A Genome-Wide Association Study Identifies a Locus on TERT for Mean Telomere Length in Han Chinese
et al. (2014) A Genome-Wide Association Study Identifies a Locus on TERT for Mean Telomere Length in Han
Chinese. PLoS ONE 9(1): e85043. doi:10.1371/journal.pone.0085043
A Genome-Wide Association Study Identifies a Locus on TERT for Mean Telomere Length in Han Chinese
Yun Liu 0 1
Lan Cao 0 1
Zhiqiang Li 0 1
Daizhan Zhou 0 1
Wanqing Liu 0 1
Qin Shen 0 1
Yanting Wu 0 1
Dan Zhang 0 1
Xun Hu 0 1
Ting Wang 0 1
Junyi Ye 0 1
Xiaoling Weng 0 1
Hong Zhang 0 1
Di Zhang 0 1
Zhou Zhang 0 1
Fatao Liu 0 1
Lin He 0 1
Yongyong Shi 0 1
0 Editor: Xiaoping Miao, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology , China
1 1 Institutes of Biomedical Sciences, Fudan University , Shanghai, PR China, 2 Bio-X Institutes , Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University , Shanghai , PR China , 3 Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, Indiana, United States of America, 4 Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences , Shanghai , PR China , 5 Women's Hospital, School of Medicine, Zhejiang University , Zhejiang , China , 6 Department of Stomatology, Ruijin Hospital Luwan Branch , Shanghai , PR China
Leukocyte telomere length (LTL) is a predictor of aging and a number of age-related diseases. We performed genome-wide association studies of mean LTL in 2632 individuals,with a two-stage replication in 3917 individuals from Chinese populations. To further validate our findings, we get the results of 696 samples from a cohort of European ancestry. We identified two loci associated with LTL that map in telomerase reverse transcriptase (TERT; rs2736100, P = 1.9361025) on chromosome 5p15.33 and near keratin 80 (KRT80; rs17653722, P = 6.9661026) on 12q13.13. In Chinese population each C allele of rs2736100 and T allele of rs17653722 was associated with a longer mean telomere length of 0.026 and 0.059 T/S, respectively, equivalent to about 3 and 7 years of average age-related telomere attrition. Our findings provide new insights into telomere regulatory mechanism and even pathogenesis of age-related diseases.
Funding: This work was supported by the Natural Science Foundation of China (81130022, 81272302, 31000553, 81121001); the National 863 project
(2012AA02A515); the 973 Program (2010CB529600); Program for Changjiang Scholars and Innovative Research Team in University (IRT1025); the Foundation for
the Author of National Excellent Doctoral Dissertation of China (201026); Shanghai Rising-Star Program (12QA1401900); Shu Guang project supported by
Shanghai Municipal Education Commission and Shanghai Education Development Foundation (12SG17); and start-up fund (WL) from the Department of
Medicinal Chemistry and Molecular Pharmacology, Purdue University. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
. These authors contributed equally to this work.
Telomeres are structures at the ends of eukaryotic
chromosomes, which are made up of a repetitive sequence (in humans,
TTAGGG) and have a major role in genomic stability. Telomere
length is important in determining telomere function, whose
dysregulation can lead to cell death, cell senescence, or abnormal
cell proliferation . In humans, leukocyte telomere length (LTL)
progressively shortens with age because of the inability of DNA
polymerase to fully replicate the 39 end of the DNA strand in
mitotic division, and is frequently reported to be relatively shorter
in aging-related diseases: such as Alzheimers disease  and
vascular dementia . LTL varies among individuals with the
same age, and is found to be inheritable in quantitative-trait
linkage analyses of sib pairs, with heritability estimates ranging
from 36% to 86% .
In some cells, such as germ cells and proliferative stem cells that
require renewal, telomere length is maintained by telomerase, a
large RNAprotein complex consisting of a reverse transcriptase
(TERT) and an RNA template (TERC) [8,9]. The latter
component complexes with TERT and provides the template
for the synthesis of telomere repeats. Mutations in the TERT and
TERC genes cause telomere shortening and are the major risk
factors for rare syndromes, including idiopathic pulmonary
fibrosis, aplastic anemia and dyskeratosis congenita . TERT
gene at 5p15.33 encodes the catalytic protein component of
telomerase. However, a consistent conclusion could not be drawn
from the association studies between TERT and LTL. Atzmon et
al. reported that one haplotype of TERT (consisting of rs2853669,
rs2736098, rs33954691 and rs2853691) was associated with LTL
, but this association couldnt be verified by Soerensen et al.
. SNPs at 5p15.33 have been reported to be associated with
risk of lung cancer in Chinese Populations [13,14]. TERC, which
serves as a template for addition of multiple 6 bp (TTAGGG)
telomere repeats, is another main component of telomerase. The
association between TERC and LTL has been identified by two
recent genome-wide association studies (GWAS) [15,16], and this
association was validated in different studies including a recently
report in the Han Chinese population [12,17]. Notably, recent
genome-wide studies of populations of European ancestry have
revealed additional variants associated with LTL [7,16,18,19,20].
However, much of the heritability of LTL remains unaccounted
for, and the pathways or biological mechanisms that underlie LTL
variation are still largely unknown, e.g. TERC only accounts for no
more than 1% of variation in telomere length [15,16]. To identify
more variants that affect telomere length, we performed GWAS
analyses in one Han Chinese cohort and then replicated promising
signals in two further Han Chinese cohorts.
The discovery cohort comprised 2,632 individuals (1,318 Type
2 diabetes patients and 1,312 health controls), which had been
used and described in our previous study . Further details of
the cohort are given in Table S1. Principal-component analysis
(PCA) was used to evaluate the population structure of samples of
the discovery stage in comparison to the Hapmap populations,
and the first two eigenvectors were plotted in Figure S1, which
differentiating the Asian populations (CHB and JPT) and the study
samples from the Caucasian (CEU) and Yoruba (YRI) samples
clearly. LTL were approximately distributed normally (Figure S2)
and showed the expected decline according to ages (Figure S3).
We analyzed the association of T/S ratio with individuals
genotype in the discovery cohort, adjusted for age, gender and
disease status. The quantile-quantile plots for the discovery cohort
are shown in Figure S4, which showed little evidence for inflation
due to population stratification (genomic inflation factor
l = 1.011). In the initial study, no SNP reached genome-wide
significance (P,5.061028), including previously reported SNPs on
TERC  and OBFC1  (Figure S5). We set a pragmatic
significance threshold of P,161024 in the discovery cohort to
determine which SNPs to be selected for replication study in an
independent cohort comprised of 2,533 individuals (1,173 subjects
with Type 2 diabetes and 1,360 healthy controls) (Rep1 cohort).
Details of the Rep1 cohort are given in Table S1.
Totally, 20 SNPs were selected for replication, including one
SNP rs2736100 (PGWAS = 8.2961023), located on TERT (Table
S2). We also z-transformed mean leukocyte telomere lengths in
individual cohort to obtain comparable results and performed a
meta-analysis (Table S3). We successfully replicated the association
findings near genes KRT80 and TERT in Rep1 cohort, and the
associations in the GWAS-Rep1 reached nominal significance
(KRT80: rs17653722, PGWAS-Rep1 = 5.8561027, p-values for the
heterogeneity test: Phet = 0.463; TERT: rs2736100, PGWAS-Rep1 =
4.0361024, Phet = 0.942) (Table 1), and the direction of the effect
was consistent with our findings in the discovery stage. And, we
examined the association of these two SNPs with telomere length
in additional cohort of Han Chinese (Rep2 cohort) comprised of
1,384 individuals (618 Type 2 diabetes and 766 healthy controls).
As shown in Table 1, the meta-analysis results of both rs17653722
(PGWAS-Rep1-Rep2 = 6.9661026, Phet = 0.071) and rs2736100
(PGWAS-Rep1-Rep2 = 1.9361025, Phet = 0.790) still showed
significant associations with telomere length. For those markers reported
to have associations with LTL in previously GWAS, we only found
rs12638862, which was in high linkage disequilibrium (r2 = 0.928
in CHB) with rs1317082 in TERC, showed association with LTL
in our GWAS results (PGWAS = 5.5761023, A allele b = 0.027). A
full listing of SNPs is provided in Table S5.
To further validate our findings, we get the genotyping results
of rs2736100 in an additional cohort of European ancestry
(Rep3 cohort), including 696 samples in total (Table S1). By
meta-analysis, we got significant association of rs2736100
(PGWAS-Rep1-Rep2-Rep3 = 4.4561026, Phet = 0.915), which strongly
support our results.
Notably, rs2736100 is located in intron 2 of TERT, which
encodes telomerase reverse transcriptase (Figure 1b), played a
crucial role in protection of telomere integrity. Interestingly,
rs2736100 has been reported to be associated with lung cancer
risks in populations of both European descent and Asian ancestry
. However, it is unclear whether LTL is predictive of lung
cancer risk: retrospective studies report that lung cancer patients at
the time of or after diagnosis have shorter telomeres than
unaffected controls , but prospective studies report that
longer telomere length is associated with lung cancer risk including
a study in China [26,27]. We found the risk allele C in lung cancer
susceptibility corresponds to a longer telomere length, which is
consistent with the results that individuals with longer LTL may
have an increased risk of lung cancer. Our finding suggests the
TERT locus may influence the cancers development through
variation in LTL.
To date, the TERT locus has been identified in several
independent populations for its association with telomere length.
Atzmon et al. identified a common TERT haplotype that was
associated with telomere length, but their association analysis of
common TERT variants showed no significant association .
Bojesen et al. found SNPs at the TERT locus were associated with
telomere length and SNP rs7705526 had the largest effect .
However, in their study, SNP rs2736100 was not highly correlated
with rs7705526 (r2 = 0.52), nor did it show independent
associations with telomere length after adjustment for rs7705526. Codd et
al. identified seven loci affecting mean telomere length including
rs2736100 at 5p15.33 (TERT) locus . We found rs2736100 at
the TERT locus was associated with mean LTL in Chinese
population, observing the association between the minor allele [C]
of rs2736100 and LTL (joint P = 1.9361025) with an effect size
and direction consistent with that of Codd et al.
On 12q13.13, rs17653722 is located ,2 kb downstream of
KRT80, encoding keratin 80 (Figure 1a). This protein is involved
in cell differentiation, localizing near desmosomal plaques in
earlier stages of differentiation but then dispersing throughout the
cytoplasm in terminally differentiating cells. In expression data
from the lymphoblastoid cell lines, rs17653722 is nominally
associated with the expression of several genes (ACVR1B, C12orf44
and KRT80; P,0.05) (Table S4);
According to the cohort used in GWAS stage, we deduced that
the age-telomere declining formula for Chinese population is (T/
S ratios) = 20.00816YEAR+1.56, R2 = 0.052, P,10216), which
indicates that LTL declines on average by 0.0081 T/S per year
between the ages of 20 and 90. In Chinese population each C
allele of rs2736100 and T allele of rs17653722 was associated with
a longer mean telomere length of 0.026 and 0.059 T/S,
respectively, equivalent to about 3 and 7 years of average
agerelated telomere attrition (Table S2), which showed similar effect
size as TERC, a key component of telomere length maintaining
One limitation of this study is that the sample size was not large
enough and the statistical power was relatively low to identify
common variants of small effects with genome-wide significance.
Another limitation is that we were unable to control more
environmental factors because the information was not available.
The third limitation is our study used different samples as the
calibrator sample of telomere length measurement in each cohort.
Therefore, we z-transformed the individual telomere length
measurements in each cohort to obtain comparable results.
Despite these limitations, we conducted a genome-wide association
study of mean LTL in Han Chinese for the first time. In summary,
rs2736100 1339516 C
0.0759 (0.0288) 8.36E-03 0.0728(0.0309) 1.85E-02 0.0744 4.03E-04 0.942 0.1071(0.0430) 1.29E-02 0.0808 1.93E-05 0.79
rs17653722 50873785 T
0.1731 (0.0419) 3.74E-05 0.1284(0.0442) 3.69E-03 0.1519 5.85E-07 0.463 0.0033(0.0609) 9.57E-01 0.1223 6.96E-06 0.071
Note: In each panel, markers (SNP) are given along with chromosomal (CHR) and base pair (BP) positions.
A1, minor allele. SE, standard error. Beta coefficients based on z-scores.
Phet: p-values for the heterogeneity test.
we have identified common genetic variants on 5p15.33 and
12q13.13 that affect telomere length in the Han Chinese
population. Several promising candidate genes, including TERT,
are implicated in these regions. Given the importance of telomeres
in cellular function and the central role of telomere length in
determining telomere function, the identification of these new
common genetic risk variants is the first step of elucidating the
telomere regulatory mechanism and even pathogenesis of
agerelated diseases. And, future studies aimed at accurately mapping
these regions could be fruitful.
Materials and Methods
The study protocols were approved by the institutional review
board of the ethics committee of the Shanghai Institute for
Biological Sciences and conducted according to the Declaration of
The characteristics details of the three cohorts are shown in
Table S1.Clinical information, such as age and family history, was
collected by questionnaire. Written informed consent was obtained
from all subjects before enrollment.
Venous blood samples anti-coagulated with EDTA were
collected from all participants. High-molecular-weight genomic
DNA was prepared from venous blood using the Quick Gene
610L Automatic DNA/RNA Extraction System (Fujifilm, Tokyo,
Japan) and diluted to working concentrations of 50 ng/ml for SNP
chip genotyping and 1020 ng/ml for replication genotyping.
Leukocyte telomere length (LTL) measurement
Mean LTL was measured using a previously described modified
version  of the quantitative real-time PCR-based assay .
Relative telomere length was calculated as a T/S ratio with Rnase
P as a reference (ABI) for each sample. For each sample the
quantity of telomere repeats and the quantity of Rnase P reference
were determined in duplicate in 10 ml reactions in the same plate
on an ABI-Applied Biosystems 7900 HT Thermal Cycler (Applied
Telomere reaction contained 16 Sybr green Taqman Gene
Expression master mix (Applied Biosystems, Foster City,
California), 300 nM of Tel-F, 300 nM Tel-R primers and 1 ng of
template DNA. (Primers: Tel-F:
59-GGCTTGCCTTACCCTTACCCTTACCCTTACCCTTACCCT-39). A commercial kit was used
according to the manufacturers instructions to estimate the
expression of the RNase P gene as an internal standard
(TaqManRNase P Detection Reagents Kit, Applied Biosystems),
using 16 Primers and TaqManH probe reagent, 16 TaqManH
Genotyping Master mix and 3 ng of template DNA. Cycling
conditions for telomere and RNase P were 90uC incubation for
10 mins followed by either 50 cycles of 95uC for 15 sec or 60uC
for 1 min.
Alongside the samples each run also contained a Calibrator
sample using pooled DNA. Dilution series (0.6755 ng in two-fold
dilutions) were run for both telomere and RNase P assays to
establish the linear range. Good linearity was observed across this
range (R2.0.99). Any samples outside this range were diluted and
run again. For quality control all samples were run in duplicate
and the duplicate values were checked for concordance. Samples
showing a CV .2% were excluded and re-run. In addition, to test
reproducibility of the assay, multivariable samples were randomly
chosen and run again and a high level of agreement between the
T/S ratios from the two runs was observed (R2.0.85, P,0.0001).
GWAS genotyping and quality control
The genome-wide association analysis was performed using the
Affymetrix Genome-Wide Human SNP Array 6.0. Quality control
(QC) filtering of the GWAS data was performed by excluding
arrays with Contrast QC ,0.4 from further data analysis.
Genotype data were generated using the birdseed algorithm
. For sample filtering, arrays that generated genotypes at
,95% of loci were excluded. For SNP filtering (after sample
filtering), SNPs with call rates ,95% in either cases or controls
were removed in each geographic group. SNPs with minor allele
frequency (MAF) ,1% or significant deviation from
HardyWeinberg equilibrium (HWE; P,0.001) in controls were also
excluded. SNPs passing QC were used for further analysis. After
QC filtering, there were 585,206 SNPs remaining in the discovery
SNP selection criteria and replication genotyping
We selected representative SNPs fromthe ones with
PGWAS,1024 for the replication study. Genotyping for the
replication study was performed MGB Taqman probe assays
from Applied Biosystems, using TaqMan Universal PCR Master
Mix reagent kits under the guidelines. PCR plates were read on an
ABI7900 system (Applied Biosystems, Foster City, CA, USA).
Duplicates of randomly chosen samples were genotyped for quality
control and duplicate concordance rates were 100%.
Analysis of population substructure
Population substructure was evaluated using
principal-components analysis (PCA) (Figure S1) using EIGENSTRAT software
Figure 1. Regional plots of the two loci associated with telomere length. Results are shown for the 12q13.13 (a) and 5p15.33 (b) regions.
Top, 2log10P values are shown for SNPs for the region 350 kb on either side of the marker SNPs. PGWAS is for results obtained from the discovery
stage and is shown for genotyped (circle). PGWAS-Rep1-Rep2 is for results obtained from the combination of the initial and replication study data
(diamond). The marker SNP is shown in purple, and the r2 values of the other SNPs are indicated by color. The genes within the relevant regions are
annotated and shown as arrows.
[32,33]. Twenty components, some of which were predicted to
reflect ancestry differences among subjects, were generated for
each sample. Logistic regression was used to determine whether
there was a significant difference in component scores between
cases and controls; significant components were used as covariates
in the association analysis to correct for population stratification.
Mean telomere length was considered as a quantitative trait,
and expressed as a T/S ratio. Multivariable linear regression was
conducted to analyze the association of the T/S ratio with SNPs,
after adjusted for age, gender, and diabetes status using PLINK
. HWE analysis was performed using PLINK, and Haploview
 was used to generate genome-wide P plots. Quantile-quantile
plots were created using the R package, and regional plots were
generated using LocusZoom (see URLs). We z-transformed the
individual telomere length measurements in each cohort by
subtracting the mean and division by the standard deviation and a
meta-analysis using the random-effect model was carried out on
the basis of the results of the three cohorts using PLINK.
Heterogeneity across the data sets was evaluated using the
Cochrans Q test. The meta-analysis was carried out using the
Mantel-Haenszel method with a random-effects model.
Expression quantitative trait loci (eQTL) analysis
Expression profiles were analyzed in the lymphoblastoid cell
lines eQTL data sets [36,37]. The expression data set were
downloaded from the NCBI GEO database. The data setconsists
of gene expression profiles generated using RNA extracted from
lymphoblastoid cell lines derived from the 210 unrelated HapMap
individuals from four sample groups (60 CEU, 45 CHB, 45 JPT
and 60 Yoruba in Ibadan (YRI)). Expression analysis was
performed using Sentrix Human-6 Expression BeadChips
(Illumina). The SNP genotypes from HapMap 2 were used in the
analysis. The eQTLs were tested by linear regression of
normalized expression levels on SNP genotypes (coded as the
number of minor alleles at each SNP: 0, 1 or 2). Analyses were
conducted for each population and the combined data set.
Figure S1 The principal components analysis (PCA) of
samples of discovery stage and HapMap individuals
Figure S3 Age-related LTL plot in four cohorts. Figure S3
shows the age relationship of the T/S ratio in the four cohorts. All
cohorts showed the expected decline in LTL in individuals of
increasing age. Regression lines are shown in black. In the cohort
used in GWAS stage, we derived an age-telomere declining
formula for Chinese population as (T/S ratios) = 20.00816
YEAR+1.56, R2 = 0.052, P,10216), which indicates that, LTL
declined on average by 0.0081 T/S per year between the ages of
20 and 90.
Quantile-quantile Plots of GWAS stage
Manhattan plot in the discovery stage.
Descriptive statistics of our samples.
We are grateful to all subjects who participated in this study.
Conceived and designed the experiments: YL LH YS. Performed the
experiments: LC DZ QS TW JY XW HZ FL. Analyzed the data: LC ZL.
Contributed reagents/materials/analysis tools: ZL DZ WL YW DZ XH
DZ ZZ. Wrote the paper: YL LC ZL LH YS.
1. Autexier C , Lue NF ( 2006 ) The structure and function of telomerase reverse transcriptase . Annu Rev Biochem 75 : 493 - 517 .
2. Panossian LA , Porter VR , Valenzuela HF , Zhu X , Reback E , et al. ( 2003 ) Telomere shortening in T cells correlates with Alzheimer's disease status . Neurobiol Aging 24 : 77 - 84 .
3. von Zglinicki T , Serra V , Lorenz M , Saretzki G , Lenzen-Grossimlighaus R , et al. ( 2000 ) Short telomeres in patients with vascular dementia: an indicator of low antioxidative capacity and a possible risk factor? Lab Invest 80 : 1739 - 1747 .
4. Andrew T , Aviv A , Falchi M , Surdulescu GL , Gardner JP , et al. ( 2006 ) Mapping genetic loci that determine leukocyte telomere length in a large sample of unselected female sibling pairs . Am J Hum Genet 78 : 480 - 486 .
5. Vasa-Nicotera M , Brouilette S , Mangino M , Thompson JR , Braund P , et al. ( 2005 ) Mapping of a major locus that determines telomere length in humans . Am J Hum Genet 76 : 147 - 151 .
6. Njajou OT , Cawthon RM , Damcott CM , Wu SH , Ott S , et al. ( 2007 ) Telomere length is paternally inherited and is associated with parental lifespan . Proc Natl Acad Sci U S A 104 : 12135 - 12139 .
7. Prescott J , Kraft P , Chasman DI , Savage SA , Mirabello L , et al. ( 2011 ) Genomewide association study of relative telomere length . PLoS One 6 : e19635 .
8. Blackburn EH , Greider CW , Szostak JW ( 2006 ) Telomeres and telomerase: the path from maize, Tetrahymena and yeast to human cancer and aging . Nat Med 12 ( 10 ): 1133 - 1138 .
9. Shay JW , Wright WE ( 2007 ) Hallmarks of telomeres in ageing research . J Pathol 211 : 114 - 123 .
10. Armanios M , Blackburn EH ( 2012 ) The telomere syndromes . Nat Rev Genet 13 : 693 - 704 .
11. Atzmon G , Cho M , Cawthon RM , Budagov T , Katz M , et al. ( 2010 ) Evolution in health and medicine Sackler colloquium: Genetic variation in human telomerase is associated with telomere length in Ashkenazi centenarians . Proc Natl Acad Sci U S A 107 Suppl 1 : 1710 - 1717 .
12. Soerensen M , Thinggaard M , Nygaard M , Dato S , Tan Q , et al. ( 2012 ) Genetic variation in TERT and TERC and human leukocyte telomere length and longevity: a cross-sectional and longitudinal analysis . Aging cell 11 : 223 - 227 .
13. Ke J , Zhong R , Zhang T , Liu L , Rui R , et al. ( 2013 ) Replication study in Chinese population and meta-analysis supports association of the 5p15.33 locus with lung cancer . PLoS One 8 : e62485 .
14. Zhong R , Liu L , Zou L , Zhu Y , Chen W , et al. ( 2013 ) Genetic variations in TERT-CLPTM1L locus are associated with risk of lung cancer in chinese population . Mol Carcinog 52 Suppl 1 : 118 - 126 .
15. Codd V , Mangino M , van der Harst P , Braund PS , Kaiser M , et al. ( 2010 ) Common variants near TERC are associated with mean telomere length . Nat Genet 42 : 197 - 199 .
16. Levy D , Neuhausen SL , Hunt SC , Kimura M , Hwang SJ , et al. ( 2010 ) Genomewide association identifies OBFC1 as a locus involved in human leukocyte telomere biology . Proc Natl Acad Sci U S A 107 : 9293 - 9298 .
17. Shen Q , Zhang Z , Yu L , Cao L , Zhou D , et al. ( 2011 ) Common variants near TERC are associated with leukocyte telomere length in the Chinese Han population . Eur J Hum Genet 19 : 721 - 723 .
18. Gu J , Chen M , Shete S , Amos CI , Kamat A , et al. ( 2011 ) A genome-wide association study identifies a locus on chromosome 14q21 as a predictor of leukocyte telomere length and as a marker of susceptibility for bladder cancer . Cancer Prev Res (Phila) 4 : 514 - 521 .
19. Mangino M , Hwang SJ , Spector TD , Hunt SC , Kimura M , et al. ( 2012 ) Genome-wide meta-analysis points to CTC1 and ZNF676 as genes regulating telomere homeostasis in humans . Hum Mol Genet 21 : 5385 - 5394 .
20. Codd V , Nelson CP , Albrecht E , Mangino M , Deelen J , et al. ( 2013 ) Identification of seven loci affecting mean telomere length and their association with disease . Nat Genet 45 : 422 - 427 .
21. Liu Y , Yu L , Zhang D , Chen Z , Zhou DZ , et al. ( 2008 ) Positive association between variations in CDKAL1 and type 2 diabetes in Han Chinese individuals . Diabetologia 51 : 2134 - 2137 .
22. Hu Z , Wu C , Shi Y , Guo H , Zhao X , et al. ( 2011 ) A genome-wide association study identifies two new lung cancer susceptibility loci at 13q12.12 and 22q12.2 in Han Chinese . Nat Genet 43 : 792 - 796 .
23. Hosgood HD 3rd, Cawthon R , He X , Chanock S , Lan Q ( 2009 ) Genetic variation in telomere maintenance genes, telomere length, and lung cancer susceptibility . Lung Cancer 66 : 157 - 161 .
24. Jang JS , Choi YY , Lee WK , Choi JE , Cha SI , et al. ( 2008 ) Telomere length and the risk of lung cancer . Cancer Sci 99 : 1385 - 1389 .
25. Wu X , Amos CI , Zhu Y , Zhao H , Grossman BH , et al. ( 2003 ) Telomere dysfunction: a potential cancer predisposition factor . J Natl Cancer Inst 95 : 1211 - 1218 .
26. Shen M , Cawthon R , Rothman N , Weinstein SJ , Virtamo J , et al. ( 2011 ) A prospective study of telomere length measured by monochrome multiplex quantitative PCR and risk of lung cancer . Lung Cancer 73 : 133 - 137 .
27. Lan Q , Cawthon R , Gao Y , Hu W , Hosgood HD 3rd , et al. ( 2013 ) Longer telomere length in peripheral white blood cells is associated with risk of lung cancer and the rs2736100 (CLPTM1L-TERT) polymorphism in a prospective cohort study among women in China . PLoS One 8 : e59230 .
28. Bojesen SE , Pooley KA , Johnatty SE , Beesley J , Michailidou K , et al. ( 2013 ) Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer . Nat Genet 45 : 371 - 384 .
29. Shen Q , Zhao X , Yu L , Zhang Z , Zhou D , et al. ( 2012 ) Association of leukocyte telomere length with type 2 diabetes in mainland Chinese populations . J Clin Endocrinol Metab 97 : 1371 - 1374 .
30. Gil ME , Coetzer TL ( 2004 ) Real-time quantitative PCR of telomere length . Mol Biotechnol 27 : 169 - 172 .
31. Korn JM , Kuruvilla FG , McCarroll SA , Wysoker A , Nemesh J , et al. ( 2008 ) Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs . Nat Genet 40 : 1253 - 1260 .
32. Patterson N , Price AL , Reich D ( 2006 ) Population structure and eigenanalysis . PLoS Genet 2 : e190 .
33. Price AL , Patterson NJ , Plenge RM , Weinblatt ME , Shadick NA , et al. ( 2006 ) Principal components analysis corrects for stratification in genome-wide association studies . Nat Genet 38 : 904 - 909 .
34. Purcell S , Neale B , Todd-Brown K , Thomas L , Ferreira MAR , et al. ( 2007 ) PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses . The American Journal of Human Genetics 81 : 559 - 575 .
35. Barrett JC , Fry B , Maller J , Daly MJ ( 2005 ) Haploview: analysis and visualization of LD and haplotype maps . Bioinformatics 21 : 263 - 265 .
36. Stranger BE , Forrest MS , Dunning M , Ingle CE , Beazley C , et al. ( 2007 ) Relative impact of nucleotide and copy number variation on gene expression phenotypes . Science 315 : 848 - 853 .
37. Stranger BE , Nica AC , Forrest MS , Dimas A , Bird CP , et al. ( 2007 ) Population genomics of human gene expression . Nat Genet 39 : 1217 - 1224 .