.17 (0.50) 3.3 × 10
–8
94,590 7.8 × 10
–8
0.26 (0.28) 2.2 × 10
–9
93,141
Directly genotyped and imputed SNPs were tested for association with fasting glucose, fasting insulin and homeostasis model assessment of beta-cell function (HOMA-B) and
insulin resistance (HOMA-IR). Twenty-one discovery cohorts with genome-wide data were meta-analyzed (stage 1 discovery), and 25 SNPs were promoted for replication of the same
trait in a set of 33 additional cohorts with in silico (n = 7) or de novo (n = 26) genotype data (n = 31 for fasting insulin, HOMA-B and HOMA-IR; for stage 2 replication P values and
effect sizes, see Table 2). A joint analysis was then performed (global). Heterogeneity in the discovery sample was assessed using the I
2
index
48
. Replication was not attempted for
SNPs in two known T2D-associated genes (SLC30A8 and TCF7L2) that achieved genome-wide significance for fasting glucose in stage 1. Freq denotes the allele frequency of the
glucose-raising allele. n = sample size. Note that the previously reported GCKR SNP has associations with glucose-related and insulin-related traits.
© 2010 Nature America, Inc. All rights reserved.
Nature GeNetics VOLUME 42 | NUMBER 2 | FEBRUARY 2010 107
A R T I C L E S
We further conducted a global meta-analysis of cohort results
adjusted for body mass index (BMI) to test whether these diabetes-
related quantitative trait associations may be mediated by associations
with adiposity. The adjustment for BMI did not materially affect the
strength of the associations with any of the traits (data not shown).
Effect size estimates for genome-wide significant loci
We restricted our effect size estimates (Table 2 and Supplementary
Table 2) to the stage 2 replication samples (up to n = 76,558) to avoid
inflation introduced by the discovery cohorts (the so-called ‘win-
ner’s curse’
26
). The previously identified loci in G6PC2, MTNR1B
and GCK showed the largest effects on fasting glucose (0.075, 0.067
and 0.062 mmol/l per allele, respectively), with the remaining loci
examined showing smaller effects (0.008 to 0.030 mmol/l per allele;
Table 2). The proportion of variance in fasting glucose explained by
the 14 fasting glucose–associated loci with replication data (that is,
all fasting glucose loci except for those on TCF7L2 and SLC30A8)
ranged from 3.2%–4.4% in the six replication studies providing this
information. Because results from our largest unselected commu-
nity-based cohort (Framingham) were on the lower bound of these
estimates (3.2%), we felt reassured that the winner’s curse was not a
major concern in this instance and selected the Framingham cohort
to estimate the proportion of heritability explained and the geno-
type score. With a heritability estimate of 30.4% in the Framingham
cohort, these 14 loci explain a substantial proportion (~10%) of the
inherited variation in fasting glucose. Given the possibility that these
same loci harbor additional independent variants (for example, those
due to low-frequency alleles not captured by this analysis) that also
influence fasting glucose
27
, this estimate of the heritability attribut-
able to these loci is likely to be conservative.
We estimated the combined impact of the 16 loci associated with
fasting glucose (the 14 loci included in the effect size estimates plus
those on TCF7L2 and SLC30A8) in some of the largest cohorts
(Framingham, the Northern Finland Birth Cohort (NFBC) of 1966
and the Atherosclerosis Risk in Communities (ARIC) study) by con-
structing a genotype score equal to the sum of the expected number
of risk alleles at each SNP weighted by their effect sizes (see Online
Methods). Fasting glucose levels were higher in individuals with
higher genotype scores (Fig. 3), with mean differences of ~0.4 mmol/l
(5.93 versus 5.51 mmol/l in NFBC 1966; 5.36 versus 5.03 mmol/l in
a b c
d e f
g h i
j
rs11708067
P = 8.4 × 10
–22
rs174550
P = 1.7 × 10
–15
rs11605924
P = 1.1 × 10
–14
rs11920090
P = 7.0 × 10
–13
rs7944584
P = 2.0 × 10
–18
rs10885122
P = 3.1 × 10
–16
ADCY5
FADS1 CRY2 SLC2A2
MADD ADRA2A
60
40
20
Recombination rate (cM/Mb)
Recombination rate (cM/Mb)
0
–log
10
P value
–log
10
P value
–log
10
P value
23
8
4
0
–log
10
P value
15
8
4
0
124,300
PDIA5 SEC22A
SYT7
DAGLA
C11orf9
C11orf10
FEN1
FADS2
FADS1
FADS3
RAB3L1
BEST1
CHST1 SLC35C1
CRY2 GYLTL1B
PEX16
LOC143678
PHF21A
RPL22L1
EIF5A2
SLC2A2 TNIK
MAPK8IP1
FTH1
VMD2
ADCY5 PTPLB
MYLK
C11orf49
DDB2
NR1H3
SLC39A13
MADD
PACSIN3
ARFGAP2
ACP2
MYBPC3
SPI1
PSMC3
RAPSN
PTPMT1
CUGBP1
NDUFS3
KBTBD4
C1QTNF4
SHOC2
ADRA2A
124,500
124,700
Chromosome 3 position (kb)
61,100 61,300 61,500 45,600
45,800 46,000
172,000
172,200 172,400
Chromosome 11 position (kb)
Chromosome 11 position (kb) Chromosome 3 position (kb)
16
12
8
4
0
60
40
20
0
Recombination rate (cM/Mb)
60
40
20
0
18
16
8
6
4
2
0
60
40
20
Recombination rate (cM/Mb)
Recombination rate (cM/Mb)
0
–log
10
P value
–log
10
P value
11
4
0
60
40
20
0
Recombination rate (cM/Mb)
60
40
20
0
16
8
12
4
0
rs7034200
P = 1.0 × 10
–12
rs340874
P = 5.6 × 10
–12
rs11071657
P = 3.6 × 10
–8
GLIS3 PROX1 C2CD4B
–log
10
P value
12
10
4
0
2
GLIS3 SLC1A1
PROX1
SMYD2
VPS13C
FAM148A
FAM148B
4,100 4,300 4,500 210,300 210,500 210,700 60,000
60,200 60,400
Chromosome 9 position (kb) Chromosome 1 position (kb) Chromosome 15 position (kb)
60
40
20
Recombination rate (cM/Mb)
Recombination rate (cM/Mb)
0
–log
10
P value
–log
10
P value
8
4
0
60
40
20
0
Recombination rate (cM/Mb)
60
40
20
0
rs35767
P = 3.1 × 10
–8
IGF1
C12orf148
PMCH
IGF1
101,200 101,400
101,600
Chromosome 12 position (kb)
–log
10
P value
8
6
4
2
0
Recombination rate (cM/Mb)
60
40
20
0
8
12
4
0
47,100
47,300 47,500
112,800
113,000 113,200
Chromosome 11 position (kb) Chromosome 10 position (kb)
Figure 1 Regional plots of ten newly discovered genome-wide significant associations.
(a) ADCY5. (b) MADD. (c) ADRA2A. (d) FADS1. (e) CRY2. (f) SLC2A2. (g) GLIS3. (h) PROX1.
(i) C2CD4B. (j) IGF1. For each region, directly genotyped and imputed SNPs are plotted with
their meta-analysis P values (as −log
10
values) as a function of genomic position (NCBI Build 35).
In each panel, the stage 1 discovery SNP taken forward to stage 2 replication is represented by
a blue diamond (with global meta-analysis P value), with its stage 1 discovery P value denoted
by a red diamond. Estimated recombination rates (taken from HapMap) are plotted to reflect the
local LD structure around the associated SNPs and their correlated proxies (according to a white-
to-red scale from r
2
= 0 to 1, based on pairwise r
2
values from HapMap CEU). Gene annotations
were taken from the UCSC genome browser.
© 2010 Nature America, Inc. All rights reserved.
108 VOLUME 42 | NUMBER 2 | FEBRUARY 2010 Nature GeNetics
A R T I C L E S
Framingham; 5.70 versus 5.29 mmol/l in ARIC) when comparing
individuals with a score of 23 or higher (5.6% of the sample) to those
with a score of 12 or lower (2.9% of the sample). The 0.4 mmol/l
(7.2 mg/dl) difference between the two tails of the distribution of risk
score in the population (top 5.6% compared to the bottom 2.9%) is of
clinical relevance, as it represents a shift of approximately 25 centile
points in the distribution of fasting glucose. Prospective evidence
has shown that a difference of this magnitude in fasting glucose is
associated with a relative risk of 1.54–1.73 for future T2D, account-
ing for other risk factors
28
. The impact of individual SNPs on fasting
glucose in the combined discovery and replication samples is shown
in Supplementary Figure 3.
We also analyzed data from 1,602 self-reported white European
children aged 5.9–17.2 from two studies. Though directionally con-
sistent with observations in adults, some effect size estimates in these
children were of smaller magnitude (data not shown). As in adults, the
largest effect sizes were observed for risk alleles in GCK (b = 0.085,
P = 1.2 × 10
−5
, n = 1,602), G6PC2 (b = 0.062, P = 1.9 × 10
−4
, n = 1,582)
and MTNR1B (b = 0.033, P = 0.058, n = 1,309).
Impactofreproducibly associated loci onadditional glycemictraits
We sought to investigate all 17 loci associated with fasting glucose,
HOMA-B, fasting insulin or HOMA-IR at genome-wide significance
for their effects on other continuous glycemic traits. Whereas most
of the 16 loci associated with fasting glucose are also strongly associ-
ated with HOMA-B (Tables 1 and 2), the associations between fasting
glucose loci and fasting insulin were weak at best; GCKR is the only
locus reaching genome-wide significant associations for both fasting
glucose and fasting insulin or HOMA-IR, with the glucose-raising
C allele being associated with increased fasting insulin (global P = 3.6 ×
10
−20
) and HOMA-IR (global P = 3.0 × 10
−24
). These patterns are
consistent with the gross trait correlations obtained in Framingham
for fasting glucose and HOMA-B (r = −0.43) and for fasting glucose
and fasting insulin (r = 0.25).
Impairment of glucose homeostasis may be characterized by ele-
vated fasting glucose or fasting insulin, elevated glucose or insulin
at 2 h after oral glucose tolerance test (OGTT), or elevated glycated
hemoglobin (HbA
1c
). We tested associations of each of the 17 loci of
interest in a subset of MAGIC cohorts with GWAS data informative
for these traits. Because HbA
1c
is a measure of average glycemia over
the preceding 2–3 months, we hypothesized that if an association of
these loci with additional traits was present, it should be direction-
ally consistent. The three loci with the largest effect sizes on fasting
glucose—G6PC2, MTNR1B and GCK—all showed genome-wide sig-
nificant and directionally consistent associations with HbA
1c
; DGKB-
TMEM195, ADCY5, SLC2A2, PROX1, SLC30A8 and TCF7L2 showed
nominal (P < 0.05) evidence of directionally consistent association
(Table 2). The fasting glucose–raising alleles at TCF7L2, SLC30A8,
GCK and ADCY5 were associated (P < 0.0002) with increased 2-h
glucose (Table 2); a parallel MAGIC project reports the genome-wide
significant association with 2-h glucose of another ADCY5 SNP in
strong linkage disequilibrium (LD) with our lead SNP (r
2
= 0.82)
29
. In
contrast, and consistent with previous reports that the fasting glucose–
raising allele of GCKR is associated with greater insulin release during
OGTT
11,12,30
, this allele was associated with lower 2-h glucose.
Testing of these loci for association with T2D as a dichotomous trait
in up to 40,655 cases and 87,022 nondiabetic controls demonstrated
that the fasting glucose–raising alleles at seven loci (in or near ADCY5,
PROX1, GCK, GCKR and DGKB-TMEM195 and the known T2D
genes TCF7L2 and SLC30A8) are robustly associated (P < 5 × 10
−8
)
with increased risk of T2D (Table 2). The association of a highly
correlated SNP in ADCY5 with T2D in partially overlapping
samples is reported by our companion manuscript
29
. We found less
significant T2D associations (P < 5 × 10
−3
) for variants in or near
CRY2, FADS1, GLIS3 and C2CD4B (Table 2). These data clearly show
that loci with similar fasting glucose effect sizes may have very different
T2D risk effects (see, for example, ADCY5 and MADD in Table 2).
Given that several alleles associated with higher fasting glucose
levels were also associated with increased T2D risk and that the T2D-
related genes TCF7L2 and SLC30A8 showed association with fasting
glucose, we systematically investigated association of all established
T2D loci with the same four fasting diabetes–related quantitative traits.
We found directionally consistent nominal associations (P < 0.05) of
T2D risk alleles with higher fasting glucose for 11 of 18 established
T2D loci, including MTNR1B (Supplementary Table 3). These data
demonstrate that a large T2D effect size does not always translate to
an equivalently large fasting glucose effect in nondiabetic persons, as
clearly highlighted when contrasting the remarkably small effects of
TCF7L2 on fasting glucose compared to MTNR1B (Table 2).
Impact of new glycemic loci on other metabolic traits
Next, we used available GWAS results for additional metabolic
phenotypes (BMI from GIANT
31
, blood pressure from Global
BPgen
32
and lipids from ENGAGE
33
) to assess the impact of the
newly discovered glycemic loci on these traits. None of the newly
discovered loci had significant (P < 0.01) associations with BMI or
blood pressure (Table 3). Notably, the FADS1 glucose-raising allele
was associated with increased total cholesterol (P = 2.5 × 10
−6
),
70
a b
c d
25
20
15
10
5
0
60
Fasting glucose
HOMA-B
50
40
30
20
10
0
0 2 4
Expected (–log
10
P value) Expected (–log
10
P value)
Observed (–log
10
P value)
Observed (–log
10
P value)
6 8 0
2 4 6 8
25
20
15
10
5
0
Fasting insulin
Expected (–log
10
P value)
Observed (–log
10
P value)
0 2 4 6 8
25
20
15
10
5
0
HOMA-IR
Expected (–log
10
P value)
Observed (–log
10
P value)
0 2 4 6 8
Figure 2 Quantile-quantile plots. (a) Fasting glucose. (b) Beta-cell
function by homeostasis model assessment (HOMA-B). (c) Fasting insulin.
(d) Insulin resistance by homeostasis model assessment (HOMA-IR). In
each plot, the expected null distribution is plotted along the red diagonal,
the entire distribution of observed P values is plotted in black and a
distribution that excludes the ten newly discovered loci shown in Figure 1
is plotted in green. For fasting glucose and HOMA-B, the distribution that
excludes the four genome-wide significant fasting glucose–associated
loci reported previously (in GCK, GCKR, G6PC2 and MTNR1B) is plotted
in blue. A comparison of the observed P values for each trait shows that
fasting glucose and HOMA-B associations are much more likely to be
detected than fasting insulin and HOMA-IR associations.
© 2010 Nature America, Inc. All rights reserved.
Nature GeNetics VOLUME 42 | NUMBER 2 | FEBRUARY 2010 109
A R T I C L E S
low-density lipoprotein cholesterol (P = 8.5 × 10
−6
) and high-
density lipoprotein cholesterol (P = 2.9 × 10
−5
), but was associated
with lower triglyceride levels (P = 1.9 × 10
−6
) (Table 3); a consist-
ent association of this locus with lipid levels has been previously
reported
34
. The fasting glucoseassociated variant in MADD was
not associated with lipid levels and is not in LD (r
2
< 0.1) with
a previously reported high-density lipoprotein cholesterol SNP
(rs7395662)
33
, suggesting two independent signals within the same
locus, one affecting lipid levels and the other affecting fasting glu-
cose levels (Table 3).
Potential functional roles of newly discovered loci
We investigated the likely functional role of genes mapping closest to
the lead SNPs using several sources of data, including human disease
Table 2 Association of newly discovered SNPs with glycemic traits in MAGIC and type 2 diabetes replication meta-analyses
SNP
Nearest
gene(s)
Alleles
(effect/
other)
Fasting
glucose
(mmol/l) HOMA-B
Fasting
insulin (pmol/l) HOMA-IR HbA
1c
(%)
2-h glucose
(mmol/l)
2-h insulin
(pmol/l)
Type 2
diabetes
b
rs560887 G6PC2 C/T Effect
a
0.075 (0.003) –0.042 (0.004) –0.007 (0.004) 0.006 (0.004) 0.032 (0.004) 0.017 (0.020) –0.031 (0.013) 0.97 (0.95–0.99)
P 8.5 × 10
–122
7.6 × 10
–29
0.11 0.16 1.0 × 10
−17
0.41 0.01 0.012
rs10830963 MTNR1B G/C Effect
a
0.067 (0.003) –0.034 (0.004) –0.006 (0.004) 0.004 (0.004) 0.024 (0.004) 0.056 (0.022) 0.034 (0.015) 1.09 (1.06–1.12)
P 1.1 × 10
–102
1.1 × 10
–22
0.14 0.37 3.0 × 10
−9
0.01 0.02 8.0 × 10
−13
rs4607517 GCK A/G Effect
a
0.062 (0.004) –0.025 (0.005) 0.004 (0.006) 0.015 (0.006) 0.041 (0.005) 0.097 (0.026) –0.012 (0.015) 1.07 (1.05–1.10)
P 1.2 × 10
–44
1.2 × 10
–6
0.46 0.01 6.3 × 10
−19
2.0 × 10
−4
0.42 5.0 × 10
−8
rs2191349 DGKB-
TMEM195
T/G Effect
a
P
0.030 (0.003)
5.3 × 10
–29
–0.017 (0.003)
6.4 × 10
–8
–0.002 (0.003)
0.48
0.002 (0.004)
0.61
0.008 (0.003)
0.01
0.000 (0.019)
0.98
–0.006 (0.012)
0.60
1.06 (1.04–1.08)
1.1 × 10
−8
rs780094 GCKR C/T Effect
a
0.029 (0.003) 0.014 (0.003) 0.032 (0.004) 0.035 (0.004) 0.004 (0.004) –0.091 (0.019) 0.000 (0.011) 1.06 (1.04–1.08)
P 1.7 × 10
–24
1.4 × 10
–5
3.6 × 10
−19
5.0 × 10
−20
0.32 1.4 × 10
−6
1.00 1.3 × 10
−9
rs11708067 ADCY5 A/G Effect
a
0.027 (0.003) –0.023 (0.004) –0.011 (0.004) –0.006 (0.005) 0.015 (0.004) 0.094 (0.023) 0.008 (0.015) 1.12 (1.09–1.15)
P 1.7 × 10
–14
3.6 × 10
–8
0.01 0.16 5.1 × 10
−4
6.6 × 10
−5
0.60 9.9 × 10
−21
rs7944584 MADD A/T Effect
a
0.021 (0.003) –0.007 (0.004) 0.002 (0.004) 0.005 (0.004) 0.001 (0.004) –0.017 (0.022) –0.019 (0.013) 1.01 (0.99–1.03)
P 5.1 × 10
–11
0.07 0.60 0.26 0.84 0.44 0.15 0.30
rs10885122 ADRA2A G/T Effect
a
0.022 (0.004) –0.010 (0.005) 0.001 (0.005) 0.004 (0.005) 0.007 (0.005) 0.004 (0.030) –0.051 (0.019) 1.04 (1.01–1.07)
P 9.7 × 10
–8
0.03 0.90 0.47 0.21 0.89 0.007 0.020
rs174550 FADS1 T/C Effect
a
0.017 (0.003) –0.020 (0.003) –0.011 (0.004) –0.008 (0.004) 0.007 (0.004) 0.013 (0.019) –0.003 (0.012) 1.04 (1.02–1.06)
P 8.3 × 10
–9
5.3 × 10
–10
2.7 × 10
−3
0.03 0.053 0.49 0.82 2.3 × 10
−4
rs11605924 CRY2 A/C Effect
a
0.015 (0.003) –0.005 (0.003) 0.001 (0.004) 0.003 (0.004) 0.001 (0.003) 0.023 (0.018) 0.006 (0.011) 1.04 (1.02–1.06)
P 8.1 × 10
–8
0.13 0.73 0.34 0.72 0.20 0.62 1.7 × 10
−4
rs11920090 SLC2A2 T/A Effect
a
0.020 (0.004) –0.012 (0.005) 0.002 (0.005) 0.005 (0.005) 0.017 (0.005) 0.015 (0.027) –0.022 (0.016) 1.01 (0.99–1.04)
P 3.3 × 10
–6
0.02 0.77 0.37 5.8 × 10
−4
0.58 0.19 0.34
rs7034200 GLIS3 A/C Effect
a
0.018 (0.003) –0.020 (0.004) –0.014 (0.004) –0.011 (0.004) 0.003 (0.003) 0.037 (0.018) 0.010 (0.011) 1.03 (1.01–1.05)
P 1.2 × 10
–9
8.9 × 10
–9
2.7 × 10
−4
4.6 × 10
−3
0.32 0.04 0.36 1.3 × 10
−3
rs340874 PROX1 C/T Effect
a
0.013 (0.003) –0.008 (0.003) –0.002 (0.004) 0.001 (0.004) 0.009 (0.004) 0.030 (0.020) –0.007 (0.012) 1.07 (1.05–1.09)
P 6.6 × 10
–6
0.02 0.68 0.74 9.5 × 10
−3
0.13 0.56 7.2 × 10
−10
rs11071657 C2CD4B A/G Effect
a
0.008 (0.003) –0.013 (0.004) –0.009 (0.004) –0.008 (0.004) 0.001 (0.004) –0.065 (0.020) –0.006 (0.013) 1.03 (1.01–1.05)
P 0.01 8.1 × 10
–4
0.03 0.07 0.79 0.001 0.65 2.9 × 10
−3
rs13266634 SLC30A8 C/T Effect
a
0.027 (0.004) –0.016 (0.004) –0.004 (0.005) –0.0002 (0.005) 0.016 (0.004) 0.093 (0.022) –0.011 (0.015) 1.15 (1.10–1.21)
c
P 5.5 × 10
–10
2.4 × 10
–5
0.44 0.97 3.3 × 10
−5
2.0 × 10
−5
0.47 1.5 × 10
−8
rs7903146 TCF7L2 T/C Effect
a
0.023 (0.004) –0.020 (0.004) –0.012 (0.004) –0.010 (0.005) 0.013 (0.003) 0.118 (0.021) 0.010 (0.013) 1.40 (1.34–1.46)
c
P 2.8 × 10
–8
1.4 × 10
–7
0.004 0.03 1.8 × 10
−4
2.6 × 10
−8
0.42 2.2 × 10
−51
rs35767 IGF1 G/A Effect
a
0.012 (0.005) 0.009 (0.005) 0.010 (0.006) 0.013 (0.006) 0.010 (0.005) 0.027 (0.025) 0.015 (0.016) 1.04 (1.01–1.07)
P 0.01 0.09 0.10 0.04 0.050 0.28 0.33 6.6 × 10
−3
Sample size
for each trait
45,049–
76,558
35,435–
61,907
37,199–
62,264
35,901–
62,001
33,718–
44,856
15,221–
15,234
7,051–
7,062
40,655
cases/87,022
controls
a
Per-allele effect (SE) for quantitative traits was estimated from stage 2 replication samples for fasting glucose, homeostasis model assessment of beta-cell function (HOMA-B),
fasting insulin, and homeostasis model assessment of insulin resistance (HOMA-IR), and from discovery meta-analyses of MAGIC GWAS for glycated hemoglobin (HbA
1c
), 2-h
glucose after an oral glucose tolerance test (BMI-adjusted) and 2-h insulin (BMI-adjusted). For the first four traits, the regression coefficients are obtained from the replication
cohorts so as to avoid an overestimate of the effect size caused by the ‘winner’s curse’. Results from replication samples were unavailable for rs7903146 and rs13266634; thus,
discovery meta-analysis results are shown for both SNPs for fasting glucose (n = 45,049–45,051), HOMA-B (n = 35,435–35,437), fasting insulin (n = 37,199–37,201) and
HOMA-IR (n = 35,901–35,903).
b
Replication genotyping was undertaken in 27 independent type 2 diabetes (T2D) case/control samples for all except the TCF7L2 and SLC30A8
signals.
c
Association with T2D for SNPs in TCF7L2 and SLC30A8 loci was estimated from the DIAGRAM+ meta-analysis for a total of 8,130 cases/38,987 controls. For these loci,
we have included data on the most commonly associated SNPs with T2D in previously published data.
0
200
400
600
800
12
13 14 15 16 17 18 19 20 21 22
23
5
5.1
5.2
5.3
5.4
Figure 3 Variation in levels of fasting glucose depending on the number
of risk alleles at newly identified loci, weighted by effect size in an
aggregate genotype score for the Framingham Heart Study. The bar plots
show the average and standard error of fasting glucose in mmol/l for
each value of the genotype score based on the regression coefficient
(right y axis), and the histogram denotes the number of individuals in
each genotype score category (left y axis). Comparable results were
obtained for the NFBC 1966 and ARIC cohorts. On average, the range
spans ~0.4 mmol/l (~7.2 mg/dl) from low to high genotype score.
© 2010 Nature America, Inc. All rights reserved.
110 VOLUME 42 | NUMBER 2 | FEBRUARY 2010 Nature GeNetics
A R T I C L E S
databases, evidence from animal models and bioinformatic analy-
ses (see Box 1, Online Methods and Supplementary Table 4). The
newly discovered and previously established glycemic loci represent
various biological functions: signal transduction (DGKB-TMEM195,
ADCY5, FADS1, ADRA2A, SLC2A2, GCK, GCKR, G6PC2 and IGF1),
cell proliferation and development (GLIS3, MADD and PROX1),
glucose transport and sensing (SLC2A2, GCK, GCKR and G6PC2)
and circadian rhythm regulation (MTNR1B and CRY2). All of these
pathways represent further avenues for physiological characterization
and possible therapeutic intervention for T2D. However, we note that
other genes could be causal (Box 1 and Supplementary Table 4), and
further experimental evidence will be needed to unequivocally link
specific genes with phenotypes.
Expression analyses
We measured expression of the genes mapping closest to our lead
SNPs (in DGKB-TMEM195, ADCY5, MADD, its neighboring gene
SLC39A13 (a member of a family of zinc transporters mapping ~45 kb
from the MADD lead SNP), ADRA2A, FADS1, CRY2, SLC2A2, GLIS3,
PROX1 and C2CD4B) in human pancreas and other metabolically
relevant tissues (Supplementary Fig. 4a). Although there was evidence
of expression in human islets for nearly all genes tested (with the
sole exception of TMEM195), we found that DGKB and MADD were
most strongly expressed in brain, SLC2A2, FADS1, TMEM195 and
PROX1 were most strongly expressed in liver and ADCY5 was most
strongly expressed in heart, whereas SLC39A13, ADRA2A and CRY2
were broadly expressed. Notably, C2CD4B was highly expressed in
the whole pancreas with lower levels in isolated islets, suggesting
that it is also present in exocrine cells. A duplicate experiment in a
different laboratory obtained similar results (Supplementary Fig. 4b).
We further examined expression of these transcripts in flow-sorted
human beta cells from two separate individuals and documented
beta-cell expression for all but TMEM195, with SLC39A13, CRY2,
GLIS3 and PROX1 being particularly highly expressed in these cells
(Supplementary Fig. 4c). Expression levels in metabolically relevant
tissues for DGKB (beta cells) and TMEM195 (liver) provided equally
credible evidence for their respective candidacies as the causal gene at
these loci. Furthermore, based on its relatively high expression levels
in beta cells, SLC39A13 (neighboring gene to MADD) constitutes a
possible candidate gene that may merit further investigation.
Potential causal variants, eQTLs and copy number variants
Our results interrogate only a fraction of the common variants in
any given genomic region; we therefore expect that for the major-
ity of the loci here described, the underlying causal variant has yet
to be identified. Nevertheless, for some loci there are possible SNP
Table 3 Association of newly discovered SNPs with related metabolic traits in other GWAS datasets
SNP
Nearest
gene(s)
Alleles
(effect/other) BMI (kg/m
2
)
Diastolic blood
pressure (mm Hg)
Systolic blood
pressure (mm Hg) Hypertension HDL LDL Total cholesterol Triglycerides
rs560887 G6PC2 C/T Effect
a
–0.013 (0.010) –0.146 (0.091) –0.105 (0.135) –0.023 (0.028) –0.004 (0.004) 0.01 (0.011) 0.019 (0.011) 0.004 (0.005)
P 0.18 0.12 0.46 0.41 0.32 0.35 0.10 0.52
rs10830963 MTNR1B G/C Effect
a
0.002 (0.010) 0.034 (0.098) 0.088 (0.146) –0.003 (0.030) 0.005 (0.004) –0.015 (0.013) 0.002 (0.014) –0.004 (0.007)
P 0.86 0.74 0.56 0.91 0.26 0.25 0.88 0.58
rs4607517 GCK A/G Effect
a
0.004 (0.011) –0.136 (0.111) –0.128 (0.165) –0.013 (0.033) –0.006 (0.005) 0.012 (0.014) –0.002 (0.015) 0.013 (0.007)
P 0.75 0.23 0.45 0.70 0.21 0.38 0.87 0.054
rs2191349 DGKB–TMEM195 T/G Effect
a
0.001 (0.009) –0.075 (0.082) –0.046 (0.122) 0.007 (0.025) 0.002 (0.003) 0.009 (0.01) 0.015 (0.011) 0.004 (0.005)
P 0.95 0.37 0.71 0.79 0.64 0.40 0.18 0.44
rs780094 GCKR C/T Effect
a
0.012 (0.009) 0.052 (0.084) 0.006 (0.124) 0.020 (0.025) 0.009 (0.003) 0.007 (0.01) –0.019 (0.011) –0.055 (0.005)
P 0.17 0.55 0.96 0.45 8.7x10
−3
0.51 0.08 9.6 × 10
−27
rs11708067 ADCY5 A/G Effect
a
–0.010 (0.011) –0.056 (0.104) 0.047 (0.156) 0.028 (0.031) 0.0004(0.004) –0.014 (0.013) –0.013 (0.013) –0.003 (0.006)
P 0.35 0.60 0.77 0.37 0.92 0.26 0.32 0.62
rs7944584 MADD A/T Effect
a
0.023 (0.010) –0.208 (0.093) –0.170 (0.140) –0.038 (0.028) 0.007 (0.004) –0.013 (0.012) –0.016 (0.012) –0.007 (0.006)
P 0.02 0.03 0.24 0.18 0.06 0.27 0.18 0.26
rs10885122 ADRA2A G/T Effect
a
–0.021 (0.014) –0.079 (0.131) 0.168 (0.193) 0.073 (0.039) 0.01 (0.007) –0.019 (0.02) –0.02 (0.021) –0.02 (0.01)
P 0.14 0.56 0.40 0.07 0.15 0.34 0.33 0.04
rs174550 FADS1 T/C Effect
a
0.003 (0.009) –0.208 (0.086) –0.108 (0.128) 0.013 (0.026) 0.014 (0.003) 0.046 (0.010) 0.052 (0.011) –0.025 (0.005)
P 0.73 0.02 0.42 0.62 2.9 × 10
−5
8.5 × 10
−6
2.5 × 10
−6
1.9 × 10
−6
rs11605924 CRY2 A/C Effect
a
0.011 (0.009) 0.123 (0.082) –0.003 (0.123) 0.004 (0.025) 0.005 (0.004) 0.005 (0.011) 0.008 (0.011) –0.009 (0.005)
P 0.21 0.15 0.98 0.87 0.13 0.62 0.46 0.10
rs11920090 SLC2A2 T/A Effect
a
0.010 (0.012) –0.034 (0.117) –0.023 (0.174) –0.030 (0.036) 0.003 (0.005) –0.004 (0.014) –0.009 (0.015) –0.015 (0.007)
P 0.42 0.78 0.90 0.41 0.60 0.81 0.57 0.04
rs7034200 GLIS3 A/C Effect
a
–0.002 (0.009) 0.093 (0.082) 0.087 (0.122) 0.006 (0.025) 0.0002(0.003) 0.015 (0.01) 0.028 (0.011) 0.005 (0.005)
P 0.86 0.27 0.49 0.80 0.94 0.15 8.3 × 10
−3
0.37
rs340874 PROX1 C/T Effect
a
–0.007 (0.009) 0.113 (0.085) 0.093 (0.127) 0.029 (0.026) –0.007 (0.003) 0.009 (0.01) 0.003 (0.011) 0.007 (0.005)
P 0.46 0.20 0.48 0.27 0.04 0.39 0.81 0.19
rs11071657 C2CD4B A/G Effect
a
–0.006 (0.010) 0.132 (0.091) –0.007 (0.135) 0.020 (0.028) –0.004 (0.004) 0.012 (0.011) 0.002 (0.011) 0.006 (0.005)
P 0.54 0.16 0.96 0.49 0.22 0.28 0.86 0.30
rs13266634 SLC30A8 C/T Effect
a
–0.026 (0.011) –0.081 (0.094) –0.072 (0.139) 0.010 (0.029) 0.003 (0.004) 0.016 (0.011) 0.013 (0.011) 0.005 (0.005)
P 0.01 0.40 0.62 0.74 0.47 0.13 0.24 0.33
rs7903146 TCF7L2 T/C Effect
a
–0.033 (0.009) 0.026 (0.091) 0.025 (0.137) 0.003 (0.028) 0.005 (0.004) 0.007 (0.012) 0.007 (0.012) –0.006 (0.006)
P 4.4 × 10
−4
0.78 0.86 0.92 0.22 0.53 0.55 0.31
rs35767 IGF1 G/A Effect
a
0.003 (0.012) –0.102 (0.113) –0.078 (0.167) –0.005 (0.034) 0.003 (0.005) –0.009 (0.015) –0.012 (0.015) –0.002 (0.007)
P 0.81 0.38 0.65 0.87 0.56 0.52 0.43 0.84
n 28,225–
32,530
28,591–
34,130
28,557–
34,135
8,145–
9,553 cases
21,045 17,521 17,529 21,104
8,175–9,749
controls
a
Per-allele effect (s.e.m.). Results for BMI, blood pressure traits and lipid levels were kindly provided by the GIANT
31
, GlobalBPGen
32
and ENGAGE
33
consortia, respectively.
© 2010 Nature America, Inc. All rights reserved.
Nature GeNetics VOLUME 42 | NUMBER 2 | FEBRUARY 2010 111
A R T I C L E S
candidates; in SLC2A2, the lead SNP (rs11920090) is in perfect LD
(r
2
= 1.0) with rs5400 (stage 1 discovery association P = 5.9 × 10
−6
),
which codes for the amino acid substitution T110I, predicted to be
“possibly damaging” by PolyPhen
35
and PANTHER (Pdel = 0.92)
36
.
In GCKR, the lead SNP is in strong LD (r
2
= 0.93) with rs1260326,
encoding P446L, a nonsynonymous variant previously associated with
fasting glucose and HOMA-IR
11,12,30
and predicted by PolyPhen to
be “probably damaging. A recent functional study has demonstrated
that this variant indirectly leads to increased GCK activity, resulting in
the observed effects on fasting glucose and triglyceride levels
37
. Both
the SLC2A2 T110I and GCKR P446L substitutions were predicted to
be “tolerated” by SIFT
38
, highlighting the difficulties in obtaining con-
sensus functional predictions from different informatic approaches.
We used publicly available expression quantitative trait locus (eQTL)
datasets for liver
39
, cortex
40
and Epstein-Barr virus–transformed lym-
phoblastoid cell lines
41
to explore additional possible causal mecha-
nisms by testing for association between replicated loci and mRNA
expression levels of nearby genes (Online Methods). The lead SNP
in FADS1, rs174550, is in strong LD with (r
2
= 0.80) and is in close
proximity (130 bp) to rs174548, a SNP highly associated with FADS1
mRNA expression levels in liver (P = 1.7 × 10
−5
) and with FADS2
mRNA expression levels in lymphoblastoid cells (P = 3.1 × 10
−4
).
The SNP rs174548 has also been associated (up to P = 4.5 × 10
−8
)
with a number of serum glycerophospholipid concentrations in a
GWAS investigating metabolomic profiles
42
, and rs174550 also
showed strong associations (P < 5.2 × 10
−7
) with the same metabolites
(data not shown). These results are substantiated by previous work
associating SNPs in this region with the fatty acid composition of
phospholipids
43
. The latter data suggest that the minor allele variant
of rs174550 results in a reduced efficiency of the fatty acid delta-5
desaturase reaction
42
. Finally, bioinformatic analysis identifies a
perfect proxy, rs174545 (r
2
= 1.0 with rs174550), whose glucose-
raising allele abolishes a predicted target site for the miR-124
microRNA (see Online Methods). Taken together, these data support
the hypothesis that not only the abundance of fatty acids, but also
their precise composition and degree of desaturation, may influence
glucose homeostasis.
Although our study was not designed to explicitly investigate the
impact of copy number variation on glycemic traits, we took advan-
tage of existing data
44
to investigate whether any of our lead SNPs are
Box 1: Genes nearest to loci associated with fasting diabetes-related quantitative traits
The DGKB-TMEM195 locus was recently reported to be associated with fasting glucose
24
; here we report genome-wide significant replication of that finding and
evaluate the genes mapping closest to the lead SNP in further detail. DGKB encodes the β (1 of 10) isotype of the catalytic domain of diacylglycerol kinase, which
regulates the intracellular concentration of the second messenger diacylglycerol. In rat pancreatic islets, glucose increases diacylglycerol
49
, which activates protein
kinase C (PKC) and thus potentiates insulin secretion
50
. TMEM195 encodes transmembrane protein 195, an integral membrane phosphoprotein highly expressed in liver.
ADCY5 encodes adenylate cyclase 5, which catalyzes the generation of cAMP. Upon binding to its receptor in pancreatic beta cells, glucagon-like peptide 1 (GLP-1)
induces cAMP-mediated activation of protein kinase A, transcription of the proinsulin gene and stimulation of insulin secretory processes
51
.
MADD encodes mitogen-activated protein kinase (MAPK) activating death domain, an adaptor protein that interacts with the tumor necrosis factor α receptor to
activate MAPK. Both PKC and MAPK have been implicated in the proliferation of beta cells induced by GLP-1 (ref. 51), suggesting that DGKB and MADD may
contribute to beta-cell mass and insulin secretion in this manner as well. Also in this region, SLC39A13 encodes a putative zinc transporter required for connec-
tive tissue development and BMP/TGF-β signaling
52
. NR1H3 encodes the liver X receptor alpha (LXRA) protein, which contains the retinoid response element.
Glucose stimulates the transcriptional activity of LXR, which acts as a molecular switch that integrates hepatic glucose metabolism and fatty acid synthesis
53
.
ADRA2A encodes the α
2A
adrenergic receptor, which is expressed in beta cells and whose activation leads to an outward potassium current independent of the
islet potassium-sensitive ATP (K
ATP
) channel, thus possibly modifying insulin release
54
. Mice with null mutations display abnormal glucose homeostasis in
addition to cardiac hypertrophy and abnormal heart rate and blood pressure.
FADS1 encodes fatty acid desaturase 1, which catalyzes the biosynthesis of highly unsaturated fatty acids from precursor essential polyunsaturated fatty acids.
One such product is arachidonic acid; in rodent beta cells, arachidonic acid liberated by phospholipase A
2
augments glucose-mediated insulin release
55
. Two
other members of the same family, FADS2 and FADS3, also reside in this region. By directing fatty acids down this metabolic pathway, increased activity of these
enzymes may lower circulating triglyceride concentrations.
CRY2 encodes cryptochrome 2, an integral component of the mammalian circadian pacemaker
56
. Mice with null mutations in this gene present with abnormal
circadian rhythmicity and several metabolic abnormalities including impaired glucose tolerance, increased insulin sensitivity, decreased body weight and adipose
tissue, and abnormal heart rate. Together with MTNR1B
15–17
, this is the second circadian gene associated with fasting glucose in humans, contributing further
evidence to the emerging idea that this pathway regulates glucose homeostasis
57
. In the same region, MAPK8IP1 encodes the scaffolding protein JIP1. Cross-talk
between JIP1 and JIP3 has been implicated in the regulation of ASK1-SEK1-JNK signaling during glucose deprivation
58
. A missense mutation in this gene (lead-
ing to a S59N amino acid substitution) segregates with diabetes in one family affected with a Mendelian form of the disease
59
.
SLC2A2 encodes the GLUT2 transporter responsible for transporting glucose into beta cells and triggering the glucose-mediated insulin secretion cascade. In
humans, recessive mutations in this gene lead to Fanconi-Bickel syndrome, a rare disorder characterized by hepatorenal glycogen accumulation, proximal renal
tubular dysfunction and impaired utilization of glucose and galactose
60
; mouse mutants also show hyperglycemia and abnormal glucose homeostasis
61
.
GLIS3 encodes the transcription factor GLIS family zinc finger 3 isoform, a Krüppel-like zinc finger protein that both activates and represses transcription and
participates in beta-cell ontogeny
62,63
. Functional mutations in this gene cause a syndrome of neonatal diabetes and congenital hypothyroidism
63
. Polymorphisms
within this gene have recently been associated with type 1 diabetes risk (t1dgc.org).
PROX1 encodes the prospero homeobox protein 1, a novel co-repressor of hepatocyte nuclear factor 4α
64
that plays a crucial role in beta-cell development; muta-
tions in its target gene HNF4A cause maturity-onset diabetes of the young, type 1 (ref. 65).
C2CD4B (formerly FAM148B) encodes the nuclear localized factor 2 (NLF2). It is expressed in endothelial cells and upregulated by proinflammatory cytokines
66
.
As shown here, it has a high level of expression in the pancreas, although its putative molecular connection with glucose homeostasis is presently unclear.
IGF1 encodes the insulin-like growth factor 1 and is the sole genome-wide significant locus associated with HOMA-IR in our study. Humans and mice null for
IGF1 display abnormal glucose homeostasis, with insulin resistance, increased circulating insulin and insensitivity to growth hormone
67
.
© 2010 Nature America, Inc. All rig