To identify causal socio-genetics longevity mechanisms protecting from (multi)morbidity in humans:1. The primary objective is to disentangle novel rare-, and structural gene variants that can explain longevity and protection from (multi)morbidity in…
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- Other condition
- Economic and housing issues
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Health condition
veroudering, leeftijdsgerelateerde ziektes
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Outcome measures
Primary outcome
Our primary analyses are A) within cases: a non-parametric linkage analysis to
establish likely genomic regions of longevity genes in researched families, to
this end we will solely use the families defined as cases (LRC>=30%). B) between
cases and control: we compare social and behavioural factor that associate with
familial longevity in the cases and controles. A Genetic analysis. Primary
analysis. In the linkage analysis you compare familymember on marker positions
, bases on single-nucleotide polymorphism (SNP) -arrya genotyping data, and you
test whether the 'longevity-affected- familymember share more alleles than
chance (allele frequentie in the population) then expected on these marker
positions. The more member from one family you can include the more power you
will have in you analysis. From this comes a logarithm-of-the-odds (LOD)-score.
We shall focus on regions with a peak LOD-score of 3 or higher (this indicates
that it is at least 1000x likely that the longevity gene lies on that position
and not somewhere else on the genome) and encompasses a region between the peak
and 1-LOD-drop. In the DNA of member of these families contributing to these
linkage signals we shall perform whole-genome-sequencing (WGS). From this
sequencing data we shall select genetic variant that are shared between all
familymember that positively contribute to the linkage signals. Relevant
genevariants shall be selected within the linkage-regions in one of two ways.
Firstly, we shall select very rare genvariant that have a predicted functional
through an in sillico procedure, based on predicted rare protein altering gene
variants (minor allele frequency (MAF) < 0,2%)> Secondly we select on more
common variants (common SNPs) in chromosome regions under the linkage signals.
Secundaire analyses: Variants in the same gene in multiple families
contributing to the linkage signals shall be candidates for further studies
into the function of how these genes contribute to healthy ageing. We shall
compare carriers of relevant variants versus non-carriers within the LOF-NL
study or in existing data of, among other, the Leiden Longevity Study (LLS), to
research whether whether these variants associatie with changes in RNA
expression of the genes or changes in health calculated from grip strength
measurements and 'moleculaire clocks or predictors' that are predicitve for
frailty, mortality and disease outcomes. In this we we can get an indication of
health while the measurements are minimal invasive. The new moleculair
predictors shall be mainly base on nuclear magnetic resonance (NMR)- based
metabolomics score (MetaboHealth, MetaboAge) with in the future space for
clocks based on DNA methylation, transcriptome and proteome based predictors.
B) Analysis of socio- and behavioralfactors. Primaire analysis. We compare 75+
year old cases from long-lived families (LRC>=30%) with 75+ year old controls
from average families (LRC=0%) on different socio- and behavioralfactors that
are measured through questionnaires. Secundaire analyses: Firstly we will look
into SNP array data from cases and control whether there is a difference in
polygenic risk score (PRS) that explain the heritable component of personality
and behaviour. A PRS is a summation of multiple genetic variants (SNPs) that
are associated with a phenotype. A PRS reflects the genetic predisposition of a
certain trait, that can be used as a predictive factor for that trait, which in
this case are social and behavioral traits that can contribute to a high
LRC-score and can contribute to longevity. A second secondairy analysis concers
the health of cases and controls. To get a indiclation of differences in health
without the need of withdrawing blood we shall do an associationanalysis based
on DNA-methylation profiles in DNA collected from saliva that can be collected
in the cases and controls. The on DNA-methylome based health status in cases
and controls can be coupled on differences in social- behaviour and status
within these groups.
Secondary outcome
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Background summary
The demographic life expectancy enhancement of the past 150 years imposes an
urgent challenge in Western and economically growing societies to stimulate the
healthy lifespan that is lagging behind. Families surviving into exceptionally
high ages (longevity) in good physical and mental health illustrate that this
is physiologically possible. Such families harbor cross generational
socio-genetic mechanisms that mediate healthy aging and protection from
(multi)morbidity. Identifying the genetic loci contributing to extended health-
and lifespan in the population at large is challenging due to the uncertainty
in defining long-lived cases with the heritable longevity trait amongst
long-living phenocopies. So far, only variants in the APOE and FOXO3 were
consistently identified. These loci do not explain the full quantitative
longevity trait that we observed being transmitted in families. In the past we
have built up a study of longevity families (the Leiden Longevity Study). We
will expand the collection of such families using a more strict definition of
heritable longevity to be used in genetic studies.
As a first step to improve the potential for finding novel longevity loci in
socio-genetic research, for the past 5 years we explored the nature of familial
transmission of longevity in historical genealogical data and we generated a
novel longevity case definition. For practical applications we developed the
Longevity Relatives Count (LRC) -score which is based on the number of
long-lived ancestors of a proband case. An LRC of 30% indicates that 30% of
ancestors of a proband survived into the top 10 % survivors of their birth
cohort. By applying our LRC-score in the genealogical data, we identified
families and elderly persons that meet the novel criteria of heritable
longevity and likely harbour longevity loci protecting against
(mutli)morbidity.
Here we propose that in order to identify the causal factors driving familial
longevity, we collect an LRC based selection of highly aged cases and their
families for the purpose of performing genome wide genetic studies to identify
longevity loci along with a focus on socio-economic behaviour and environmental
background of longevity families, which has not as yet received sufficient
attention so far,
Study objective
To identify causal socio-genetics longevity mechanisms protecting from
(multi)morbidity in humans:
1. The primary objective is to disentangle novel rare-, and structural gene
variants that can explain longevity and protection from (multi)morbidity in the
families.
2. The secondary objective is to focus more in depth on socio-behavioural and
environmental components of familial longevity, their role in (multi)morbidity
and interaction with the genetic longevity component.
3. The third objective is to extend objective 1 also to common variants and to
link the socio-behavioural and genetic longevity components to metabolomics and
epigenetic health biomarkers.
Study design
Family based observational study consisting of cases from families with
multiple long-lived ancestors (LRC>=30%) and controls from families with no
long-lived ancestors (LRC=0%).
Study burden and risks
There are no direct benefits to the subjects. We include a limited number of
questionnaires about socio-economic status and lifestyle, which might be
experienced as burdensome for some participants. Blood withdrawal of 3 tubes
with in total 22,5 mL will be performed, which is a minimally invasive
procedure. Social network questions might be confronting for some of the
participants.
Einthovenweg 20
Leiden 2333ZC
NL
Einthovenweg 20
Leiden 2333ZC
NL
Listed location countries
Age
Inclusion criteria
Longevity Relatives Count (LRC) expresses a score indicating the percentage of
ancestors surviving into a specific top percentile of their birth cohort. We
focus on LRC 30%.
Cases LRC>=30%:
- Able to give written consent
- Willing and able to follow the study protocol
- LRC-score 30% and up in
- Age >= 75 years
- At least one sibling or first cousin with age >= 75 years who has given
written consent
Controls LRC=0%:
- Able to give written consent
- Willing and able to follow the study protocol
- LRC-score 0% in our
- Age >= 75 years
Exclusion criteria
- Not be able to give (written) informed consent
Design
Recruitment
metc-ldd@lumc.nl
metc-ldd@lumc.nl
metc-ldd@lumc.nl
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In other registers
| Register | ID |
|---|---|
| CCMO | NL81887.058.23 |