The Healthy Elderly Active Longevity Study, nicknamed the “Wellderly” Study, began enrollment in 2007 and focuses on understanding the genetic components of healthy aging. 

The objective of this study is to obtain blood and/or saliva samples in order to help model health and disease phenotypes through population genomics. Initially enrollment was open to individuals 80 years of age or older, but was later raised to 85 years of age and older. 

Individuals enrolled into the study must be healthy or may have mild medical conditions associated with the normal aging process. Phenotypic information collected includes basic demographic information such as birthdate, height and weight at time of enrollment. It also includes data on alcohol use, tobacco use, exercise, highest level of education, the birthplace of their grandparents, prescription medications used, any activities of daily living they require assistance with; any chronic conditions (which are accepted in the study inclusion criteria) and lastly the date of their last physical exam. 

Eric Topol
Scripps Research Translational Institute

Ali Torkamani
Scripps Research Translational Institute

Acknowledgment statement for any data distributed by NIAGADS:

Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer's Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689), funded by the National Institute on Aging.

For investigators using Wellderly – Healthy Elderly Active Longevity (HEAL) data:

Development of the Wellderly resource was supported by the National Center For Advancing Translational Sciences of the National Institutes of Health under Award Number UM1TR004407.

Weißbach, Stephan et al. Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines. BMC genomics vol. 22,1 62. 19 Jan. 2021, doi:10.1186/s12864-020-07362-8. PubMed Link

Dewan, Ramita et al. Pathogenic Huntingtin Repeat Expansions in Patients with Frontotemporal Dementia and Amyotrophic Lateral Sclerosis. Neuron vol. 109,3 (2021): 448-460.e4. doi:10.1016/j.neuron.2020.11.005. PubMed Link

Bandres-Ciga, S et al. Large-scale pathway specific polygenic risk and transcriptomic community network analysis identifies novel functional pathways in Parkinson disease. Acta neuropathologica vol. 140,3 (2020): 341-358. doi:10.1007/s00401-020-02181-3. PubMed Link

Erikson, Galina A et al. Whole-Genome Sequencing of a Healthy Aging Cohort. Cell vol. 165,4 (2016): 1002-11. doi:10.1016/j.cell.2016.03.022. PubMed Link

Pham, Phillip H et al. Scripps Genome ADVISER: Annotation and Distributed Variant Interpretation SERver. PloS one vol. 10,2 e0116815. 23 Feb. 2015, doi:10.1371/journal.pone.0116815. PubMed Link

Erikson, Galina A et al. SG-ADVISER CNV: copy-number variant annotation and interpretation. Genetics in medicine : official journal of the American College of Medical Genetics vol. 17,9 (2015): 714-8. doi:10.1038/gim.2014.180. PubMed Link

Cruchaga, Carlos et al. Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature vol. 505,7484 (2014): 550-554. doi:10.1038/nature12825. PubMed Link

Tewhey, Ryan et al. Enrichment of sequencing targets from the human genome by solution hybridization. Genome biology vol. 10,10 (2009): R116. doi:10.1186/gb-2009-10-10-r116. PubMed Link