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Journal Article•DOI•

Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases A Mendelian Randomization Study

Philip C Haycock1, Stephen Burgess2, Aayah Nounu1, Jie Zheng1  +194 more•Institutions (88)
01 May 2017-JAMA Oncology (American Medical Association)-Vol. 3, Iss: 5, pp 636-651
TL;DR: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases, as well as single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population.
Abstract: IMPORTANCE: The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. OBJECTIVE: To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. DATA SOURCES: Genomewide association studies (GWAS) published up to January 15, 2015. STUDY SELECTION: GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available. DATA EXTRACTION AND SYNTHESIS: Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. MAIN OUTCOMES AND MEASURES: Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. RESULTS: Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]). CONCLUSIONS AND RELEVANCE: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

Summary (3 min read)

Jump to: [Study Design] – [Comparison With Prospective Observational Studies] – [Key Points] – [Results] – [Discussion] – [Comparison With Previous Studies] – [Mechanisms of Association] – [Clinical Relevance of Findings] – [Study Limitations] and [Conclusions]

Study Design

  • As a first step, the authors searched the GWAS catalog 15, 16 on January 15, 2015, to identify single-nucleotide polymorphisms (SNPs) associated with telomere length.
  • As part of this step, the authors invited principal investigators of noncommunicable disease studies curated by the GWAS catalog 15, 26 to share summary data for their study.
  • Primary outcomes were defined as diseases with sufficient numbers of cases and controls for greater than 50% statistical power, and secondary outcomes were defined as diseases with 50% or less statistical power to detect odds ratios (ORs) of 2.0 or higher per standard deviation (SD) change in genetically increased telomere length (α assumed to be .01).
  • Risk factors with less than 50% statistical power were excluded.

Comparison With Prospective Observational Studies

  • The authors searched PubMed for prospective observational studies of the association between telomere length and disease (see eTables 3 and 4 in Supplement 1 for details of the search strategy and inclusion criteria).
  • Study-specific relative risks for disease per unit change or quantile comparison of telomere length were transformed to an SD scale using previously described methods.
  • 27 Hazard ratios, risk ratios, and ORs were assumed to approximate the same measure of relative risk.
  • Where multiple independent studies of the same disease were identified, these were combined by fixed effects meta-analysis, unless there was strong evidence of between-study heterogeneity (Cochran Q P < .001), in which case they were kept separate.

Key Points

  • Genetically longer telomeres were associated with higher odds of disease for 9 of 22 primary cancers tested but with reduced odds of disease for 6 of 32 primary non-neoplastic diseases, including cardiovascular diseases.
  • 30, 31 The assumptions are that (1) the selected SNPs are associated with telomere length; (2) the selected SNPs are not associated with confounders; and (3) the selected SNPs are associated with disease exclusively through their effect on telomere length.
  • The authors used meta-regression to appraise potential sources of heterogeneity in their findings for cancer.

Results

  • This indi-cates that the genetic instrument constructed from these 10 independent genomic regions is strongly associated with telomere length .
  • For 9 of the 83 noncommunicable diseases, additional summary data were available from 10 independent studies for replication analyses, corresponding to 40 465 cases (median, 1416 per disease) and 52 306 controls (median, 3537 per disease) (eTable 1 in Supplement 1).
  • The strongest evidence of association was observed for glioma, lung adenocarcinoma, neuroblastoma, and serous LMP ovarian cancer .
  • The strongest evidence of association was observed for coronary heart disease, abdominal aortic aneurysm, celiac disease, and interstitial lung disease .

Discussion

  • The authors show that genetically increased telomere length is associated with increased risk of several cancers and with reduced risk of some non-neoplastic diseases.
  • Given the random distribution of genotypes in the general population with respect to lifestyle and other environmental factors, as well as the fixed nature of germline genotypes, these results should be less susceptible to confounding and reverse causation than those generated by observational studies.
  • The authors results could, however, reflect violations of Mendelian randomization assumptions, such as confounding by pleiotropy, population stratification, or ancestry.
  • Confounding by population stratification or ancestry is also unlikely, given the adjustments made for ancestry in the original disease GWASs .
  • The authors results are therefore compatible with causality.

Comparison With Previous Studies

  • The authors findings for cancer are generally contradictory to those based on retrospective studies, which tend to report increased risk for cancer in individuals with shorter telomeres.
  • The contradictory findings may reflect reverse causation in the retrospective studies, whereby shorter telomeres arise as a result of disease, or of confounding effects, eg, due to case patients being slightly older than controls even in age-matched analyses.
  • Individuals with dyskeratosis congenita, a disease caused by germline loss-of-function mutations in the telomerase component genes TERC and TERT have chronically short telomeres and are at increased risk of some cancers, particularly acute myeloid leukemia and squamous cell carcinomas arising at sites of leukoplakia, 125, 126 presumably due to increased susceptibility to genome instability and chromosomal end-toend fusions.

Mechanisms of Association

  • The authors cancer findings are compatible with known biology.
  • This could explain the approximately 6-fold variation in ORs observed across cancer types in the present study as well as the tendency of their results to be stronger at tissue sites with lower rates of stem cell division.
  • The association was strongest for glioma (OR, 5.27) and comparatively weak for colorectal cancer (OR, 1.09), and the rates of stem cell division in the tissues giving rise to these cancers differ by several orders of magnitude.
  • In neural stem cells, which give rise to gliomas, the number of divisions is about 270 million, and for colorectal stem cells it is about 1.2 trillion over the average lifetime of an individual.
  • 34 The observation that genetically increased telomere length was more strongly associated with rarer cancers potentially reflects the same mechanism, since rarer cancers also tend to show lower rates of stem cell division.

Clinical Relevance of Findings

  • The authors findings suggest that potential clinical applications of telomere length, eg, as a tool for risk prediction or as an intervention target for disease prevention, may be subject to a trade-off in risk between cancer and non-neoplastic diseases.
  • A number of companies have been established that offer telomere length measurement services to the public (via a requesting physician) under the claim that shorter telomeres are a general indicator of poorer health status and older biological age and that such information can be used to motivate healthy lifestyle choices in individuals.
  • The conflicting direction of association between telomere length and risk of cancer and non-neoplastic diseases indicated by their findings suggests that such services to the general public may be premature.

Study Limitations

  • The authors study is subject to some limitations, in addition to the Mendelian randomization assumptions already considered.
  • First, their method assumes that the magnitude of the association between SNPs and telomere length is consistent across tissues.
  • Fifth, their results may not be fully representative of noncommunicable diseases (since not all studies shared data, and their analyses were underpowered for the secondary disease outcomes).
  • The diseases represented in their primary analyses probably account for more than 60% of all causes of death in American adults.

Conclusions

  • It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.
  • Further research is required to resolve whether telomere length is a useful predictor of risk that can help guide therapeutic interventions, to clarify the shape of any dose-response relationships, and to characterize the nature of the association in population subgroups.

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Copyright 2017 American Medical Association. All rights reserved.
Association Between Telomere Length and Risk
of Cancer and Non-Neoplastic Diseases
A Mendelian Randomization Study
The Telomeres Mendelian Randomization Collaboration
IMPORTANCE
The causal direction and magnitude of the association between telomere
length and incidence of cancer and non-neoplastic diseases is uncertain owing to the
susceptibility of observational studies to confounding and reverse causation.
OBJECTIVE To conduct a Mendelian randomization study, using germline genetic variants as
instrumental variables, to appraise the causal relevance of telomere length for risk of cancer
and non-neoplastic diseases.
DATA SOURCES Genomewide association studies (GWAS) published up to January 15, 2015.
STUDY SELECTION GWAS of noncommunicable diseases that assayed germline genetic
variation and did not select cohort or control participants on the basis of preexisting diseases.
Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were
available.
DATA EXTRACTION AND SYNTHESIS Summary association statistics for single nucleotide
polymorphisms (SNPs) that are strongly associated with telomere length in the general
population.
MAIN OUTCOMES AND MEASURES Odds ratios (ORs) and 95% confidence intervals (CIs) for
disease per standard deviation (SD) higher telomere length due to germline genetic variation.
RESULTS Summary data were available for 35 cancers and 48 non-neoplastic diseases,
corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls
(median, 6789 per disease). Increased telomere length due to germline genetic variation was
generally associated with increased risk for site-specific cancers. The strongest associations
(ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for
glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung
adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19
(1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55
(1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer
cancers and at tissue sites with lower rates of stem cell division. There was generally little
evidence of association between genetically increased telomere length and risk of
psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except
for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR,
0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung
disease (OR, 0.09 [95% CI, 0.05-0.15]).
CONCLUSIONS AND RELEVANCE It is likely that longer telomeres increase risk for several
cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.
JAMA Oncol. 2017;3(5):636-651. doi:10.1001/jamaoncol.2016.5945
Published online February 23, 2017.
Supplemental content
The Authors/Members of the
Telomeres Mendelian
Randomization Collaboration
appear at the end of this article.
Corresponding Author: Philip C.
Haycock, PhD, MRC Integrative
Epidemiology Unit, University of
Bristol, Bristol, England
(philip.haycock@bristol.ac.uk).
Research
JAMA Oncology | Original Investigation
636 (Reprinted) jamaoncology.com
Copyright 2017 American Medical Association. All rights reserved.
Downloaded From: https://jamanetwork.com/ on 08/09/2022
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A
t the ends of chromosomes, telomeres are DNA-
protein structures that protect the genome from dam-
age, shorten progressively over time in most somatic
tissues,
1
and are proposed physiological markers of aging.
2,3
Shorter leukocyte telomeres are correlated with older age, male
sex, and other known risk factors for noncommunicable
diseases
4-6
and are generally associated with higher risk for car-
diovascular diseases,
7,8
type 2 diabetes,
9
and nonvascular, non-
neoplastic causes of mortality.
8
Whether these associations are
causal, however, is unknown. Telomere length has also been
implicated in risk of cancer, but the direction and magnitude
of the association is uncertain and contradictory across ob-
servational studies.
10-14
The uncertainty reflects the consid-
erable difficulty of designing observational studies of telo-
mere length and cancer incidence that are sufficiently robust
to reverse causation, confounding, and measurement error.
The aim of the present report was to conduct a Mende-
lian randomization study, using germline genetic variants as
instrumental variables for telomere length, to help clarify the
nature of the association between telomere length and risk of
cancer and non-neoplastic diseases. The approach, which mim-
ics the random allocation of individuals to the placebo and in-
tervention arms of a randomized clinical trial, allowed us to:
(1) estimate the direction and broad magnitude of the associa-
tion of telomere length with risk of multiple cancer and non-
neoplastic diseases; (2) appraise the evidence for causality in
the estimated etiological associations; (3) investigate poten-
tial sources of heterogeneity in findings for site-specific can-
cers; and (4) compare genetic estimates with findings based
on directly measured telomere length in prospective obser-
vational studies.
Methods
Study Design
The design of our study, illustrated in eFigure 1 in Supplement
1, had 3 key components: (1) the identification of genetic vari-
ants to serve as instruments for telomere length; (2) the acqui-
sition of summary data for the genetic instruments from ge-
nomewide association studies (GWASs) of diseases and risk
factors for noncommunicable diseases; and (3) the classifica-
tion of diseases and risk factors into primary or secondary out-
comes based on a priori statistical power. As a first step, we
searched the GWAS catalog
15,16
on January 15, 2015, to identify
single-nucleotide polymorphisms (SNPs) associated with telo-
mere length. To supplement the list with additional potential
instruments, we also searched the original study reports
curated by the GWAS catalog (using a P value threshold of
5×10
−8
).
17-25
We acquired summary data for all SNPs identi-
fied by our search from a meta-analysis of GWASs of telomere
length, involving 9190 participants of European ancestry.
18
The second key component of our design strategy in-
volved the acquisition of summary data, corresponding to the
selected genetic instruments for telomere length, from GWASs
of noncommunicable diseases and risk factors (eFigure 1 in
Supplement 1). As part of this step, we invited principal inves-
tigators of noncommunicable disease studies curated by the
GWAS catalog
15,26
to share summary data for our study. We also
downloaded summary data for diseases and risk factors from
publically available sources, including study-specific web-
sites, dbGAP, ImmunoBase, and the GWAS catalog (eFigure 1
in Supplement 1).
The third key component of our design strategy was the
classification of diseases and risk factors into either primary
or secondary outcomes, which we defined on the basis of a
priori statistical power to detect associations with telomere
length. Primary outcomes were defined as diseases with suf-
ficient numbers of cases and controls for greater than 50% sta-
tistical power, and secondary outcomes were defined as dis-
eases with 50% or less statistical power to detect odds ratios
(ORs) of 2.0 or higher per standard deviation (SD) change in
genetically increased telomere length (α assumed to be .01).
All risk factors were defined as secondary outcomes. Risk fac-
tors with less than 50% statistical power were excluded.
Further details on our design strategy can be found in
Supplement 1.
Comparison With Prospective Observational Studies
We searched PubMed for prospective observational studies of
the association between telomere length and disease (see
eTables 3 and 4 in Supplement 1 for details of the search strat-
egy and inclusion criteria). Study-specific relative risks for dis-
ease per unit change or quantile comparison of telomere length
were transformed to an SD scale using previously described
methods.
27
Hazard ratios, risk ratios, and ORs were assumed
to approximate the same measure of relative risk. Where mul-
tiple independent studies of the same disease were identi-
fied, these were combined by fixed effects meta-analysis, un-
less there was strong evidence of between-study heterogeneity
(Cochran Q P < .001), in which case they were kept separate.
Statistical Analysis
We combined summary data across SNPs into a single instru-
ment, using maximum likelihood to estimate the slope of the
relationship between β
GD
and β
GP
and a variance-covariance
matrix to make allowance for linkage disequilibrium be-
tween SNPs,
28
where β
GD
is the change in disease log odds or
risk factor levels per copy of the effect allele, and β
GP
is the SD
Key Points
Question What is the causal relevance of telomere length for risk
of cancer and non-neoplastic diseases?
Findings In this Mendelian randomization study, genetically
longer telomeres were associated with higher odds of disease for 9
of 22 primary cancers tested but with reduced odds of disease for
6 of 32 primary non-neoplastic diseases, including cardiovascular
diseases.
Meaning It is likely that longer telomeres increase risk for several
cancers but reduce risk for some non-neoplastic diseases,
including cardiovascular diseases. This trade-off in risk should be
carefully considered in any diagnostic, prognostic, or therapeutic
applications based on telomere length.
Telomere Length and Risk of Cancer and Non-Neoplastic Diseases Original Investigation Research
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Copyright 2017 American Medical Association. All rights reserved.
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change in telomere length per copy of the effect allele (see eAp-
pendix 1 in Supplement 1 for technical details). The slope from
this approach can be interpreted as the log OR for binary out-
comes, or the unit change for continuous risk factors, per SD
change in genetically increased telomere length. P values for
heterogeneity among SNPs in the estimated associations of ge-
netically increased telomere length with disease and risk fac-
tors were estimated by likelihood ratio tests.
28
Associations be-
tween genetically increased telomere length and continuous
risk factors were transformed into SD units. For 5 secondary
disease outcomes where only a single SNP was available for
analysis, we estimated associations using the Wald ratio: β
GD
/
β
GP,
with standard errors approximated by the delta method.
29
Inference of causality in the estimated etiological associa-
tions between telomere length and disease depends on satis-
faction of Mendelian randomization assumptions (eFigure 7 in
Supplement 1; also see eTable 5 in Supplement 1 for a glossary
of terms).
30,31
The assumptions are that (1) the selected SNPs
are associated with telomere length; (2) the selected SNPs are
not associated with confounders; and (3) the selected SNPs are
associated with disease exclusively through their effect on telo-
mere length. If these assumptions are satisfied, the selected
SNPs are valid instrumental variables, and their association with
disease can be interpreted as a causal effect of telomere length.
We modeled the impact of violations of these assumptions
through 2 sets of sensitivity analyses: a weighted median
function
32
and MR-Egger regression (see eAppendix 1 in
Supplement 1 for technical details).
30
We restricted our sensi-
tivity analyses to diseases showing the strongest evidence of
association with genetically increased telomere length (defined
as Bonferroni P ≤ .05).
We used meta-regression to appraise potential sources of
heterogeneity in our findings for cancer. The association of ge-
netically increased telomere length with the log odds of cancer
was regressed on cancer incidence, survival time, and median
age at diagnosis (downloaded from the National Cancer Institute
Surveillance, Epidemiology, and End Results [SEER] Program
33
),
and tissue-specific ratesof stem cell division from Tomasetti and
Vogelstein.
34
As the downloaded cancer characteristics from
SEER correspond to the United States population, 77% of which
was of white ancestry in 2015,
35
the meta-regression analyses
excluded genetic studies conducted in East Asian populations.
All analyses were performed in R, version 3.1.2,
36
and Stata
release 13.1 (StataCorp LP). P values were 2-sided, and evi-
dence of association was declared at P < .05. Where indicated,
Bonferroni corrections were used to make allowance for mul-
tiple testing, although this is likely to be overly conservative
given the nonindependence of many of the outcomes tested.
Results
We selected 16 SNPs as instruments for telomere length (eFig-
ure1inSupplement 1 and Table 1). The selected SNPs corre-
spond to 10 independent genomic regions that collectively ac-
count for 2% to 3% of the variance in leukocyte telomere length,
which would be equivalent to an F statistic of 18 to 28 in the
sample used to define the instruments (Table 1). This indi-
cates that the genetic instrument constructed from these 10
independent genomic regions is strongly associated with telo-
mere length (details in eAppendix 1 in Supplement 1).
37
Sum-
mary data for the genetic instruments were available for 83
noncommunicable diseases, corresponding to 420 081 cases
(median, 2526 per disease), 1 093 105 controls (median, 6789
per disease), and 44 risk factors (eFigure 1 and eTable 1 in
Supplement 1; Table 2). The median number of SNPs avail-
able across diseases was 11 (minimum, 1; maximum, 13) and
across risk factors was 12 (minimum, 11; maximum, 13). Of the
83 diseases, 56 were classified as primary outcomes and 27 as
secondary outcomes (Table 2; eFigure 1 and eTable 1 in
Supplement 1). For 9 of the 83 noncommunicable diseases, ad-
ditional summary data were available from 10 independent
studies for replication analyses, corresponding to 40 465 cases
(median, 1416 per disease) and 52 306 controls (median, 3537
per disease) (eTable 1 in Supplement 1).
The results from primary analyses of noncommunicable
diseases are presented in Figure 1 and the eTable in Supplement
2; results from secondary analyses of risk factors and dis-
eases with low a priori power are presented in eFigures 2, 5,
and6inSupplement 1. Genetically increased telomere length
was associated with higher ORs (95% CIs) of disease for 9 of
22 primary cancers (P < .05): glioma (5.27 [3.15-8.81]), endo-
metrial cancer (1.31 [1.07-1.61]), kidney cancer (1.55 [1.08-
2.23]), testicular germ-cell cancer (1.76 [1.02-3.04]), mela-
noma (1.87 [1.55-2.26]), bladder cancer (2.19 [1.32-3.66]),
neuroblastoma (2.98 [1.92-4.62]), lung adenocarcinoma (3.19
[2.40-4.22]) and serous low-malignancy-potential (LMP) ovar-
ian cancer (4.35 [2.39-7.94]) (Figure 1). The associations were,
however, highly variable across cancer types, varying from an
OR (95% CI) of 0.86 (0.57-1.30) for head and neck cancer to 5.27
(3.15-8.81) for glioma. Substantial variability was also ob-
served within tissue sites. For example, the OR (95% CI) for
lung adenocarcinoma was 3.19 (2.40-4.22) compared with 1.07
(0.82-1.39) for squamous cell lung cancer. For serous LMP ovar-
ian cancer, the OR (95% CI) was 4.35 (2.39-7.94) compared with
1.21 (0.87-1.68) for endometrioid ovarian cancer, 1.12 (0.94-
1.34) for serous invasive ovarian cancer, 1.04 (0.66-1.63) for
clear-cell ovarian cancer, and 1.04 (0.73-1.47) for mucinous
ovarian cancer. The strongest evidence of association was ob-
served for glioma, lung adenocarcinoma, neuroblastoma, and
serous LMP ovarian cancer (Figure 1). Results for glioma and
bladder cancer showed evidence for replication in indepen-
dent data sets (independent data sets were not available for
other cancers) (eFigure 3 in Supplement 1).
Genetically increased telomere length was associated with
lower ORs (95% CIs) of disease for 6 of 32 primary non-neoplastic
diseases (P < .05): coronary heart disease (0.78 [0.67-0.9]), ab-
dominal aortic aneurysm (0.63 [0.49-0.81]), Alzheimer dis-
ease (0.84 [0.71-0.98]), celiac disease (0.42 [0.28-0.61]), in-
terstitial lung disease (0.09 [0.05-0.15]) and type 1 diabetes
(0.71 [0.51-0.98]) (Figure 1). The strongest evidence of asso-
ciation was observed for coronary heart disease, abdominal aor-
tic aneurysm, celiac disease, and interstitial lung disease
(Figure 1). The associations with coronary heart disease and
interstitial lung disease showed evidence for replication in in-
dependent data sets (eFigure 3 in Supplement 1).
Research Original Investigation Telomere Length and Risk of Cancer and Non-Neoplastic Diseases
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Our genetic findings were generally similar in direction and
magnitude to estimates based on observational prospective
studies of leukocyte telomere length and disease (Figure 2).
10,97
Our genetic estimates for lung adenocarcinoma, melanoma,
kidney cancer, and glioma were, however, stronger than the
observational estimates.
In sensitivity analyses, we appraised the potential
impact of confounding by pleiotropic pathways on our
results. Associations estimated by the weighted median
and MR-Egger were broadly similar to the main results for
glioma, lung adenocarcinoma, serous LMP ovarian
cancer, neuroblastoma, abdominal aortic aneurysm, coro-
nary heart disease, and interstitial lung disease (eFigure 4 in
Supplement 1). We found little evidence for the presence of
pleiotropy, as indicated by the MR-Egger intercept test
(eFigure 4 in Supplement 1). The MR-Egger analyses were,
however, generally underpowered, as reflected by the wide
confidence intervals in the estimated odds ratios (eFigure 4
in Supplement 1).
In meta-regression analyses, we observed that geneti-
cally increased telomere length tended to be more strongly as-
sociated with rarer cancers and cancers at tissue sites with
lower rates of stem cell division (Figure 3). The associations
showed little evidence of varying by percentage survival 5 years
after diagnosis or median age at diagnosis.
Discussion
In this report, we show that genetically increased telomere
length is associated with increased risk of several cancers and
with reduced risk of some non-neoplastic diseases. Given the
random distribution of genotypes in the general population
with respect to lifestyle and other environmental factors, as
well as the fixed nature of germline genotypes, these results
should be less susceptible to confounding and reverse causa-
tion than those generated by observational studies. Our re-
sults could, however, reflect violations of Mendelian random-
ization assumptions, such as confounding by pleiotropy,
population stratification, or ancestry.
98
Although we cannot
entirely rule out this possibility, the majority of our results per-
sisted in sensitivity analyses that made allowance for viola-
tions of Mendelian randomization assumptions. Confound-
ing by population stratification or ancestry is also unlikely,
given the adjustments made for ancestry in the original dis-
ease GWASs (see eAppendix 1 in Supplement 1). Our results are
therefore compatible with causality.
Comparison With Previous Studies
Our findings for cancer are generally contradictory to those
based on retrospective studies, which tend to report increased
Table 1. Single-Nucleotide Polymorphisms Associated With Telomere Length
SNP Chr Pos Gene EA OA EAF
a
β
a
SE
a
P Value
a
P
het
a
No. of
Studies
a
Sample
Size
a
Discovery
P Value
Variance
Explained,
%
Discovery
Study
rs11125529 2 54248729 ACYP2 A C 0.16 0.065 0.012 6.06 × 10
−3
0.313 6 9177 8.00 × 10
−10
0.080 Codd
et al
21
rs6772228 3 58390292 PXK T A 0.87 0.041 0.014 .0497 0.77 6 8630 3.91 × 10
−10
0.200 Pooley
et al
17
rs12696304 3 169763483 TERC C G 0.74 0.090 0.011 5.41 × 10
−8
0.651 6 9012 4.00 × 10
−14
0.319 Codd
et al
22
rs10936599 3 169774313 TERC C T 0.76 0.100 0.011 1.76 × 10
−9
0.087 6 9190 3.00 × 10
−31
0.319 Codd
et al
21
rs1317082 3 169779797 TERC A G 0.71 0.097 0.011 4.57 × 10
−9
0.029 6 9176 1.00 × 10
−8
0.319 Mangino
et al
18
rs10936601 3 169810661 TERC C T 0.74 0.087 0.011 8.64 × 10
−8
0.433 6 9150 4.00 × 10
−15
0.319 Pooley
et al
17
rs7675998 4 163086668 NAF1 G A 0.80 0.048 0.012 .01 0.077 6 9161 4.35 × 10
−16
0.190 Codd
et al
21
rs2736100 5 1286401 TERT C A 0.52 0.085 0.013 2.14 × 10
−5
0.54 4 5756 4.38 × 10
−19
0.310 Codd
et al
21
rs9419958 10 103916188 OBFC1 T C 0.13 0.129 0.013 5.26 × 10
−11
0.028 6 9190 9.00 × 10
−11
0.171 Mangino
et al
18
rs9420907 10 103916707 OBFC1 C A 0.14 0.142 0.014 1.14 × 10
−11
0.181 6 9190 7.00 × 10
−11
0.171 Codd
et al
21
rs4387287 10 103918139 OBFC1 A C 0.14 0.120 0.013 1.40 × 10
−9
0.044 6 8541 2.00 × 10
−11
0.171 Levy
et al
25
rs3027234 17 8232774 CTC1 C T 0.83 0.103 0.012 2.75 × 10
−8
0.266 6 9108 2.00 × 10
−8
0.292 Mangino
et al
18
rs8105767 19 22032639 ZNF208 G A 0.25 0.064 0.011 <.001 0.412 6 9096 1.11 × 10
−9
0.090 Codd
et al
21
rs412658 19 22176638 ZNF676 T C 0.35 0.086 0.010 1.83 × 10
−8
0.568 6 9156 1.00 × 10
−8
0.484 Mangino
et al
18
rs6028466 20 39500359 DHX35 A G 0.17 0.058 0.013 .004 0.533 6 9190 2.57 × 10
−8b
0.041 Mangino
et al
18
and
Gu et al
20
rs755017 20 63790269 ZBTB46 G A 0.17 0.019 0.0129 .34 0.757 5 8026 6.71 × 10
−9
0.090 Codd
et al
21
Abbreviations: β, standard deviation change in telomere length per copy of the
effect allele; Chr, chromosome; EA, effect allele; EAF, EA frequency; OA, other
allele; P
het
, P value for between-study heterogeneity in association between
SNP and telomere length; Pos, base-pair position (GRCh38.p3); SE, standard
error; SNP, single-nucleotide polymorphism.
a
Summary data from Mangino et al.
18
b
From a meta-analysis of Mangino
18
and Gu
20
performed in the present study.
Telomere Length and Risk of Cancer and Non-Neoplastic Diseases Original Investigation Research
jamaoncology.com (Reprinted) JAMA Oncology May 2017 Volume 3, Number 5 639
Copyright 2017 American Medical Association. All rights reserved.
Downloaded From: https://jamanetwork.com/ on 08/09/2022
Copyright 2017 American Medical Association. All rights reserved.
Table 2. Study Characteristics for Primary Noncommunicable Diseases
Disease
Cases,
No.
Controls,
No.
SNPs,
No.
Statistical
Power Population Source
Cancer
Bladder cancer 1601 1819 10 0.62 EUR NBCS
38
Breast cancer 48 155 43 612 13 1.00 EUR BCAC
17,39
Estrogen receptor negative 7465 42 175 13 1.00 EUR BCAC
17,39
Estrogen receptor positive 27 074 41 749 13 1.00 EUR BCAC
17,39
Colorectal cancer 14 537 16 922 9 1.00 EUR CORECT/GECCO
40,41
Endometrial cancer 6608 37 925 12 1.00 EUR ECAC
42,43
Esophageal squamous cell carcinoma 1942 2111 11 0.64 EA Abnet et al
44
Glioma 1130 6300 12 0.72 EUR Wrensch et al
45
and
Walsh et al
46
Head and neck cancer 2082 3477 12 1.00 EUR McKay et al
47
Kidney cancer 2461 5081 12 0.99 EUR KIDRISK
48
Lung cancer 11 348 15 861 13 1.00 EUR ILCCO
49
Adenocarcinoma 3442 14 894 13 1.00 EUR ILCCO
49
Squamous cell carcinoma 3275 15 038 13 1.00 EUR ILCCO
49
Skin cancer
Melanoma 12 814 23 203 13 1.00 EUR MC
50
Basal cell carcinoma 3361 11 518 13 1.00 EUR NHS/HPFS
51
Neuroblastoma 2101 4202 12 0.87 EUR Diskin
52
Ovarian cancer 15 397 30 816 13 1.00 EUR OCAC
17,53
Clear cell 1016 30 816 13 0.76 EUR OCAC
17,53
Endometrioid 2154 30 816 13 0.98 EUR OCAC
17,53
Mucinous 1643 30 816 13 0.94 EUR OCAC
17,53
Serous invasive 9608 30 816 13 1.00 EUR OCAC
17,53
Serous low malignant potential 972 30 816 13 0.73 EUR OCAC
17,53
Pancreatic cancer 5105 8739 12 1.00 EUR PanScan (incl. EPIC)
54
Prostate cancer 22 297 22 323 11 1.00 EUR PRACTICAL
55,56
Testicular germ-cell cancer 986 4946 11 0.52 EUR Turnbull et al
57
and
Rapley et al
58
Autoimmune/Inflammatory Diseases
Alopecia areata 2332 5233 7 0.60 EUR Betz
59
Atopic dermatitis 10 788 30 047 13 1.00 EUR EAGLE
60
Celiac disease 4533 10 750 3 0.82 EUR Dubois
61
Inflammatory bowel disease
Crohn disease 5956 14 927 11 1.00 EUR IIBDGC
62
Ulcerative colitis 6968 20 464 12 1.00 EUR IIBDGC
62
Juvenile idiopathic arthritis 1866 14 786 11 0.87 EUR Thompson et al
63a
Multiple sclerosis 14 498 24 091 3 1.00 EUR IMSGC
64
Aggressive periodontitis 888 6789 13 0.63 EUR Schaefer et al
65
Rheumatoid arthritis 5538 20 163 11 1.00 EUR Stahl et al
66
Cardiovascular Diseases
Abdominal aortic aneurysm 4972 99 858 13 1.00 EUR AC
67-72
Coronary heart disease 22 233 64 762 13 1.00 EUR CARDIoGRAM
73
Heart failure 2526 20 926 13 0.99 EUR CHARGE-HF
74
Hemorrhagic stroke 2963 5503 12 0.96 EUR METASTROKE/ISGC
75
Ischemic stroke 12 389 62 004 13 1.00 EUR METASTROKE/ISGC
76,77
Large-vessel disease 2167 62 004 13 0.99 EUR METASTROKE/ISGC
76,77
Small-vessel disease 1894 62 004 13 0.97 EUR METASTROKE/ISGC
76
Cardioembolic disease 2365 62 004 13 0.99 EUR METASTROKE/ISGC
76
Sudden cardiac arrest 3954 21 200 13 1.00 EUR Unpublished
Diabetes
Type 1 7514 9045 6 0.95 EUR T1DBase
78,79
Type 2 10 415 53 655 11 1.00 EUR DIAGRAM
80
Eye Disease
Age-related macular degeneration 7473 51 177 13 1.00 EUR AMD Gene
81
Retinopathy 1122 18 289 12 0.75 EUR Jensen et al
82
(continued)
Research Original Investigation Telomere Length and Risk of Cancer and Non-Neoplastic Diseases
640 JAMA Oncology May 2017 Volume 3, Number 5 (Reprinted) jamaoncology.com
Copyright 2017 American Medical Association. All rights reserved.
Downloaded From: https://jamanetwork.com/ on 08/09/2022
Citations
More filters
Journal Article•DOI•
The MR-Base platform supports systematic causal inference across the human phenome

[...]

Gibran Hemani1, Jie Zheng1, Benjamin Elsworth1, Kaitlin H Wade1, Valeriia Haberland1, Denis Baird1, Charles Laurin1, Stephen Burgess2, Jack Bowden1, Ryan Langdon1, Vanessa Y Tan1, James Yarmolinsky1, Hashem A Shihab1, Nicholas J. Timpson1, David M. Evans1, David M. Evans3, Caroline L Relton1, Richard M. Martin1, George Davey Smith1, Tom R. Gaunt1, Philip C Haycock1 •
Medical Research Council1, University of Cambridge2, University of Queensland3
30 May 2018-eLife
TL;DR: MR-Base is a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR, and includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions.
Abstract: Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base ( http://www.mrbase.org ): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.

2,520 citations

Cites methods from "Association Between Telomere Length..."

  • ...In order to gain insight into potential opportunities for repurposing or adverse effects of LDL cholesterol lowering - an established intervention stategy for CHD prevention - we conducted a hypothesis-free MR-PheWAS analysis (Millard et al., 2015; Haycock et al., 2017)....

    [...]

Identification of seven loci affecting mean telomere length and their association with disease

[...]

Veryan Codd, Christopher P. Nelson, Eva Albrecht, Massimo Mangino, Joris Deelen, Jessica L. Buxton, Jouke-Jan Hottenga, Krista Fischer, Tõnu Esko, Ida Surakka, Linda Broer, Dale R. Nyholt, Irene Mateo Leach, Perttu Salo, Sara Hägg, Mary K. Matthews, Jutta Palmen, Giuseppe Danilo Norata, Paul F. O'Reilly, Danish Saleheen, Najaf Amin, Anthony J. Balmforth, Marian Beekman, Rudolf A. de Boer, Stefan Böhringer, Peter S. Braund, Paul Burton, Anton J. M. de Craen, Matthew Denniff, Yanbin Dong, Konstantinos Douroudis, Elena Dubinina, Johan G. Eriksson, Katia Garlaschelli, Dehuang Guo, Anna-Liisa Hartikainen, Anjali K. Henders, Jeanine J. Houwing-Duistermaat, Laura Kananen, Lennart C. Karssen, Johannes Kettunen, Norman Klopp, Vasiliki Lagou, Elisabeth M. van Leeuwen, Pamela A. F. Madden, Reedik Maegi, Patrik K. E. Magnusson, Satu Männistö, Mark I. McCarthy, Sarah E. Medland, Evelin Mihailov, Grant W. Montgomery, Ben A. Oostra, Aarno Palotie, Annette Peters, Helen Pollard, Anneli Pouta, Inga Prokopenko, Samuli Ripatti, Veikko Salomaa, H. Eka D. Suchiman, Ana M. Valdes, Niek Verweij, Ana Viñuela, Xiaoling Wang, H-Erich Wichmann, Elisabeth Widen, Gonneke Willemsen, Margaret J. Wright, Kai Xia, Xiangjun Xiao, Dirk J. van Veldhuisen, Alberico L. Catapano, Martin D. Tobin, Alistair S. Hall, Alexandra I. F. Blakemore, Wiek H. van Gilst, Haidong Zhu, Jeanette Erdmann, Muredach P. Reilly, Sekar Kathiresan, Heribert Schunkert, Philippa J. Talmud, Nancy L. Pedersen, Markus Perola, Willem H. Ouwehand, Jaakko Kaprio, Nicholas G. Martin, Cornelia M. van Duijn, Iris Hovatta, Christian Gieger, Andres Metspalu, Dorret I. Boomsma, Marjo-Riitta Järvelin, P. Eline Slagboom, John R Thompson, Tim D. Spector, Pim van der Harst, Nilesh J. Samani 
01 Jan 2013
TL;DR: In this article, a genome-wide meta-analysis of 37,684 individuals with replication of selected variants in an additional 10,739 individuals was carried out to identify seven loci, including five new loci associated with mean leukocyte telomere length (LTL).
Abstract: Interindividual variation in mean leukocyte telomere length (LTL) is associated with cancer and several age-associated diseases. We report here a genome-wide meta-analysis of 37,684 individuals with replication of selected variants in an additional 10,739 individuals. We identified seven loci, including five new loci, associated with mean LTL (P < 5 × 10−8). Five of the loci contain candidate genes (TERC, TERT, NAF1, OBFC1 and RTEL1) that are known to be involved in telomere biology. Lead SNPs at two loci (TERC and TERT) associate with several cancers and other diseases, including idiopathic pulmonary fibrosis. Moreover, a genetic risk score analysis combining lead variants at all 7 loci in 22,233 coronary artery disease cases and 64,762 controls showed an association of the alleles associated with shorter LTL with increased risk of coronary artery disease (21% (95% confidence interval, 5–35%) per standard deviation in LTL, P = 0.014). Our findings support a causal role of telomere-length variation in some age-related diseases.

604 citations

Proceedings Article•
Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility

[...]

Yuanyuan Xiao
08 Jul 2009
TL;DR: Wrensch et al. as mentioned in this paper found that variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility and showed that the direction of association was the same in discovery and replication phases.
Abstract: LETTERS Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility Margaret Wrensch 1,2,12 , Robert B Jenkins 3,12 , Jeffrey S Chang 4,12 , Ru-Fang Yeh 4,12 , Yuanyuan Xiao 4 , Paul A Decker 5 , Karla V Ballman 5 , Mitchel Berger 1 , Jan C Buckner 6 , Susan Chang 1 , Caterina Giannini 3 , Chandralekha Halder 3 , Thomas M Kollmeyer 3 , Matthew L Kosel 5 , Daniel H LaChance 7 , Lucie McCoy 1 , Brian P O’Neill 7 , Joe Patoka 1 , Alexander R Pico 8 , Michael Prados 1 , Charles Quesenberry 9 , Terri Rice 1 , Amanda L Rynearson 3 , Ivan Smirnov 1 , Tarik Tihan 10 , Joe Wiemels 2,4 , Ping Yang 11,13 & John K Wiencke 1,2,13 The causes of glioblastoma and other gliomas remain obscure 1,2 To discover new candidate genes influencing glioma susceptibility, we conducted a principal component– adjusted 3 genome-wide association study (GWAS) of 275,895 autosomal variants among 692 adult high-grade glioma cases (622 from the San Francisco Adult Glioma Study (AGS) and 70 from the Cancer Genome Atlas (TCGA)) 4 and 3,992 controls (602 from AGS and 3,390 from Illumina iControlDB (iControls)) For replication, we analyzed the 13 SNPs with P o 10 A6 using independent data from 176 high-grade glioma cases and 174 controls from the Mayo Clinic On 9p21, rs1412829 near CDKN2B had discovery P ¼ 34 Â 10 A8 , replication P ¼ 00038 and combined P ¼ 185 Â 10 A10 On 20q133, rs6010620 intronic to RTEL1 had discovery P ¼ 15 Â 10 A7 , replication P ¼ 000035 and combined P ¼ 340 Â 10 A9 For both SNPs, the direction of association was the same in discovery and replication phases Subject characteristics, including participation rates for the discovery GWAS and replication phases, are listed in Supplementary Table 1a,b The distribution of P values from the principal component–adjusted logistic regression additive model across the genome for high-grade glioma cases versus controls (Fig 1) suggests potentially meaningful associations for several SNPs on chromosomes 1, 5, 9, 11 and 20 Supplementary Table 2 summarizes results for the 13 SNPs with P o 10 A6 for association with high-grade glioma in discovery data along with results from replication data; SNPs with Hardy-Weinberg P o 10 A5 in controls or with 45% missing data in any case or control group were excluded Three of these 13 SNPs (rs1412829 on 9p21, and rs6010620 and rs4809324 intronic to RTEL1 on 20q133) had significant association with high-grade glioma risk in the discovery phase (principal component analysis P o 18 Â 10 A7 ), were inde- pendent risk predictors in a multivariable analysis of 13 top hits, and were replicated in the Mayo Clinic dataset (Table 1) As shown in Table 1 and Supplementary Table 2, the minor allele frequencies for the three SNPs consistently differed in the same direction between high-grade glioma cases and controls regardless of data source (AGS, TCGA, iControls or Mayo Clinic) Supplementary Table 3 shows results from the multivariable model of discovery data that included all 13 SNPs (four from the 9p21 region, three in RTEL1, plus six others in other locations) Eight SNPs, including one in the 9p21 region and two intronic to RTEL1, remained independently associated with high- grade glioma risk after adjustment for other SNPs This was expected given the strong linkage disequilibrium (LD) evident for the four 9p21 SNPs and two of the three RTEL1 SNPs (Supplementary Table 4) In discovery data, only the interaction between chromosome 9p21 SNP rs1412829 and TERT SNP rs2736100 on chromosome 5 was statistically significant with Wald test P ¼ 0019 (see Supplementary P value Chromosome © 2009 Nature America, Inc All rights reserved Figure 1 Distribution of P values from principal component–adjusted logistic regression additive model across the genome for high-grade glioma cases versus controls The 13 SNPs with P o 10 A6 are shown in red 1 Department of Neurological Surgery, University of California, San Francisco, San Francisco, California, USA 2 Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA 3 Department of Experimental Pathology, Mayo Clinic, Rochester, Minnesota, USA 4 Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA 5 Division of Biostatistics, 6 Department of Oncology and 7 Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA 8 Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, San Francisco, California, USA 9 Division of Research, Kaiser Permanente, Oakland, California, USA 10 Department of Pathology, University of California, San Francisco, San Francisco, California, USA 11 Division of Epidemiology, Mayo Clinic, Rochester, Minnesota, USA 12 These authors contributed equally to this work 13 These authors jointly directed the work Correspondence should be addressed to MW (margaretwrensch@ucsfedu) Received 13 March; accepted 1 June; published online 5 July 2009; doi:101038/ng408 NATURE GENETICS ADVANCE ONLINE PUBLICATION

427 citations

Journal Article•DOI•
Recent Developments in Mendelian Randomization Studies

[...]

Jie Zheng1, Denis Baird1, Maria Carolina Borges1, Jack Bowden1, Gibran Hemani1, Philip C Haycock1, David M. Evans1, David M. Evans2, George Davey Smith1 •
University of Bristol1, University of Queensland2
22 Nov 2017-Current Epidemiology Reports
TL;DR: In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.
Abstract: Mendelian randomization (MR) is a strategy for evaluating causality in observational epidemiological studies. MR exploits the fact that genotypes are not generally susceptible to reverse causation and confounding, due to their fixed nature and Mendel’s First and Second Laws of Inheritance. MR has the potential to provide information on causality in many situations where randomized controlled trials are not possible, but the results of MR studies must be interpreted carefully to avoid drawing erroneous conclusions. In this review, we outline the principles behind MR, as well as assumptions and limitations of the method. Extensions to the basic approach are discussed, including two-sample MR, bidirectional MR, two-step MR, multivariable MR, and factorial MR. We also consider some new applications and recent developments in the methodology, including its ability to inform drug development, automation of the method using tools such as MR-Base, and phenome-wide and hypothesis-free MR. In conjunction with the growing availability of large-scale genomic databases, higher level of automation and increased robustness of the methods, MR promises to be a valuable strategy to examine causality in complex biological/omics networks, inform drug development and prioritize intervention targets for disease prevention in the future.

406 citations

Journal Article•DOI•
Genetic and molecular epidemiology of adult diffuse glioma

[...]

Annette M. Molinaro1, Jennie Taylor1, John K. Wiencke1, Margaret Wrensch1•
University of California, San Francisco1
21 Jun 2019-Nature Reviews Neurology
TL;DR: This Review focuses on the two dominant trends in glioma science: the characterization of diagnostic and prognostic tumour markers and the identification of genetic and other risk factors included in the 2016 WHO integrated classification system.
Abstract: The WHO 2007 glioma classification system (based primarily on tumour histology) resulted in considerable interobserver variability and substantial variation in patient survival within grades. Furthermore, few risk factors for glioma were known. Discoveries over the past decade have deepened our understanding of the molecular alterations underlying glioma and have led to the identification of numerous genetic risk factors. The advances in molecular characterization of glioma have reframed our understanding of its biology and led to the development of a new classification system for glioma. The WHO 2016 classification system comprises five glioma subtypes, categorized by both tumour morphology and molecular genetic information, which led to reduced misclassification and improved consistency of outcomes within glioma subtypes. To date, 25 risk loci for glioma have been identified and several rare inherited mutations that might cause glioma in some families have been discovered. This Review focuses on the two dominant trends in glioma science: the characterization of diagnostic and prognostic tumour markers and the identification of genetic and other risk factors. An overview of the many challenges still facing glioma researchers is also included.

368 citations

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R Core Team
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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Shaun Purcell1, Shaun Purcell2, Benjamin M. Neale3, Benjamin M. Neale2, Kathe Todd-Brown1, Lori Thomas1, Manuel A. R. Ferreira1, David Bender2, David Bender1, Julian Maller2, Julian Maller1, Pamela Sklar1, Pamela Sklar2, Paul I.W. de Bakker1, Paul I.W. de Bakker2, Mark J. Daly2, Mark J. Daly1, Pak C. Sham4 •
Harvard University1, Massachusetts Institute of Technology2, University of London3, University of Hong Kong4
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TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Abstract: Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

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"Association Between Telomere Length..." refers methods in this paper

  • ...9 (strong linkage disequilibrium), using the 187 ‘indep’ command in PLINK.(16) The base pair position and chromosome id for each SNP, in 188 GCRCh38 format, was extracted from Ensembl through the R biomart package....

    [...]

Journal Article•DOI•
The Hallmarks of Aging

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Carlos López-Otín1, Maria A. Blasco, Linda Partridge2, Manuel Serrano, Guido Kroemer2 •
University of Oviedo1, Max Planck Society2
06 Jun 2013-Cell
TL;DR: Nine tentative hallmarks that represent common denominators of aging in different organisms are enumerated, with special emphasis on mammalian aging, to identify pharmaceutical targets to improve human health during aging, with minimal side effects.

9,980 citations

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Gonçalo R. Abecasis1, Adam Auton2, Lisa D. Brooks3, Mark A. DePristo4, Richard Durbin5, Robert E. Handsaker4, Robert E. Handsaker6, Hyun Min Kang1, Gabor T. Marth7, Gil McVean8 •
University of Michigan1, Yeshiva University2, National Institutes of Health3, Broad Institute4, Wellcome Trust Sanger Institute5, Harvard University6, Boston College7, University of Oxford8
01 Nov 2012-Nature
TL;DR: It is shown that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites.
Abstract: By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

7,710 citations

"Association Between Telomere Length..." refers methods in this paper

  • ...180 181 Prior to calculating the associations of genetically increased telomere length with diseases and risk 182 factors, we estimated the pairwise r(2) for all telomere-associated SNPs residing on the same 183 chromosome using PLINK(16) and 1000 Genomes phase 3 data for European samples.(17) SNPs 184...

    [...]

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Veryan Codd, Christopher P. Nelson, Eva Albrecht, Massimo Mangino, Joris Deelen, Jessica L. Buxton, Jouke-Jan Hottenga, Krista Fischer, Tõnu Esko, Ida Surakka, Linda Broer, Dale R. Nyholt, Irene Mateo Leach, Perttu Salo, Sara Hägg, Mary K. Matthews, Jutta Palmen, Giuseppe Danilo Norata, Paul F. O'Reilly, Danish Saleheen, Najaf Amin, Anthony J. Balmforth, Marian Beekman, Rudolf A. de Boer, Stefan Böhringer, Peter S. Braund, Paul Burton, Anton J. M. de Craen, Matthew Denniff, Yanbin Dong, Konstantinos Douroudis, Elena Dubinina, Johan G. Eriksson, Katia Garlaschelli, Dehuang Guo, Anna-Liisa Hartikainen, Anjali K. Henders, Jeanine J. Houwing-Duistermaat, Laura Kananen, Lennart C. Karssen, Johannes Kettunen, Norman Klopp, Vasiliki Lagou, Elisabeth M. van Leeuwen, Pamela A. F. Madden, Reedik Mägi, Patrik K. E. Magnusson, Satu Männistö, Satu Männistö, Mark I. McCarthy, Mark I. McCarthy, Mark I. McCarthy, Sarah E. Medland, Evelin Mihailov, Grant W. Montgomery, Ben A. Oostra, Aarno Palotie, Annette Peters, Helen Pollard, Anneli Pouta, Anneli Pouta, Inga Prokopenko, Samuli Ripatti, Veikko Salomaa, Veikko Salomaa, H. Eka D. Suchiman, Ana M. Valdes, Niek Verweij, Ana Viñuela, Xiaoling Wang, Xiaoling Wang, H-Erich Wichmann, Elisabeth Widen, Gonneke Willemsen, Margaret J. Wright, Kai Xia, Xiangjun Xiao, Dirk J. van Veldhuisen, Alberico L. Catapano, Martin D. Tobin, Alistair S. Hall, Alexandra I. F. Blakemore, Wiek H. van Gilst, Haidong Zhu, Haidong Zhu, Jeanette Erdmann, Muredach P. Reilly, Sekar Kathiresan, Sekar Kathiresan, Heribert Schunkert, Philippa J. Talmud, Nancy L. Pedersen, Markus Perola, Markus Perola, Markus Perola, Willem H. Ouwehand, Jaakko Kaprio, Nicholas G. Martin, Cornelia M. van Duijn, Iiris Hovatta, Iiris Hovatta, Christian Gieger, Andres Metspalu, Dorret I. Boomsma, Marjo-Riitta Järvelin, P. Eline Slagboom, John R Thompson, Tim D. Spector, Pim van der Harst, Nilesh J. Samani, Nilesh J. Samani 
Human telomere biology: A contributory and interactive factor in aging, disease risks, and protection

[...]

04 Dec 2015-Science
Elizabeth H. Blackburn, Elissa S. Epel, Jue Lin
Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

[...]

01 Apr 2015-International Journal of Epidemiology
Jack Bowden, George Davey Smith, Stephen Burgess
Telomere measurement by quantitative PCR

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15 May 2002-Nucleic Acids Research
Richard M. Cawthon
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator.

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07 Apr 2016-Genetic Epidemiology
Jack Bowden, George Davey Smith, Philip C Haycock, Stephen Burgess 
Frequently Asked Questions (12)
Q1. What contributions have the authors mentioned in the paper "Association between telomere length and risk of cancer and non-neoplastic diseases: a mendelian randomization study" ?

The strongest associations ( ORs [ 95 % CIs ] per 1-SD change in genetically increased telomere length ) were observed for glioma, 5.27 ( 3.15-8.81 ) ; serous low-malignant-potential ovarian cancer, 4.35 ( 2.39-7.94 ) ; lung adenocarcinoma, 3.19 ( 1.92-4.22 ) ; neuroblastoma, 2.98 ( 1.32-3.66 ) ; melanoma, 1.55-2.26 ) ; 

127 By limiting the proliferative potential of cells, telomere shortening may serve as a tumor suppressor, and individuals with longer telomeres may be more likely to acquire somatic mutations owing to increased proliferative potential. 

The contradictory findings may reflect reverse causation in the retrospective studies, whereby shorter telomeres arise as a result of disease, or of confounding effects, eg, due to case patients being slightly older than controls even in age-matched analyses. 

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. 

The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. 

The inverse associations observed for some nonneoplastic diseases may reflect the impact of telomere shortening on tissue degeneration and an evolutionary trade-off for greater resistance to cancer at the cost of greater susceptibility to degenerative diseases, particularly cardiovascular diseases. 

Genome-wide association analysis of juvenile idiopathic arthritis identifies a new susceptibility locus at chromosomal region 3q13. 

As the downloaded cancer characteristics from SEER correspond to the United States population, 77% of which was of white ancestry in 2015,35 the meta-regression analyses excluded genetic studies conducted in East Asian populations. 

30,31 The assumptions are that (1) the selected SNPs are associated with telomere length; (2) the selected SNPs are not associated with confounders; and (3) the selected SNPs are associated with disease exclusively through their effect on telomere length. 

Genome-wide association study identifies variants in casein kinase II (CSNK2A2) to be associated with leukocyte telomere length in a Punjabi Sikh diabetic cohort. 

Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. 

For 9 of the 83 noncommunicable diseases, additional summary data were available from 10 independent studies for replication analyses, corresponding to 40 465 cases (median, 1416 per disease) and 52 306 controls (median, 3537 per disease) (eTable 1 in Supplement 1).