Genetic variation in healthy oldest-old.
- Authors
- Halaschek-Wiener, Julius; Amirabbasi-Beik, Mahsa; Monfared, Nasim; Pieczyk, Markus; Sailer, Christian; Kollar, Anita; Thomas, Ruth; Agalaridis, Georgios; Yamada, So; Oliveira, Lisa; Collins, Jennifer A; Meneilly, Graydon; Marra, Marco A; Madden, Kenneth M; Le, Nhu D; Connors, Joseph M; Brooks-Wilson, Angela R
- Year
- 2009
- Journal
- PloS one
- PMID
- 19680556
- DOI
- 10.1371/journal.pone.0006641
- PMCID
- PMC2722017
Individuals who live to 85 and beyond without developing major age-related diseases may achieve this, in part, by lacking disease susceptibility factors, or by possessing resistance factors that enhance their ability to avoid disease and prolong lifespan. Healthy aging is a complex phenotype likely to be affected by both genetic and environmental factors. We sequenced 24 candidate healthy aging genes in DNA samples from 47 healthy individuals aged eighty-five years or older (the 'oldest-old'), to characterize genetic variation that is present in this exceptional group. These healthy seniors were never diagnosed with cancer, cardiovascular disease, pulmonary disease, diabetes, or Alzheimer disease. We re-sequenced all exons, intron-exon boundaries and selected conserved non-coding sequences of candidate genes involved in aging-related processes, including dietary restriction (PPARG, PPARGC1A, SIRT1, SIRT3, UCP2, UCP3), metabolism (IGF1R, APOB, SCD), autophagy (BECN1, FRAP1), stem cell activation (NOTCH1, DLL1), tumor suppression (TP53, CDKN2A, ING1), DNA methylation (TRDMT1, DNMT3A, DNMT3B) Progeria syndromes (LMNA, ZMPSTE24, KL) and stress response (CRYAB, HSPB2). We detected 935 variants, including 848 single nucleotide polymorphisms (SNPs) and 87 insertion or deletions; 41% (385) were not recorded in dbSNP. This study is the first to present a comprehensive analysis of genetic variation in aging-related candidate genes in healthy oldest-old. These variants and especially our novel polymorphisms are valuable resources to test for genetic association in models of disease susceptibility or resistance. In addition, we propose an innovative tagSNP selection strategy that combines variants identified through gene re-sequencing- and HapMap-derived SNPs.
TagSNP selection strategy.HapMap genotypes for European individuals were obtained from the HapMap database. For each candidate gene, SNPs within the gene regionΒ±10 Kb were included. A MAF β₯5% and an r2 = 0.8 were used for tagSNPs selection using Haploview. Variants with a MAF β₯2% were analyzed in the gene re-sequencing set, with r2 = 1.0. Using a two-stage approach, we selected 682 tagSNPs that represent 1550 non-redundant variants from gene re-sequencing and HapMap data sets. tagSNPs (120) representing the 179 shared variants found in both data sets were determined in the gene re-sequencing set. CNS = conserved non-coding sequences.
LLM interpretation
This figure is a flow diagram illustrating a two-stage strategy for selecting tagSNPs from HapMap (1045 SNPs, MAF $\ge$ 5%) and gene re-sequencing (684 variants, MAF $\ge$ 2%) datasets. A Venn diagram shows 179 shared variants, which are represented by 120 tagSNPs selected using $r^2=1.0$. Additional tagSNPs were selected using $r^2=0.8$ for HapMap (220 SNPs) and $r^2=1.0$ for re-sequencing (342 SNPs), resulting in a final set of 340 HapMap tagSNPs and 462 re-sequencing tagSNPs.
| # | Section | Preview |
|---|---|---|
| 40 | Materials and Methods β TagSNP selection | For all 24 candidate genes we inferred tagSNPs from our sequenced variants as well as from dataβ¦ |
| 41 | Figure 1 | TagSNP selection strategy.HapMap genotypes for European individuals were obtained from the HapMap⦠|
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