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
    rs1533484 ADARB2 0.81 0.0109 877 vs 1808 Southern Italian, age 90–109 (median 96): 459 vs 429. Ashkenazi Jewish, age 95–112 (median 100): 299 vs 269. Japanese, age 100-116 (median 106): 470 vs 538. Northern European 96–119 (median: 103) AA/AG — — 20011587
    rs2676192 ADARB2 0.81 0.0096 877 vs 1808 Southern Italian, age 90–109 (median 96): 459 vs 429. Ashkenazi Jewish, age 95–112 (median 100): 299 vs 269. Japanese, age 100-116 (median 106): 470 vs 538. Northern European 96–119 (median: 103) AA — — 20011587
    rs2387952 ADARB2 1.37 0.0004 877 vs 1808 Southern Italian, age 90–109 (median 96): 459 vs 429. Ashkenazi Jewish, age 95–112 (median 100): 299 vs 269. Japanese, age 100-116 (median 106): 470 vs 538. Northern European 96–119 (median: 103) AA — — 20011587
    rs17294019 ADARB2 0.69 0.0005 877 vs 1808 Southern Italian, age 90–109 (median 96): 459 vs 429. Ashkenazi Jewish, age 95–112 (median 100): 299 vs 269. Japanese, age 100-116 (median 106): 470 vs 538. Northern European 96–119 (median: 103) AA/AG — — 20011587
    rs3758391 SIRT1 1.45 0.026 246 vs 236 — Chinese 71.2±7.1 years T — Candidate Region/Gene 20633545
    rs4746720 SIRT1 1.35 0.022 246 vs 236 — Chinese 71.2±7.1 years T — Candidate Region/Gene 20633545
    rs12413082 MSRB2 0.53 3.33e-06 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs7915479 CDH23 1.92 2.81e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs1556758 SORCS1 0.50 3.47e-05 410 vs 553 — Italian 90–109 — — Genome-Wide Association Study 21612516
    rs1887922 IDE 2.02 0.0004 829 vs 1033 — Australian, Caucasian Mixed — — Candidate Region/Gene 18448515
    CYP2C19*1 (wt allele) / CYP2C19*2( rs4244285 / rs17878459 ) CYP2C19 1.21 0.2 239 vs 64 — Danish 95 and over — — Candidate Region/Gene 9754988
    rs740746 ADRB1 — 0.556 384 vs 384 579 vs 644 Chinese Mean age 102.1±0.20 — — Candidate Region/Gene 23020224
    rs982764 FAS 1.10 0.000335327 801 vs 914 — Caucasian Median age 104 G A Genome-Wide Association Study 22279548
    rs3758391 SIRT1 1.45 0.026 246 vs 236 — Chinese 60-90 — C Candidate Region/Gene 20633545
    rs11005328 ZWINT 3.16 0.002342056 801 vs 914 — Caucasian Median age 104 A C Genome-Wide Association Study 22279548
    rs12257410 FRMD4A — 3.77e-05 1364 — American, British Age range 50-108; mean age at death 80.2 A C Genome-Wide Association Study 22445811
    rs1516507 KCNMA1 1.36 9.67e-05 403 vs 1670 3746 vs 5912 Dutch Mean age 94 — G Genome-Wide Association Study 21418511
    rs4751140 EBF3 1.21 0.000268865 801 vs 914 — Caucasian Median age 104 A G Genome-Wide Association Study 22279548
    rs4918255 SORCS1 1.27 0.00063068 801 vs 914 — Caucasian Median age 104 A G Genome-Wide Association Study 22279548
    rs10509271 CTNNA3 1.21 0.002944036 801 vs 914 — Caucasian Median age 104 A G Genome-Wide Association Study 22279548

    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

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