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 (13)   No Age Effect (17)  
  • Study type: + -
    Candidate Region/Gene (30)   Complete Mitochondrial Genome (1)  
  • Variant type: + -
    SNP « 
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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
    rs4746720 SIRT1 2.10 0.0001 223 vs 277 — Chinese Average age 93 CC, TT CT Candidate Region/Gene 23450480
    rs2291727 ADCY6 — 0.003 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs28359172 MT-ND5 0.30 0.003 98 vs 117 — Chinese 90–100 G — Candidate Region/Gene 18759861
    rs189037 (genotype) ATM — 0.004 875 (280 m, 595 f) vs 886 (491 m, 395 f) — Chinese Mean age 94,6, healthy — CT (Article); GA (ENSEMBL) Candidate Region/Gene 20816691
    mt146 C>T — — 0.005 113 (cases) vs 200 — Chinese 90-110 mt146T mt146C Complete Mitochondrial Genome 22729909
    rs2227956 HSPA1L — 0.01 191 vs 53 — Chinese >90 TC TT/CC Candidate Region/Gene 19840767
    rs1042718 ADRB2 1.27 0.014 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 C A Candidate Region/Gene 23020224
    rs4746720 — 1.35 0.022 246 vs 236 — Chinese 60-90 — C Candidate Region/Gene 20633545
    rs3758391 SIRT1 1.45 0.026 246 vs 236 — Chinese 60-90 — C Candidate Region/Gene 20633545
    rs3928306 MT-RNR2 0.49 0.027 98 vs 117 — Chinese 90–100 A — Candidate Region/Gene 18759861
    XbaI APOB 1.30 0.03 191 (case) vs 53 — Chinese > 90 baseline, Mean age 97 +/- 3 — X+X+ Candidate Region/Gene 17393087
    rs2266968 MAPK1 — 0.048 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs1061581 HSPA1B — 0.05 191 vs 53 — Chinese >90 A G Candidate Region/Gene 19840767
    rs708272 CETP — 0.068 523 vs 498 — Chinese Mean age 93.4 — — Candidate Region/Gene 22336474
    rs2053044 ADRB2 — 0.138 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs1042713 ADRB2 — 0.149 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs5749998 MAPK1 — 0.152 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs1892848 MAPK1 — 0.218 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs5999521 MAPK1 — 0.218 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs2853826 MT-ND3 — 0.219 98 vs 117 — Chinese 90–100 A — Candidate Region/Gene 18759861
    rs9861425 ADCY5 — 0.251 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs4482616 ADCY5 — 0.398 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs740746 ADRB1 — 0.556 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs1042714 ADRB2 — 0.558 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs2877709 ADCY5 — 0.767 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    • Page 1 of 2
    • 25 of 31 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