The 24.9 Data Release includes data from the following studies:
Human data
The Long Life Family Study (LLFS)
The Long Life Family Study (LLFS) is an international collaborative study of the genetics and familial components of exceptional survival, longevity, and healthy aging. Families were recruited through elderly probands (generally in their 90s) who self-reported on the survival history of their parents and siblings, and on the basis of this information, families which showed clustering of exceptional survival were recruited. This large collection of families, selected on the basis of clustering for exceptional survival, is a unique resource for the study of human longevity and healthy aging. We examined and extensively phenotyped in all major domains of healthy aging, 4,953 individuals in 539 families through comprehensive in-home visits. Phenotypes cover the major domains of the aging process (demographic, anthropometric, cognitive, activities of daily living, ankle-brachial index, blood pressure, physical performance measure, pulmonary function measurements, carotid ultrasound, digital pen data from cognitive testing, audio recordings of cognitive testing, Pittsburgh fatigueability score, biochemical assays, and blood samples (serum, plasma, lymphocytes, red cells, and DNA) for future studies). This study will generate GWAS chip data using ~2.5 million SNPs from the Illumina Omni chip was performed using DNA collected from the first visit; and multiple OMIC analyses (whole genome sequencing, whole genome bisulfite sequencing (methylome), RNA-sequencing (transcriptome), metabolome, and proteome).
- This release provides whole genome bisulfite sequencing (methylome) data
- This release provides metabolomics data
- The LLFS used untargeted metabolomics to analyze human EDTA plasma samples. The workflow involved solid-phase extraction to separate polar and lipid metabolites, followed by LC/MS analysis using an Agilent system. Polar metabolites were separated with a ZIC®-pHILIC column, while lipids were analyzed on an Acquity UPLC® HSS T3 column. Both metabolite classes were subjected to MS/MS fragmentation for identification. Data were processed using DecoID and the Agilent Lipid Annotator. ComBat correction was applied to correct for batch effects in the data, ensuring minimal batch variance while preserving research sample variability.