Evidence map›Paper›PMID 36114182›Full record

ArticleNature communications2022

Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks.

Rounak Dey, Wei Zhou, Tuomo Kiiskinen, Aki Havulinna, Amanda Elliott, Juha Karjalainen, Mitja Kurki, Ashley Qin, FinnGen, Seunggeun Lee and 4 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
6.7field-weighted citation impact, top 3% of its field
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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 43 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Scalable and accurate rare-variant association tests for whole genome sequencing time-to-event analysis in large biobanks.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  12. SPANature communications · 2025
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  15. Fast and scalable ensemble learning method for versatile polygenic risk prediction.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  16. Multi-organ imaging-derived polygenic indexes for brain and body health.medRxiv : the preprint server for health sciences · 2024
    Article
  17. Article
  18. Distinct explanations underlie gene-environment interactions in the UK Biobank.medRxiv : the preprint server for health sciences · 2024
    Article
  19. Article
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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

14 authors at 4 institutions in 3 countries.

Rounak Dey *Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.ORCID 0000-0002-6540-8280
Wei Zhou *Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.ORCID 0000-0001-7719-0859
Tuomo KiiskinenInstitute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland.ORCID 0000-0002-6306-8227
Aki HavulinnaInstitute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, Finland.
Amanda ElliottDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
Juha KarjalainenAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
Mitja KurkiAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
Ashley QinDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
FinnGen
Seunggeun LeeGraduate School of Data Science, Seoul National University, Seoul, Korea.ORCID 0000-0002-8097-3878
Aarno PalotieAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.ORCID 0000-0002-2527-5874
Benjamin NealeAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.ORCID 0000-0003-1513-6077
Mark DalyAnalytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
Xihong LinDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. xlin@hsph.harvard.edu.ORCID 0000-0001-7067-7752
Broad Institute · USHarvard University · USUniversity of Helsinki · FISeoul National University · KR

Funding

Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Christopher I. Amos, Rayjean J. Hung · 2017 to 2026
$23.7M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Statistical methods to localize disease heritability and identify biological mechanismsR37MH107649 · NIMH · BROAD INSTITUTE, INC. · PI Benjamin Michael Neale · 2019 to 2026
$7.0M
Statistical Methods for the Spatio-Temporal Assessment of Social Disparities in CancerP01CA134294 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI HANEUSE, SEBASTIEN, HERNAN, MIGUEL · 2008 to 2017
$6.8M
Powering whole genome sequence-based genetic discovery for common human diseases- Extended 2021-2022.U01HG009088 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG, NEALE, BENJAMIN MICHAEL · 2016 to 2021
$5.1M
Leveraging Family Data to Identify Genetic Variants for Sleep ApneaR01HL113338 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI LIN, XIHONG, REDLINE, SUSAN S. · 2012 to 2016
$4.4M
Partners Healthcare Training Program in Precision and Genomic MedicineT32HG010464 · NHGRI · MASSACHUSETTS GENERAL HOSPITAL · PI HEIDI L REHM, JORDAN W SMOLLER · 2019 to 2026
$3.1M
Statistical Methods for Integrative Analysis of Large-Scale Whole Genome Sequencing Studies and Biobanks of Common DiseasesR01HL163560 · NHLBI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2022 to 2026
$2.6M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NCI NIH HHS P01 CA134294NCI NIH HHS R35 CA197449NCI NIH HHS U19 CA203654NHGRI NIH HHS T32 HG010464NHGRI NIH HHS U01 HG009088NHLBI NIH HHS R01 HL113338NHLBI NIH HHS R01 HL163560NIMH NIH HHS R37 MH107649
6 · The paper itself

Abstract

With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser.

Indexed as

Biological Specimen BanksFrailtyGenome-Wide Association StudyHumansPhenomicsPhenotype

Identifiers

PMID36114182
PMCPMC9481565
OpenAlexW4296161411

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.