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 (1027)   No Age Effect (543)  
  • Study type: + -
    GWAS (2)   ‑ (27)   Candidate Region/Gene (784)   Meta‑Analysis (38)   Complete Mitochondrial Genome (2)   Whole‑Genome Linkage Scan (1)   Genome‑Wide Association Study (710)   Mitochondrial Haplogroup (36)   Mitochondrial genome (3)  
  • Variant type: + -
    SNP (572)   Locus (5)   Haplotype (87)   Other (1)   STR (11)   Deletion (5)   CNV (1)    (1)   VNTR (1)  
    • 1 2 3 4 5 6 7 8 9 10 11 12
    13 14 15 16 17 18 19 20 21 X Y MT
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    Populations | Study Types | Variant Types


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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
    –11391 G/A ADIPOQ10 8.48 0.026 148 vs 414 — Polish Mean age 101.1 +/- 1.2 G A Candidate Region/Gene 23339001
    V465A GABRR3 — 0.04 390 vs 410 — Ashkenazi Jewish Centenarians — — Candidate Region/Gene 23376243
    rs9868286 CCDC50 1.48 7.4e-06 403 vs 1670 3746 vs 5912 Dutch Mean age 94 — C Genome-Wide Association Study 21418511
    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
    rs964403 SUMF1 2.07 4.91e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs712773 GRM7 0.66 9.99e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs6801173 EIF4E3 1.52 8.16e-06 801 vs 914 — Caucasian Median age 104 A G Genome-Wide Association Study 22279548
    rs6443429 TBL1XR1 1.13 0.000251569 801 vs 914 — Caucasian Median age 104 A C Genome-Wide Association Study 22279548
    rs572169 GHSR 0.72 0.0045 1089 vs 736 1613 vs 1104 Danish 92-93 years old A G Candidate Region/Gene 22406557
    rs572169 GHSR 0.72 0.0045 1089 vs 736 1613 vs 1104 Danish 92.2–93.8 (mean age 93.2 A — Candidate Region/Gene 22406557
    rs571391 IMPG2 0.55 2.67e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs5189 AGTR1 0.75 0.945 173 vs 376 — Italian 99-106 (mean 100.9±1.7) — — Candidate Region/Gene 22569962
    rs5186 AGTR1 1.15 0.875 173 vs 376 — Italian 99-106 (mean 100.9±1.7) — — Candidate Region/Gene 22569962
    rs5183 AGTR1 0.27 1.0 173 vs 376 — Italian 99-106 (mean 100.9±1.7) — — Candidate Region/Gene 22569962
    rs5182 AGTR1 0.97 1.0 173 vs 376 — Italian 99-106 (mean 100.9±1.7) — — Candidate Region/Gene 22569962
    rs513154 IMPG2 0.52 4.65e-06 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs4681554 C3orf16 0.64 9.82e-05 403 vs 1670 3746 vs 5912 Dutch Mean age 94 A — Genome-Wide Association Study 21418511
    rs4677882 ADCY5 — 0.999 384 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
    rs422858 AGTR1 3.57 0.007 173 vs 376 — Italian 99-106 (mean 100.9±1.7) AG CC Candidate Region/Gene 22569962
    rs3864051 SUMF1 2.07 5.49e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs361072 PIK3CB — 0.829 122 vs 122 — Japanese mean age 106.8 — — Candidate Region/Gene 15582274
    rs2877709 ADCY5 — 0.767 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs275653 AGTR1 3.57 0.007 173 vs 376 598 vs 422 Italian 99-106 (mean 100.9±1.7) A G Candidate Region/Gene 22569962
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    • 25 of 53 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