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Diabetologia

, Volume 60, Issue 1, pp 107–115 | Cite as

Exome-chip association analysis reveals an Asian-specific missense variant in PAX4 associated with type 2 diabetes in Chinese individuals

  • Chloe Y. Y. Cheung
  • Clara S. Tang
  • Aimin Xu
  • Chi-Ho Lee
  • Ka-Wing Au
  • Lin Xu
  • Carol H. Y. Fong
  • Kelvin H. M. Kwok
  • Wing-Sun Chow
  • Yu-Cho Woo
  • Michele M. A. Yuen
  • JoJo S. H. Hai
  • Ya-Li Jin
  • Bernard M. Y. Cheung
  • Kathryn C. B. Tan
  • Stacey S. Cherny
  • Feng Zhu
  • Tong Zhu
  • G. Neil Thomas
  • Kar-Keung Cheng
  • Chao-Qiang Jiang
  • Tai-Hing LamEmail author
  • Hung-Fat TseEmail author
  • Pak-Chung ShamEmail author
  • Karen S. L. LamEmail author
Article

Abstract

Aims/hypothesis

Genome-wide association studies (GWASs) have identified many common type 2 diabetes-associated variants, mostly at the intronic or intergenic regions. Recent advancements of exome-array genotyping platforms have opened up a novel means for detecting the associations of low-frequency or rare coding variants with type 2 diabetes. We conducted an exomechip association analysis to identify additional type 2 diabetes susceptibility variants in the Chinese population.

Methods

An exome-chip association study was conducted by genotyping 5640 Chinese individuals from Hong Kong, using a custom designed exome array, the Asian Exomechip. Single variant association analysis was conducted on 77,468 single nucleotide polymorphisms (SNPs). Fifteen SNPs were subsequently genotyped for replication analysis in an independent Chinese cohort comprising 12,362 individuals from Guangzhou. A combined analysis involving 7189 cases and 10,813 controls was performed.

Results

In the discovery stage, an Asian-specific coding variant rs2233580 (p.Arg192His) in PAX4, and two variants at the known loci, CDKN2B-AS1 and KCNQ1, were significantly associated with type 2 diabetes with exome-wide significance (p discovery < 6.45 × 10−7). The risk allele (T) of PAX4 rs2233580 was associated with a younger age at diabetes diagnosis. This variant was replicated in an independent cohort and demonstrated a stronger association that reached genome-wide significance (p meta-analysis [p meta] = 3.74 × 10−15) in the combined analysis.

Conclusions/interpretation

We identified the association of a PAX4 Asian-specific missense variant rs2233580 with type 2 diabetes in an exome-chip association analysis, supporting the involvement of PAX4 in the pathogenesis of type 2 diabetes. Our findings suggest PAX4 is a possible effector gene of the 7q32 locus, previously identified from GWAS in Asians.

Keywords

Asian-specific Exome-chip association analysis PAX4 Type 2 diabetes 

Abbreviations

CSN1S1

Casein α S1

Chr

Chromosome

FGF21

Fibroblast growth factor 21

FGFR1

Fibroblast growth factor receptor 1

FPG

Fasting plasma glucose

GBCS

Guangzhou Biobank Cohort Study

GWAS

Genome-wide association studies

HKU-TRS

University of Hong Kong Theme-based Research Scheme

HWE

Hardy–Weinberg equilibrium

LD

Linkage disequilibrium

MAF

Minor allele frequency

PAX

Paired box

PC

Principal component

QC

Quality control

SGLT1

Solute carrier family 5 member 1

SNP

Single nucleotide polymorphism

TTBK2

Tau tubulin kinase 2

Introduction

Type 2 diabetes is a common disease resulting from the complex interactions between multiple genetic and environmental factors. Insights into the genetic basis of type 2 diabetes will facilitate the discovery of novel treatment targets. Since 2007, the success in genome-wide association studies (GWAS) has led to the identification of a large number of independent loci for type 2 diabetes. However, the disease-susceptibility single nucleotide polymorphisms (SNPs) identified from these GWAS are common variants which tend to confer relatively small effect sizes, altogether accounting for only 10–15% of the type 2 diabetes heritability [1]. The functional consequences of these susceptibility variants, which are mostly present in intronic or intergenic regions, remained difficult to interpret. In the past few years, the role of low-frequency (minor allele frequency [MAF] = 1–5%) and rare (MAF < 1%) coding variants with various complex traits [2, 3, 4, 5, 6, 7] is being increasingly studied. It has been suggested that most current rare variants were introduced by mutational events during the recent explosive growth of the human population [8, 9]. These rare variants are believed to confer a greater effect than the common variants because of the limited time for purifying selection to act [9, 10]. The majority of efforts to reveal type 2 diabetes susceptibility variants have been made in populations of European ancestry. Using advanced technologies, such as the exome chip and whole-genome/exome sequencing, researchers have detected associations of additional novel coding variants, both common and rare, for type 2 diabetes [4, 5] and several quantitative glycaemic traits, such as fasting glucose and insulin levels, in European populations [3, 6, 7]. As samples of European ancestry represent only a subset of human genetic variations [11], the risk variants in other populations are likely to be insufficiently characterised. A genome-wide trans-ancestry meta-analysis reported several type 2 diabetes-susceptibility variants which showed significant differences in effect sizes and associations in different populations [12]. For instance, the effect size of TCF7L2 rs7903146 was higher in Europeans than in East Asians, whilst the association signal of PEPD rs3786897 was specific to the populations of East Asians and the association signal of KLF14 rs13233731 was only significant in the European samples [12]. Such observations highlight the importance of conducting association analyses in non-European populations to detect novel loci affecting the risk of type 2 diabetes.

The advancement in array-based genotyping technology, such as exome arrays, has provided a more cost-effective approach than whole-genome or exome sequencing for assessing the association of rare and low-frequency coding variants that may be population specific. In a joint collaborative study, our group has recently reported several novel or Asian-specific coding variants associated with blood lipids [2] using a tailored Illumina HumanExome BeadChip (Asian Exomechip [13]). In the present study, we aimed to detect novel loci for type 2 diabetes in the Chinese population using this Asian Exomechip. We first conducted an exome-chip association analysis based on 5640 participants from the University of Hong Kong Theme-based Research Scheme (HKU-TRS) cohort, and genotyped 15 SNPs for replication in an independent Southern Han Chinese cohort from Guangzhou (n = 12,362).

Methods

Participants

Discovery cohort

The discovery stage involved a total of 5640 Southern Han Chinese participants (3652 cases and 1988 controls) from the HKU-TRS cohort who participated in a previous exome-chip association study for blood lipid traits [2]. The study participants were recruited from the Hong Kong West Diabetes Registry (HKWDR) [14], the Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS) [15] and the Hong Kong Chinese coronary artery disease (CAD) cohort. All participants were recruited from these clinic-based or community-based studies conducted at the Queen Mary Hospital, Hong Kong, People’s Republic of China. Details of the corresponding cohorts have been previously reported [2]. Type 2 diabetes cases were defined as meeting at least one of the following criteria: fasting plasma glucose (FPG) ≥ 7 mmol/l, 2 h glucose during OGTT ≥ 11.1 mmol/l, taking glucose-lowering agents or physician-diagnosed diabetes. All controls had no documented history of diabetes and were not receiving treatment for diabetes. Written informed consent was obtained from each participant and the study protocol was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster.

Replication cohort

The replication stage involved a total of 12,362 Southern Han Chinese participants (3537 cases and 8825 controls) of the Guangzhou Biobank Cohort Study (GBCS). The clinical characteristics and glycaemic status (outlined in the electronic supplementary material [ESM], Table 1) were based on cross-sectional data obtained at the time of blood sample collection. Details of the GBCS have been described previously [16], however, in brief, the GBCS is a collaborative project between Guangzhou No. 12 Hospital, the University of Hong Kong and the University of Birmingham (Birmingham, UK). The GBCS was established to examine the effect of genetic and environmental influences on health problems and the development of chronic diseases. Baseline recruitment was conducted from 2003 to 2008 (n = 30,519; age, ≥50 years) in Guangzhou [17]. Participants were invited to have a second examination from August 2008 to December 2012. The present study included participants who attended the second examination and from whom sufficient information was obtained to determine type 2 diabetes status. Type 2 diabetes cases were defined as meeting at least one of the following criteria: FPG ≥ 7 mmol/l, 2 h glucose during OGTT ≥ 11.1 mmol/l, HbA1c ≥ 6.5% (≥47.5 mmol/mol), taking glucose-lowering agents or self-reported physician-diagnosed diabetes. All controls were not receiving treatment for diabetes, had no documented history of diabetes and had FPG < 6.1 mmol/l and 2 h glucose during OGTT < 7.8 mmol/l. Written informed consent was obtained from each participant and the study protocol was approved by the Guangzhou Medical Ethics Committee of the Chinese Medical Association.

Genotyping and data quality control

Discovery stage

All participants were genotyped using the Asian Exomechip, which is a specially designed exome array with an add-on content of 58,317 variants in addition to the standard content of the Infinium HumanExome BeadChip (HumanExome-12v1_A; Illumina, San Diego, CA, USA). A detailed description of the Asian Exomechip design has been presented elsewhere [2, 13]; briefly, the standard content of the exome array includes 242,901 markers, including: (1) >200,000 protein-altering variants identified from approximately 12,000 sequenced genomes and exomes of primarily European ancestry; (2) >20,000 non-exonic variants contributed by multiple consortia, such as the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project; (3) variants designed for ancestry differentiation, sample tracking and for establishing segments of identity by descent (http://genome.sph.umich.edu/wiki/Exome_Chip_Design; accessed 1 May 2016). The European-based design of the Infinium HumanExome BeadChip has led to an under-representation of non-European genomes and thereby limited the coverage of low-frequency variants among non-European populations. To allow comprehensive genotyping across the full allele frequency spectrum in Asians, a custom panel of ~30,000 missense or nonsense variants identified from three independent Asian sequencing datasets of ~1000 Chinese samples were integrated into the Asian Exomechip. Additionally, a custom set of common variants selected for GWAS follow-up or fine mapping studies was also included. Genotype calling was conducted by GenTrain version 2.0 in GenomeStudio V2011.1 (Illumina). We first conducted manual inspection of genotype clusters for >55,000 variants that had either a GenTrain score <0.8, high missingness (>1%), or a poor genotype clustering, as determined by exome-chip genotyping of > 9000 individuals by collaborators [13, 18]. A total of 4550 variants with poor genotype clustering were removed. Individual-level quality control (QC) was conducted with regard to sex mismatch, duplication, biological relatedness and possible sample contamination. A principal component (PC) analysis was conducted to examine the existence of non-Chinese samples using a panel of >20,000 independent common SNPs (MAF > 0.05), with outliers excluded from the analysis. For SNP-level QC, we excluded 217,455 SNPs with MAF < 0.1%, of which 179,107 SNPs were monomorphic, 154 SNPs deviated from Hardy–Weinberg equilibrium (HWE) with p < 1 × 10−5 in controls, 3854 SNPs had >2% missingness and 8086 SNPs were originally designed for the purpose of QC (including the fingerprint SNPs for sample tracking, ancestry informative markers [AIMs] for distinguishing Europeans from native and African-Americans, and grid SNPs for the identification of identity by descent segments). After QC measures, a total of 5640 participants and 77,468 variants were included in the association analysis.

Replication stage

In the replication stage, we genotyped all SNPs that achieved p discovery < 5 × 10−4 and with potential functional relevance, except CDKN2B-AS1/DMRTA1 rs10965250 and KCNQ1 rs2237896, which have been previously reported to be of genome-wide significance (p < 5 × 10−8) in GWAS [1] and also reached exome-wide significance (p discovery < 6.45 × 10−7) in the current study. By SNPs with potential functional relevance, we refer to SNPs at or near genes that showed protein–protein interactions, shared a pathway with known type 2 diabetes susceptibility genes or were implicated in the pathogenesis of diabetes. These included PAX4 rs2233580, CDKAL1 rs10440833, FGFR1 rs2288696, ANKRD55/MAP3K1 rs456867, IGF2BP2 rs11711477, TTBK2 rs56017612 and DUSP26/UNC5D rs4739563, HCG27/HLA-C rs3869115, SCN1B rs67701503, DAP rs267939, CSN1S1 rs10030475, ZNF283/ZNF404 rs138993781, STAB1 rs740903, CARNS1 rs868167 and PDPN chromosome (Chr)1:13937002. All 15 selected SNPs were then genotyped using the MassARRAY Sequenom platform (San Diego, CA, USA) at the Beijing Genomics Institute (BGI), Beijing, People’s Republic of China. Four SNPs that either showed a low genotyping call rate (<90%; PDPN Chr1:13937002 and IGF2BP2 rs11711477) or deviated from HWE in controls (p HWE < 0.003 = 0.05/15; CDKAL1 rs10440833 and SCN1B rs67701503) in the replication study were excluded from further analysis. Thus, for the final analysis, a total of 11 SNPs were included. PAX4 rs2233580 did not deviate from HWE in controls and was therefore retained in the analysis, even though it significantly deviated from HWE in the case group (p HWE < 0.003). PAX4 has been implicated in the pathogenesis of type 2 diabetes [19], and it is recognised that a true association can lead to deviation from HWE in cases [20]. The average genotyping call rate of these 11 SNPs was 98.2%.

Variants annotation and in silico functional analysis

Function of variants and protein changes for non-synonymous SNPs were annotated by KGGSeq (version 1.0; http://grass.cgs.hku.hk/limx/kggseq/; accessed 15 April 2016) [21] according to the RefGene annotation. The pathogenic potential of the non-synonymous variants was assessed through various deleteriousness and conservation prediction tools implemented in KGGSeq, including SIFT [22] and PolyPhen [23].

Asian-specific variants

Variants were classified as ‘Asian-specific’ if they were monomorphic in both the European and African populations but polymorphic (MAF > 0) in the Asian population, according to the 1000 Genomes Project [11].

Data analysis

All statistical analyses in the discovery and replication stages were conducted using PLINK, version 1.9 (http://pngu.mgh.harvard.edu/~purcell/plink/) [24]. In the discovery stage, multiple logistic regression analysis with adjustment for age, sex and the first two PCs was employed to examine for the associations with type 2 diabetes under the additive genetic model. To assess the adiposity independent association of the top SNPs (with p discovery < 5 × 10−4) with type 2 diabetes, we further included BMI in the multiple logistic regression model. Exome-wide significance was defined as p < 6.45 × 10−7 (=0.05/77,468). To address the between-SNP linkage disequilibrium (LD), the p value-informed LD-based clumping approach, with the ‘--clump’ command implemented in PLINK, was conducted. The index SNP had the most significant p value from each clumped association region. Each index SNP formed clumps with other variants which were in LD with the index SNP (r 2 ≥ 0.2) and were within ±500 kb from the index SNP. The association between PAX4 rs2233580 and age of diabetes diagnosis was examined by univariate linear regression analysis. In the replication stage, age and sex were included as covariates in the multiple logistic regression model to assess for associations with type 2 diabetes. A Bonferroni corrected one-tailed p value <4.54 × 10−3 (=0.05/11) was used as the threshold for successful replication. Meta-analysis of the association results of the discovery and replication stages were conducted using METAL (http://csg.sph.umich.edu//abecasis/Metal/; accessed 1 May 2016) [25]. The inverse variance fixed-effect method was employed to pool the summary statistics of the two stages and heterogeneity of effect was assessed using Cochran’s Q test and I 2 index.

Results

A total of 5640 Chinese (Hong Kong) participants (see Table 1 for details) were genotyped using a custom Asian Exomechip. Single-variant association analysis was performed to assess the associations with type 2 diabetes for 77,468 polymorphic variants (Fig. 1). Of these, 48% altered protein composition and 21% were Asian-specific variants with a MAF between 0.1% and 5%.
Table 1

Clinical characteristics of study participants in the discovery stage

Variable 

Controls

Cases

p value

Number

1988

3652

-

Male (%)

56.4

60.9

<0.001

Age (years)

58.7 ± 12.1

64.8 ± 11.8

<0.001

Fasting glucose (mmol/l)

5.1 ± 0.6

7.6 ± 2.5

<0.001

BMI (kg/m2)

24.2 ± 3.7

25.9 ± 4.0

<0.001

Waist circumference (cm)

  Male

86.3 ± 8.4

91.5 ± 10.2

<0.001

  Female

79.1 ± 9.1

86.3 ± 10.9

<0.001

Coronary artery disease (%)

37.2

43.8

<0.001

Hypertension (%)

40.3

85.5

<0.001

Use of anti-hypertensive drug (%)

31.5

83.0

<0.001

Use of lipid-lowering drug (%)

37.5

65.6

<0.001

Ever smoker (%)

34.4

35.9

0.251

Data are mean ± SD

Fig. 1

Manhattan plot of discovery stage results. The y-axis represents the –log10(p value), and the x-axis represents the genomic position. The dots represent the 77,468 SNPs analysed, relative to their position on each chromosome (alternating black and grey). The black horizontal dashed line indicates exome-wide significance (6.45 × 10−7). Diamonds show the exome-wide significant SNPs

In the discovery stage, single-variant association analysis was conducted in 3652 cases and 1988 controls, adjusted for age, sex and the first two PCs. We detected 34 index SNPs within 32 loci significantly associated with type 2 diabetes at p discovery < 5 × 10−4 (ESM Table 2), of which three variants reached exome-wide significance (p discovery < 6.45 × 10−7) (Table 2). These included the known associations at CDKN2B-AS1/DMRTA1 rs10965250 (p discovery = 5.93 × 10−8, OR [95% CI] 0.80 [0.74, 0.87]) and KCNQ1 rs2237896 (p discovery = 1.82 × 10−7; OR [95% CI] 0.80 [0.73, 0.87]) reported in previous GWAS, as well as an Asian-specific variant, rs2233580 (p.Arg192His) of PAX4 (p discovery = 1.75 × 10−7; OR [95% CI] 1.39 [1.23, 1.56]). As PAX4 is a known gene for MODY [26], we further examined its association with age of diabetes diagnosis. The risk allele (T) of rs2233580 was found to be significantly associated with younger age of diabetes diagnosis (p = 6.01 × 10−4 [ESM Table 3]; β [95% CI] −1.45 [−2.28, −0.62] [data not shown]; mean age of diagnosis ± SD [years] TT 52 ± 13, CT 53 ± 13, CC 54 ± 13 [ESM Table 3]). In addition, we identified several loci not previously reported to be associated with type 2 diabetes: FGFR1 rs2288696 (p discovery = 2.29 × 10−5; OR [95% CI] 0.73 [0.63, 0.85]), TTBK2 rs56017612 (p discovery = 7.40 × 10−5; OR [95% CI] 0.72 [0.61, 0.84]) and DUSP26/UNC5D rs4739563 (p discovery = 7.48 × 10−5; OR [95% CI] 0.80 [0.72, 0.90]) (ESM Table 2). The association of all SNPs with type 2 diabetes remained statistically significant after further adjustment for BMI (ESM Table 2).
Table 2

Association results of SNPs reaching exome-wide significance (p < 6.45 × 10−7) in the discovery stage

Nearest gene(s)

SNP

Position

Annotation

A1

A2

MAF

OR (95% CI)

p discovery a

p discovery b

Cases

Controls

Asian-specific variant

   PAX4

rs2233580

7:127253550

p.Arg192His

T

C

0.145

0.113

1.39 (1.23, 1.56)

1.75 × 10−7

7.62 × 10−6

Established type 2 diabetes susceptibility variants

   CDKN2B-AS1/DMRTA1

rs10965250

9:22133284

Intergenic

A

G

0.384

0.430

0.80 (0.74, 0.87)

5.93 × 10−8

8.80 × 10−10

   KCNQ1

rs2237896

11:2858440

Intronic

A

G

0.311

0.359

0.80 (0.73, 0.87)

1.82 × 10−7

1.53 × 10−8

A1, minor allele; A2, major allele ORs are relative to the minor allele

aAdjusted for age, sex, PC1 and PC2

bAdjusted for age, sex, BMI, PC1 and PC2

In the replication stage, 11 of the 15 selected SNPs passed QC and were analysed in 3537 cases and 8825 controls. Replication and combined association results of these SNPs are shown in Table 3. Of these SNPs, eight showed consistent direction of effects. Only the association of the PAX4 missense variant, rs2233580, with type 2 diabetes was successfully replicated (one-tailed p replication = 1.22 × 10−9; OR [95% CI] 1.28 [1.18, 1.39]; remained significant after Bonferroni correction). Meta-analysis of the association results gave a genome-wide significant association, with no evidence of heterogeneity in effect size (p meta = 3.74 × 10−15, OR [95% CI] 1.31 [1.23, 1.40] [Table 3]; I 2 = 10, p heterogeneity = 0.292 [data not shown]). The associations of FGFR1 rs2288696, TTBK2 rs56017612 and DUSP26/UNC5D rs4739563 were not significant in the replication cohort. However, the direction of effects for both FGFR1 rs2288696 and TTBK2 rs56017612 were consistent with those from the discovery stage. A modest association was observed at a missense variant of CSN1S1 (rs10030475 [p.Pro137Thr]; one-tailed p replication = 7.5 × 10−3, OR [95% CI] 0.93 [0.87, 0.99]). However, this association did not pass Bonferroni correction for multiple testing in the replication stage.
Table 3

Replication and combined association results

Gene(s)

SNP

A1

Discovery Hong Kong (3652 cases vs 1988 controls)

Replication Guangzhou (3537 cases vs 8825 controls)

Combined Hong Kong + Guangzhou (7189 cases vs 10,813 controls)

OR (95% CI)

p discovery a

OR (95% CI)

One-tailed

p replication a

Dir

OR (95% CI)

p meta a

PAX4

rs2233580

T

1.39 (1.23, 1.56)

1.75 × 10−7

1.28 (1.18, 1.39)

1.22 × 10−9b

+ +

1.31 (1.23, 1.40)

3.74 × 10−15

FGFR1

rs2288696

A

0.73 (0.63, 0.85)

2.29 × 10−5

0.98 (0.88, 1.09)

0.350

− −

0.88 (0.81, 0.96)

4.57 × 10−3

ANKRD55/MAP3K1

rs456867

T

0.84 (0.77, 0.91)

4.78 × 10−5

0.99 (0.93, 1.05)

0.363

− −

0.94 (0.89, 0.98)

8.57 × 10−3

TTBK2

rs56017612

C

0.72 (0.61, 0.84)

7.40 × 10−5

0.90 (0.80, 1.02)

0.046

− −

0.83 (0.75, 0.92)

2.11 × 10−4

DUSP26/UNC5D

rs4739563

T

0.80 (0.72, 0.90)

7.48 × 10−5

1.00 (0.93, 1.08)

0.518

− +

0.93 (0.87, 0.99)

0.020

HCG27/HLA-C

rs3869115

C

0.81 (0.73, 0.90)

1.04 × 10−4

0.99 (0.92, 1.07)

0.418

− −

0.93 (0.87, 0.99)

0.016

DAP

rs267939

A

0.79 (0.69, 0.89)

1.85 × 10−4

1.01 (0.92, 1.10)

0.545

− +

0.92 (0.86, 0.99)

0.035

CSN1S1

rs10030475

T

0.85 (0.78, 0.92)

1.86 × 10−4

0.93 (0.87, 0.99)

7.50 × 10−3

− −

0.90 (0.85, 0.94)

3.28 × 10−5

ZNF283/ZNF404

rs138993781

G

0.27 (0.13, 0.55)

2.93 × 10−4

0.69 (0.34, 1.39)

0.148

− −

0.43 (0.26, 0.71)

1.03 × 10−3

STAB1

rs740903

T

1.19 (1.08, 1.31)

3.02 × 10−4

1.00 (0.94, 1.07)

0.450

+ +

1.06 (1.01, 1.12)

0.030

CARNS1

rs868167

A

0.67 (0.54, 0.83)

3.33 × 10−4

1.05 (0.90, 1.23)

0.730

− +

0.90 (0.79, 1.02)

0.109

ORs are relative to the minor allele.

aAdjusted for age and sex

bRetained significance following Bonferroni correction for multiple testing in the replication analysis

For effects in the same direction as in the discovery stage analysis, one-tailed p values were calculated as p/2; for effects in the opposite direction as in the discover stage analysis, one-tailed p values were calculated as 1 − p/2

A1, minor allele; Dir, direction of effect

Discussion

The present study reports the first exome-chip association analysis on type 2 diabetes in a Chinese population. By genotyping 5640 Chinese participants using a custom Asian Exomechip, which interrogated 77,468 polymorphic SNPs, we identified the association of an Asian-specific coding variant in PAX4 and replicated the associations of some known type 2 diabetes-susceptibility loci. We also detected a few possible candidates which showed potential functional relevance in the pathogenesis of type 2 diabetes, such as TTBK2, FGFR1 and CSN1S1.

The identification of the Asian-specific and probably damaging variant of PAX4 is the major finding of this study. PAX4 encodes a member of the paired box (PAX) family of paired-homeodomain factors. PAX4 functions as a transcription repressor and plays a crucial role in pancreatic beta cell function and development [27]. It also plays a role in beta cell proliferation and survival [28, 29]. Heterozygous Pax4-knockout mice harbour less mature pancreatic beta and delta cells, but have numerous abnormally clustered alpha cells, indicating the essential role of PAX4 in the differentiation of beta and delta cell lineages [30]. PAX4 has been shown to repress the transcriptional activity of insulin [19] and glucagon [31] promoters. PAX4 is located at 7q32, a region reported to be associated with type 2 diabetes in previous GWAS of Asians [32, 33]. An intergenic variant rs6467136 located near GRIP and GCC1–PAX4 was reported to be associated with type 2 diabetes in a meta-analysis of eight GWAS in East Asians [32], whilst rs10229583, located downstream of PAX4, was identified as a risk variant in a GWAS for type 2 diabetes in a Chinese population [33]. Such observations, together with our findings, suggest that the effect of PAX4 may be more evident in East Asians than in other populations. The association of type 2 diabetes with both of these SNPs appear to be independent of the missense variant rs2233580 (p.Arg192His), which was identified in the current study. According to the 1000 Genomes project [11], rs2233580 shows very low LD with both rs6467136 (r 2 = 0.03) and rs10229583 (r 2 = 0.02). The association of rs6467136 was not significant (p discovery = 0.284). Data for rs10229583 were not available for analysis in the present study. Our findings provide evidence that PAX4 is a possible effector gene at 7q32, a GWAS locus for type 2 diabetes. Our exome chip achieves 50% coverage of the coding variants within this gene region. Nonetheless, we were unable to eliminate the possibility that the association of rs2233580 with type 2 diabetes that was identified in the current study resulted from tagging of other causative coding variants which were not covered by our exome chip. However, its functional significance, as demonstrated by in silico [22, 23] and in vitro [34, 35] analyses, suggests that this SNP is likely to be the causative variant. While in silico analysis of the two previously reported intergenic variants was unable to define their functional relevance (RegulomeDB score = 5 for rs10229583 and = 6 for rs6467136), rs2233580 was predicted to be damaging by multiple prediction tools (SIFT score = 0; PolypPhen2 HDIV score = 1; Polyphen2 HVAR score = 0.99) [22, 23]. An in vitro study showed that the transcriptional repressor activities of PAX4 p.Arg192His on human insulin and glucagon promoters were reduced when compared with wild-type PAX4 [34]. The Arg192 residue is highly conserved across different species, including human, mouse, rat and chimpanzee, and this residue has been shown to make direct contact with the major groove of DNA-binding sequences [35]. An amino acid change in the homeodomain of PAX4 may cause a defect in its transcriptional activity. It has been proposed that this variant may affect diabetes risk through its effect on beta cell proliferation in the adult pancreas, or beta cell differentiation and maturation during development, leading to beta cell mass reduction [34]. While rs2233580 has a frequency of ~10% among Asian populations, according to the 1000 Genomes project this variant was found to be monomorphic in European and African individuals [11], suggesting interrogation in less-studied non-European populations would facilitate the identification of novel population-specific associations. Our finding of an Asian-specific variant also has implications for the construction of polygenic genetic scores to predict type 2 diabetes in Asian populations.

Our observation of the significant association of PAX4 rs2233580 with type 2 diabetes was in agreement with findings from a large-scale whole-genome/exome sequencing study conducted by the GoT2D and T2D-GENES consortia [36], which was recently published during the review process of our manuscript. rs2233580 was reported to be associated with type 2 diabetes exclusively in 2165 East Asian individuals at genome-wide significance (p = 9.3 × 10−9), and this association was further replicated in three independent East Asian cohorts [36]. Mutations in PAX4 have been found to cause the rare monogenic form, MODY, in Thai individuals [26]. On the other hand, common variants of a number of established MODY genes have been found to be associated with type 2 diabetes, including GCK, HNF1α (also known as HNF1A), HNF4α (also known as HNF4A), HNF1β (also known as HNF1B) and PDX1 [37, 38, 39]. Findings from the current study and those reported by the GoT2D and T2D-GENES consortia suggest that PAX4 also harbours common variants that confer susceptibility to type 2 diabetes. Interestingly, in a previous GWAS of East Asians, the risk allele of a common variant, rs10229583, located downstream of PAX4, was reported to be associated with higher risk of type 2 diabetes and a younger age of diagnosis [33]. Among the 3652 cases in the current study, individuals who carried the risk allele (T) of the PAX4 missense variant rs2233580 were also significantly younger at the time of diagnosis. In contrast, the GoT2D and T2D-GENES consortia reported no significant association between rs2233580 and age of diagnosis in a total of 1619 cases from three independent cohorts of East Asian ancestry (Hong Kong Chinese, Korean and Singapore Chinese) [36]. This contradictory observation could be attributed to the much larger sample size of the current study, which provided sufficient power to detect the association (ESM Table 3). Furthermore, study heterogeneity caused by different ascertainment criteria for type 2 diabetes cases in the studies may have also contributed to the discordant observations. A meta-analysis of our data with those of the three independent cohorts has provided evidence to support the association of PAX4 rs2233580 with younger age at diagnosis (p meta = 0.007; z score = −2.717; I 2 = 58.5, p heterogeneity = 0.065; ESM Table 3).

Although unable to reach genome/exome-wide significance, the potential functions of TTBK2, FGFR1 and CSN1S1 have made them possible candidates for type 2 diabetes. Tau tubulin kinase 2 (TTBK2) is a serine/threonine kinase known to phosphorylate tau and tubulin [40]. TTBK2 is involved in regulation of the sodium-dependent glucose transporter, solute carrier family 5 member 1 (SGLT1) [41], which is responsible for the absorption of glucose and galactose in the intestine and is involved in the reabsorption of glucose in the kidney [42]. Depletion of TTBK2 has been shown to decrease SGLT1 stability in the cell membrane and lead to loss of glucose transport capacity in Xenopus oocytes [41]. Mice with attenuated fibroblast growth factor receptor 1 (FGFR1) signalling exhibited a reduced number of beta cells, impaired expression of glucose transporter 2, enhanced proinsulin content in beta cells and developed diabetes with age [43]. FGFR1 is the primary receptor of fibroblast growth factor 21 (FGF21) and hence regulates FGF21 responsiveness. FGF21 has shown beneficial metabolic effects in animals and humans [44] and our team previously demonstrated that high FGF21 levels could predict type 2 diabetes development [15]. The paradoxical increase in FGF21 levels in patients with type 2 diabetes suggest that FGF21 resistance may play a role in the pathogenesis of type 2 diabetes [44]. Our finding that a variant of FGFR1 is associated with type 2 diabetes is supportive of such a possibility. Casein α S1 (CSN1S1) is a member of the casein family that has been shown to possess proinflammatory properties, such as the upregulation of IL-1β [45]. Data from animal studies and clinical trials is suggestive of a causative role for IL-1β in the loss of beta cell mass in type 2 diabetes [46]. Overall, given their potential functional relevance in the pathogenesis of type 2 diabetes, more detailed investigation of these genes, such as deep sequencing analysis, is warranted.

A potential limitation of the present study was the under-representation of rare functional variants specific to the Chinese populations in the exome chip. With an attempt to ameliorate this limitation, we included additional coding variants to augment the coverage. Small sample size has always been a major limitation hindering the identification of rare variants, as demonstrated in this study. The sample size of the discovery stage was relatively small and therefore lacked statistical power to detect variants with modest effect size or very low frequency. Future large-scale meta-analysis with other Asian cohorts may serve to identify more functional variants that are specific to our population. Trans-ethnic meta-analysis will help to enhance the fine-mapping resolution of causal variants. An additional limitation of the present study may be the strategy used for selecting SNPs for replication.

In summary, the significant association of an Asian-specific coding variant, rs2233580 (p.Arg192His), with type 2 diabetes was identified in an exome-chip association analysis in a Chinese population. Our findings provide compelling evidence that PAX4 could be a possible effector gene of the 7q32 locus and support its involvement in the pathogenesis of type 2 diabetes.

Notes

Acknowledgements

The authors thank all the study participants and the clinical and research staff of the University of Hong Kong Theme-based Research Scheme (HKU-TRS) and the Guangzhou Biobank Cohort Study (GBCS) for their contribution to this research study.

Data availability

Summary statistics of the current study are available from the corresponding author (ksllam@hku.hk) on reasonable request.

Funding

This work was supported by: the Hong Kong Research Grant Council Theme Based Research Scheme (T12-705/11) and Collaborative Research Fund (HKU2/CRF/12R); the University of Hong Kong Foundation for Educational Development and Research (SN/1f/HKUF-DC;C20400.28505200); the Guangzhou Public Health Bureau (201102A211004011); and the Guangzhou Science and Technology Bureau, Guangzhou, People’s Republic of China (2002Z2-E2051; 2012J5100041; 2013J4100031).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

KSLL, PCS, HFT and THL conceived the study, undertook project leadership and are guarantors of this work. CYYC and CST analysed the data and wrote the first draft of the manuscript. All authors contributed to the drafting and critical revision of the manuscript. PCS, CST and SSC interpreted the data and provided useful comments on the data analysis. CYYC, AX, CHL, KWA, LX, CHYF, KHMK, WSC, YCW, MMAY, JSHH, YLJ, BMYC, KCBT, FZ, TZ, GNT, KKC and CQJ were involved in the sample collection, selection and phenotype data preparation for the University of Hong Kong Theme-based Research Scheme (HKU-TRS) and the Guangzhou Biobank Cohort Study (GBCS) cohorts. KSLL, HFT, THL, GNT, KKC and CQJ were involved in the database management for the HKU-TRS and GBCS cohorts. All authors approved the final version of the manuscript.

Supplementary material

125_2016_4132_MOESM1_ESM.pdf (76 kb)
ESM (PDF 76 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Chloe Y. Y. Cheung
    • 1
  • Clara S. Tang
    • 2
  • Aimin Xu
    • 3
    • 4
    • 5
  • Chi-Ho Lee
    • 1
  • Ka-Wing Au
    • 1
  • Lin Xu
    • 6
  • Carol H. Y. Fong
    • 1
  • Kelvin H. M. Kwok
    • 1
  • Wing-Sun Chow
    • 1
  • Yu-Cho Woo
    • 1
  • Michele M. A. Yuen
    • 1
  • JoJo S. H. Hai
    • 1
  • Ya-Li Jin
    • 7
  • Bernard M. Y. Cheung
    • 1
  • Kathryn C. B. Tan
    • 1
  • Stacey S. Cherny
    • 8
  • Feng Zhu
    • 7
  • Tong Zhu
    • 7
  • G. Neil Thomas
    • 9
  • Kar-Keung Cheng
    • 9
  • Chao-Qiang Jiang
    • 7
  • Tai-Hing Lam
    • 6
    • 7
    Email author
  • Hung-Fat Tse
    • 1
    • 10
    Email author
  • Pak-Chung Sham
    • 8
    • 11
    • 12
    Email author
  • Karen S. L. Lam
    • 1
    • 3
    • 4
    Email author
  1. 1.Department of MedicineUniversity of Hong Kong, Queen Mary HospitalHong KongPeople’s Republic of China
  2. 2.Department of SurgeryUniversity of Hong KongHong KongPeople’s Republic of China
  3. 3.State Key Laboratory of Pharmaceutical BiotechnologyUniversity of Hong KongHong KongPeople’s Republic of China
  4. 4.Research Centre of Heart, Brain, Hormone and Healthy Ageing, Li Ka Shing Faculty of MedicineUniversity of Hong KongHong KongPeople’s Republic of China
  5. 5.Department of Pharmacology & PharmacyUniversity of Hong KongHong KongPeople’s Republic of China
  6. 6.School of Public Health, Room 505, Faculty of Medicine Building, William M.W. Mong BlockUniversity of Hong KongHong KongPeople’s Republic of China
  7. 7.Molecular Epidemiological Research CentreGuangzhou Number 12 HospitalGuangzhouPeople’s Republic of China
  8. 8.Department of PsychiatryUniversity of Hong KongHong KongPeople’s Republic of China
  9. 9.Institute of Applied Health ResearchUniversity of BirminghamBirminghamUK
  10. 10.Hong Kong-Guangdong Joint Laboratory on Stem Cell and Regenerative MedicineUniversity of Hong KongHong KongPeople’s Republic of China
  11. 11.Centre for Genomic Sciences, Centre for Genomic SciencesUniversity of Hong KongHong KongPeople’s Republic of China
  12. 12.State Key Laboratory in Brain and Cognitive SciencesUniversity of Hong KongHong KongPeople’s Republic of China

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