Evidence map›Paper›PMID 29727688›Full record

ArticleAmerican journal of human genetics2018

Profiling and Leveraging Relatedness in a Precision Medicine Cohort of 92,455 Exomes.

Jeffrey Staples, Evan K Maxwell, Nehal Gosalia, Claudia Gonzaga-Jauregui, Christopher Snyder, Alicia Hawes, John Penn, Ricardo Ulloa, Xiaodong Bai, Alexander E Lopez and 17 more

Open access · greenAbstract read
In one paragraph

Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
7.6field-weighted citation impact, top 2% 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

48 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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  11. Rare missense variants inJournal of medical genetics · 2025
    Article
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  13. Article
  14. A rare variant inProceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  15. Article
  16. Article
  17. CORRECTING MODEL MISSPECIFICATION IN RELATIONSHIP ESTIMATES.bioRxiv : the preprint server for biology · 2024
    Article
  18. Article
  19. Article
  20. Article
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

27 authors at 3 institutions in 1 country.

Jeffrey StaplesRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Evan K MaxwellRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Nehal GosaliaRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Claudia Gonzaga-JaureguiRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Christopher SnyderRochester Institute of Technology, Rochester, NY 14623, USA.
Alicia HawesRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
John PennRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Ricardo UlloaRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Xiaodong BaiRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Alexander E LopezRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Cristopher V Van HoutRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Colm O'DushlaineRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Tanya M TeslovichRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Shane E McCarthyRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Suganthi BalasubramanianRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
H Lester KirchnerGeisinger Health System, Danville, PA 17822, USA.
Joseph B LeaderGeisinger Health System, Danville, PA 17822, USA.
Michael F MurrayGeisinger Health System, Danville, PA 17822, USA.
David H LedbetterGeisinger Health System, Danville, PA 17822, USA.
Alan R ShuldinerRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
George D YancoupolosRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Frederick E DeweyRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
David J CareyGeisinger Health System, Danville, PA 17822, USA.
John D OvertonRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Aris BarasRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Lukas HabeggerRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA.
Jeffrey G ReidRegeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA. Electronic address: jeffrey.reid@regeneron.com.
Regeneron (United States) · USGeisinger Health System · USRochester Institute of Technology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large-scale human genetics studies are ascertaining increasing proportions of populations as they continue growing in both number and scale. As a result, the amount of cryptic relatedness within these study cohorts is growing rapidly and has significant implications on downstream analyses. We demonstrate this growth empirically among the first 92,455 exomes from the DiscovEHR cohort and, via a custom simulation framework we developed called SimProgeny, show that these measures are in line with expectations given the underlying population and ascertainment approach. For example, within DiscovEHR we identified ∼66,000 close (first- and second-degree) relationships, involving 55.6% of study participants. Our simulation results project that >70% of the cohort will be involved in these close relationships, given that DiscovEHR scales to 250,000 recruited individuals. We reconstructed 12,574 pedigrees by using these relationships (including 2,192 nuclear families) and leveraged them for multiple applications. The pedigrees substantially improved the phasing accuracy of 20,947 rare, deleterious compound heterozygous mutations. Reconstructed nuclear families were critical for identifying 3,415 de novo mutations in ∼1,783 genes. Finally, we demonstrate the segregation of known and suspected disease-causing mutations, including a tandem duplication that occurs in LDLR and causes familial hypercholesterolemia, through reconstructed pedigrees. In summary, this work highlights the prevalence of cryptic relatedness expected among large healthcare population-genomic studies and demonstrates several analyses that are uniquely enabled by large amounts of cryptic relatedness.

Indexed as

Precision MedicineCohort StudiesComputer SimulationElectronic Health RecordsExomeExonsFamilyFemaleGenetics, PopulationGeographyHeterozygoteHumansMaleMutationPedigreePhenotypecompound heterozygous mutation phasingcryptic relatednessde novo mutationsexome sequencingfamilial hypercholesterolemiafamily structurehealthcare population-based genetic studyidentity by decentpedigree reconstructionprecision medicinerelationship inference

Identifiers

PMID29727688
PMCPMC5986700
OpenAlexW2761309312

What OpenQuestion holds

Textmetadata
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.