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: + -
    Longevity‑Associated (27)   No Age Effect (73)  
  • Study type: + -
    Candidate Region/Gene « 
  • Variant type: + -
    SNP (85)   CNV (1)  
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    13 14 15 16 17 18 19 20 21 X Y MT
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    Populations | Study Types | Variant Types


    LVDB_word_cloud.png
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    variants.jpg
    polymorphism factor odds ratio pvalue initial number replication number Population age of cases shorter lived allele longer lived allele study type reference
    rs3758391 SIRT1 — 1.0 386 vs 640 vs 547 — German Mean age 101 — — Candidate Region/Gene 16257164
    rs1885472 SIRT1 — 1.0 386 vs 640 vs 547 — German Mean age 101 — — Candidate Region/Gene 16257164
    rs2273773 SIRT1 — 1.0 386 vs 640 vs 547 — German Mean age 101 — — Candidate Region/Gene 16257164
    rs10997870 SIRT1 — 1.0 386 vs 640 vs 547 — German Mean age 101 — — Candidate Region/Gene 16257164
    rs2234975 SIRT1 — 1.0 386 vs 640 vs 547 — German Mean age 101 — — Candidate Region/Gene 16257164
    rs1801274 FCGR2A — 1.0 408 vs 446 vs 454 — German Centenarians — — Candidate Region/Gene 16893392
    rs4986790 TLR4 — 0.61 273 vs 594 — German Mean age 97.9 — — Candidate Region/Gene 17493663
    rs11188059 CYP2C — 0.04 616 centenarians versus control group of 945 younger individuals — German — — — Candidate Region/Gene 21798861
    rs2071069 TPI1 — 0.05 1422 vs 967 — German mean age: 98.8, age range: 95–110 (SNP is G/A) — Candidate Region/Gene 18510744
    rs2071065 TPI1 — 0.05 1422 vs 967 — German mean age: 98.8, age range: 95–110 (SNP is T/C) — Candidate Region/Gene 18510744
    rs770087 DUSP6 1.53 0.002 386 vs 410 541 vs 469 German 100 to 110, mean age = 101.3 C — Candidate Region/Gene 20800603
    rs2301582 NALP1 1.30 0.011 386 vs 410 541 vs 469 German 100 to 110, mean age = 101.3 C — Candidate Region/Gene 20800603
    rs648802 PERP 1.29 0.012 386 vs 410 541 vs 469 German 100 to 110, mean age = 101.3 C — Candidate Region/Gene 20800603
    rs4073591 DEAF1 — 0.002618 1600 samples (cases and controls combined) — German, Italian — — — Candidate Region/Gene 19367319
    rs4073590 DEAF1 — 0.004259 1600 samples (cases and controls combined) — German, Italian — — — Candidate Region/Gene 19367319
    rs11040489 KRTAP5-6 — 0.005869 1600 samples (cases and controls combined) — German, Italian — — — Candidate Region/Gene 19367319
    rs800140 TSPAN32 — 0.0081 1600 samples (cases and controls combined) — German, Italian — — — Candidate Region/Gene 19367319
    rs16928120 — — 0.00057 1600 samples (cases and controls combined) — German, Italian — — — Candidate Region/Gene 19367319
    rs4998557 SOD1 — 0.52 1612 vs 1104 — German 90+ — — Candidate Region/Gene 24163049
    rs2070424 SOD1 — 0.77 1612 vs 1104 — German 90+ — — Candidate Region/Gene 24163049
    rs1041740 SOD1 — 0.998 1612 vs 1104 — German 90+ — — Candidate Region/Gene 24163049
    rs6917589 SOD2 — 0.741 1612 vs 1104 — German 90+ — — Candidate Region/Gene 24163049
    rs2842980 SOD2 — 0.689 1612 vs 1104 — German 90+ — — Candidate Region/Gene 24163049
    rs7855 SOD2 — 0.842 1612 vs 1104 — German 90+ — — Candidate Region/Gene 24163049
    rs8031 SOD2 — 0.186 1612 vs 1104 — German 90+ — — Candidate Region/Gene 24163049
    • Page 1 of 5
    • 25 of 102 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