TY - JOUR
T1 - How Well Does the Family Longevity Selection Score Work
T2 - A Validation Test Using the Utah Population Database
AU - Arbeeva, Liubov S.
AU - Hanson, Heidi A.
AU - Arbeev, Konstantin G.
AU - Kulminski, Alexander M.
AU - Stallard, Eric
AU - Ukraintseva, Svetlana V.
AU - Wu, Deqing
AU - Boudreau, Robert M.
AU - Province, Michael A.
AU - Smith, Ken R.
AU - Yashin, Anatoliy I.
N1 - Funding Information:
We thank the Pedigree and Population Resource of the Huntsman Cancer Institute, University of Utah (funded in part by the Huntsman Cancer Foundation) for its role in the ongoing collection, maintenance and support of the Utah Population Database (UPDB). We also acknowledge partial support for the UPDB through grant P30 CA2014 from the National Cancer Institute, University of Utah and from the University of Utah's Program in Personalized Health and Center for Clinical and Translational Science. Funding. This work was supported by the National Institute on Aging at the National Institutes of Health (grant numbers U01 AG023712, P01 AG043352, R01 AG022095). The Long Life Family Study is funded by U01AG023749, U01AG023744, and U01AG023712 from the National Institute on Aging. Work of HH was supported by the grant 5K12HD085852 Building Interdisciplinary Research Careers in Women's Health.
Funding Information:
This work was supported by the National Institute on Aging at the National Institutes of Health (grant numbers U01 AG023712, P01 AG043352, R01 AG022095). The Long Life Family Study is funded by U01AG023749, U01AG023744, and U01AG023712 from the National Institute on Aging. Work of HH was supported by the grant 5K12HD085852 Building Interdisciplinary Research Careers in Women’s Health.
Funding Information:
We thank the Pedigree and Population Resource of the Huntsman Cancer Institute, University of Utah (funded in part by the Huntsman Cancer Foundation) for its role in the ongoing collection, maintenance and support of the Utah Population Database (UPDB). We also acknowledge partial support for the UPDB through grant P30 CA2014 from the National Cancer Institute, University of Utah and from the University of Utah’s Program in Personalized Health and Center for Clinical and Translational Science.
Publisher Copyright:
© Copyright © 2018 Arbeeva, Hanson, Arbeev, Kulminski, Stallard, Ukraintseva, Wu, Boudreau, Province, Smith and Yashin.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - The Family Longevity Selection Score (FLoSS) was used to select families for the Long Life Family Study (LLFS) but has never been validated in other populations. The goal of this paper is to validate how well the FLoSS-based selection procedure works in an independent dataset. In this paper, we computed FLoSS using the lifespan data of 234,155 individuals from a large comprehensive genealogically-based resource, the Utah Population Database (UPDB), born between 1779 and 1910 with mortality follow-up through 2012–2013. Computations of FLoSS in a specific year (1980) confirmed the survival advantage of the “exceptional” sibships (defined by LLFS FLoSS threshold, FLoSS ≥ 7). We found that the subsample of the UPDB participants born after 1900 who were from the “exceptional” sibships had survival curves similar to that of the US participants from the LLFS probands' generation. Comparisons between the offspring of parents with “exceptional” and “ordinary” survival showed the survival advantage of the “exceptional” offspring. Investigators seeking to explain the extent genetics and environment contribute to exceptional survival will benefit from the use of exceptionally long-lived individuals and their relatives. Appropriate ranking of families by survival exceptionality and their availability for the purposes of providing genetic and phenotypic data is critical for selecting participants into such studies. This study validated the FLoSS as selection criteria in family longevity studies using UPDB.
AB - The Family Longevity Selection Score (FLoSS) was used to select families for the Long Life Family Study (LLFS) but has never been validated in other populations. The goal of this paper is to validate how well the FLoSS-based selection procedure works in an independent dataset. In this paper, we computed FLoSS using the lifespan data of 234,155 individuals from a large comprehensive genealogically-based resource, the Utah Population Database (UPDB), born between 1779 and 1910 with mortality follow-up through 2012–2013. Computations of FLoSS in a specific year (1980) confirmed the survival advantage of the “exceptional” sibships (defined by LLFS FLoSS threshold, FLoSS ≥ 7). We found that the subsample of the UPDB participants born after 1900 who were from the “exceptional” sibships had survival curves similar to that of the US participants from the LLFS probands' generation. Comparisons between the offspring of parents with “exceptional” and “ordinary” survival showed the survival advantage of the “exceptional” offspring. Investigators seeking to explain the extent genetics and environment contribute to exceptional survival will benefit from the use of exceptionally long-lived individuals and their relatives. Appropriate ranking of families by survival exceptionality and their availability for the purposes of providing genetic and phenotypic data is critical for selecting participants into such studies. This study validated the FLoSS as selection criteria in family longevity studies using UPDB.
KW - Long Life Family Study
KW - Utah population database
KW - exceptional survival
KW - familial longevity
KW - family longevity selection score
UR - http://www.scopus.com/inward/record.url?scp=85075615880&partnerID=8YFLogxK
U2 - 10.3389/fpubh.2018.00277
DO - 10.3389/fpubh.2018.00277
M3 - Article
AN - SCOPUS:85075615880
SN - 2296-2565
VL - 6
JO - Frontiers in Public Health
JF - Frontiers in Public Health
M1 - 277
ER -