Genome-Wide Association Study Identifies Variants in Casein Kinase II (CSNK2A2) to be Associated With Leukocyte Telomere Length in a Punjabi Sikh Diabetic CohortCLINICAL PERSPECTIVE
Background—Telomere length is a heritable trait, and short telomere length has been associated with multiple chronic diseases. We investigated the relationship of relative leukocyte telomere length with cardiometabolic risk and performed the first genome-wide association study and meta-analysis to identify variants influencing relative telomere length in a population of Sikhs from South Asia.
Methods and Results—Our results revealed a significant independent association of shorter relative telomere length with type 2 diabetes mellitus and heart disease. Our discovery genome-wide association study (n=1616) was followed by stage 1 replication of 25 top signals (P<10–6) in an additional Sikhs (n=2397). On combined discovery and stage 1 meta-analysis (n= 4013), we identified a novel relative telomere length locus at chromosome 16q21 represented by an intronic variant (rs74019828) in the CSNK2A2 gene (β=−0.38; P=4.5×10−8). We further tested 3 top variants by genotyping in UK cardiovascular disease (UKCVD) (whites n=2952) for stage 2. Next, we performed in silico replication of 139 top signals (P<10–5) in UK Twin, Nurses Heart Study, Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, and MD Anderson Cancer Controls (n=10 033) and joint meta-analysis (n=16 998). The observed signal in CSNK2A2 was confined to South Asians and could not be replicated in whites because of significant difference in allele frequencies (P<0.001). CSNK2A2 phosphorylates telomeric repeat binding factor 1 and plays an important role for regulation of telomere length homoeostasis.
Conclusions—By identification of a novel signal in telomere pathway genes, our study provides new molecular insight into the underlying mechanism that may regulate telomere length and its association with human aging and cardiometabolic pathophysiology.
Leukocyte telomere length has been inversely associated with multiple diseases including osteoporosis, hypertension, myocardial infarction, coronary heart disease (CHD), type 2 diabetes mellitus (T2D), and Alzheimer disease.1–5 Growing evidence suggests that telomere length plays a critical role in cellular aging.6,7 Because loss of telomeric DNA is affected by cell division and oxidative stress, and induces cellular senescence, telomere length has been postulated as a biomarker of senescence and human aging in many published studies.6,8 Interindividual variation in telomere length at birth and subsequent years is attributed to by both genetic and environmental factors that start in utero.9 Ethnic differences in the relative leukocyte telomere length (RTL) have been reported in multiethnic studies where populations such as blacks and Hispanics had significant age-associated differences in RTL than white participants.10 Furthermore, a cumulative effect of differential exposures to oxidative stress and other environmental stressors on telomere attrition in different ethnic groups have been shown to be predictors of cellular and biological aging that may affect race and ethnicity-related health outcomes.11–13
Clinical Perspective on p 295
Gene mapping and twin studies have confirmed the strong influence of genetic factors for controlling RTL with heritability estimates ranging from 0.36 to 0.84.14–16 Genome-wide association studies (GWAS) and candidate gene studies have identified common genetic variation contributing to RTL in healthy and disease conditions.13,17–20 Most GWAS on RTL have been performed in populations of European ancestry. With exception of 3 small studies (composed of 40 men and women from Chennai, India,12 218 men from United Kingdom,21 and the largest with 238 patients who had undergone coronary artery bypass graft and 238 controls from Mumbai, India), no previous study has comprehensively evaluated the association of RTL with cardiometabolic risk or the role of genetic factors on RTL in South Asians. The goals of this investigation were (1) to test association of RTL with cardiometabolic traits in this diabetic sample with a high risk of CHD, (2) to confirm whether gene variants identified in earlier GWAS replicate in a diabetic case–control cohort of Punjabi Sikhs, and (3) to identify new genomic regions associated with RTL by GWAS, replication, and meta-analysis studies in cohorts of South Asian and European ancestry.
Materials and Methods
Sample and Characteristics
Our primary Punjabi Sikh discovery and replication study comprised 4013 individuals including 1616 in the GWAS (discovery) and 2397 in the replication (stage 1; Tables I and IIA in the Data Supplement; Figure 1). These subjects were part of the Asian Indian Diabetic Heart Study (AIDHS), also named the Sikh Diabetes Study (SDS) as described previously.22 The AIDHS/SDS has unique characteristics that are ideal for genetic studies. Sikhs are strictly a nonsmoking population, and ≈50% of participants are teetotalers and life-long vegetarians. All individuals for the GWAS discovery cohort were recruited from 1 geographical location. Diagnosis of T2D was confirmed by scrutinizing medical records for symptoms, use of medication, and measuring fasting glucose levels following the guidelines of the American Diabetes Association23 as described previously.24 Details of sample recruitment and clinical phenotypes are described previously.22 The selection of controls was based on a fasting glucose of <100.8 mg/dL or a 2-hour glucose <141.0 mg/dL. All blood samples were obtained at the baseline visits.
CHD was considered if there was use of nitrate medication (nitroglycerine), electrocardiographic evidence of angina pain, coronary angiographic evidence of severe (>50%) stenosis, or echocardiographic evidence of myocardial infarction. Diagnosis was based on date of coronary artery bypass graft or angioplasty and medication usage obtained from patient records as described previously.25 In this study, ≈11% of participants had CHD in discovery and 21% in replication cohorts. All participants signed a written informed consent for the investigations. The study was reviewed and approved by the University of Oklahoma Health Sciences Center’s Institutional Review Board, as well as the Human Subject Protection Committees at the participating institutes in India. Description of the data sets used in stage 2 and stage 3 replication is explained in section in the Data Supplement. Clinical characteristics of the stage 2 replication and stage 3 and look-up cohorts are described in Table IIB in the Data Supplement.
Anthropometric and Metabolic Measures
Body mass index (BMI) was calculated as (weight [kg]/height [meter]),2 and waist-to-hip ratio was calculated as the ratio of abdomen or waist circumference to hip circumference. Details of demographic, anthropometric, and clinical traits are summarized in Table IIA in the Data Supplement. Insulin was measured by radioimmuno assay (Diagnostic Products, Cypress, USA). Serum lipids (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, very-low-density lipoprotein cholesterol, and triglycerides) were measured by using standard enzymatic methods (Roche, Basel, Switzerland) as described.26,27 C-peptide, leptin, amylin, and monocyte chemoattractant protein-1 (MCP-1) measures were simultaneously quantified using Millipore’s Magnetic MILLIPLEX Human Metabolic panel (St. Charles, MO) and analyzed on a Bio-plex 200 multiplex system (Bio-Rad Hercules, CA) as described previously.28
Punjabi Sikh Discovery GWAS
Study design for the RTL GWAS is shown in Figure 1. Clinical characteristics of GWAS (discovery) and stage 1 (replication) in Punjabi Sikh cohorts are described in Table IIA in the Data Supplement. After quality control as described previously,22 474 231 directly genotyped single-nucleotide polymorphisms (SNPs) (minor allele frequency [MAF] ≥5%) in 1616 subjects (842 cases and 774 controls) from 1850 total subjects were available for association testing. To increase genome coverage, genotypes were imputed for untyped SNPs and in-dels using the 1kG multiethnic reference panel, yielding a total of 6 378 483 variants, and of these, 5 904 251 with MAF ≥5% were analyzed in the current investigation.
Measurement of RTL
Genomic DNA was extracted from blood buffy coats as described previously.29 DNA was quantified and equilibrated using Quant-iTed with PicoGreen and using lambda DNA standard (Invitrogen, Eugene, OR). Telomere length was measured on the Applied Biosystems 7900HT Genetic Analyzer using a modified version of Cawthon quantitative polymerase chain reaction–based method.30 Each individual DNA sample of 20 ng/μL was assayed in duplicate for measuring telomere length. Primer sequences used to amplify the single copy gene (36b4) and telomere repeats are listed in Table III in the Data Supplement. Each run also contained no template controls and 5 to 6 technical replicates previously determined to have short, medium, and long telomere. Samples from patients with T2D and CHD and healthy controls were run blindly for RTL measurements both in discovery and stage 1 replication. A standard curve was used with a range of concentrations (2-fold dilutions) and mixing multiple samples during initial optimization. Any sample that fell out of range was repeated, and outliers (≈2%) were discarded. RTL was calculated using telomere/single copy ratio of telomere repeats (T) to single copy gene (S). Assays were conducted blind to the disease status, age, or sex of the individuals. The overall coefficient of variation for the telomere length variable in AIDHS/SDS is 2.83% in telomere, 1.34% in single copy run, and 7.80% in T/S ratio for the entire sample. The interplate mean coefficient of variation was 4.84% in telomere, 3.26% in single copy, and 7.41% in T/S ratio.
In most consortium cohorts (UKCVD, UKTWIN, MD Anderson Cancer Controls, Nurses Heart Study, and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO]), the telomere length was measured similarly using quantitative polymerase chain reaction–based methods.19,31–33 Despite measured similarly, the mean (±SD) of RTL ranged from 1.19 (0.37) to 3.71 (0.69) and varied across studies (Table IIB in the Data Supplement).
Genotyping and Statistical Analysis
Genome-Wide Genotyping and Quality Control: (Discovery)
Genome-wide genotyping was performed using the Human 660W Quad BeadChip panel (Illumina, Valencia, CA) described in detail previously.22 Briefly, samples with genotyping call rate <95%, cryptic relatedness, population outliers, and extremes of heterozygosity (±3 SD) were removed, and SNPs with genotyping call rate <95%, departures from Hardy–Weinberg equilibrium (P<10–7), or MAF <5% were excluded using the software PLINK34 before association testing or imputation. As described previously in Saxena et al,22 the inbreeding coefficient and measures of autozygosity were determined using the PLINK. We identified runs of homozygosity using the metrics defined in Nalls et al,35 evaluating 1 Mb autosomal regions with ≥50 adjacent SNPs, with a sliding window of 50 SNPs including no more than 2 SNPs with missing genotypes and 1 possible heterozygous genotype.
RTL Association to Cardiometabolic Traits
Associations between natural log transformed (ln) RTL and cardiometabolic traits including anthropometric traits (age, BMI, waist), diabetes mellitus (fasting glucose, homeostasis model assessment for insulin resistance and homeostasis model assessment for β cell function, C-peptide, leptin, and amylin, and so on), and cardiovascular traits (CHD, blood pressure, and MCP-1) were assessed using linear and logistic regression in SPSS, adjusting for significant covariates, including age, sex, and T2D status.
Sikh RTL Genome-Wide Association Analysis (Discovery)
Associations of SNPs with ln-RTL were tested using linear regression and an additive genetic model. Age, sex, BMI, T2D status, and 5 principal components to adjust for residual population stratification were included as covariates. Because the existing HapMap2 or HapMap3 and 1kG data do not include Sikhs, the 5 principal components used for this correction were estimated using our Sikh population sample. After association analyses the genomic control inflation factor, λ was 1.0, so no adjustments were made (Figure 2A).
In addition to analysis of directly genotyped SNPs, we performed imputation using the Impute 2 program.36–38 Imputation was based on the entire multiethnic 1000 genomes reference panel of 28.3 M autosomal SNPs and short in-dels (release v2) in 1092 individuals from Africa, Asia, Europe, and the Americas.39 Imputed SNPs were analyzed using linear regression for ln-RTL using SNPTEST,36,38 adjusted for covariates age, sex, BMI, T2D, and the 5 principal component (Figure I in the Data Supplement). Postimputation quality control included removal of SNPs with an information score of ≥0.5 and MAF <5%. The inflation factor, λ, for imputed SNPs was 1.021 (Figure 2B)
Punjabi Sikh Discovery GWAS
Study design for the RTL GWAS is shown in Figure 1. Clinical characteristics of GWAS (discovery) and stage 1 (replication) in Punjabi Sikh cohorts are described in Table IIA in the Data Supplement. Principal components analysis revealed little population structure (Figure I in the Data Supplement). After quality control as described previously,22 474 231 directly genotyped SNPs (MAF ≥5%) in 1616 subjects (842 cases and 774 controls) from 1850 total subjects were available for association testing after removing samples showing cryptic relatedness through identity by descent sharing. Also, as reported previously in Saxena et al,22 average inbreeding coefficients (F=0.041±0.018) were comparable to other Indian populations but higher than European outbred populations. To increase genome coverage, genotypes were imputed for untyped SNPs and in-dels using the 1kG multiethnic reference panel, yielding a total of 6 378 483 variants, and of these 5 904 251 with MAF ≥5% were analyzed in the current investigation for association analysis. We performed a genome-wide association analysis for ln-RTL using multiple linear regression and adjusting for covariates age, sex, BMI, T2D, and the 5 principal components of ancestry (Figures 3 and 4; see Methods). No association signals exceeded genome-wide significance, but strong signals (P<10–7) were seen at 3 loci: chr 16q21 (CSNK2A2), 5p13.2 (C5ORF42), and 5q21.3 (FER; Figure 3; Table IV in the Data Supplement). The association at the CSNK2A2 locus remained strongly significant when performed separately in diabetic cases (rs74019828 [β±SE] −0.40±0.13), P=0.0019, and controls (rs74019828 [β±SE] −0.41±0.11), P=0.00036.
Replication Samples and Characteristics
Recruitment and diagnostic details of T2D for stage 1 (Sikh) replication sample are similar as described above for discovery cohort. Clinical and demographical details of the replication data set used for the stage 1 and stage 2 and 3 replication cohorts are provided in the section in the Data Supplement and Table IIB in the Data Supplement.
Replication Through De Novo Genotyping (Stage 1)
Genotyping of 25 SNPs selected for validation in the Punjabi replication sample (n=2397) was performed on the BioMark HD MX/HX (Fluidigm) using the Fluidigm 96.96 GT Dynamic Array chip and SNPtype assays (Fluidigm, San Francisco, CA) at Rutgers’s Core laboratory. On completion of polymerase chain reaction amplification, end point data were collected using the Fluidigm BioMarkHD Genetic Analysis instrument. Individual genotype calls and data analysis were performed using the Fluidigm SNP Genotyping Analysis Software v 3.0.2.
Replication Through De Novo Genotyping (Stage 2)
We further selected 3 SNPs from the 2 top independent loci identified by meta-analysis of the discovery and stage 1 populations for replication in the UKCVD cohort (n=2952). These 3 SNPs included rs7196068 and rs74019828 for CSNK2A2 and rs78341307 for FER signals and were genotyped using TaqMan assays designed by Applied Biosystems (Foster City, CA) and KASPar technology (KBiosciences, Herts, United Kingdom).
Statistical Analysis (Replication Studies)
In each replication sample, genetic association analysis for ln-RTL was performed using a linear regression model, with SNPs coded in an additive genetic model and cohort-specific adjustment for covariates. To identify RTL association signals common to Punjabi and other ethnic populations, we analyzed the association of 139 top independent signals (P<10–5) derived from the discovery cohort and stage 1 meta-analysis using genotyping data available from 4 previously published GWAS in leukocyte telomere length. These studies comprised a total of 10 033 individuals from the UKTWIN,17 Nurses Heart Study, PLCO,19 and MD Anderson Cancer Controls from lung, bladder, and kidney cancer study32 as part of stage 3 replication (Figure 1; Table I in the Data Supplement).
A fixed-effect, inverse variance meta-analysis (as implemented in METAL)40 was the primary approach used to combine the results for individual studies. A random-effects approach in METASOFT41 and subset-based approach for heterogeneous traits in ASSET42 were also performed to allow for heterogeneity between populations under study and between measurements of telomere length.
Association of RTL With Cardiometabolic Traits in Sikhs
Our results revealed a significant independent association of shorter RTL with T2D and CHD. Patients with T2D had shorter RTL (1.57±0.26) compared with controls (3.11±0.19; p= 4.2×10−14) and CHD had shorter RTL (1.83±0.16) compared with non-CHD subjects (2.12±0.34; p=2.2×10−3). Shorter RTL was also associated with elevated systolic blood pressure (β=−1.03; p=2×10−3), diastolic blood pressure (β=−0.68; P=2×10−3), arterial pressure (β=−0.83; P=5.8×10−2), and MCP-1 (β=−0.05; P=7×10−7) in analysis adjusted for age, sex, BMI, and T2D (Table V in the Data Supplement). Interestingly, the results were similar when restricted to the control group for all these phenotypes (data not shown). In addition, mean RTL showed a gradual decline from healthy subjects (with no disease) to individuals with T2D and CHD, showing respective mean RTL of 2.67±0.16 in healthy, 2.08±0.14 in CHD, 1.83±0.34 in T2D, and 0.77±0.14 in T2D+CHD individuals showing highly significant difference between healthy subjects versus patients with T2D and CHD (P=3×10−15; Figure 5). Mean RTL levels were also significantly lower (P=0.014) in Sikh men (1.82±0.11) compared with women (2.29±0.15), irrespective of the disease status, consistent with observations in South Asians and other ethnic groups (Figure II in the Data Supplement).21,31,43,44
We performed a genome-wide association analysis for ln-RTL using multiple linear regression and adjusting for covariates age, sex, BMI, T2D, and 5 principal components of ancestry (Figures 3 and 4; see Methods). No association signals exceeded genome-wide significance, but strong signals (P<10−7) were seen at 3 loci: chr 16q21 (CSNK2A2), 5p13.2 (C5ORF42), and 5q21.3 (FER; Figure 3; Table IV in the Data Supplement). The association at the CSNK2A2 locus remained strongly significant when performed separately in diabetic cases (rs74019828 [β±SE] −0.40±0.13), P=0.0019, and controls (rs74019828 [β±SE] −0.41±0.11), P=0.00036. A consistent allelic effect was observed for 3 of 6 previously replicated RTL association signals from GWAS20 (Table VI in the Data Supplement); the results for the most significant SNP for the seventh signal (RTEL1) did not pass quality control based on low frequency in the Sikh population. Notably, at the TERC locus, we observed moderately significant association, but the allelic effect of the previously identified index SNP (rs10936599) was in the opposite direction (Table VI in the Data Supplement), perhaps because of population differences in linkage disequilibrium (LD) between the marker and causal SNP at this locus. A combined genotype risk score of previously associated variants from the 6 known RTL loci from Codd et al20 trended toward association with RTL (β±SE; −0.80±0.45; P=0.075). The differences in the LD patterns between the Sikh population and European populations (in which previous index SNPs were identified) suggest the possibility that independent signals at these loci may exist in our population. For instance, using other variants from the same previously associated loci, in 5 of 9 regions, we observed association effects in Sikhs with P values ranging from 10−03 to 10−07. The most significant association was seen in a variant representing C5orf42 (≈35 Mb) from TERT (β=−0.33; P=2.1×10−7) and a variant at NAF1 (different from the reported)33 in Sikhs (β=0.26, 8.9×10−5; Figure III in the Data Supplement).
Two-Stage Replication and Meta-Analysis
We undertook a 2-stage replication including T2D case–control samples of Punjabi Sikh ancestry (stage 1) and genotyping or in silico replication in 5 studies of European ancestry (stages 2 and 3; Figure 1). The analyses were adjusted for age, sex, BMI, and T2D.
In stage 1 replication, top SNPs representing 25 putatively novel signals with P<10−4 from the discovery GWAS were directly genotyped and analyzed for association with RTL in 2397 additional Punjabi Sikhs comprising 1108 T2D cases and 1289 controls (Table I in the Data Supplement). The analyses were adjusted for age, sex, BMI, and disease. In discovery and stage 1 meta-analysis (n=4013), we identified a novel signal at CSNK2A2 (16q21; located in intron 4) and represented by rs74019828 to be associated with shorter RTL (β=−0.38±SE 0.06; P=4.5×10−8; Table; Figures 4 and 6). Five additional independent signals showed suggestive association (P<10−6–<10−7): chromosome 5p13.2 C5orf42 (rs2098713), 5q21.3 FER (rs78869517), 5q35.2 (an uncharacterized gene) LOC101928726 (rs244731), 4q34.2 SPATA4 (rs10004325), 12p11.23 PTF1BP1 (rs4409879), and 1p31.2 an unknown gene (rs9988609; Table; Table VII in the Data Supplement). We also performed sensitivity analysis by removing T2D and BMI covariates from the model. Our results looked similar to the previous findings after excluding T2D from model and including only age and sex as covariates (Table VIIIA in the Data Supplement). In the BMI-stratified analyses including age, sex, and T2D as covariates, the association signals for RTL for FER at chromosome 5 and chromosome 8 were significantly improved at BMI <25; however, the strongest P value for the SNP association remained consistent at CSNK2A2 region (Table VIIIA–VIIIC in the Data Supplement; Table IXA–IXC in the Data Supplement).
Next, we analyzed our data using alternative models (dominant, recessive, and codominant) for the top variant in CSNK2A2 and other 2 variants in chromosome 5 near TERT and FER regions. These analyses did not improve the earlier outcome using additive model (Table X in the Data Supplement).
For stage 2 replication, we directly genotyped 3 lead SNPs representing 2 top novel independent (r2<0.25) association signals (CSNK2A2 and FER) in a UKCVD cohort available with RTL measures on 2952 patients with myocardial infarction and healthy controls. Combined AIDHS/SDS and UKCVD meta-analysis revealed rs74019828 (CSNK2A2) and rs112020835 (FER) to be significantly associated with RTL (Figure 6; Table XI in the Data Supplement). However, although allelic affects were in the same direction, the association of the top variant (rs7401928) did not achieve the GWAS significance threshold (P=3.2×10−4), partly because of lower frequency in UKCVD (Table).
In stage 3 lookup in RTL GWAS of European ancestry, none of our top independent signals could be confirmed. In joint multiethnic meta-analysis on individuals from all studies, our top signal in the CSNK2A2 (rs1393203 used as proxy for rs74019828; r2 Euro-whites=1.00) did not reach GWAS significance, although there was a moderately significant trend in the same direction (β=−0.03±SE 0.01; P=2.1×10−3; Table; Table XII in the Data Supplement; Figure 6) along with significant heterogeneity (I2=73.41%; P=2×10−4). Using a random-effects model, nominal significance was retained (β=−0.04±SE 0.02; P=0.069). Meta-analysis allowing for heterogeneity of phenotypic measurements42 revealed a negative z score comprising our discovery GWAS, Sikh stage 1, and UKCVD stage 2 replication studies (β=−0.05; P=3.5×10−6) and a positive z score subset comprising the stage 3 replication studies (β=+0.04; P=1). The gene-based analysis of each GWAS replication data set could not confirm association of any SNP within 20Kb± of the CSNK2A2 locus with log RTL.
Association of CSNK2A2 and previously identified variants with cardiometabolic traits in the Sikh population: we first examined the relationship between top SNP in the CSNK2A2 (rs74019828 or proxy rs1393203) with cardiovascular and metabolic traits. We did not observe any association of these variants with T2D, T2D age of onset, or cardiovascular traits (Table XV in the Data Supplement). We tested for association of the lead CSNK2A2 SNP rs74019828 or proxy rs1393203 and other previously established loci with cardiometabolic traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, CHD, systolic and diastolic blood pressure, mean arterial pressure, pulse pressure, and T2D) in Sikhs and found marginal association of the telomere shortening allele at rs10936599 (TERC) with lower total cholesterol (P=0.0152) and triglycerides (P=0.0188), rs2736100 (TERT) with lower blood pressure measures (P=0.0027), and rs9420907 (OBCF1) with increased triglycerides (P=0.0225; Table XIII in the Data Supplement). A combined genotype risk score of 6 established shorter telomere length alleles from GWAS20 showed a trend toward association with increased total cholesterol levels in the entire sample and with increased pulse pressure in nondiabetic controls (Table XIII in the Data Supplement).
In this study, we report an independent association of shorter RTL in the Punjabi Sikh population with T2D, CHD, and other cardiometabolic traits including inverse association with blood pressure, MCP-1, and positive association with homeostasis model assessment for β cell function. The Sikh men had significantly shorter mean RTL compared with Sikh women, P=0.014. These findings are in agreement with several previous studies from multiple ethnic populations reporting presence of shorter RTL associated with age-related cardiometabolic diseases including T2D, insulin resistance, myocardial infarction, and CHD,31,43,44 containing 3 previous studies on South Asians.12,21,45 We also report discovery of a novel signal represented by an intronic variant in CSNK2A2 gene associated with shorter RTL (P=4.52×10−8) in Punjabi Sikhs (Figure IV in the Data Supplement). The observed association was confined to South Asian Sikhs and was not replicated in GWAS of European ancestry and, therefore, could be population specific. The significance of association of the key variant (rs74019828) remained unchanged after controlling for MCP-1, blood pressure, pulse pressure, CHD, and T2D in Sikhs, implies independent effect of CSNK2A2 genetic variants on RTL.
Interestingly, the LD patterns in the region (≈1.2 Mb) surrounding CSNK2A2 varied between South Asian Sikhs and HapMap founder populations including Euro-whites, Gujarati Indians, East Asians, and Yorubans (Figure V in the Data Supplement). The frequency of the susceptibility allele (A) of our key variant (rs74019828) at the CSNK2A2 locus was 0.17 in Punjabi Sikhs and 0.20 in Gujarati Indians, 0.09 in East Asians, 0.03 to 0.07 in Euro-whites, and 0.05 in Yorubans populations. The difference in allele frequencies between Sikhs and Europeans could have contributed to nonreplication of this association. Also, the association of the established TERC variant (rs10936599) is in opposite direction in Sikhs from the Europeans (Table VI in the Data Supplement), suggests a further evidence that there may be population-specific causal variant not in LD with these SNPs from these genes. A similar population-specific association was observed in our T2D GWAS in which a novel signal for T2D susceptibility represented by a directly genotyped SNP in the SGCG gene (rs9552911, P=1.82×10−8) found in the Sikhs was monomorphic in subjects of European ancestry.22 The Sikh sample comprising discovery and replication data sets (n=4013) has >92% power to detect the CSNK2A2 SNP association to RTL with genome-wide significance (Table XIV in the Data Supplement). Furthermore, because the discovery effect size is influenced by the winner’s curse, the actual effect size may be smaller, requiring even larger sample sizes for European data sets to observe significant replication.
CSNK2A2 encodes an enzyme, casein kinase II subunit α, that phosphorylates a large number of substrates and regulates numerous cellular processes, such as cell cycle progression, apoptosis, and transcription.46,47 It is affiliated with the members of the shelterin complex involved in chromosome end protection, telomere regulation, and maintenance.48 Interestingly, the telomeric repeat binding factor 1 serves as a substrate for CSNK2A2, which phosphorylates and initiates its binding to telomeres (Figure 7).48,49 Partial knockdown of csnk2a2 with small interfering RNA resulted in removal of telomeric repeat binding factor 1 from telomeres and degradation of telomeric repeat binding factor 1.49 CSNK2A2 also influences Wnt signaling via β-catenin phosphorylation and the phosphatidylinositol-3-kinase signaling pathway via the phosphorylation of Akt. CSNK2A2 also interacts with multiple genes and miRNAs in pathway controlling telomere length and CHD.50
There are only a few studies connecting CSNK2A2 to telomere length. Our results substantiate the need for a deeper examination and characterization of genetic variation in CSNK2A2 in conjunction with environmental influences for affecting cardiometabolic risk. Because none of the variants within the CSNK2A2 locus revealed any independent association with CHD in Sikhs (Table XV in the Data Supplement), it is also possible that the CSNK2A2 does not have any direct role in T2D/CHD pathophysiology.
Limitations of our study include multiple sources of interstudy variability. First, variability across studies because of well-known limitations of telomere length measurement techniques could have contributed to nonreplication in whites; the mean RTL varied widely across studies (Table IIB in the Data Supplement). Second, the presence of population heterogeneity and variation in observed telomere length has resulted in significant heterogeneity in global meta-analysis. Notably, most replication cohorts had 9% to 20% smokers, whereas our Sikh population is a strictly nonsmoking population, and gene–environment interactions with smoking may have obscured true association signals (Table IIB in the Data Supplement).11,51,52 Third, a significant proportion of sex bias existed in most white replication cohorts including 91% women in UKTWIN, 100% women in Nurses Heart Study, 100% men in PLCO and UKCVD, and 72% men in MD Anderson Cancer Controls compared with AIDHS/SDS (55% men), which could have partly contributed in nonreplication as shorter RTL correlates remarkably with male sex in Sikhs, as well as in previous studies (Table IIB in the Data Supplement).53 Finally, performing analysis from findings derived from T2D case–control study with cohorts including prostate cancer survivors and postmenopausal women could have resulted in replication bias in addition to other factors. For example, shorter telomeres in cancer cases because of disease-related secondary effects may reduce power to detect a genetic effect. Differences in LD between marker and causal SNPs in Sikhs and non-Sikh replication cohorts have certainly contributed to nonreplication in the European sample and to the limited association of 6 European SNPs in Sikh populations. However, despite these limitations, our original association results were replicated in an independent validation cohort of Punjabi Sikhs
We report association of shorter RTL with T2D and cardiometabolic risk in Punjabi Sikhs from South Asia. Our GWAS and meta-analysis identified a new signal within the CSNK2A2 gene associated with RTL in South Asian Sikhs. CSNK2A2 phosphorylates telomeric repeat binding factor 1, which initiates and regulates its binding to telomere. CSNK2A2 also interacts with multiple genes and miRNAs in pathways controlling telomere length and cardiovascular disease. Thus far, no other GWAS has been conducted for telomere length and association of genome-wide variants with T2D and cardiovascular traits in conjunction with shorter RTL in South Asian populations. Therefore, future confirmation of our findings in other South Asian populations will be necessary to evaluate this population-specific association signal. Also, targeted resequencing of CSNK2A2 in Sikhs and other multiethnic populations and functional studies may provide clinically important insights into the causal relationship of possible rare variants in CSNK2A2 with telomere attrition and cardiometabolic risk in diabetes mellitus and other age-dependent chronic disorders.
We thank all the participants of Asian Indian Diabetic Heart Study/Sikh Diabetes Study and are grateful for their contribution in this study. We also thank Anuradha Subramanian for her technical and administrative help in the article preparation. Study-specific acknowledgments are provided in the Material in the Data Supplement. URLs: IMPUTE, http://mathgen.stats.ox.ac.uk/impute/impute.html; METAL, http://www.sph.umich.edu/csg/abecasis/Metal; SNAP, http://www.broadinstitute.org/mpg/snap/; LocusZoom, http://csg.sph.umich.edu/locuszoom/; GenABEL, http://www.genabel.org/; and ProbABEL, http://www.genabel.org/packages/ProbABEL.
Sources of Funding
This work was partly supported by National Institutes of Health grants—R01DK082766 funded by the National Institute of Diabetes and Digestive and Kidney Diseases and NOT-HG-11-009 funded by National Genome Research Institute and Vice President for Research Bridge grant from University of Oklahoma Health Sciences Center, Oklahoma City, OK. The genotyping and analysis for UKCVD was supported by the British Heart Foundation (PG08/008). Study-specific funding sources for the replication studies are provided in the Material in the Data Supplement.
From the Center for Human Genetic Research, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston (R.S., A.B., J.L.); Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA (J.P., D.J.H., I.D.V.); Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge (R.S., A.B., J.L., D.J.H.); Department of Pediatrics, College of Medicine (P.D., P.N., D.K.S.) and Department of Surgery (M.L., D.B., D.S., M.P.), University of Oklahoma Health Sciences Center, Oklahoma City; Program in Molecular and Genetic Epidemiology, Department of Epidemiology (J.P., P.K., D.J.H., I.D.V.) and Department of Biostatistics (P.K.), Harvard School of Public Health, Boston, MA; Cardiovascular Genetics, BHF Laboratories, Institute Cardiovascular Sciences, University College London, London, United Kingdom (J.A.C., K.W.L., C.G.M., K.D.S., S.E.H.); Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston (Y.Y., J.G., X.W.); National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester, United Kingdom (V.C., M.M., N.J.S., T.D.S.); Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD (L.M., S.A.S.); Department of Urology, The University of Texas MD Anderson Cancer Center, Houston (C.P.D.); Hero DMC Heart Institute, Punjab, India (S.R., G.S.W.); and All India Institute of Medical Sciences and Research, New Delhi, India (N.K.M.).
Guest Editor for this article is Paivi Pajukanta, MD, PhD.
The Data Supplement is available at http://circgenetics.ahajournals.org/lookup/suppl/doi:10.1161/CIRCGENETICS.113.000412/-/DC1.
- Received September 3, 2013.
- Accepted April 1, 2014.
- © 2014 American Heart Association, Inc.
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Leukocyte telomere length is inversely associated with multiple diseases including hypertension, coronary heart disease, type 2 diabetes mellitus, and Alzheimer disease. Because loss of telomeric DNA is affected by cell division and oxidative stress, and induces cellular senescence, telomere length has been postulated as a biomarker of senescence and human aging in many published studies. Interindividual variation in telomere length is attributed to by both genetic and environmental factors. Gene mapping and twin studies have confirmed the strong influence of genetic factors for controlling relative telomere length (RTL) with heritability ≤84%. Ethnic differences in the RTL have been reported in multiethnic studies where populations such as blacks and Hispanics had significant age-associated differences in RTL than whites. Recent genome-wide association studies have identified common genetic variation contributing to RTL in healthy and disease conditions. In this study, we report association of shorter RTL with type 2 diabetes mellitus and cardiometabolic risk in Punjabi Sikhs from South Asia. Our genome-wide association study and meta-analysis study have identified a novel signal within the CSNK2A2 gene to be associated with the RTL. CSNK2A2 phosphorylates telomeric repeat binding factor 1, which initiates and regulates its binding to telomere. CSNK2A2 also interacts with multiple genes and miRNAs in pathways controlling telomere length and cardiovascular disease. Future functional studies may provide clinically important insights into the causal relationship of possible rare variants in CSNK2A2 with telomere attrition and cardiometabolic risk in type 2 diabetes mellitus and other age-dependent chronic disorders.