Project Overview

Overview

Contact MPI: Michael A. Province

Exceptional Longevity (EL) is a complex trait that is likely influenced by multiple genes with small effects interacting with lifetime exposures.  The multicenter Long Liife Family Study (LLFS) enrolled and studied a unique sample of 4,953 participants in 539 pedigrees in the USA and Denmark that are all enriched for familial EL.  Fewer than 1% of Framingham Heart Study (FHS) families would meet the EL selection criteria for LLFS.  Through two extensive in-person visits  we show they have key Healthy Aging Phenotypes (HAPs) in major domains of the aging process (cognitive, cardiovascular, metabolic, inflammatory, etc.).  In this project, we concentrate on why and how these families and participants are protected.  Our long-term objective is to gain a deeper biological insight into EL and HAPs, including resistance to Alzheimer’s Disease and other dementias, enabling future novel preventive and therapeutic strategies.

Aim P1

Find novel rare and lineage-specific genetic variants and heritable familial factors associated with and linked to HAPs and EL in the unique LLFS pedigrees through Whole Genome Sequencing (WGS).

Aim P2

Leverage cross-sectional, longitudinal phenotyping and comprehensive OMICs information on LLFS pedigrees to discover the biological mechanisms leading to the heterogeneous familial patterns of HAPs and EL in LLFS pedigrees, and to discover additional causal variants. 

Aim P3

Systems Biology, Data Integration, Replication, and Future Functional Follow-up. 

Aim P1 Rationale and Subaims

Aim P1: Find novel rare and lineage-specific genetic variants and heritable familial factors associated with and linked to HAPs and EL in the unique LLFS pedigrees through Whole Genome Sequencing (WGS). 

Rationale:  LLFS analyses find that the families are on average much healthier than their age/sex matched peers and their phenotypes are highly heritable cross-sectionally and longitudinally. Yet, there is considerable familial phenotypic heterogeneity with different families showing different healthy aging characteristics and profiles in key pathways of healthy aging (cognitive, metabolic, inflammatory, etc.).  Linkage peaks for various HAPs are highly heterogeneous, with exceptionally large heterogeneity linkage peaks (HLODs), each driven by small numbers of (largely) different families.  These peaks are NOT explained by common GWAS imputed variants.  We hypothesize that multiple novel, rare, lineage-specific protective variants are driving HAPs and their longitudinal trajectories in different families for different phenotypes that will be discoverable through WGS by combined linkage/association.

  1. Re-measure LLFS families in a third longitudinal visit [Core B Phenotyping] to increase precision and to characterize rates of change and non-linear longitudinal trajectories of HAPs, such as compression of morbidity. Add formal Alzheimer’s Disease diagnosis and dementia subtyping to allow testing if exceptional cognitive performance variants also protect against Alzheimer’s Disease.
  2. Extend LLFS pedigrees to the grandchildren generation in selected pedigrees driving multiple HLOD peaks for key HAPs and trajectories, to increase genetic association power linearly and linkage power exponentially. Strong evidence from Danish Registry data shows HAP protection persists into the 3rd generation
  3. Combine heterogeneity-linkage and association analysis to identify rare and lineage-specific variants for cross-sectional and longitudinal HAPs and EL and their interactions with lifestyle exposures.

Aim P2 Rationale and Subaims

Aim P2: Leverage cross-sectional, longitudinal phenotyping and comprehensive OMICs information on LLFS pedigrees to discover the biological mechanisms leading to the heterogeneous familial patterns of HAPs and EL in LLFS pedigrees, and to discover additional causal variants.

Rationale:  Results suggest heterogeneous familial protective mechanisms leading to HAPs and EL, but few endophenotypes along the causal pathways explain this heterogeneity in LLFS. Further, as TOPMed is discovering, it is difficult to move from “statistical variant” to “gene of action” without mediating omic endophenotypes.  We hypothesize there are distinct OMICs features that are measurable mediators in the causal pathways from sequence variants to HAPs and EL, that are discoverable through OMICs analyses.

  1. Generate comprehensive omics profiling of key LLFS pedigrees driving multiple linkage peaks [Core C Biospecimen]. Conduct omics-QTL mapping to reveal distinct, novel HAP and EL genes and epigenetic modifiers through which causal variants are operating, to move from “statistical variants” to “genes of action.”
  2. Generate metabolomics on all LLFS samples longitudinally. Correlate these and other omic features with HAPs and EL to identify novel biomarkers which (when replicated) become new HAPs for all aims P1-P3.

Aim P3 Rationale and Subaims

Aim P3:  Systems Biology, Data Integration, Replication, and Future Functional Follow-up. 

Rationale:  Biology works through gene networks, not genes in isolation. Integrative analyses can be more powerful and comprehensive in explaining the biology.   We hypothesize that systems biology and network analysis scans across multiple omics spaces will discover novel variants and biology for HAPs and EL.

  1. Use systems biology approaches to integrate data [guided by Core D Analysis Methods]. Efficiently screen the “Big Data” omics space for promising features jointly associated with phenotypes AND genetic variants using a novel “Correlated Meta-Analysis.”  Generate comprehensive Path/Network causal models of key pathways from sequence to expression through epigenetics to proteins to metabolites to HAPs and EL.
  2. Replicate LLFS discoveries in other cohorts (Framingham, TOPMed, BLSA, NECS, etc.). Contribute LLFS data to Consortia (e.g. CHARGE, TOPMed) to increase discovery power using population methods.
  3. Enable future functional follow-up studies in appropriate model systems tailored to our individual findings.