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: + -
  • Variant type: + -

  • 1 2 3 4 5 6 7 8 9 10 11 12
    13 14 15 16 17 18 19 20 21 X Y MT
    Export results (Download)

    Populations | Study Types | Variant Types



    LVDB_word_cloud.png
    logo.png
    variants.jpg
    polymorphism factor odds ratio pvalue initial number replication number Population age of cases shorter lived allele longer lived allele study type reference
    APOE e2,e3,e4 APOE 0.05 185 vs 486 Finn Over 100 E4 e2 Candidate Region/Gene 8018664
    many SNPs TH 0.05 1321 vs 1140 Italian Mean age 101.9 (southern Italian), 101.0 (northern-central Italian), 104.41 (German), 103.1 (French) Candidate Region/Gene 19367319
    many SNPs IGF2 0.05 1321 vs 1140 Italian Mean age 101.9 (southern Italian), 101.0 (northern-central Italian), 104.41 (German), 103.1 (French) Candidate Region/Gene 19367319
    many SNPs INS 0.05 1321 vs 1140 Italian Mean age 101.9 (southern Italian), 101.0 (northern-central Italian), 104.41 (German), 103.1 (French) Candidate Region/Gene 19367319
    many SNPs HRAS1 0.05 1321 vs 1140 Italian Mean age 101.9 (southern Italian), 101.0 (northern-central Italian), 104.41 (German), 103.1 (French) Candidate Region/Gene 19367319
    indel ACE1 0.05 82 vs 252 Italian Mean age 100 +/- 2 Candidate Region/Gene 12954489
    Not specified IL2 0.05 470 vs 120 Italian, Bulgarian Age range: 65-99 Candidate Region/Gene 21299522
    Not specified IFNG 0.05 707 vs 339 Turkish, Polish, Italian, Bulgarian Age range: 65-99 Candidate Region/Gene 21299522
    Arg156Arg ERCC2 0.05 149 vs 413 Polish Mean age 101.1 Candidate Region/Gene 19707883
    Asp312Asn ERCC2 0.05 149 vs 413 Polish Mean age 101.1 Candidate Region/Gene 19707883
    rs147610191 APOA4 0.05 229 vs 571 Calabrian 81–109 (median 101) Candidate Region/Gene 12556235
    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
    rs2764264 FOXO3 0.05 1089 vs 736 Danish 92–93 T Candidate Region/Gene 20849522
    rs7762395 FOXO3 0.05 1089 vs 736 Danish 92–93 G Candidate Region/Gene 20849522
    rs13217795 FOXO3 0.05 1089 vs 736 Danish 92–93 T Candidate Region/Gene 20849522
    rs9400239 FOXO3 0.05 1089 vs 736 Danish 92–93 C Candidate Region/Gene 20849522
    rs9398172 FOXO3 0.05 1089 vs 736 Danish 92–93 A Candidate Region/Gene 20849522
    rs479744 FOXO3 0.05 1089 vs 736 Danish 92–93 (can't figure out using ensemble and NCBI) Candidate Region/Gene 20849522
    APOC3-SstI-RFLP (3'UTR, 3238 nt) APOC3 0.05 229 vs 571 Calabrian 81–109 (median 101) Candidate Region/Gene 12556235
    rs1801274 FCGR2A 0.05 408 vs 446 vs 454 German 100–110/ mean101.3 (SNP is His/Arg) Candidate Region/Gene 16893392
    HP1/2 common polymorphism HP 0.05 200 (cases) vs 539 (initial) (initial) Italian >88 Candidate Region/Gene 21703254
    -351A/G ESR1 0.05 148 vs 414 Polish 99.6-107.2; mean= 101.1±1.2 Candidate Region/Gene 20599431
    mt5178A ND2 0.05 95 vs 105 Chinese Mean age 76 АА Candidate Region/Gene 12384792
    rs11279109 - SI000565Q APOB 0.05 191 (case) vs 53 Chinese > 90 baseline, Mean age 97 +/- 3 D - S D - M Candidate Region/Gene 17393087

    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