Candidate gene resequencing to identify rare, pedigree-specific variants influencing healthy aging phenotypes in the long life family study

Todd E. Druley, Lihua Wang, Shiow J. Lin, Joseph H. Lee, Qunyuan Zhang, E. Warwick Daw, Haley J. Abel, Sara E. Chasnoff, Enrique I. Ramos, Benjamin T. Levinson, Bharat Thyagarajan, Anne B. Newman, Kaare Christensen, Richard Mayeux, Michael A. Province

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Background: The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes. Methods: We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests. Results: We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3' UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL). Conclusions: Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants.

Original languageEnglish
Article number80
JournalBMC Geriatrics
Volume16
Issue number1
DOIs
StatePublished - Apr 9 2016

Keywords

  • Aging
  • Family
  • Genetics
  • Genomics
  • Geriatrics
  • Pedigrees
  • Sequencing

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