Denigma

Longevity Variant Database

Narrow results by variant, study, or associated fields (e.g. gene symbol ADRB2):

Use ontology terms to further narrow results (e.g. aging, insulin, etc.):

  • Variant effect: + -
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    ‑ (19)   Candidate Region/Gene (382)   Meta‑Analysis (8)   Genome‑Wide Association Study (103)   Mitochondrial Haplogroup (27)  
  • Variant type: + -
    SNP (314)   Haplotype (43)   Other (1)   STR (7)   Deletion (1)  
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    Populations | Study Types | Variant Types


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    polymorphism factor odds ratio pvalue initial number replication number Population age of cases shorter lived allele longer lived allele study type reference
    −438 G/A TAFI 0.53 0.003 2224 — American, Caucasian 65+ G (men) — Candidate Region/Gene 15939070
    XRCC5 120 bp 5` (GAPyA)n STR — — 0.0005 430 (case) vs 290 — British Mean 70 16+2 15 Candidate Region/Gene 16518718
    XRCC3 intron 3 STR — — 0.0005 430 (case) vs 290 — British Mean age 70 15 13 Candidate Region/Gene 16518718
    VNTR DC-SIGNR — 0.041 361 vs 342 — Chinese >90 non-6/7 genotype 6/7 genotype Candidate Region/Gene 20003397
    V465A GABRR3 — 0.04 390 vs 410 — Ashkenazi Jewish Centenarians — — Candidate Region/Gene 23376243
    TP53 TP53 — 1.0 224 vs 441 — Ashkenazi Jewish 75 — — Candidate Region/Gene 15621215
    SNP at chromosome location 36750804 CDKN1A 0.83 0.414 184 vs 184 — Italian 100 — — Candidate Region/Gene 20126416
    SI000565Q APOB — 0.001 191 (case) vs 53 — Chinese > 90 baseline, Mean age 97 +/- 3 — LL Candidate Region/Gene 17393087
    S65C HFE — 1.0 35 vs 106 — 91-105 — — Candidate Region/Gene 11857056
    rs9995651 SNCA — 0.98 769 total (see notes) — Caucasian >90 (oldest age group) — — Candidate Region/Gene 22912757
    rs9861425 ADCY5 — 0.251 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs9844212 ADCY5 — 0.901 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs969845 — 5.87 7.22e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs964403 SUMF1 2.07 4.91e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs9603776 FOXO1 1.05 0.778 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 — — Candidate Region/Gene 21388494
    rs9595687 — 0.49 2.09e-05 763 vs 1085 754 vs 850 German Mean age 99.7 — — Genome-Wide Association Study 21740922
    rs9486913 FOXO3A 1.18 0.053 304 vs 603;279 vs 797;383 vs 363 — Caucasian 95.3 ± 2.2_x000D_;+M19994.5 ± 2.1_x000D_;mean 97.7, 95– 119 — — Candidate Region/Gene 19489743
    rs9486902 FOXO3A 1.18 0.073 303 vs 603;279 vs 797;383 vs 363 — Caucasian 95.3 ± 2.2_x000D_;+M19994.5 ± 2.1_x000D_;mean 97.7, 95– 118 — — Candidate Region/Gene 19489743
    rs9470367 CDKN1A 1.01 0.949 184 vs 184 — Italian 100 — — Candidate Region/Gene 20126416
    rs9462209 CDKN1A 1.25 0.228 184 vs 184 — Italian 100 — — Candidate Region/Gene 20126416
    rs9400239 FOXO3 — 0.05 1089 vs 736 — Danish 92–93 C — Candidate Region/Gene 20849522
    rs939915 SIRT3 — 0.0145 2461 samples (cases and controls combined) — Italian — — — Candidate Region/Gene 19367319
    rs9398172 FOXO3 — 0.05 1089 vs 736 — Danish 92–93 A — Candidate Region/Gene 20849522
    rs9366292 — 0.64 8.65e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs9340799 ESR1 — 1.0 148 vs 414 — Polish mean age 101.1 — — Candidate Region/Gene 20599431
    • Page 1 of 22
    • 25 of 543 variants

    The Longevity Variant Database (LVDB) is a collaborative effort to catalogue all published genetic variants relevant to human longevity.

    The project is directed by the Health Extension Research Foundation [http://www.healthextension.co/about/], and the online content is managed by the members of the Global Computing Initiative.

    LVDB is driven by an international collaboration of scientists, programmers, and volunteers, including Joe Betts-LaCroix, Kristen Fortney, Daniel Wuttke, Eric K. Morgen, Nick Schaum, John M. Adams, Jessica Choi, Barry Goldberg, Amir Levine, Maria Litovchenko, Aiste Narkeviciute, Emily Quist, Navneet Ramesh, Justin Rebo, Dmitri Shytikov, and Jimi Vyas. o

    Suggest a new study Report a database issue