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 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
    rs2984121 FOXO1 1.05 0.178 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 Candidate Region/Gene 21388494
    rs2721044 FOXO1 0.90 0.097 1447 vs 1029 (German) 166 vs 216 (Italian) German Candidate Region/Gene 21388494
    rs4943794 FOXO1 0.93 0.313 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 Candidate Region/Gene 21388494
    rs1986649 FOXO1 0.92 0.229 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 Candidate Region/Gene 21388494
    rs17630266 FOXO1 1.11 0.51 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 Candidate Region/Gene 21388494
    rs12876443 FOXO1 1.00 0.989 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 Candidate Region/Gene 21388494
    rs7981045 FOXO1 1.06 0.349 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 Candidate Region/Gene 21388494
    rs9603776 FOXO1 1.05 0.778 1447 vs 1029 (German) 166 vs 216 (Italian) German Mean age 98.8 Candidate Region/Gene 21388494
    rs9315385 DCAMKL1 1.74 8.13e-05 410 vs 553 Italian 90–109 Genome-Wide Association Study 21612516
    rs285097 RNF113B 0.47 7.76e-05 410 vs 553 Italian 90–109 Genome-Wide Association Study 21612516
    rs1805097 IRS2 2.03 0.0003 144 vs 418 Italian 85-100 years old non-AA AA Candidate Region/Gene 19887537
    rs1805097 IRS2 2.07 0.04 144 vs 418 Caucasian 85 to 104, mean age = 96 ± 4 C Candidate Region/Gene 19887537
    rs1207362 KL 0.63 0.0001 1089 vs 736 1613 vs 1104 Danish 92-93 years old A C Candidate Region/Gene 22406557
    rs1207362 KL 0.63 0.0001 1089 vs 736 1613 vs 1104 Danish 92.2–93.8 (mean age 93.2 A Candidate Region/Gene 22406557
    rs1334241 FOXO1A 1.01 0.87 297 vs 603;279 vs 797;383 vs 363 Caucasian 95.3 ± 2.2_x000D_;+M19994.5 ± 2.1_x000D_;mean 97.7, 95– 112 Candidate Region/Gene 19489743
    rs17630266 FOXO1 1.0 761 vs 1056 350 vs 350 Chinese mean age 102.3 Candidate Region/Gene 19793722
    rs2755209 FOXO1 0.80 0.0046 761 vs 1056 350 vs 350 Chinese mean age 102.3 C T Candidate Region/Gene 19793722
    rs2755213 FOXO1 0.75 7.4e-05 761 vs 1056 350 vs 350 Chinese mean age 102.3 C T Candidate Region/Gene 19793722
    KL-VS KLOTHO 5.88 0.004 216 vs 309 Ashkenazi Jewish 95 years and over homozygous Candidate Region/Gene 15677572
    rs9536314 KL 0.007 216 vs 309 Ashkenazi Jewish >95 TG (genotype) Candidate Region/Gene 15677572
    rs2755209 FOXO1 0.48 213 vs 402 Japanese minimum 95; mean 97.9 Candidate Region/Gene 18765803
    rs2721069 FOXO1 0.62 213 vs 402 Japanese minimum 95; mean 97.9 Candidate Region/Gene 18765803
    rs2755213 FOXO1 0.77 213 vs 402 Japanese minimum 95; mean 97.9 Candidate Region/Gene 18765803
    rs1054016 TNFSF11 1.09 0.579 137 vs 213 Korean Mean age 90 T Genome-Wide Association Study 19641380
    rs9517320 STK24 0.00204 1173 vs 570 American 85-100 C Genome-Wide Association Study 22533364

    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