genetic networks associated with human longevity, stress
TRANSCRIPT
Genetic Networks Associated with Human Longevity,
Stress Resistance, & Aging
Evidence that lifespan may be 20-30% heritable.Potentially higher for extreme longevity.Evidence that extreme longevity may be linked to stress resistance.
The search for “longevity genes”may benefit from incorporating:1) Environmental influences2) Known biological pathways3) Polygenic effects
1. Investigate genes related to longevity among persons with hazardous environmental exposure.
2. Use prior knowledge of biological pathways and protein interaction networks to select candidate genes from GWAS results.
3. Examine the ability of polygenetic scores to predict other aging and longevity phenotypes in the general population.
HRS DATA: • 12,507 individuals• 2.5 million SNPs
ANALYTIC SMAPLE (GWAS)• Whites only• 90 Cases (Current smokers ages 80+)• 730 Controls (Current smokers <70 years)• 1,224,285 SNPs after performing QC and MAF >.05
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
5,184 SNPs <5x10-03
Mapped to 784 unique genesUsing WebGestaltNCBI dbSNP IDs linked to
Ensembl gene IDs
Used Cytoscape plugin, Reactome FI, to construct functional interaction networks and run pathway enrichment analysis.
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
217 genes
Top Pathways Enriched in the Network:PI3K-Akt signaling pathwaySignaling by PDGFRas signaling pathwayFocal adhesion
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
• Composite (additive) scores for the 217 SNPs in the FI Networks
• Assumes a dose-response effect0=homozygous for the negatively associated allele
1=heterozygous
2=homozygous for the positively associated allele
0.0
2.0
4.0
6D
ensi
ty
120 140 160 180 200 220Polygenic Risk Score
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation 120
140
160
180
200
220
Poly
geni
c R
isk
Sco
re (P
RS
)
Smokers Under Age 70 Smokers Ages 80+
Cases vs. Controls (Current Smokers)
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
PRS and Longevity in the replication HRS sample
PRS was significantly associated with longevity (age 90+) for never and former smokers, controlling for EV1-4, sex, smoking, and self-reported race (N=5,570)
OR=1.02P=.006
.02
.04
.06
.08
.1.1
2
Pro
babi
lity
of b
eing
90+
(Yea
rs)
130 150 170 190 210Polygenic Risk Score
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
Accumulation of comorbid conditions
Used all 10 waves of the RAND HRS data (N=7,331).
Quadratic growth curve models, allowing for random intercepts and slopes, and controlling for EV1-4, sex, smoking, education, BMI, and race.
Main effect (PRS): = -0.006, P=.001
Interaction Effect (PRS x Age): = -.0002, P=.023)0
24
68
Num
ber o
f Com
orbi
d C
ondi
tions
50 60 70 80 90 100Age (Years)
PRS=130 PRS=200
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
Risk of Disease
Disease-Specific Cox Proportional Hazard Models (incidence). All significant at P<.01, except cancer (P=.22)
Data
GWAS
Pathway and Network Analysis
Polygenic Risk Score
Prediction and Validation
Does using Networks really improve PRS models?
1. Evidence that longevity may influence stress resistance.
2. Longevity and aging appear to be polygenic traits.
3. Using prior knowledge of biological networks and pathways may improved our PRS predictions.
1. Develop PRS that allows for gene-gene interactions (Machine Learning).
2. Examine pleiotropy.
This research was supported by the National Institute on Aging:Grants P30AG017265 and T32AG0037
Co-Contributors:
Eileen Crimmins, PhD
Jasmine Zhou, PhD
