Evidence map›Paper›PMID 40778169›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Utility of Polygenic Risk Scores in Families with Exceptional Longevity.

Laura Xicota, Rong Cheng, Sandra Barral, Lawrence S Honig, Nicole Schupf, Yian Gu, Stacy Andersen, Stephanie Cosentino, Joseph Zmuda, Thomas Perls and 2 more

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Laura XicotaDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.ORCID 0000-0003-2832-9214
Rong ChengDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.
Sandra BarralDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.
Lawrence S HonigDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.ORCID 0000-0002-9703-2265
Nicole SchupfDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.
Yian GuDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.
Stacy AndersenSection of Geriatrics, Department of Medicine, Boston University School of Medicine, 72 East Concord St., Boston, MA 02118, USA.
Stephanie CosentinoDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.
Joseph ZmudaDepartment of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15261 USA.
Thomas PerlsSection of Geriatrics, Department of Medicine, Boston University School of Medicine, 72 East Concord St., Boston, MA 02118, USA.
Michael ProvinceDepartment of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15261 USA.
Joseph H LeeDepartment of Neurology, Columbia University Irving Medical Center, 630W 168th street, New York City, New York, 10032, USA.

Funding

The Long Life Family StudyU19AG063893 · NIA · WASHINGTON UNIVERSITY · PI Stacy Andersen, KAARE CHRISTENSEN · 2019 to 2026
$125.4M
Research Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI PHILIP L DE JAGER · 2020 to 2026
$30.1M
Long Life Family Study: Data Management and Coordinating CenterU01AG023746 · NIA · WASHINGTON UNIVERSITY · PI PROVINCE, MICHAEL A. · 2004 to 2018
$29.2M
The Long Life Family StudyU01AG023755 · NIA · BOSTON MEDICAL CENTER · PI PERLS, THOMAS T · 2004 to 2018
$13.2M
Plasma beta-amyloid in long lived families: genetics and risk of ADU01AG023749 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI COSENTINO, STEPHANIE ANN, LEE, JOSEPH HYUNGWOO · 2004 to 2018
$13.0M
Multicenter Study on Exceptional Survival in Families: The Long Life Family StudyU01AG023744 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI NEWMAN, ANNE B., ZMUDA, JOSEPH M. · 2004 to 2018
$12.0M
Long Life Family Study: Duke Univ./Univ. of Southern Denmark Field CenterU01AG023712 · NIA · DUKE UNIVERSITY · PI CHRISTENSEN, KAARE · 2004 to 2018
$10.4M
Protective genetic factors of Alzheimer disease in PSEN1 Mutation Carriers in Puerto RicoR01AG058918 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LEE, JOSEPH HYUNGWOO · 2018 to 2022
$4.3M
NIA NIH HHS P30 AG066462NIA NIH HHS R01 AG058918NIA NIH HHS U01 AG023712NIA NIH HHS U01 AG023744NIA NIH HHS U01 AG023746NIA NIH HHS U01 AG023749NIA NIH HHS U01 AG023755NIA NIH HHS U19 AG063893
6 · The paper itself

Abstract

Introduction: Polygenic risk scores (PRS) have been used to assess an individual's risk for various diseases, including Alzheimer's disease (AD). This study applied the PRS approach to a cohort of families ascertained for healthy aging, that have shown a reduced risk of AD. Using the SNPs identified as significantly associated with AD in the study by Kunkle and colleagues, we examined the utility of PRS for predicting AD risk in a cohort ascertained for familial healthy aging. Methods: We restricted the study to US LLFS study participants who have been evaluated for AD and have available whole genome sequencing (WGS) data. AD diagnosis was based on consensus diagnosis, and for those without consensus diagnosis, we used the algorithm based on standardized memory scores. PRS were calculated using a published weighted formula. To further examine the predictability of PRS, we assessed the relationship between PRS and AD biomarkers, including Aβ Results: We observed that PRS had limited predictive power for AD in this healthy aging cohort. Yet, allele frequencies for the SNPs used in PRS estimation differed between the two studies in a small number (9.7%) of SNPs, suggesting that the lack of effect of the PRS is likely to be due to the small number of AD associated SNPs (12.3% and 16.1%). Subsequent analysis observed no significant association between PRS and biomarkers. This was explained by the low number of SNPs significantly associated with each of the biomarkers. Conclusions: This study highlights the importance of ascertainment of study population in interpreting PRS. In LLFS, a population at reduced risk of AD, PRS based on genetic variants identified from the general population may be inadequate to explain the variability in AD risk. Our results suggest that genetic risk variants, the basis of PRS, may need to be adjusted according to the study population of interest.

Identifiers

PMID40778169
PMCPMC12330441

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.