Evidence map›Paper›PMID 29779949›Full record

ArticleCell2018

Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Fernanda C G Polubriaginof, Rami Vanguri, Kayla Quinnies, Gillian M Belbin, Alexandre Yahi, Hojjat Salmasian, Tal Lorberbaum, Victor Nwankwo, Li Li, Mark M Shervey and 11 more

Abstract read
In one paragraph

Article in Cell, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
68citing papers in PubMed, 1 pooled it
–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

68 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The CODATwins Project: The Current Status and Recent Findings of COllaborative Project of Development of Anthropometrical Measures in Twins.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2019
    Trial
  3. Trial
  4. Article
  5. Article
  6. Family history of cardiovascular disease and its modifiable risk factors and association with offspring risk profile.International journal of cardiology. Cardiovascular risk and prevention · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Identifying family structures from obituaries and matching them to patients in an electronic heath record.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  13. Article
  14. Review
  15. Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

8 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors.

Fernanda C G PolubriaginofDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Rami VanguriDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Kayla QuinniesDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Institute for Genomic Medicine, Columbia University, New York, NY, USA.
Gillian M BelbinDepartment of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
Alexandre YahiDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Hojjat SalmasianDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Value Institute, NewYork-Presbyterian Hospital, New York, NY, USA.
Tal LorberbaumDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Department of Physiology and Cellular Biophysics, Columbia University, New York, NY, USA.
Victor NwankwoDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
Li LiDepartment of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
Mark M SherveyDepartment of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
Patricia GloweDepartment of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
Iuliana Ionita-LazaMailman School of Public Health, Columbia University, New York, NY, USA; Department of Biostatistics, Columbia University, New York, NY, USA.
Mary SimmerlingDepartment of Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA; Quality and Patient Safety, NewYork-Presbyterian Hospital, New York, NY, USA.
George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Medical Informatics Services, NewYork-Presbyterian Hospital, New York, NY; Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA.
Suzanne BakkenDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; School of Nursing, Columbia University, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA.
David GoldsteinInstitute for Genomic Medicine, Columbia University, New York, NY, USA.
Krzysztof KirylukDepartment of Medicine, Columbia University, New York, NY, USA.
Eimear E KennyDepartment of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
Joel DudleyDepartment of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
David K VawdreyDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Value Institute, NewYork-Presbyterian Hospital, New York, NY, USA.
Nicholas P TatonettiDepartment of Biomedical Informatics, Columbia University, New York, NY, USA; Institute for Genomic Medicine, Columbia University, New York, NY, USA; Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA; Department of Medicine, Columbia University, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA. Electronic address: nick.tatonetti@columbia.edu.

Funding

OMOP information model for eMERGE phenotypingU01HG008680 · NHGRI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Wendy K Chung, GEORGE M HRIPCSAK · 2015 to 2026
$13.3M
DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
Genetics of IgA nephropathy by integrative network-based association studiesR01DK105124 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Krzysztof Kiryluk · 2015 to 2026
$4.8M
Novel Statistical methods for DNA Sequencing Data, and applications to Autism.R01MH095797 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA · 2012 to 2022
$3.1M
Biomedical Data Translator Technical Feasibility Assessment and Architecture DesignOT3TR002027 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI DUMONTIER, MICHEL, TATONETTI, NICHOLAS P · 2016 to 2019
$2.7M
Drug Effect Discovery Through Data Mining and Integrative Chemical BiologyR01GM107145 · NIGMS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI TATONETTI, NICHOLAS P · 2014 to 2018
$2.5M
Addressing Hospital Patient Information Needs Using a PHR PortalR01HS021816 · AHRQ · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI VAWDREY, DAVID K. · 2013 to 2016
$2.0M
Integrative methods for the identification of causal variants in mental disorderR01MH106910 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA, XU, BIN · 2016 to 2018
$1.2M
WICER 4UR01HS022961 · AHRQ · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE · 2013 to 2013
$750k
AHRQ HHS R01 HS021816AHRQ HHS R01 HS022961NCATS NIH HHS OT3 TR002027NHGRI NIH HHS U01 HG008680NIDDK NIH HHS R01 DK105124NIGMS NIH HHS R01 GM107145NIMH NIH HHS R01 MH095797NIMH NIH HHS R01 MH106910NLM NIH HHS R01 LM006910
6 · The paper itself

Abstract

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Indexed as

Electronic Health RecordsAlgorithmsDatabases, FactualFamily RelationsGenetic Diseases, InbornGenotypeHumansPedigreePhenotypeQuantitative Trait, Heritabledata miningdisease heritabilityelectronic health recordfamilial relationshipsfamily historygeneticsobservational databases

Identifiers

PMID29779949
PMCPMC6015747

What OpenQuestion holds

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Read underepoch 390

Registered trials

None linked

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.