Genome‐wide linkage analysis for human longevity: Genetics of Healthy Aging Study

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DOI: 10.1111/acel.12039 · Source: PubMed
Abstract
Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in fifteen study centers of eleven European countries as part of the Genetics of Healthy Ageing (GEHA) project. In the joint linkage analyses we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD=3.47), chromosome 17q12-q22 (LOD=2.95), chromosome 19p13.3-p13.11 (LOD=3.76) and chromosome 19q13.11-q13.32 (LOD=3.57). To fine map these regions linked to longevity, we performed association analysis using GWAS data in a subgroup of 1,228 unrelated nonagenarian and 1,907 geographically matched controls. Using a fixed effect meta-analysis approach, rs4420638 at the TOMM40/APOE/APOC1 gene locus showed significant association with longevity (p-value=9.6 x 10(-8) ). By combined modeling of linkage and association we showed that association of longevity with APOEε4 and APOEε2 alleles explain the linkage at 19q13.11-q13.32 with p-value=0.02 and p-value=1.0 x 10(-5) , respectively. In the largest linkage scan thus far performed for human familial longevity, we confirm that the APOE locus is a longevity gene and that additional longevity loci may be identified at 14q11.2, 17q12-q22 and 19p13.3-p13.11. Since the latter linkage results are not explained by common variants, we suggest that rare variants play an important role in human familial longevity. © 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland.
Genome-wide linkage analysis for human longevity: Genetics
of Healthy Aging Study
Marian Beekman,
1,2
H
el
ene Blanch
e,
3
Markus Perola,
4
Anti
Hervonen,
5
Vladyslav Bezrukov,
6
Ewa Sikora,
7
Friederike
Flachsbart,
8
Lene Christiansen,
9
Anton J. M. De Craen,
2,10
Tom B. L. Kirkwood,
11
Irene Maeve Rea,
12
Michel
Poulain,
13,14
Jean-Marie Robine,
15
Silvana Valensin,
16
Maria
Antonietta Stazi,
17
Giuseppe Passarino,
18
Luca Deiana,
19
Efstathios S. Gonos,
20
Lavinia Paternoster,
21,22
Thorkild I. A.
Sørensen,
23,24
Qihua Tan,
9,25
Quinta Helmer,
26
Erik B. van
den Akker,
1,27
Joris Deelen,
1
Francesca Martella,
26,28
Heather J. Cordell,
11
Kristin L. Ayers,
11
James W. Vaupel,
29
Outi T
ornwall,
4
Thomas E. Johnson,
30
Stefan Schreiber,
8
Mark Lathrop,
3
Axel Skytthe,
9
Rudi G. J. Westendorp,
10
Kaare Christensen,
9,25
Jutta Gampe,
29
Almut Nebel,
8
Jeanine
J. Houwing-Duistermaat,
2,26
Pieternella Eline Slagboom
1,2
and Claudio Franceschi
16
On behalf of the GEHA
consortium
1
Molecular Epidemiology, Leiden University Medical Centre, Leiden, ZC,
2333, The Netherlands
2
Netherlands Consortium for Healthy Ageing, Leiden, ZC, 2333, The
Netherlands
3
Foundation Jean Dausset, CEPH, 75010, Paris, France
4
The National Institute for Health and Welfare, THL, Helsinki, FI-00271, Finland
5
Tampere School of Public Health, Tampere, FI-33014, Finland
6
Institute of Gerontology, Kiev, 04114, Ukraine
7
Nencki Istitute for Experimental Biology, NENCKI, Warszawa, 02-093,
Poland
8
Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel
(CAU), Kiel, 24118, Germany
9
Danish Aging Research Center, Institute of Public Health, University of
Southern Denmark, Odense, DK-5230, Denmark
10
Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, ZA,
2333, The Netherlands
11
Institute for Ageing and Health, Newcastle University, UNEW, Newcastle,
NE1 7RU, UK
12
Queens University of Belfast, QUB, Belfast, Northern Ireland, BT7 1NN, UK
13
Catholic University of Louvain, UCL, Louvain-la-Neuve B-1348, Belgium
14
Tallinn University, Tallinn, 10120, Estonia
15
INSERM, Health & Demography, CRLC, Montpellier, 34298, France
16
Interdepartmental Centre “Luigi Galvani CIG, University of Bologna
UNIBO, Bologna, 40126, Italy
17
Istituto Superiore di Sanit
a, ISS, Rome, 00161, Italy
18
UNICAL, University of Calabria, Rende, 87030, Italy
19
UNISS, University of Sassari, 07100, Sassari, Italy
20
National Hellenic Researcher Foundation, NHRF, Athens, 11635, Greece
21
MRC CAiTE centre, University of Bristol, Bristol, BS8 2BN, UK
22
School of Social and Community Medicine, University of Bristol, Bristol, BS8
2BN, UK
23
Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of
Health Sciences, University of Copenhagen, 2100, Copenhagen, Denmark
24
Institute of Preventive Medicine, Copenhagen University Hospitals, 1357,
Copenhagen, Denmark
25
Department of Clinical Genetics and department of Clinical Biochemistry and
Pharmacology, Odense University Hospital, DK-5000, Odense C, Denmark
26
Medical Statistics and Bioinformatics, Leiden University Medical Centre,
Leiden, ZC, 2333, The Netherlands
27
Delft Bioinformatics Lab, Delft University of Technology, Delft, CD, 2628,
The Netherlands
28
Dipartimento di Scienze Statistiche, Sapienza University of Rome, 00185,
Rome, Italy
29
Max Planck Institute for Demographic Research, MPIDR, 18057, Rostock,
Germany
30
Institute for Behavioral Genetics, University of Colorado at Boulder,
Boulder, CO 80309-0447, USA
Summary
Clear evidence exists for heritability of human longevity, and much
interest is focused on identifying genes associated with longer
lives. To identify such longevity alleles, we performed the largest
genome-wide linkage scan thus far reported. Linkage analyses
included 2118 nonagenarian Caucasian sibling pairs that have been
enrolled in 15 study centers of 11 European countries as part of the
Genetics of Healthy Aging (GEHA) project. In the joint linkage
analyses, we observed four regions that show linkage with
longevity; chromosome 14q11.2 (LOD = 3.47), chromosome
17q12-q22 (LOD = 2.95), chromosome 19p13.3-p13.11
(LOD = 3.76), and chromosome 19q13.11-q13.32 (LOD = 3.57). To
fine map these regions linked to longevity, we performed associ-
ation analysis using GWAS data in a subgroup of 1228 unrelated
nonagenarian and 1907 geographically matched controls. Using a
fixed-effect meta-analysis approach, rs4420638 at the TOMM40/
APOE/APOC1 gene locus showed significant association with
longevity (P-value = 9.6 3 10
8
). By combined modeling of link-
age and association, we showed that association of longevity with
APOEe4 and APOEe2 alleles explain the linkage at 19q13.11-q13.32
with P-value = 0.02 and P-value = 1.0 3 10
5
, respectively. In the
largest linkage scan thus far performed for human familial
longevity, we confirm that the APOE locus is a longevity gene
and that additional longevity loci may be identified at 14q11.2,
17q12-q22, and 19p13.3-p13.11. As the latter linkage results are not
explained by common variants, we suggest that rare variants play
an important role in human familial longevity.
Key words: APOE gene; association analysis; genome-wide
linkage analysis; Human familial longevity; nonagenarian
sibling pairs.
Introduction
Nonagenarians, centenarians, and their first degree family members
have a life-long survival advantage (Perls et al., 2002; Schoenmaker
et al., 2006) that can be attributed to a lower risk of coronary artery
disease, cancer and type-2 diabetes (Terry et al., 2003; Westendorp
et al., 2009). In middle age, members of long-lived families display
Correspondence
Marian Beekman, Molecular Epidemiology, Leiden University Medical Center,
PO Box 9600, Leiden, RC 2300, The Netherlands. Tel.: +31715269735;
fax: +31715268280; e-mail: m.beekman@lumc.nl
These authors contributed equally to this work.
Accepted for publication 27 November 2012
184 ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
Aging Cell (2013) 12, pp184–193 Doi: 10.1111/acel.12039
Aging
Cell
characteristic of metabolic health such as low glucose levels and
preserved insulin sensitivity (Rozing et al., 2010; Slagboom et al.,
2011). The clustering of longevity in families suggests a heritable
component (Gudmundsson et al., 2000; Perls et al., 2000) which
has been estimated at approximately 25% in the general population
(Skytthe et al., 2003) with increasing importance at the highest ages
(Hjelmborg et al., 2006). However, the genetic basis of longevity,
first clearly identified as a research priority by Sch
achter et al.
(Schachter et al., 1993), still remains to be elucidated.
Previously, it has been demonstrated that long-lived families carry
as many GWAS-identified disease susceptibility alleles as the general
population (Beekman et al., 2010). It can therefore be hypothesized
that long-lived families carry gene variants that promote healthy
aging and protect from disease. The genomic location of such
protective variants can be identified in a genome-wide linkage scan
among long-lived siblings. In the past, by genotyping 400 micro-
satellite markers in 137 long-lived sibships, Puca et al. (Puca et al.,
2001) detected linkage at 4q25 (LOD = 3.65). In a subsequent
study, modest support was provided for this locus by a genome-
wide linkage scan in 95 male sibling pairs concordant for healthy
aging (Reed et al., 2004). However, in a targeted study of 4q25 in
164 nonagenarian sibships no linkage was observed (Beekman
et al., 2006). By association of the positional candidate genes at
4q25, MTP was suggested to explain the linkage (Geesaman et al.,
2003), but this association could not be replicated in other studies
(Bathum et al., 2005; Nebel et al., 2005; Beekman et al., 2006).
Extension of the original group of long-lived siblings as investigated
by Puca et al. to 279 pairs also resulted in lack of evidence for
linkage at 4q25, although novel linkage regions for longevity were
observed at chromosome 3p24-22 and chromosome 9q31-24
(Boyden & Kunkel, 2010).
A number of studies have performed genome-wide association
(GWA) analysis for longevity to uncover common genetic variation
involved in this trait. A rather large association study form the
Cohorts for Heart and Aging Research in Genomic Epidemiology
Consortium of 1836 individuals aged over 90 years and 1955
controls between 55 and 80 years did not reveal genome-wide
significant loci (Newman et al., 2010) and neither did the analyses
of all-cause mortality and survival free of major disease in this cohort
(Walter et al., 2011). A smaller Dutch study of 403 nonagenarians
and 1670 controls younger than 65 years identified the APOE gene
as a mortality locus (Deelen et al., 2011), which was confirmed in a
German study of 763 long-lived individuals and 1085 younger
controls (Nebel et al., 2011) and a longitudinal study of 1606 Danes
showed that the effect size of this association increases at the
highest ages (Jacobsen et al., 2010). Apparently, the influence of
the common genetic variation on longevity is small which requires
large meta-GWA studies for identification. Alternatively, rare
genetic variants may play a more important role in longevity. As
the previous linkage studies showed contradictory results potentially
due to heterogeneity in the longevity phenotype, it is expected that
longevity is influenced by many private rare variants.
In this study, we performed genome-wide linkage analysis among
2118 European nonagenarian sibships of the Genetics of Healthy
Aging (GEHA) Study (Franceschi et al., 2007; Skytthe et al., 2011).
This integrated European project was initiated in 2004 with the aim
of identifying genes involved in healthy aging and longevity. The
GEHA selection criterion of nonagenarian sibling pairs has been
shown to result in families enriched for genetic influences on
longevity by a smaller previous study of the same design (the Leiden
Longevity Study) (Schoenmaker et al., 2006). This study is charac-
terized by a survival benefit in multiple generations, low prevalence
of disease, and beneficial metabolic phenotypes, which is compa-
rable to the observations reported for families of centenarian
singletons such as the Longevity Study at Albert Einstein College of
Medicine (Barzilai et al., 2010). Sibships over 90 years of age and
controls between 55 and 75 years of age have been recruited in 10
European countries and their genomic DNA has been isolated and
genotyped centrally. This logistic achievement resulted in the largest
linkage study for human longevity.
Results
In the Genetics of Healthy Aging Study (GEHA), nonagenarian
sibling pairs have been recruited in 11 countries among 15 study
centers (Skytthe et al., 2011). Genotypings of 5734 SNPs (Illumina
HumanLinkage12 set) were available after quality control in 4445
persons belonging to 2118 full sibships with a mean sibship size of
2.1 (Table 1).
To investigate potential substructure, identical-by-state (IBS)
estimates for all pairs of individuals in the data set were computed
using EIGENSTRAT (Price et al., 2006). The first two resulting
principal components (C1 and C2) were plotted against each other
(Fig. 1), which gives a representation of the data in two dimensions.
In the resulting scatter plot, each point represents an individual and
each recruitment center is marked by its own symbol. After visual
inspection of component 1 (C1) and component 2 (C2), C1
appeared to reflect the North-south localization and C2 dissociated
the Fins from the rest of Europe. On the basis of this genetic
heterogeneity among the study samples, the linkage analyses were
performed in three geographical clusters: (i) Finland with
N
sibships
= 150; (ii) Northern Europe with N
sibships
= 1378, consisting
of Ukraine, Poland, Germany, Denmark, The Netherlands, United
Kingdom, Belgium, and France; (iii) Southern Europe with
N
sibships
= 590, consisting of Italy and Greece.
We calculated nonparametric linkage scores over the whole
genome for the three clusters combined (Fig. 2, blue line; Data S2).
Besides the inflated LOD score probably due to linkage disequilib-
rium between markers at the beginning of chromosome 3, the
combined linkage score for the GEHA study exceeded the genome-
wide significant LOD score of 3 at chromosome 14q11.2 (8 cM),
19p13.3-p13.11 (36 cM), and 19q13.11-q13.32 (68 cM) (Fig. 3).
For centers for which population-based mortality rates were
available, i.e., Italy, UK, Denmark, France, Netherlands, Germany,
and Finland, a weighted NPL statistic was computed. The weights
are the products of the age- and sex-specific cumulative hazard for
the two siblings (Houwing-Duistermaat et al., 2009) that are based
on country-specific mortality rates. In this weighted NPL analysis
extremely old sibling pairs obtain more weight (Fig. 2, yellow line)
and the LOD score at chromosome 17q12-q22 (82 cM) increased
Genome-wide linkage analysis for human longevity, M. Beekman et al.
185
ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
from 2.95 to 3.71 (Fig. 3; Table 2). This increase has been observed
in each of the three clusters Northern Europe, Southern Europe, and
Finland. The northern European cluster contributes most to the
linkage at 14q11.2 (8 cM), 19p13.3-p13.11 (36 cM), and
19q13.11-q13.32 (68 cM), whereas all three clusters contribute to
linkage at 17q12-q22 (Fig. S1).
Fig. 1 Population structure among GEHA study and the grouping of countries into three clusters. Cluster 1: Finland; Cluster 2: Northern Europe consisting of Ukraine,
Poland, Germany, Denmark, The Netherlands, United Kingdom, Belgium, and France; Cluster 3: Southern Europe consisting of Italy and Greece.
Table 1 Number of sibships and sibship sizes for the GEHA sample sets
Cluster Country Center
Sibship size
Sibships Mean age (years) Male sibpairs Female sibpairs2345
1 Finland Tampere 124 24 1 1 150 92.6 11 102
2 Ukraine Kiev 46 1 0 0 47 93.2 1 38
2 Poland Warsaw 131 4 0 0 135 93.0 11 87
2 Germany Kiel 90 4 0 0 94 92.8 12 53
2 Denmark Odense 384 45 5 0 434 92.3 50 215
2 The Netherlands Leiden 150 14 2 0 166 93.0 20 82
2 UK Newcastle 98 1 0 0 99 92.8 7 55
2 UK Belfast 52 4 1 0 57 92.9 4 40
2 Belgium Louvain 76 1 0 0 77 92.9 13 40
2 France Montpellier 238 27 3 1 269 93.3 30 157
3 Italy Bologna 178 23 6 1 208 93.4 14 126
3 Italy Rome 51 2 0 0 53 92.5 6 32
3 Italy Calabria 181 7 1 0 189 93.0 24 79
3 Italy Sassari 43 2 0 0 45 93.4 3 24
3 Greece Athens 92 3 0 0 95 92.9 57 15
Total 1934 162 19 3 2118 92.9 263 1145
Genome-wide linkage analysis for human longevity, M. Beekman et al.
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ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
As gender-specific effects play a role in human longevity,
linkage analysis was also performed in male-only (N
pairs
= 263) and
female-only (N
pairs
= 1145) sibling pairs. Although NPL analysis did
not reveal any linkage for male-only pairs, significant linkage
(Weighted-NPL 3.61) was observed at 8p11.21-q13.1 (70 cM)
among the male-only pairs in the age-weighted analysis (Fig. 4,
lower panel; Table 3). Among the female-only sibpairs (Fig. 4,
upper panel) significant linkage was observed at chromosome
15q12-q14 (22 cM) (Weighted-NPL 3.16) and at 19q13.33-q13.41
(80 cM) (NPL = 4.97) (Table 3). Further investigation of these
linkage areas revealed that the Northern Europe cluster and the
Finland cluster provide evidence for the 8p11.21-q13.1 and
15q12-q14 loci, and that the 19q13.33-q13.41 locus seems only
to link with female longevity in the Northern Europe cluster
(Fig. S2).
When we consider the genes in the 1-LOD drop intervals of
the linkage analysis as candidate genes for longevity, we have a
list of 1151 unique position candidate genes (Data S3) for which
we have performed gene set enrichment analysis (Table S3,
Data S4). The 1-LOD drop intervals were investigated for
Chromosome
LOD
01
234
1234567
8 9 10 11 12
13 14 15 16 17 18 19 2021 22
Fig. 2 Genome-wide linkage graphs of
among 2118 sibships of the GEHA study.
The blue line displays the NPL LOD scores
and the yellow line the age-weighted LOD
scores.
0 102030405060
01234
Chromosome 14
position in cM
lod score
40 60 80 100 120 140
0
12
34
Chromosome 17
position in cM
lod score
0 20 40 60 80 100
01234
Chromosome 19
position in cM
lod score
Fig. 3 Chromosomal regions linked to
longevity with LOD score above 3. The blue
line displays the NPL LOD scores and the
yellow line the age-weighted LOD scores.
Genome-wide linkage analysis for human longevity, M. Beekman et al.
187
ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
association with longevity by comparison of unrelated nonage-
narians to geographically matched younger controls. Of each
sibpair of the centers that provided the largest contribution to the
linkage results [i.e., Netherlands, Denmark, France, Italy (Bologna),
and UK (Newcastle)] (Table S1), the eldest sibs were taken as
cases and as controls for France and Bologna, GEHA controls
were taken. As controls for UK, Netherlands and Denmark already
genotyped available younger population controls were used. We
performed a meta-analysis for 57 607 imputed SNPs in the seven
candidate regions with longevity. We tested for differences in
genotype frequencies between cases and geographically matched
controls using a trend test, i.e., assuming additive genetic effects.
The test statistic was adjusted for uncertainty in imputations
and for the inflation factor before computing the p-values. We
used a fixed-effect meta-analysis approach to combine the results
across centers. Finally, we used a Bonferroni correction based on
the number of SNPs within the region to correct for multiple
testing.
No significant association (P-value <8.7 9 10
7
) was observed
at the loci at chromosome 14q11.2, 17q12-q22, and 19p13.3-
p13.11, and neither at the sex-specific loci. At 19q13.11-q13.32
locus, rs4420638 at the TOMM40/APOE/APOC1 gene locus
showed significant association with longevity (P-
value = 9.6 9 10
8
). As the linkage plots per center show that
especially the sample from Denmark provides the statistical
evidence for an increased LOD score at this position (Fig. S3),
we tested in the Danish data set with the software LAMP (Burdick
et al., 2006) for residual linkage at the 19q13.11-q13.21 locus
if the APOEe2e3e4 rs7412 and rs429358 genotypes were taken
into account. It appeared that the APOEe4 allele, although
with moderate significance, contributed to the linkage peak
(P-value = 0.02). Remarkably, with a much higher significance the
APOEe2 allele also explained the linkage peak (P-value = 1.0
9 10
5
).
Table 2 Four linkage regions with LOD score above 3 in combined GEHA samples
for NPL and age-weighted linkage analyses
14q11.2 17q12-q22
19p13.3-
p13.11
19q13.11-
q13.32
1LOD-drop interval 012 cM
6895 cM
2042 cM
5885 cM
Start SNP Rs10484218 Rs2429990 Rs432001 Rs7250748
End SNP Rs977870 Rs12947910 Rs9193 33 Rs10403760
NPL 3.47 2.95 3.76 3.57
NPL_subsets
3.15 3.26 3.85 3.80
Weighted_
NPL_subsets
§
2.69 3.71 2.96 1.85
According to genome build 35.
Population mortality rates were available in: Cluster 1: Finland; Cluster 2:
Denmark, UK, The Netherlands, France; Cluster 3: Italy.
§
weighted with ages of the siblings.
Wom en
Chromosome
LOD
012345
12345678910
11 12 13 14 15 16
17 18 19 20 21
22
Men
Chromosome
LOD
0.0 0.5 1.0 1.5 2.0 2.5 3.0
12345678910111213141516171819202122
Fig. 4 Genome-wide linkage graphs of
among 1145 female-only sibships and 263
male-only sibships of the GEHA study. The
blue line displays the NPL LOD scores and
the yellow line the age-weighted LOD
scores.
Genome-wide linkage analysis for human longevity, M. Beekman et al.
188
ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
Discussion
To identify chromosomal regions involved in longevity, we per-
formed the largest linkage analysis thus far reported. This analysis
included 2118 nonagenarian sibling pairs from 11 European
countries enrolled as part of the Genetics of Healthy Aging (GEHA)
project. We identified four regions linked to human longevity at
14q11.2, 17q12-q22, 19p13.3-p13.11, and 19q13.11-q13.32. In
addition to these loci, we identified three loci that were linked to
longevity in a sex-specific manner: 8p11.21-q13.1 (men), 15q12-
q14 (women), and 19q13.33-q13.41 (women). In these seven linked
regions, we performed association analysis using GWAS data that
was available in a subset of the cohorts, and we showed that
genetic variation at the APOE gene locus was significantly associated
with longevity. The absence of the APOEe4 allele among the
nonagenarians is obvious (Deelen et al., 2011) and contributed to
the linkage, suggesting that APOE is a mortality gene. However, the
linkage was mainly explained by the shared presence of the APOEe2
allele, indicating that the APOE gene indeed is a longevity gene.
Affected sibpair analysis is a powerful tool to map strong
dominant as well as recessive gene effects, but to map rare genetic
variation with relatively small effect a group of at least 600 sibling
pairs with one sibling over age 95 years should be examined (Tan
et al., 2004). As the GEHA project yielded an impressive number of
2118 genotyped nonagenarian sibling pairs to investigate for
linkage (Skytthe et al., 2011), of which 899 sibling pairs contained
one sib aged over 95 years of age, this linkage study has sufficient
power to detect the loci with relatively small effects.
Previous studies for longevity loci detected linkage at 4q25 (Puca
et al., 2001), 3p24-22, 9q31-34, 12q24 (Boyden & Kunkel, 2010),
6p12.1, 7q11.21, 14q22.1 (Edwards et al., 2011). None of these
loci showed linkage in the GEHA project. The largest of the previous
linkage studies examined 279 long-lived sibling pairs, which is
considered a relatively small study and therefore the previous
linkage results should be considered with caution, given the
complexity of human longevity (Tan et al., 2004). Otherwise, the
effect of previously detected loci on longevity could be private for a
specific genetic background or specific environment, or even private
for a small number of families.
In the chromosomal region linked to familial longevity at
19q13.11-q13.32, we found rs4420638 to be significantly associ-
ated with familial longevity. This SNP tags the LD block harboring
the TOMM40, APOE, and APOC1 genes. It is known that the APOE
gene exhibit three isoforms that are caused by two functional SNPs
rs7412 (APOEe2) and rs429358 (APOEe4) which have been associ-
ated with longevity (Schachter et al., 1994). The APOE e4 allele
frequency in GEHA nonagenarian sibling cases was 6.8%, which
was significantly lower than the allele frequency of 12.7% among
the geographically matched controls. APOEe4 allele carriers have
about 50% lower chance to become a nonagenarian then non-
APOEe4 allele carriers (OR = 0.48, 95% CI = 0.420.55). Recently,
this gene region has been explored for other functional variation
and multiple cis-elements are found to influence both APOE and
TOMM40 promoter activity and a complex transcriptional regulatory
structure has been suggested modulates regional expression (Bekris
et al., 2012). Hence, future research should consider exploring these
other functional variants for their role in familial longevity.
Genetic association analysis as an approach is suitable to detect
the effect on a trait of common genetic variation (minor allele
frequency larger than 1%). We did not observe significant associ-
ations in the families that contributed to linkage of common variants
in the linked regions with longevity which can be explained by the
possibility that the positive linkage signals may be based on allelic
and/or locus heterogeneity, i.e., different genetic variants in the
same gene or gene region contributed to the trait in different
families. The less likely explanation is that the power, which was
large enough for detecting linkage, may be too small to detect
genetic association among 57K SNPs in the linkage regions in 1228
cases. Thus, common variants may not contribute to the longevity
phenotype and by applying next generation sequencing analyses of
the nonagenarians contributing to linkage rare, private genetic
variation in the linkage regions may be found.
This genome-wide linkage scan did not provide substantial
evidence for linkage to FOXO3A
on chromosome 6, of which the
T allele of rs2802295 predisposes to survive into old age (reviewed
in Chung et al., 2010). This may be due to the relatively small effect
sizes conferred by SNPs at that locus or it could be that the
nonagenarian sibling design is underpowered to detect the associ-
ation of FOXO3A with longevity because its effect seems the
strongest at ages above 95, in which age range the GEHA study has
limited numbers of participants. Besides the NPL analyses we
performed linkage analyses in which the contribution of the sibling
pairs has been weighted for the age of the eldest sibling. Thus,
when a locus is involved in survival after an extremely old age, as is
suggested for the FOXO3A locus, linkage signals would increase in
this weighted analyses. Despite the use of these analysis tools, we
did not observe any LOD score above LOD score of 1 at the FOXO3A
locus. We therefore conclude that genetic variation at the FOXO3A
gene might not contribute to longevity in the GEHA Study.
Previously, it has been suggested that genetic variation in the
FOXO1 gene is specifically contributing to human female longevity
(reviewed in Chung et al., 2010). However, at chromosome
13q14.11 harboring the FOXO1 gene we found no evidence for
linkage with female longevity (LOD < 0.05) and at the gene position
Table 3 Sex-specific linkage regions with LOD score above 3 in combined GEHA
samples for NPL and age-weighted linkage analyses
Sex
8p11.21-q13.1 15q12-q14 19q13.33-q13.41
Men Women Women
1LOD-drop interval 6482 cM
1428 cM
6690 cM
Start SNP Rs801100 Rs1871009 Rs1236093
End SNP Rs4368961 Rs580839 Rs1661965
NPL 1.14 0.62 4.97
NPL_subsets
1.96 1.44 3.92
Weighted_NPL_subsets
§
3.61 3.16 3.60
According to genome build 35.
Population mortality rates were available in: Cluster 1: Finland; Cluster 2:
Denmark, UK, The Netherlands, France; Cluster 3: Italy.
§
weighted with ages of the siblings.
Genome-wide linkage analysis for human longevity, M. Beekman et al.
189
ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
of FOXO1 we found no evidence for association in the females-only
meta-analysis (P-values >0.042) in the GEHA Study. Potentially, the
effect of this locus is not only influenced by gender but also by
genetic background.
When we investigated the cluster-specific linkage results, we
observed that Northern Europe cluster contributes to all linkage loci,
the Southern Europe cluster only contributes to the 17q12-q22 and
19p13.3-p13.11 loci and the Finland cluster only contributes to the
14q11.2 and 17q12-q22 loci (Fig. S3). The Southern Europe cluster
and the Finland cluster did not obtain LOD scores above 3 on their
own and the Northern Europe cluster obtained LOD scores above 3
only at chromosome 19 at 20, 68, and 82 cM. This illustrates that
probably, in addition to the sample size of the cluster, the
environment and genetic background play a role in the complex
phenotype of human familial longevity.
Besides linkage at the APOE locus, we detected linkage at 14q11.2,
17q12-q22, and 19p13.3-p13.11 considering the total GEHA study.
These chromosomal regions linked to human longevity cover many
genes that are potential candidate genes involved in neurodegener-
ative disease, auto-immune disease, cardiovascular disease, and
cancer. In the NCBI GWAS catalog (http://www.genome.gov/gwa-
studies/) genome-wide association has been reported at the 14q locus
for the T-cell receptor alpha with narcolepsy. At the 17q12-q22 locus,
genome-wide association has been reported for MAPT and NSF with
Parkinson’s disease, for CA10 with age of onset of menarche, and for
STAT3 with Multiple Sclerosis and Crohn’s disease. At the chromo-
some 19p13.3-p13.11 locus, genome-wide association has been
reported for TYK2 and ICAM3 with Crohn’s disease, for LDLR with
myocardial infarction, for SMARCA4 with coronary heart disease, and
for ABHD8 and ANKLE1 with breast cancer.
In conclusion, we identified four chromosomal regions linked to
human familial longevity and we are the first identifying genetic
linkage with familial longevity at the APOE gene locus. As genetic
variation at the APOE gene explains the linkage, we confirm that the
APOE locus is indeed a longevity gene. Identification of additional
longevity genes in the linkage regions will provide biological insight
into the pathways underlying human familial longevity, metabolic
health, and the resistance to age-related disease.
Experimental procedures
Study subjects
The individuals investigated in this study participate in Genetics of
Healthy Aging (GEHA) Study (Skytthe et al., 2011). Families partic-
ipating in the GEHA study have at least two siblings meeting four
inclusion criteria: (i) participants are at least 90 years old, (ii)
participants have at least one living brother or sister who fulfills the
first criterion and is willing to participate, (iii) the nonagenarian
sibship has an identical mother and father, and (iv) the parents of
the nonagenarian sibship are European and Caucasian. In total,
2249 sibships have been recruited.
In accordance with the Declaration of Helsinki, written informed
consent was obtained from all participants prior to entering the
study. Good clinical practice guidelines were maintained. The study
protocol was approved by the local medical ethical committees of
the 11 participating countries before the start of the study.
DNA isolation
The National Institute for Health and Welfare (THL, Helsinki),
extracted high molecular DNA from 7 ml of whole blood using
automated DNA purification, Autopure LS (Qiagen), based on
Puregene salting out methodology. DNA concentrations were
adjusted to 50 ng/lL, verified using PicoGreen dsDNA Quantitation
kit (Molecular Probes, Life Technologies, Paisley, UK). The samples
were then subjected to quality control by ABI 3730 DNA analyzer
(Applied Biosystems, Life Technologies) using two sex-specific and
four autosomal microsatellite markers.
Genotyping for linkage analysis
The Centre National de G
enotypage (CNG, Paris) performed
genotyping of 6090 SNPs in the nonagenarian siblings of the
2249 sibships recruited in the GEHA project with the Illumina
HumanLinkage-12 Genotyping BeadChip. Genotyping call rate was
larger than 95% per sample. We observed 19 duplicates that are
either MZ twins or duplicate samples of which two were found in
two different families. Five full sibs were identified as unrelated and
eight sample pairs that are supposed to be unrelated were identified
as potential half-sibs. Of all sibships, 35 appeared to be composed
of half-sibs. Finally, two sample pairs that are supposed to be
unrelated were found full sibs. After removing the samples with
unexpected relations, we obtained reliable genotyping for 4445
individuals from 2118 sibships (Table 1). As the recruitment of the
GEHA study has been a major enterprise, it turned out in a later
phase of the project that some cases were aged below 90 years of
age at the time of interview (Table S2 and Fig. S4).
We applied the EIGENSTRAT program (Price et al., 2006) with
default parameters to infer axes of variation with the 6090 SNPs per
individual to identify genetic population stratification.
Affected sibpair analysis
Unlikely recombinants were detected using Merlin-0.10.2 (Abecasis
et al., 2002) and erroneous genotypes were removed with Pedwipe.
In addition, Pearson’s r
2
was estimated using the total data set. For
the nonparametric (NPL) analyses, 275 SNPs were removed to obtain
a genome-wide SNP set of 5734 SNPs with pairwise r
2
smaller than
0.4. MERLIN-0.10.2 (Abecasis et al., 2002) was used to estimate the
information content over the genome (Data S1) and to estimate IBD
probabilities in the sibling pairs without parents.
Next, proportion of alleles shared identical by descent was
estimated in Merlin (Abecasis et al., 2002) allowing for center-specific
allele frequencies. Nonparametric linkage analysis (NPL) (Kruglyak &
Lander, 1996) was performed using a score test statistic for affected
sibling pairs (Callegaro et al., 2009) that was computed at a grid of
2 cM. NPL statistics per cluster were obtained by summing the NPL
statistics per involved center. This statistic compares the actual
proportion of alleles shared identical by descent by a sibling pair with
Genome-wide linkage analysis for human longevity, M. Beekman et al.
190
ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
its expectation of 0.5 under Mendelian segregation. The variance of
the statistic was obtained via simulation in Merlin. By simulation, the
variance is adjusted for incomplete marker informativeness and
correlation of sibling pairs within families. The statistic assumes that
there is no linkage disequilibrium between the markers. When this
assumption is violated, the peaks will be inflated.
For centers for which population-based mortality rates were
available, i.e., Italy, United Kingdom, Denmark, France, Netherlands,
Germany, and Finland, a weighted NPL statistic was computed. The
weights are the products of the age- and sex-specific cumulative
hazard for the two siblings (Houwing-Duistermaat et al., 2009).
These cumulative hazards are based on country-specific mortality
rates. In this analysis, extremely old sibling pairs obtain more weight
in the analysis.
Subjects for association analysis within linked regions
To fine map the 1-LOD drop linkage regions, association analysis
was performed in the largest centers and those most contributing to
the linkage results. From each pair the eldest siblings from the
United Kingdom, Netherlands, Denmark, France, and Bologna, and
GEHA controls from France and Bologna were genotyped. As
controls for United Kingdom, Netherlands, and Denmark available
geographically matched control data sets were used: TwinUK for UK
nonagenarians (Perola et al., 2007), controls from the Leiden
Longevity Study (LLS) for Dutch nonagenarians (Beekman et al.,
2010), and Danish GOYA controls (Paternoster et al., 2011) for
Danish nonagenarians. The characteristics of the samples are
displayed in table S1.
Genotyping for association analysis within linked regions
The GEHA nonagenarians from the UK, Netherlands, Denmark, and
France (CNG, Paris, France) and the LLS controls (genotyping facility
Erasmus MC, Rotterdam, The Netherlands) have been genotyped
using Illumina Infinium HD Human660W-Quad BeadChips, the
GEHA nonagenarians from Bologna, their controls and the France
controls have been genotyped using Illumina HumanOmniExpress
BeadChips (genotyping facility Erasmus MC, Rotterdam, The
Netherlands) and the Danish controls (GOYA) and UK controls
(TwinsUK) have been genotyped using Illumina Infinium HD
Human610-Quad BeadChips. Autosomal SNPs were included in
analysis if they had less than 5% missing data, HardyWeinberg
P-values in cases and controls greater than 1 9 10
4
and minor
allele frequency (MAF) was larger than 1% (if n
cases
and n
controls
200) or 5%. SNPs that passed this quality control in both cases
and controls were used as input for imputation to HAPMAP2 release
22 using IMPUTE2. SNPs with a R
2
T value lower than 40 and a MAF
lower than 1% (if n
cases
and n
controls
200) or 5% were excluded
from analyses (Uh et al., 2011).
Association analysis within linked regions
Association analysis has been performed using CC-assoc applying
score test (Uh et al., 2011). For meta-analysis, a fixed-effect
approach was used and adjustment for population stratification
per study was performed by multiplying the variances with the
genomic inflation factor (k), which measures overdispersion of test
statistics from association tests. Scores and adjusted variances of the
five countries were combined to obtain a single metastatistic. We
tested 57 607 SNPs within the 1-LOD drop intervals and according
to Bonferroni adjustment for multiple testing, P-values below
8.7 9 10
7
were considered genome-wide significant.
Combined linkage and association at APOEe2e3e4 locus
To investigate whether the linkage identified can be explained by
the APOEe2e3e4 polymorphisms, we performed linkage and asso-
ciation modeling in pedigrees (LAMP) (Li et al., 2005) in the Danish
sample in which rs7412 and rs429358 have been genotyped by
TaqMan using standard protocol of the supplier.
Gene set enrichment analyses
For each linkage region, the 1-LOD drop intervals have been
determined. Next, the genes located in these 1-LOD drop intervals
have been listed and tested for pathway enrichment using default
settings in DAVID (http://david.abcc.ncifcrf.gov/).
Acknowledgments
Funding: The work described in this study was funded mainly by the
EU GEHA Project contract no. LSHM-CT-2004-503-270. The work
has additionally been supported by the following programs and
agencies: The Competitive Research Funding of the Tampere
University Hospital and Academy of Finland (Tampere); United
States National Institute of Aging (PO1-AG08761) (Odense); The
Innovation Oriented Research Program on Genomics (Senter-Novem
IGE05007), the Centre for Medical Systems Biology (CMSB), and the
National Institute for Healthy Ageing (NCHA 05060810), all in the
framework of the Netherlands Genomics Initiative (NGI)/Netherlands
Organisation for Scientific Research (NWO) (Leiden); The Institute for
Ageing and Health and the UK NIHR Biomedical Research Centre for
Ageing and Age-related disease award to the Newcastle-upon-Tyne
Foundation Hospitals NHS Trust (Newcastle); Fondation Caisse
d’Epargne Rh
^
one-Alpes Lyon CERAL (20042007) (Montpellier);
Regione Autonoma della Sardegna (Sassari), European Union’s
Seventh Framework Programme (FP7/2007-2011) IDEAL-ageing
under grant agreement no. 259679.
Access to genotype data from the TwinsUK cohort was kindly
provided by the Department of Twin Research (DTR) and Genetic
Epidemiology at King’s College London. The TwinsUK cohort
acknowledges funding from the Wellcome Trust, European Com-
munity’s FP7 Programme (HEALTH-F2-2008-201865 GEFOS and
HEALTH-F4-2007-201413 ENGAGE projects) and the FP5 Pro-
gramme (GenomEUtwin Project QLG2-CT-2002-01254), BBSRC
project grant G20234, and the National Eye Institute via an NIH/
CIDR genotyping project (PI: Terri Young). We thank the staff of the
Twin Research Unit for their help and support in undertaking this
project. We would also like to thank all patients, their families, and
Genome-wide linkage analysis for human longevity, M. Beekman et al.
191
ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
control subjects for their voluntary contribution to this research
project.
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Supporting Information
Additional Supporting Information may be found in the online version of this
article at the publisher’s web-site.
Fig. S1 Linkage regions in the three geographically clusters at chromosome
14 (upper left panel), chromosome 17 (upper right panel) and chromosome
19 (lower panel).
Fig. S2 Sex-specific linkage regions in the three geographically clusters. Light
blue line represents Northern Europe cluster, orange line represents Southern
Europe cluster, purple line represents Finland.
Fig. S3 Linkage at chromosome 19 for the three largest centers from the
Northern European cluster.
Fig. S4 Histogram of ages in the GEHA study.
Table S1 Nonagenarian cases and controls for association analyses in
candidate longevity regions.
Table S2 Mean age and age range of GEHA nonagenarian sibling cases.
Table S3 Gene Ontology term enrichment among the genes resided in
regions linked to longevity.
Data S1 Information content.
Data S2 Genome wide linkage results.
Data S3 Positional candidate genes.
Data S4 Gene set enrichment results from DAVID.
Genome-wide linkage analysis for human longevity, M. Beekman et al.
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ª 2013 The Authors
Aging Cell ª 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland
    • "harboring APOE gene as the single most replicated longevity-associated locus, confirming the previous single gene association studies. (Beekman et al., 2013; Deelen et al., 2011; Nebel et al., 2011; Sebastiani et al., 2012). APOE has two common missense variants, rs429358 (Cys130Arg) and rs7412 (Arg176Cys), and a combination of the two determines functional alleles of APOE: 2 (Cys130, Cys176), 3 (Cys130, Arg176) and 4 (Arg130, Arg176). "
    [Show abstract] [Hide abstract] ABSTRACT: Apolipoprotein E (APOE) gene has been the most replicated longevity-associated gene in humans. Two common APOE alleles are either significantly depleted (ε4 allele) or enriched (ε2 allele) in long-lived individuals as compared to controls. We performed high-throughput sequencing analysis of exons and 2kb proximal promoter of APOE in 450 centenarians and 500 controls of Ashkenazi Jewish decent. We found two common regulatory variants, rs405509 (p=0.006) and rs769449 (p=0.036), that were significantly depleted in centenarians. Genotyping analysis of rs7412 and rs429358 showed significant enrichment of ε2 allele (p=0.003) and ε2/ε3 genotype (p=0.005), and significant depletion of ε3/ε4 genotype (p=0.005) in centenarians. Our findings support the hypothesis that variants in both coding and regulatory regions of APOE may contribute to longevity in humans.
    Article · Mar 2016
    • "Some of these genes are known to play an important role in cellular and metabolic functions such as development (FOXO1), oxidative stress (SOD3; HSPA), genome maintenance (P53), cognitive pathways (ApoE), lipid metabolism (APOE, CETP), and glucose metabolism (IGF1). For instance, in a comprehensive study, Beekman et al. (2013) reported that a variant close to APOE gene was found to be associated with 90 year olds in the European Genetics of Healthy Aging study (GEHA). Further analysis suggested that one of the isoforms of APOE2 was associated with longevity. "
    [Show abstract] [Hide abstract] ABSTRACT: Longevity as a complex life-history trait shares an ontogenetic relationship with other quantitative traits and varies among individuals, families and populations. Heritability estimates of longevity suggest that about a third of the phenotypic variation associated with the trait is attributable to genetic factors, and the rest is influenced by epigenetic and environmental factors. Individuals react differently to the environments they are a part of, as well as to the environments they construct for their survival and reproduction; the latter phenomenon is known as niche construction. Lifestyle influences longevity at all the stages of development and levels of human diversity. Hence, lifestyle may be viewed as a component of niche construction. Here, we: a) interpret longevity using a combination of genotype-epigenetic-phenotype (GEP) map approach and niche-construction theory, and b) discuss the plausible influence of genetic and epigenetic factors in the distribution and maintenance of longevity among individuals with normal life span on the one hand, and centenarians on the other. Although similar genetic and environmental factors appear to be common to both of these groups, exceptional longevity may be influenced by polymorphisms in specific genes, coupled with superior genomic stability and homeostatic mechanisms, maintained by negative frequency-dependent selection. We suggest that a comparative analysis of longevity between individuals with normal life span and centenarians, along with insights from population ecology and evolutionary biology, would not only advance our knowledge of biological mechanisms underlying human longevity, but also provide deeper insights into extending healthy life span.
    Full-text · Article · Feb 2015
    • "A genome-wide linkage analysis on 2118 European nonagenarian full sibships of the GEHA project was performed to identify chromosomal regions involved in longevity [52]. By using Illumina HumanLinkage12 Genotyping BeadChip, four regions (14q11.2, "
    [Show abstract] [Hide abstract] ABSTRACT: Usually the genetics of human longevity is restricted to the nuclear genome (nDNA). However it is well known that the nDNA interacts with a physically and functionally separated genome, the mitochondrial DNA (mtDNA) that, even if limited in length and number of genes encoded, plays a major role in the ageing process. The complex interplay between nDNA/mtDNA and the environment is most likely involved in phenomena such as ageing and longevity. To this scenario we have to add another level of complexity represented by the microbiota, that is, the whole set of bacteria present in the different part of our body with their whole set of genes. In particular, several studies investigated the role of gut microbiota (GM) modifications in ageing and longevity and an age-related GM signature was found. In this view, human being must be considered as "metaorganism" and a more holistic approach is necessary to grasp the complex dynamics of the interaction between the environment and nDNA-mtDNA-GM of the host during ageing. In this review, the relationship between the three genetics and human longevity is addressed to point out that a comprehensive view will allow the researchers to properly address the complex interactions that occur during human lifespan.
    Full-text · Article · Apr 2014
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