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Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways

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Abstract and Figures

In genome-wide association studies (GWAS) of complex traits, single SNP analysis is still the most applied approach. However, the identified SNPs have small effects and provide limited biological insight. A more appropriate approach to interpret GWAS data of complex traits is to analyze the combined effect of a SNP set grouped per pathway or gene region. We used this approach to study the joint effect on human longevity of genetic variation in two candidate pathways, the insulin/insulin-like growth factor (IGF-1) signaling (IIS) pathway and the telomere maintenance (TM) pathway. For the analyses, we used genotyped GWAS data of 403 unrelated nonagenarians from long-lived sibships collected in the Leiden Longevity Study and 1,670 younger population controls. We analyzed 1,021 SNPs in 68 IIS pathway genes and 88 SNPs in 13 TM pathway genes using four self-contained pathway tests (PLINK set-based test, Global test, GRASS and SNP ratio test). Although we observed small differences between the results of the different pathway tests, they showed consistent significant association of the IIS and TM pathway SNP sets with longevity. Analysis of gene SNP sets from these pathways indicates that the association of the IIS pathway is scattered over several genes (AKT1, AKT3, FOXO4, IGF2, INS, PIK3CA, SGK, SGK2, and YWHAG), while the association of the TM pathway seems to be mainly determined by one gene (POT1). In conclusion, this study shows that genetic variation in genes involved in the IIS and TM pathways is associated with human longevity. Electronic supplementary material The online version of this article (doi:10.1007/s11357-011-9340-3) contains supplementary material, which is available to authorized users.
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Gene set analysis of GWAS data for human
longevity highlights the relevance of the insulin/IGF-1
signaling and telomere maintenance pathways
Joris Deelen &Hae-Won Uh &Ramin Monajemi &Diana van Heemst &
Peter E. Thijssen &Stefan Böhringer &Erik B. van den Akker &
Anton J. M. de Craen &Fernando Rivadeneira &André G. Uitterlinden &
Rudi G. J. Westendorp &Jelle J. Goeman &P. Eline Slagboom &
Jeanine J. Houwing-Duistermaat &Marian Beekman
Received: 7 July 2011 / Accepted: 28 October 2011
#The Author(s) 2011. This article is published with open access at Springerlink.com
Abstract In genome-wide association studies (GWAS)
of complex traits, single SNP analysis is still the most
applied approach. However, the identified SNPs have
small effects and provide limited biological insight. A
more appropriate approach to interpret GWAS data of
complex traits is to analyze the combined effect of a
SNP set grouped per pathway or gene region. We used
this approach to study the joint effect on human
longevity of genetic variation in two candidate path-
ways, the insulin/insulin-like growth factor (IGF-1)
signaling (IIS) pathway and the telomere maintenance
(TM) pathway. For the analyses, we used genotyped
AGE
DOI 10.1007/s11357-011-9340-3
Electronic supplementary material The online version of this
article (doi:10.1007/s11357-011-9340-3) contains supplemen-
tary material, which is available to authorized users.
J. Deelen (*):P. E. Thijssen :E. B. van den Akker :
P. E. Slagboom :M. Beekman
Section of Molecular Epidemiology,
Leiden University Medical Center,
Zone S5-P, PO Box 9600, 2300 RC Leiden,
The Netherlands
e-mail: j.deelen@lumc.nl
J. Deelen :H.-W. Uh :A. G. Uitterlinden :
R. G. J. Westendorp :P. E. Slagboom :M. Beekman
Netherlands Consortium for Healthy Ageing,
Leiden University Medical Center,
P.O. Box 9600, 2300 RC Leiden, The Netherlands
H.-W. Uh :R. Monajemi :S. Böhringer :J. J. Goeman :
J. J. Houwing-Duistermaat
Section of Medical Statistics,
Leiden University Medical Center,
P.O. Box 9600, 2300 RC Leiden, The Netherlands
D. van Heemst :A. J. M. de Craen :R. G. J. Westendorp
Department of Gerontology and Geriatrics,
Leiden University Medical Center,
P.O. Box 9600, 2300 RC Leiden, The Netherlands
P. E. Thijssen
Department of Human Genetics,
Leiden University Medical Center,
P.O. Box 9600, 2300 RC Leiden, The Netherlands
E. B. van den Akker
Department of Mediamatics, Delft Bioinformatics Lab,
Delft University of Technology,
P.O. Box 5031, 2600 GA Delft, The Netherlands
F. Rivadeneira :A. G. Uitterlinden
Department of Epidemiology, Erasmus Medical Center,
P.O. Box 2040, 3015 CE Rotterdam, The Netherlands
F. Rivadeneira :A. G. Uitterlinden
Department of Internal Medicine, Erasmus Medical Center,
P.O. Box 2040, 3015 CE Rotterdam, The Netherlands
GWAS data of 403 unrelated nonagenarians from long-
lived sibships collected in the Leiden Longevity Study
and 1,670 younger population controls. We analyzed
1,021SNPsin68IISpathwaygenesand88SNPsin
13 TM pathway genes using four self-contained
pathway tests (PLINK set-based test, Global test,
GRASS and SNP ratio test). Although we observed
small differences between the results of the different
pathway tests, they showed consistent significant
association of the IIS and TM pathway SNP sets with
longevity. Analysis of gene SNP sets from these
pathways indicates that the association of the IIS
pathway is scattered over several genes (AKT1,AKT3,
FOXO4,IGF2,INS,PIK3CA,SGK,SGK2,and
YWHAG), while the association of the TM pathway
seems to be mainly determined by one gene (POT1).
In conclusion, this study shows that genetic variation
in genes involved in the IIS and TM pathways is
associated with human longevity.
Keywords Genetics .Aging .Longevity .Gene set
analysis .Insulin/IGF-1 signaling .Telomere
maintenance
Introduction
Genome-wide association studies (GWAS) using
single SNP analysis have been very successful in
identifying loci for various quantitative traits and
diseases (Manolio et al. 2008). It became apparent
that complex traits are usually determined by many
genes with small effects and that results from single
SNP analysis provide limited biological insight and
only partly explain the genotypic variation of the
studied trait. Instead of analyzing single SNPs, the
combined effect of a SNP set, grouped per pathway or
gene region, can be tested for association with the
trait of interest. Such SNP set analysis could be used
as an alternative approach for GWAS analysis and,
since the composition of SNP sets is often based on
pathways, should be able to provide additional
biological insight of the studied trait.
Since the amount of tests in SNP set analysis is
low compared to single SNP analysis, it requires a
lower penalty for multiple testing. Therefore, SNP
set analysis is also very suitable in studies with
low power for GWAS analysis. The last couple of
years, several methods have been developed to
perform SNP set analysis on GWAS data (Wang et
al. 2010; Fridley and Biernacka 2011;Holmans
2010). There are two main types of methods, the
competitive and the self-contained tests. The compet-
itive tests compare the association between a SNP set
and trait to a standard defined by the genotyped SNPs
outside the SNP set (complement), while the self-
contained tests compare the SNP set to a fixed
standard that does not depend on the complement
(Goeman and Buhlmann 2007).
Human longevity is a complex trait that is assumed
to be determined by variation in many genes with
small effects. Previous GWA studies, in which single
SNP analyses were performed (Newman et al. 2010;
Deelen et al. 2011), have identified only one genome-
wide significant locus contributing to survival into old
age; APOE. However, the genetic contribution to
human lifespan variation, determined in twin studies,
is estimated at 2530% (Gudmundsson et al. 2000;
Hjelmborg et al. 2006; Skytthe et al. 2003) and,
although the effect of genetic variation in APOE is
relatively large, the heritability of longevity is only
partially explained by this variation (Deelen et al.
2011). Part of the remaining heritability might be
explained by functionally related SNPs with small
effects, of which the joint effect could not be detected
in a single SNP analysis. Testing of SNP sets of
candidate pathways for association with longevity
would therefore be valuable.
The insulin/insulin-like growth factor (IGF-1)
signaling (IIS) pathway is considered as a candi-
date pathway for studying human longevity. It is
involved in the adaptation of the organism to its
(changing) environment (Tatar et al. 2003). When
experimentally induced in model organisms like
worms, flies, and mice, mutations in genes that play
a role in IIS, e.g., homologues of human IGF1R,
INSR,IRS1,PI3K, and FOXO, were shown to have a
considerable effect on lifespan (Kenyon et al. 1993;
Kimura et al. 1997; Tatar et al. 2001; Holzenberger et
al. 2003; Bluher et al. 2003; Morris et al. 1996;
Friedman and Johnson 1988; Clancy et al. 2001;
Hwangbo et al. 2004; Ogg et al. 1997; Lin et al. 1997;
Giannakou et al. 2004; Selman et al. 2011). Although
the IIS pathway is evolutionarily conserved, the
complexity of the human IIS pathway (Fig. 1)is
much larger compared to that of model organisms.
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Several studies have investigated associations be-
tween single SNPs in genes from the IIS pathway
and human longevity. The most prominent results
came from FOXO3 (Willcox et al. 2008; Flachsbart et
al. 2009; Anselmi et al. 2009; Pawlikowska et al.
2009; Li et al. 2009; Soerensen et al. 2010) and AKT1
(Pawlikowska et al. 2009), which showed associa-
tions with longevity in several independent cohort
studies.
Another candidate pathway for studying human
longevity is the mechanism of telomere maintenance
(TM). Telomeres are structures at the end of chromo-
somes, consisting of TTAGGG tandem repeats (Moyzis
et al. 1988), which protect chromosomes from
degradation or rearrangement (Blackburn 1991). In
normal human cells, telomere length declines with
every cell division (Harley et al. 1990), and when a
critical length is reached, the cell will enter replicative
senescence (Allsopp 1996). In human epidemiological
studies in blood, increased telomere length has been
associated with longevity (Atzmon et al. 2010), while
decreased telomere length has been associated with
increased mortality (Cawthon et al. 2003; Bakaysa et
al. 2007; Kimura et al. 2008), although some studies
showed contradictory results (Martin-Ruiz et al. 2005;
Bischoff et al. 2006). Telomere integrity is essentially
regulated by two protein networks, telomerase and its
associated factors, which regulate telomere length, and
the shelterin complex, which covers the telomeres (de
Lange 2005; Collins and Mitchell 2002) (Fig. 2).
Several studies have investigated associations be-
tween single SNPs in telomerase and shelterin genes
and telomere length. The most promising results came
from TERC and TERT (Atzmon et al. 2010; Codd et al.
2010; Levy et al. 2010; Mirabello et al. 2010; Rafnar
et al. 2009), of which the latter has also been
associated with human longevity (Atzmon et al. 2010).
In this study, we used four self-contained tests
(PLINK set-based test, Purcell et al. 2007; GRASS,
Chen et al. 2010; Global test, Goeman et al. 2004;
and SNP ratio test, O'Dushlaine et al. 2009) and one
competitive test (the comparative approach of Global
test) to study the joint effect of genetic variation in the
IIS and TM pathways on human longevity. For the
analyses, we used genotyped GWAS data of nonage-
narian siblings from the Leiden Longevity Study
(LLS) and younger population controls from the
Rotterdam Study (RS) (Deelen et al. 2011).
Fig. 1 Insulin/IGF-1 signaling pathway. The insulin/IGF-1
signaling pathway consists of the core components IGF1R/IR/
IRR, IRS, PI3K, AKT/SGK, FOXO and SIRT, and proteins that
have a direct activating or inhibiting effect on these proteins.
The small closed circles (containing Ac, P, or Ub) indicate an
activating effect of the posttranslational modification on the
protein, while the small dashed circles indicate an inhibiting
effect. The straight arrows pointing to these small circles
indicate an activating effect on the posttranslational modifica-
tion, while the dashed arrows indicate an inhibiting effect. Ac
acetylation, Pphosphorylation, Ub ubiquitylation
AGE
Materials and methods
Study populations
Leiden longevity study
For the LLS, long-lived siblings of European descent
were recruited together with their offspring and the
partners of the offspring. Families were included if at
least two long-lived siblings were alive and fulfilled
the age criterion of 89 years or older for males and
91 years or older for females, representing less than
0.5% of the Dutch population in 2001 (Schoenmaker
et al. 2006). In total, 944 long-lived proband siblings
were included with a mean age of 94 years (range,
89104), 1,671 offspring (61 years, 3981), and 744
partners (60 years, 3679). DNA from the LLS was
extracted from samples at baseline using conventional
methods (Beekman et al. 2006). For the GWAS, 403
unrelated LLS siblings (one sibling from each sibling
pair) were included (LLS GWAS cases) (Deelen et al.
2011).
Rotterdam study
The RS is a prospective population-based study of
people aged 55 years and older, which was designed
to study neurological, cardiovascular, locomotor, and
ophthalmological diseases (Teichert et al. 2009). The
study consists of 7,983 participants from the baseline
cohort (RS-I) and 3,011 participants from an inde-
pendent extended cohort formed in 1999 (RS-II) from
which DNA was isolated between 1990 and 1993
(RS-I) or between 2000 and 2001 (RS-II). For the
GWAS, 1,731 participants from the combined cohort
who were below 60 years of age and for whom
GWAS data were available were included as controls
(LLS GWAS controls) (Deelen et al. 2011).
Population substructure
Multidimensional scaling analysis in PLINK (http://
pngu.mgh.harvard.edu/purcell/plink, Purcell et al.
2007) showed that there was no substructure in the
GWAS data to an extent that would affect the
observations (Deelen et al. 2011).
Genotyping and SNP selection
For the SNP set analyses, we used the genotype data
from the GWAS described by Deelen et al. (2011).
The LLS GWAS cases were genotyped using Illumina
Infinium HD Human660W-Quad BeadChips (Illu-
mina, San Diego, CA, USA). The RS GWAS controls
were genotyped using Illumina Infinium II Human-
Hap 550K Beadchips and Illumina Infinium II
HumanHap550-Duo BeadChips (Illumina), respec-
tively (Teichert et al. 2009). Of the 551,606 SNPs
measured in both the LLS GWAS cases and RS
GWAS controls, 516,712 SNPs passed quality control
using the following criteria: SNP call rate 0.95 or
MAF 0.01 in RS GWAS controls and LLS GWAS
cases, PHWE 10
4
and no between-chip effect in the
RS GWAS controls, and good cluster plots in the LLS
GWAS cases and RS GWAS controls if P<
1104(Deelen et al. 2011).
We analyzed SNPs within a 10-kb window around
genes encoding proteins that belonged to the IIS
(Fig. 1) and TM pathway (Fig. 2). A gene was defined
as an NCBI Entrez Gene (mRNA or RNA) cluster,
corresponding to a set of transcripts (RefSeq) for
which the alignments can be obtained from the UCSC
genome browser (http://genome.ucsc.edu/), in which
all transcripts within a cluster agree on strand and
overlap. Due to an overlap of the 10-kb windows
a
b
Fig. 2 Telomere maintenance pathway. The telomere mainte-
nance pathway consists of proteins belonging to telomerase and
its associated factors or to the shelterin complex. Telomere
elongation is performed by telomerase after binding to the
telomere (a). However, binding of the shelterin protein POT1 to
the telomere blocks this process (b)
AGE
around IGF2 and INS, two SNPs, rs4320932 and
rs7924316, were assigned to both genes.
Statistical analysis
PLINK set-based test
In the PLINK set-based test (set-test, http://pngu.
mgh.harvard.edu/purcell/plink; Purcell et al. 2007), a
single SNP analysis (in our case, a trend test) of the
original pathway or gene SNP set is performed. For
each SNP set, a mean SNP statistic is calculated from
the single SNP statistics of a maximum amount (set-
max) of independent SNPs below a certain Pvalue
threshold (set-p). If SNPs are not independent, i.e.,
in case linkage disequilibrium (r
2
) is above a certain
threshold (set-r2), the SNP with the lowest Pvalue
in the single SNP analysis is selected. The same
analysis is performed with a certain amount (mperm)
of simulated SNP sets in which the phenotype status
of the individuals is permuted. An empirical Pvalue
for the SNP set is computed by calculating the
number of times the test statistic of the simulated
SNP sets exceeds that of the original SNP set. For the
analysis in this study, the parameters were set to set-
p 0.05 set-r2 0.5, set-max 99999, and mperm
10,000.
GRASS
GRASS (http://linchen.fhcrc.org/grass.html; Chen et
al. 2010) calculates eigenSNPsfor each gene in the
pathway SNP set by summarizing the variation of a
gene using principal component analysis. Subsequently,
oneormoreoftheseeigenSNPsper gene are selected
using regularized logistic regression to calculate a test
statistic for each pathway SNP set. The same analysis is
performed with simulated SNP sets in which the
phenotype status of the individuals is permuted. The P
value per pathway SNP set is calculated by comparing
the test statistic of the original pathway SNP set with
that of the combined simulated pathway SNP sets. For
the analysis in this study, the amount of simulated
pathway SNP sets was 10,000.
Global test
In this study, we used a modified version of the
Global test (http://www.bioconductor.org/help/bioc-
views/release/bioc/html/globaltest.html;Goemanet
al. 2004), which is capable and powerful for analyz-
ing GWAS data (Chapman and Whittaker 2008; Pan
2009). This test is based on a multiple logistic
regression model that uses the phenotype as the
response variable and the SNPs in the SNP set as
covariates and which automatically takes the
correlations between SNPs into account. The null
hypothesis is tested that none of the SNPs in the
SNP set are associated with the phenotype. P
values are calculated using a permutation test based
on 10,000 permutations.
For the comparative approach, 10,000 random
SNP sets per pathway SNP set were generated and
tested to determine the chance to find a similar-
sized SNP set with a comparable or lower Pvalue
as compared to the original pathway SNP set.
SNP ratio test
The SNP ratio test (http://sourceforge.net/projects/
snpratiotest/; O'Dushlaine et al. 2009) performs a
single SNP analysis (in our case, a trend test) of the
original pathway or gene SNP set and of similar-sized
SNP sets in which the phenotype status of the
individuals is permuted. An empirical Pvalue of the
SNP set is computed by calculating the ratio between
the proportion of SNPs that shows an association
below a certain Pvalue threshold (p) in the original
GWAS dataset and in the simulated GWAS datasets.
The amount of significant SNPs in the simulated
GWAS datasets is defined as the top nSNPs with
the lowest Pvalues, where nis the amount of SNPs
with an association below pin the original GWAS
dataset. For the analysis in this study, we made use of
the scripts described in SRT_documenta-
tion_090310.pdf(http://sourceforge.net/projects/
snpratiotest/). For the analysis in this study, pwas
set to 0.05, and the amount of simulated datasets used
was 10,000.
Statistical significance
To adjust for multiple testing, the significance level
was set at the Bonferroni-corrected nominal Pvalue
(which is 0.05/(number of pathway or gene SNP sets
tested)).
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Table 1 Characteristics of the insulin/IGF-1 signaling pathway proteins
Protein Gene Entrez gene ID Chr Start (bp) End (bp) Size (kb) SNPs Coverage
AKT1 AKT1 207 14 104,306,732 104,333,125 26.39 2 25.00%
AKT2 AKT2 208 19 45,428,064 45,483,105 55.04 6 45.45%
AKT3 AKT3 10000 1 241,718,158 242,073,509 355.35 25 50.00%
BIM BCL2L11 10018 2 111,594,962 111,642,493 47.53 12 47.06%
BCL-6 BCL6 604 3 188,921,859 188,946,169 24.31 6 23.81%
CAT CAT 847 11 34,417,048 34,450,182 33.13 18 80.00%
Cyclin D1 CCND1 595 11 69,165,054 69,178,423 13.37 3 27.27%
Cyclin D2 CCND2 894 12 4,253,163 4,284,782 31.62 20 45.00%
Cyclin G2 CCNG2 901 4 78,297,381 78,310,237 12.86 4 33.33%
p27kip CDKN1B 1027 12 12,761,569 12,766,572 5.00 9 75.00%
CBP CREBBP 1387 16 3,715,057 3,870,122 155.07 15 50.00%
Deptor (mTORC2) DEPDC6 64798 8 120,955,081 121,132,338 177.26 47 61.90%
p300 EP300 2033 22 39,818,560 39,906,027 87.47 6 50.00%
Fas ligand FASLG 356 1 170,894,808 170,902,635 7.83 7 45.45%
FOXO1 FOXO1 2308 13 40,027,801 40,138,734 110.93 19 65.38%
FOXO3A FOXO3 2309 6 108,987,719 109,112,664 124.95 21 68.75%
FOXO4 FOXO4 4303 X 70,232,751 70,240,109 7.36 3 NA
G6P G6PC 2538 17 38,306,341 38,318,912 12.57 5 83.33%
IGF1 IGF1 3479 12 101,313,775 101,398,508 84.73 20 47.06%
IGF1R IGF1R 3480 15 97,010,284 97,325,282 315.00 102 56.34%
IGF2 IGF2 3481 11 2,106,923 2,127,409 20.49 7 63.64%
Insulin INS 3630 11 2,137,585 2,139,015 1.43 4 80.00%
IR INSR 3643 19 7,063,266 7,245,011 181.75 52 50.00%
IRR INSRR 3645 1 155,077,289 155,095,290 18.00 6 37.50%
IRS1 IRS1 3667 2 227,304,277 227,371,750 67.47 11 53.33%
IRS2 IRS2 8660 13 109,204,185 109,236,915 32.73 15 59.09%
IRS4 IRS4 8471 X 107,862,383 107,866,263 3.88 2 NA
PCAF KAT2B 8850 3 20,056,528 20,170,900 114.37 44 62.96%
ERK2 MAPK1 5594 22 20,443,947 20,551,970 108.02 12 58.33%
ERK1 MAPK3 5595 16 30,032,927 30,042,131 9.20 2 33.33%
JNK1 MAPK8 5599 10 49,279,693 49,313,189 33.50 6 35.71%
JNK2 MAPK9 5601 5 179,593,203 179,651,677 58.47 17 40.00%
JNK3 MAPK10 5602 4 87,155,300 87,593,307 438.01 71 55.81%
mSIN1 (mTORC2) MAPKAP1 79109 9 127,239,494 127,509,334 269.84 26 64.00%
mLST8 (mTORC2) MLST8 64223 16 2,195,451 2,199,419 3.97 4 80.00%
mTOR (mTORC2) MTOR 2475 1 11,089,176 11,245,195 156.02 11 50.00%
PEPCK PCK1 5105 20 55,569,543 55,574,919 5.38 10 33.33%
PDK1 PDPK1 5170 16 2,527,971 2,593,190 65.22 0 0.00%
PHLPP1 PHLPP1 23239 18 58,533,714 58,798,646 264.93 41 64.29%
PHLPP2 PHLPP2 23035 16 70,236,353 70,306,205 69.85 4 15.38%
PI3K PIK3CA 5290 3 180,349,005 180,435,191 86.19 10 44.44%
PIK3CB 5291 3 139,856,921 139,960,875 103.95 8 42.86%
PIK3CD 5293 1 9,634,377 9,711,759 77.38 8 42.11%
PIK3R1 5295 5 67,558,218 67,633,405 75.19 31 65.12%
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Results
For the IIS pathway, we selected genes encoding
proteins that belong to the well-described core of the
pathway, consisting of IGF1R/IR/IRR, IRS, PI3K,
AKT/SGK, FOXO, and SIRT, or that had a direct
activating or inhibiting effect on these core compo-
nents (van der Horst and Burgering 2007; Taniguchi
et al. 2006). In addition, we selected several FOXO
target genes that play a role in cell-cycle inhibition,
oxidative-stress resistance, metabolism, and apoptosis
(van der Horst and Burgering 2007) (Fig. 1). For the
TM pathway, we selected genes encoding proteins
that were specifically associated with telomeres and
belonged to telomerase and its associated factors or to
the shelterin complex (Vulliamy et al. 2008; Harrington
et al. 1997; de Lange 2005) (Fig. 2). We analyzed
SNPs within a 10-kb window around the selected
genes (based on Pawlikowska et al. 2009)from
genotyped GWAS data of 403 unrelated nonagenarian
participants from the LLS and 1,670 middle-aged
controls from the RS (Deelen et al. 2011). A
description of the investigated samples is given in
Table S1. In total, 1,021 SNPs in 68 IIS pathway
genes and 88 SNPs in 13 TM pathway genes were
analyzed (Tables 1,2,S3A, and S3B).
Four methods, PLINK set-based test, Global test,
GRASS, and SNP ratio test (Table S2), were used to
investigate the association of the SNP sets from the
IIS and TM pathways with longevity. As a biological
Table 1 (continued)
Protein Gene Entrez gene ID Chr Start (bp) End (bp) Size (kb) SNPs Coverage
PIK3R2 5296 19 18,125,016 18,142,343 17.33 5 66.67%
PIK3R3 8503 1 46,278,399 46,371,295 92.90 10 60.00%
PP2A PPP2R5B 5526 11 64,448,756 64,458,523 9.77 3 37.50%
Protor-1 (mTORC2) PRR5 55615 22 43,443,091 43,512,225 69.13 32 50.00%
PTEN PTEN 5728 10 89,613,175 89,718,512 105.34 8 47.06%
PTP1B PTPN1 5770 20 48,560,298 48,634,493 74.20 17 44.44%
p130Rb2 RBL2 5934 16 52,025,852 52,083,061 57.21 3 33.33%
RICTOR (mTORC2) RICTOR 253260 5 38,973,780 39,110,258 136.48 9 30.77%
SCP2 SCP2 6342 1 53,165,536 53,289,870 124.33 18 50.00%
SGK1 SGK1 6446 6 134,532,077 134,680,889 148.81 38 46.75%
SGK2 SGK2 10110 20 41,621,100 41,647,687 26.59 9 34.62%
SIRT1 SIRT1 23411 10 69,314,433 69,348,152 33.72 4 33.33%
SIRT2 SIRT2 22933 19 44,061,040 44,082,201 21.16 7 38.89%
SIRT3 SIRT3 23410 11 205,030 226,362 21.33 17 60.00%
SKP2 SKP2 6502 5 36,187,946 36,219,904 31.96 15 51.72%
SOCS1 SOCS1 8651 16 11,255,775 11,257,540 1.77 4 50.00%
SOCS3 SOCS3 9021 17 73,864,457 73,867,753 3.30 4 50.00%
MnSOD SOD2 6648 6 160,020,139 160,034,343 14.20 4 44.44%
USP7 USP7 7874 16 8,893,452 8,964,842 71.39 12 42.11%
14-3-3 YWHAB 7529 20 42,947,758 42,970,575 22.82 6 50.00%
YWHAE 7531 17 1,194,586 1,250,306 55.72 16 70.00%
YWHAG 7532 7 75,794,044 75,826,278 32.23 5 35.71%
YWHAH 7533 22 30,670,479 30,683,590 13.11 9 43.75%
YWHAQ 10971 2 9,641,557 9,688,557 47.00 10 58.33%
YWHAZ 7534 8 101,999,981 102,034,799 34.82 6 40.00%
Total 1,023
Chr Chromosome position of the gene according to NCBI Build 36, Start (bp) start position of the gene according to NCBI Build 36,
End (bp) end position of the gene according to NCBI Build 36, Coverage coverage of genes based on Phased data HapMap II release
22 CEU, NA not available
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negative control, we also analyzed a SNP set of 223
SNPs in 9 genes previously associated with eye and
hair color (Eriksson et al. 2010) (Tables 3and S3C).
Both candidate pathways were consistently associated
with longevity across all four tests (Table 4). We
applied Bonferroni correction to adjust for the number
of tested pathways (i.e., 2, so for significance P<
0.025). After Bonferroni correction, the IIS pathway
SNP set remained significant in GRASS and Global
test, while the TM pathway SNP set remained
significant in the PLINK set-based test, GRASS, and
Global test. Using the comparative approach in
Global test as a competitive test, we also showed that
the probability to find a random SNP set with the
same amount of genes as the IIS or TM pathway and
a comparable or lower Pvalue is less than 5% (2.11%
for the IIS and 2.95% for the TM pathway).
To determine which genes are mainly responsible
for the observed association of the pathway SNP sets
from the IIS and TM pathways with longevity, we
also investigated the association of gene SNP sets
from these pathways. Although the power to detect an
association using gene SNP set analysis is lower than
for pathway SNP set analysis, due to the larger amount
of tests, it provides a ranking of genes based on the
contribution to the observed associations of the path-
ways. To analyze the gene SNPsets, we used the PLINK
set-based test, Global test, and SNP ratio test. GRASS
was not used, since the underlying statistical method of
this test is less suitable for analysis of gene SNP sets.
Nine of the 68 IIS pathway gene SNP sets (AKT1,
AKT3,FOXO4,IGF2,INS,PIK3CA,SGK1,SGK2,
and YWHAG) and 1 of the 13 TM pathway gene SNP
sets (POT1) showed an association (P< 0.05) with
longevity in at least two tests (Tables 5and 6).
Discussion
To study the effect of the IIS and TM pathways on
longevity, SNP set analysis on GWAS data of 403
nonagenarian cases and 1,670 population controls
was performed. Both pathway SNP sets associated
significantly with longevity. The gene SNP sets
analysis showed that the association of the IIS
pathway was scattered over several genes (AKT1,
AKT3,FOXO4,IGF2,INS,PIK3CA,SGK1,SGK2,
and YWHAG), while the association of the TM
pathway seems to be mainly determined by one gene
(POT1).
The proteins encoded by the IIS gene SNP sets that
associate with longevity are involved in several parts
of the IIS pathway (Fig. 1). Akt1, Akt3, Foxo4, Igf2,
Ins2, Pik3ca, and Sgk1 knockout mice all show
Table 2 Characteristics of the telomere maintenance pathway proteins
Protein Gene Entrez gene ID Chr Start (bp) End (bp) Size (kb) SNPs Coverage
TPP1 (shelterin) ACD 65057 16 66,248,916 66,252,219 3.30 2 50.00%
Dyskerin (telomerase) DKC1 1736 X 153,644,225 153,659,158 14.93 1 NA
GAR1 (telomerase) GAR1 54433 4 110,956,115 110,965,342 9.23 1 14.29%
NHP2 (telomerase) NHP2 55651 5 177,509,072 177,513,567 4.50 2 33.33%
NOP10 (telomerase) NOP10 55505 15 32,421,209 32,422,654 1.45 7 45.45%
POT1 (shelterin) POT1 25913 7 124,249,676 124,357,273 107.60 25 55.56%
TP1 (telomerase) TEP1 7011 14 19,903,666 19,951,419 47.75 21 40.00%
TERC (telomerase) TERC 7012 3 170,965,092 170,965,542 0.45 1 25.00%
TRF1 (shelterin) TERF1 7013 8 74,083,651 74,122,541 38.89 10 60.00%
TRF2 (shelterin) TERF2 7014 16 67,946,965 67,977,375 30.41 6 57.14%
RAP1 (shelterin) TERF2IP 54386 16 74,239,136 74,248,842 9.71 4 50.00%
TERT (telomerase) TERT 7015 5 1,306,287 1,348,162 41.88 7 41.18%
TIN2 (shelterin) TINF2 26277 14 23,778,691 23,781,720 3.03 1 14.29%
Total 88
Chr Chromosome position of the gene according to NCBI Build 36, Start (bp) start position of the gene according to NCBI Build 36,
End (bp) end position of the gene according to NCBI Build 36, Coverage coverage of genes based on Phased data HapMap II release
22 CEU, NA not available
AGE
abnormalities in growth and/or increased mortality
(www.informatics.jax.org; Blake et al. 2011), which
indicates that these genes are indeed responsible for
the growth- and lifespan-regulating effects of the IIS
pathway. Previously, SNPs in several of the signifi-
cant IIS pathway genes (AKT1,FOXO4,INS, and
PIK3CA) were studied by single SNP analysis, and
only one SNP, rs3803304 in AKT1, which was not
measured in our study, showed an association with
longevity (Pawlikowska et al. 2009). However, gene
set testing, which could have detected association of
additional genes containing SNPs with many small
effects, was not applied in that study. Most signaling
cascades require cooperation of several genes in
multiple branches of the cascade. This indicates that,
for signaling pathways, mutations in different genes
could result in similar downstream effects, which
would explain the scattered association in the IIS
pathway.
Although SNPs in FOXO3A have previously been
associated with longevity in several independent studies
(Willcox et al. 2008; Flachsbart et al. 2009;Anselmiet
al. 2009; Pawlikowska et al. 2009;Lietal.2009;
Soerensen et al. 2010), the gene SNP set showed no
effect in our study in the PLINK set-based test, Global
test, and SNP ratio test (P=0.181, P=0.138, and P=
0.180, respectively) (Table 5). This might be due to
the fact that the effects of FOXO3A on longevity are
most prominent in centenarians. As was previously
reported by Flachsbart et al., centenarians represent a
highly selected phenotype even among nonagenarians
(Flachsbart et al. 2009). In addition, the genetic
contribution to longevity in general is increased at higher
ages (Hjelmborg et al. 2006), and the small effects of
longevity-promoting gene variants, relative to other
factors, may be larger in centenarians (Perls et al. 2002)
and not detectable in nonagenarians. The cases in
our study, which are from long-lived families,
have a mean age of 94 years, yet we had only 11
individuals >100 years, which may explain the absence
of significance of the FOXO3A association in our
population.
POT1 is part of the shelterin complex and is
responsible for the binding of this complex to the
TTAGGG repeats of telomeres. Binding of POT1 to
the telomere leads to decreased elongation by telo-
merase (de Lange 2005). Reduction of POT1 in
human fibroblasts by RNAi leads to induction of
Table 4 Results of gene set analysis of insulin/IGF-1 signal-
ing, telomere maintenance, and eye and hair color pathway
SNP sets
Pathway test Insulin/IGF-1
signaling
Telomere
maintenance
Eye and
hair color
PLINK set-based test
a
0.064 0.019 0.340
GRASS
a
0.010 0.023 0.540
Global test
a
0.011 0.023 0.362
SNP ratio test
a
0.044 0.034 0.337
a
Permutation (n=10,000)
Pvalue
Table 3 Characteristics of the eye and hair color pathway proteins
Protein Gene Entrez gene ID Chr Start (bp) End (bp) Size (kb) SNPs Coverage
ASIP ASIP 434 20 32,311,832 32,320,809 8.98 5 50.00%
HERC2 HERC2 8924 15 26,029,783 26,240,890 211.11 9 41.67%
IRF4 IRF4 3662 6 336,739 356,443 19.70 14 65.00%
MC1R MC1R 4157 16 88,511,788 88,514,886 3.10 3 33.33%
OCA2 OCA2 4948 15 25,673,616 26,018,053 344.44 82 58.00%
SLC24A4 SLC24A4 123041 14 91,858,678 92,037,578 178.90 62 53.68%
SLC45A2 SLC45A2 51151 5 33,980,478 34,020,537 40.06 15 44.83%
TYR TYR 7299 11 88,550,688 88,668,575 117.89 22 56.00%
TYRP1 TYRP1 7306 9 12,683,386 12,700,266 16.88 11 50.00%
Total 223
Chr Chromosome position of the gene according to NCBI Build 36, Start (bp) start position of the gene according to NCBI Build 36,
End (bp) end position of the gene according to NCBI Build 36, Coverage coverage of genes based on Phased data HapMap II release
22 CEU
AGE
apoptosis, chromosomal instability, and senescence
(Yang et al. 2005). The same effects are observed in
Pot1b knockout mice (He et al. 2009; Hockemeyer et
al. 2008). In addition, telomerase-deficient Pot1b
knockout mice show a reduction in lifespan compared
to normaltelomerase-deficient mice (Hockemeyer
et al. 2008), which stresses the importance of TM in
lifespan regulation. Most protein complexes contain
one or several proteins essential for specific functions
of the complex, e.g., binding, transport, or activation/
repression activity. This indicates that, for pathways
containing a protein complex, mutations in a single
gene, encoding such an essential protein, could be
sufficient to alter the function of the complex, which
would explain the single-gene association in the TM
pathway.
Table 5 Results of gene set analysis of insulin/IGF-1 signaling
pathway gene SNP sets
Gene PLINK set-
based test
a
Global test
a
SNP
ratio test
a
AKT1 0.003 0.002 0.099
AKT2 0.193 0.461 0.197
AKT3 0.101 0.023 0.043
BCL2L11 1 0.678 1
BCL6 1 0.539 1
CAT 1 0.661 1
CCND1 1 0.471 1
CCND2 0.248 0.073 0.073
CCNG2 1 0.528 1
CDKN1B 1 0.675 1
CREBBP 1 0.495 1
DEPDC6 1 0.378 1
EP300 1 0.823 1
FASLG 1 0.219 1
FOXO1 1 0.688 1
FOXO3 0.181 0.138 0.180
FOXO4 0.023 0.023 0.055
G6PC 0.156 0.172 0.173
IGF1 0.342 0.042 0.148
IGF1R 0.054 0.373 0.491
IGF2 0.028 0.019 0.084
INS 0.022 0.049 0.188
INSR 0.154 0.217 0.286
INSRR 0.139 0.247 0.224
IRS1 1 0.873 1
IRS2 1 0.569 1
IRS4 1 0.605 1
KAT2B 1 0.905 1
MAPK1 1 0.248 1
MAPK3 1 0.132 1
MAPK8 0.185 0.531 0.215
MAPK9 1 0.198 1
MAPK10 0.191 0.885 0.068
MAPKAP1 1 0.372 1
MLST8 1 0.593 1
MTOR 1 0.722 1
PCK1 1 0.547 1
PHLPP1 0.113 0.398 0.200
PHLPP2 1 0.364 1
PIK3CA 0.003 9:36 1040.022
PIK3CB 1 0.726 1
PIK3CD 1 0.828 1
PIK3R1 1 0.666 1
Table 5 (continued)
Gene PLINK set-
based test
a
Global test
a
SNP
ratio test
a
PIK3R2 1 0.722 1
PIK3R3 1 0.263 1
PPP2R5B 1 0.363 1
PRR5 0.355 0.163 0.257
PTEN 1 0.855 1
PTPN1 1 0.982 1
RBL2 1 0.061 1
RICTOR 1 0.343 1
SCP2 1 0.729 1
SGK1 0.091 0.007 0.016
SGK2 0.027 0.042 0.349
SIRT1 1 0.941 1
SIRT2 1 0.282 1
SIRT3 0.241 0.232 0.326
SKP2 1 0.898 1
SOCS1 1 0.349 1
SOCS3 1 0.996 1
SOD2 1 0.692 1
USP7 0.025 0.101 0.103
YWHAB 1 0.223 1
YWHAE 0.067 0.124 0.196
YWHAG 0.090 0.032 0.018
YWHAH 1 0.236 1
YWHAQ 0.228 0.175 0.293
YWHAZ 1 0.756 1
a
Permutation (n=10,000) Pvalue
AGE
There are two main kinds of pathway analyses,
explorative and candidate based. Since we want
to focus on two pathways, the IIS and TM
pathways, we performed candidate-based pathway
analysis. The advantage of testing candidate
pathways instead of explorative testing is the
decreased penalty for multiple testing, due to the
limited amount of tests performed. For information
about pathways, several databases are available, e.g.,
Gene Ontology (Ashburner et al. 2000) and Kyoto
Encyclopedia of Genes and Genomes (KEGG)
(Kanehisa and Goto 2000), which are particularly
useful for explorative studies (Wang et al. 2010).
However, to our knowledge, the IIS and TM path-
ways are not described in sufficient detail in these
databases, and we therefore assembled these path-
ways based on literature. Although the IIS pathway is
available in KEGG (hsa04910; insulin-signaling
pathway), only four of the nine IIS pathway genes
that were associated with longevity, AKT1,AKT3,
INS, and PIK3CA, were part of this pathway, which
indicates that the pathway definition used in this study
could have had a large influence on the results of the
analysis.
Different pathway tests could show contradictory
results, even when analyzing the same GWAS data
(Wang et al. 2010). These discrepancies are caused by
differences in, for example, the underlying statistical
methods of the tests. Therefore, we used several
pathway tests in parallel for our analysis. Some of the
available pathway tests require SNP Pvalues as input
data, while others require raw genotypes (Wang et al.
2010). Given that we have GWAS data available, we
selected pathway tests that make use of raw geno-
types. All four selected pathway tests are self-
contained tests which deal with the complexity of
SNP set testing by permuting the case-control status.
While, the PLINK set-based test, Global test, and
SNP ratio test do not completely incorporate LD
information, GRASS employs PCA to deal with
correlations within each gene. A simulation study
showed that in general, GRASS was more power-
ful than the PLINK set-based test (Chen et al.
2010). Simulation studies for Global test or SNP ratio
test are not yet available. However, despite the
differences between the methods, they all showed
similar results for the IIS and TM pathways in this
study.
SNP set analysis could have power to detect
significant association, even if the power to detect
associations in single SNP analysis is low (Fridley
and Biernacka 2011), as was previously shown in the
Welcome Trust Case Control Consortium (Torkamani
et al. 2008). Our study has a power <1% to detect
single SNP associations of the tested SNPs with an
OR of 1.2 and a minor allele frequency of 0.25 (the
mean frequency of the tested SNPs). However,
because the small (non-significant) effects of the
Table 6 Results of gene set analysis of telomere maintenance pathway gene SNP sets
Gene PLINK set-based test
a
Global test
a
SNP ratio test
a
ACD 1 0.491 1
DKC1 1 0.642 1
GAR1 1 0.281 1
NHP2 1 0.759 1
NOP10 1 0.208 1
POT1 0.007 0.014 0.019
TEP1 1 0.525 1
TERC 1 0.202 1
TERF1 1 0.821 1
TERF2 0.018 0.160 0.164
TERF2IP 1 0.825 1
TERT 1 0.471 1
TINF2 1 0.587 1
a
Permutation (n=10,000)
Pvalue
AGE
SNPs are jointly tested, the pathway SNP set analysis
is able to detect a significant association of the IIS
and TM pathway. This indicates that SNP set analysis
could be a useful approach for studies which showed
no significant associations in single SNP analysis.
There is still much debate about the optimal size of
the window used in SNP set analysis (Holmans 2010;
Fridley and Biernacka 2011; Wang et al. 2010), and
we choose a fixed window of 10 kb to take into
account effects of SNPs in regulatory regions
surrounding the genes. The same window was also
used in a previous study of the IIS pathway
(Pawlikowska et al. 2009). Although there is a chance
that we will miss some functional SNPs, increasing
the window would increase the chance that SNPs are
included with no functional relationship to the tested
gene.
The amount and diversity of SNPs measured per
gene/pathway is highly variable between genotyping
platforms used for GWAS. In addition, there is a large
variety in allele frequencies and presence of SNPs
between populations. For single SNP analysis, one is
dependent on association of the same SNP (or a SNP
in high LD) for replication. However, when due to
varying allele frequencies, different SNPs associate in
different populations, SNP set analysis determines the
combined effect of SNPs within a gene and is able to
overcome this problem. Therefore, replication of SNP
set analysis is assumed to be more reproducible
between genotyping platforms and populations (Luo
et al. 2010; Wang et al. 2010). To support these
assumptions, our findings should be replicated in
other cohorts.
In conclusion, we have shown that genetic varia-
tion in genes involved in the IIS and TM pathways is
associated with human longevity. In addition, we
provide evidence that different self-contained tests
show similar results when applied to candidate-based
pathway analysis.
Acknowledgments We thank all participants of the Leiden
Longevity Study and Rotterdam Study. The research leading to
these results has received funding from the European Union's
Seventh Framework Programme (FP7/2007-2011) under grant
agreement no. 259679. This study was supported by a grant
from the Innovation-Oriented Research Program on Genomics
(SenterNovem IGE05007), the Centre for Medical Systems
Biology, and the Netherlands Consortium for Healthy Ageing
(grant 050-060-810), all in the framework of the Netherlands
Genomics Initiative, Netherlands Organization for Scientific
Research (NWO), and by Unilever Colworth. The generation
and management of GWAS genotype data for the Rotterdam
study are supported by the Netherlands Organisation for
Scientific Research NWO Investments (nr. 175.010.2005.011,
911-03-012). This study is funded by the Research Institute for
Diseases in the Elderly (014-93-015; RIDE2) and the Nether-
lands Genomics Initiative (NGI)/Netherlands Organisation for
Scientific Research (NWO) project nr. 050-060-810. The
Rotterdam Study is funded by the Erasmus Medical Center
and Erasmus University, Rotterdam; the Netherlands Organiza-
tion for the Health Research and Development (ZonMw); the
Research Institute for Diseases in the Elderly (RIDE); the
Ministry of Education, Culture and Science; the Ministry for
Health, Welfare and Sports; the European Commission (DG
XII); and the Municipality of Rotterdam.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which
permits any noncommercial use, distribution, and reproduction
in any medium, provided the original author(s) and source are
credited.
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AGE
... Despite decades of aging research, the role of genetic interactions (G × G) in heterogeneity of human lifespan, and in animal to human translation, remains not fully understood. Several studies supported the involvement of G × G in human longevity (e.g., Zeng et al., 2010;Deelen et al., 2013;Fuku et al., 2017;Dato et al., 2018). Some focused specifically on the interactions between FOXO3 and other genes (Zeng et al., 2010;Fuku et al., 2017) and on relevant biology, such as DNA damage response (Tsai et al., 2008). ...
... Some focused specifically on the interactions between FOXO3 and other genes (Zeng et al., 2010;Fuku et al., 2017) and on relevant biology, such as DNA damage response (Tsai et al., 2008). Other researchers (Deelen et al., 2013) applied the pathway-based geneset approach to evaluating the joint effect of SNPs in genes from aging pathways on longevity in humans, and yielded results suggesting a major impact of the genetic variation in the IGF1 signaling pathway. Dato et al. (2018) investigated the synergic SNP × SNP interactions in nonagenarians compared with controls aged 46-55 years, using tagging SNPs in 140 genes belonging to three candidate pathways (insulin/insulin-like growth signaling, DNA repair, and pro/antioxidant ones). ...
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... Genome-wide association studies (GWAS) have been a powerful tool to infer genetic associations with diseases and quantitative traits [60][61][62]. We treated PAR as a quantitative trait and employed GWAS to determine genetic loci significantly associated with PARs for the SardiNIA study. ...
... GAR1 polymorphisms have been associated with differences in acute heart rate response to exercise, which is a predictor of all-cause mortality and cardiovascular mortality [84]. Other components and effectors of telomerase have also been linked to aging [60,62,85,86]. ...
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It is widely thought that individuals age at different rates. A method that measures "physiological age" or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual's risk of morbidity and mortality. Here we present machine learning frameworks for inferring individual physiological age from a broad range of biochemical and physiological traits including blood phenotypes (e.g., high-density lipoprotein), cardiovascular functions (e.g., pulse wave velocity) and psychological traits (e.g., neuroticism) as main groups in two population cohorts SardiNIA (~6,100 participants) and InCHIANTI (~1,400 participants). The inferred physiological age was highly correlated with chronological age (R2 > 0.8). We further defined an individual's physiological aging rate (PAR) as the ratio of the predicted physiological age to the chronological age. Notably, PAR was a significant predictor of survival, indicating an effect of aging rate on mortality. Our trait-based PAR was correlated with DNA methylation-based epigenetic aging score (r = 0.6), suggesting that both scores capture a common aging process. PAR was also substantially heritable (h2~0.3), and a subsequent genome-wide association study of PAR identified significant associations with two genetic loci, one of which is implicated in telomerase activity. Our findings support PAR as a proxy for an underlying whole-body aging mechanism. PAR may thus be useful to evaluate the efficacy of treatments that target aging-related deficits and controllable epidemiological factors.
... Telomeres are non-coding sequences of repetitive DNA located at the end of linear chromosomes that shorten with each cellular replication (Backburn and Epel, 2012), in response to cellular stressors including oxidative stress (Reichert and Stier, 2017), and under increased metabolic demand (Casagrande and Hau, 2019). Both, IGF-1 and telomere length, have been shown to diminish with age (Moverare-Skrtic et al., 2009) and are linked to longevity (Deelen et al., 2013). The association between IGF-1 and telomeres has mainly been explored in laboratory animals and human patients exhibiting pathological disorder of their somatotropic axis as well as cancer cell lines (Aulinas et al., 2013;Matsumoto et al., 2015). ...
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Hormonal pathways have been proposed to be key at modulating how fast individuals grow and reproduce and how long they live (i.e., life history trajectory). Research in model species living under controlled environment is suggesting that insulin-like growth factor 1 (IGF-1), which is an evolutionarily conserved polypeptide hormone, has an important role in modulating animal life histories. Much remains, however, to be done to test the role played by IGF-1 in shaping the phenotype and life history of animals in the wild. Using a wild long-lived bird, the Alpine swift (Tachymarptis melba), we show that adults with higher levels of IGF-1 had longer wings and shorter telomeres. Hence, telomeres being a proxy of lifespan in this species, our results support a potential role of IGF-1 at shaping the life-history of wild birds and suggest that IGF-1 may influence the growth-lifespan trade-off.
... AKT3 is a member of the AKT/PKB family of serine/threonine protein kinases. AKT3 regulates cell signaling events in response to insulin and IGF1 and has been linked to longevity control [58]. The N-myristoyltransferase-2 (NMT2) gene encodes an enzyme responsible for the addition of myristoyl groups to the N-terminal end of a number of proteins [59]. ...
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The growth hormone (GH)–insulin-like growth factor-1 (IGF1) endocrine axis is a central player in normal growth and metabolism as well as in a number of pathologies, including cancer. The GH–IGF1 hormonal system, in addition, has emerged as a major determinant of lifespan and healthspan. Laron syndrome (LS), the best characterized entity under the spectrum of the congenital IGF1 deficiencies, results from mutation of the GH receptor (GHR) gene, leading to dwarfism, obesity and other defects. Consistent with the key role of IGF1 in cellular proliferation, epidemiological studies have shown that LS patients are protected from cancer development. While reduced expression of components of the GH-IGF1 axis is associated with enhanced longevity in animal models, it is still unknown whether LS is associated with an increased lifespan. MicroRNAs (miRs) are endogenous short non-coding RNAs that regulate the expression of complementary mRNAs. While a number of miRs involved in the regulation of IGF components have been identified, no previous studies have investigated the differential expression of miRs in congenital IGF1 deficiencies. The present study was aimed at identifying miRs that are differentially expressed in LS and that might account for the phenotypic features of LS patients, including longevity. Our genomic analyses provide evidence that miR-132-3p was highly expressed in LS. In addition, we identified SIRT1, a member of the sirtuin family of histone deacetylases, as a target for negative regulation by miR-132-3p. The data was consistent with the notion that low concentrations of IGF1 in LS lead to elevated miR-132-3p levels, with ensuing reduction in SIRT1 gene expression. The impact of the IGF1-miR-132-3p-SIRT1 loop on aging merits further investigation.
... Together, these studies have identified more than 50 longevity-associated genetic loci of genome-wide significance, among which only a few, especially APOE, were replicated by multiple studies 16 . On the other hand, several previous studies have detected an association of human longevity with variants in several aging genes-including insulin signaling genes 17 and FOXO3 (refs. 18,19 )-using candidate gene approaches. ...
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Extreme longevity in humans has a strong genetic component, but whether this involves genetic variation in the same longevity pathways as found in model organisms is unclear. Using whole-exome sequences of a large cohort of Ashkenazi Jewish centenarians to examine enrichment for rare coding variants, we found most longevity-associated rare coding variants converge upon conserved insulin/insulin-like growth factor 1 signaling and AMP-activating protein kinase signaling pathways. Centenarians have a number of pathogenic rare coding variants similar to control individuals, suggesting that rare variants detected in the conserved longevity pathways are protective against age-related pathology. Indeed, we detected a pro-longevity effect of rare coding variants in the Wnt signaling pathway on individuals harboring the known common risk allele APOE4. The genetic component of extreme human longevity constitutes, at least in part, rare coding variants in pathways that protect against aging, including those that control longevity in model organisms. In this whole-exome sequencing study of the largest centenarian cohort to date, Lin et al. demonstrate that conserved pathways—for example, IIS and AMPK signaling—are as relevant to human longevity and healthy aging as they are in worms, flies and mice.
... We originally chose to examine the role of PIK3R1 in aging because its expression is downregulated in the liver in calorically restricted mice [22]. Although we noted an association of genetic variation in PIK3R1 with longevity in our cohort, a negative result was obtained in a GWAS of nonagenarians [23]. The PIK3R1 SNPs rs7713645 and rs7709243 have shown association with BMI [24]. ...
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Aging is a collection of changes that contribute to decline in maximum function and ultimately death of an organism. This process is controlled and initiated by several mechanisms including telomere shortening, oxidative stress, AMP-activated protein kinase and sirt-1. Several therapies have been reported to relieve the process of aging. Among these, diet therapy seems to be the most appropriate approach. Fruits are an important part of regular diet. They contain several compounds which have potential to handle the problem of aging and its related disorders. The present paper provides a comprehensive review on different factors present in various fruits related to the process of aging together with their antiaging mechanisms.
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Aging is a process leading to a progressive loss of physiological integrity and homeostasis, and a primary risk factor for many late-onset chronic diseases. The mechanisms underlying aging have long piqued the curiosity of scientists. However, the idea that aging is a biological process susceptible to genetic manipulation was not well established until the discovery that the inhibition of insulin/IGF-1 signaling extended the lifespan of C. elegans . Although aging is a complex multisystem process, López-Otín et al . described aging in reference to nine hallmarks of aging. These nine hallmarks include: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication. Due to recent advances in lipidomic, investigation into the role of lipids in biological aging has intensified, particularly the role of sphingolipids (SL). SLs are a diverse group of lipids originating from the Endoplasmic Reticulum (ER) and can be modified to create a vastly diverse group of bioactive metabolites that regulate almost every major cellular process, including cell cycle regulation, senescence, proliferation, and apoptosis. Although SL biology reaches all nine hallmarks of aging, its contribution to each hallmark is disproportionate. In this review, we will discuss in detail the major contributions of SLs to the hallmarks of aging and age-related diseases while also summarizing the importance of their other minor but integral contributions.
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Early-life telomere length (TL) is associated with fitness in a range of organisms. Little is known about the genetic basis of variation in TL in wild animal populations, but to understand the evolutionary and ecological significance of TL it is important to quantify the relative importance of genetic and environmental variation in TL. In this study, we measured TL in 2746 house sparrow nestlings sampled across 20 years and used an animal model to show that there is a small heritable component of early-life TL (h²=0.04). Variation in TL among individuals was mainly driven by environmental (annual) variance, but also brood and parental effects. Parent-offspring regressions showed a large maternal inheritance component in TL (h²maternal=0.44), but no paternal inheritance. We did not find evidence for a negative genetic correlation underlying the observed negative phenotypic correlation between TL and structural body size. Thus, TL may evolve independently of body size and the negative phenotypic correlation is likely to be caused by non-genetic environmental effects. We further used genome-wide association analysis to identify genomic regions associated with TL variation. We identified several putative genes underlying TL variation; these have been inferred to be involved in oxidative stress, cellular growth, skeletal development, cell differentiation and tumorigenesis in other species. Together, our results show that TL has a low heritability and is a polygenic trait strongly affected by environmental conditions in a free-living bird.
Chapter
Dietary intervention is one of the most important approaches for the treatment of metabolic diseases such as diabetes mellitus. Fasting and caloric restriction have profound effects on systemic metabolism. The energy source-producing organs, such as the liver, and peripheral tissues rewire their metabolism to meet the energy demands of the whole body. Glycogenolysis, fatty acid oxidation, and ketone body production are characteristic metabolic changes that occur during fasting and caloric restriction. These metabolic changes are regulated by various signaling cascades including PPARα and FGF21. Moderate fasting and caloric restriction have also been implicated in extending the lifespan in a variety of organisms from nematodes to vertebrates. Intensive research has unveiled several regulatory mechanisms of longevity including metabolic regulators such as mTOR and sirtuins. The epigenome has been attracting attention as a mechanism underlying metabolic diseases and longevity. The epigenome is the concept that involves covalent modifications of DNA, histones, and RNA, which are mediated by the action of epigenetic enzymes. The activity of these enzymes is regulated by energy states, i.e. metabolites including ketone bodies and intermediates of various metabolic pathways. Thus, energy states are recorded in cells as an epigenetic memory, which may cause future onset of metabolic diseases and affect lifespan.
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Studies in invertebrates have led to the identification of a number of genes that regulate lifespan, some of which encode components of the insulin or insulin-like signalling pathways. Examples include the related tyrosine kinase receptors InR (Drosophila melanogaster) and DAF-2 (Caenorhabditis elegans) that are homologues of the mammalian insulin-like growth factor type 1 receptor (IGF-1R). To investigate whether IGF-1R also controls longevity in mammals, we inactivated the IGF-1R gene in mice (Igf1r). Here, using heterozygous knockout mice because null mutants are not viable, we report that Igf1r(+/-) mice live on average 26% longer than their wild-type littermates (P < 0.02). Female Igf1r(+/-) mice live 33% longer than wild-type females (P < 0.001), whereas the equivalent male mice show an increase in lifespan of 16%, which is not statistically significant. Long-lived Igf1r(+/-) mice do not develop dwarfism, their energy metabolism is normal, and their nutrient uptake, physical activity, fertility and reproduction are unaffected. The Igf1r(+/-) mice display greater resistance to oxidative stress, a known determinant of ageing. These results indicate that the IGF-1 receptor may be a central regulator of mammalian lifespan.
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In mammals, insulin signalling regulates glucose transport together with the expression and activity of various metabolic enzymes. In the nematode Caenorhabditis elegans, a related pathway regulates metabolism, development and longevity. Wild-type animals enter the developmentally arrested dauer stage in response to high levels of a secreted pheromone, accumulating large amounts of fat in their intestines and hypodermis. Mutants in DAF-2 (a homologue of the mammalian insulin receptor) and AGE-1 (a homologue of the catalytic subunit of mammalian phosphatidylinositol 3-OH kinase) arrest development at the dauer stage. Moreover, animals bearing weak or temperature-sensitive mutations in daf-2 and age-1 can develop reproductively, but nevertheless show increased energy storage and longevity. Here we show that null mutations in daf-16 suppress the effects of mutations in daf-2 or age-1; lack of daf-16 bypasses the need for this insulin receptor-like signalling pathway. The principal role of DAF-2/AGE-1 signalling is thus to antagonize DAF-16. daf-16 is widely expressed and encodes three members of the Fork head family of transcription factors. The DAF-2 pathway acts synergistically with the pathway activated by a nematode TGF-β-type signal, DAF-7, suggesting that DAF-16 cooperates with nematode SMAD proteins in regulating the transcription of key metabolic and developmental control genes. The probable human ortholognes of DAF-16, FKHR and AFX, may also act downstream of insulin signalling and cooperate with TGF-β effectors in mediating metabolic regulation. These genes may be dysregulated in diabetes.
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Dyskeratosis congenita is a premature aging syndrome characterized by muco-cutaneous features and a range of other abnormalities, including early greying, dental loss, osteoporosis, and malignancy. Dyskeratosis congenita cells age prematurely and have very short telomeres. Patients have mutations in genes that encode components of the telomerase complex (dyskerin, TERC, TERT, and NOP10), important in the maintenance of telomeres. Many dyskeratosis congenita patients remain uncharacterized. Here, we describe the analysis of two other proteins, NHP2 and GAR1, that together with dyskerin and NOP10 are key components of telomerase and small nucleolar ribonucleoprotein (snoRNP) complexes. We have identified previously uncharacterized NHP2 mutations that can cause autosomal recessive dyskeratosis congenita but have not found any GAR1 mutations. Patients with NHP2 mutations, in common with patients bearing dyskerin and NOP10 mutations had short telomeres and low TERC levels. SiRNA-mediated knockdown of NHP2 in human cells led to low TERC levels, but this reduction was not observed after GAR1 knockdown. These findings suggest that, in human cells, GAR1 has a different impact on the accumulation of TERC compared with dyskerin, NOP10, and NHP2. Most of the mutations so far identified in patients with classical dyskeratosis congenita impact either directly or indirectly on the stability of RNAs. In keeping with this effect, patients with dyskerin, NOP10, and now NHP2 mutations have all been shown to have low levels of telomerase RNA in their peripheral blood, providing direct evidence of their role in telomere maintenance in humans. • GAR1 • bone marrow failure • telomeres
Article
The insulin/IGF1 signaling pathways affect lifespan in several model organisms, including worms, flies and mice. To investigate whether common genetic variation in this pathway influences lifespan in humans, we genotyped 291 common variants in 30 genes encoding proteins in the insulin/IGF1 signaling pathway in a cohort of elderly Caucasian women selected from the Study of Osteoporotic Fractures (SOF). The cohort included 293 long-lived cases (lifespan ≥ 92 years (y), mean ± standard deviation (SD) = 95.3 ± 2.2y) and 603 average-lifespan controls (lifespan ≤ 79y, mean = 75.7 ± 2.6y). Variants were selected for genotyping using a haplotype-tagging approach. We found a modest excess of variants nominally associated with longevity. Nominally significant variants were then replicated in two additional Caucasian cohorts including both males and females: the Cardiovascular Health Study and Ashkenazi Jewish Centenarians. An intronic single nucleotide polymorphism in AKT1, rs3803304, was significantly associated with lifespan in a meta-analysis across the three cohorts (OR = 0.78 95%CI = 0.68–0.89, adjusted P = 0.043); two intronic single nucleotide polymorphisms in FOXO3A demonstrated a significant lifespan association among women only (rs1935949, OR = 1.35, 95%CI = 1.15–1.57, adjusted P = 0.0093). These results demonstrate that common variants in several genes in the insulin/IGF1 pathway are associated with human lifespan.