Evidence map›Paper›PMID 33413596›Full record

ArticleGenome medicine2021

Family history assessment significantly enhances delivery of precision medicine in the genomics era.

Yasmin Bylstra, Weng Khong Lim, Sylvia Kam, Koei Wan Tham, R Ryanne Wu, Jing Xian Teo, Sonia Davila, Jyn Ling Kuan, Sock Hoai Chan, Nicolas Bertin and 9 more

Erratum issued 2 registry-linked trialsOpen access · goldAbstract read
In one paragraph

Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to 2 registered trials, which are not on this map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
5.4field-weighted citation impact, top 4% 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.

NCT02791152 recruitingnot on this map

SingHEART: Effects of Physical Activity, Ambulatory Blood Pressure and Calcium Score on Cardiovascular Health in Normal People

TypeobservationalSponsorNational Heart Centre SingaporeRan2015 to 2036Enrolled1,000ConditionsMyocardial Infarction
NCT03114553 completednot on this map

Integration of Family Health History Data Into a Singaporean Healthy Population Cohort Database to Understand the Associations Between Family Health History and Genomics

TypeobservationalSponsorDuke UniversityRan2017 to 2024Enrolled1,000ConditionsAllArmsMeTree
3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.

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  19. Benefits, harms, and costs of newborn genetic screening for hypertrophic cardiomyopathy: Estimates from the PreEMPT model.Genetics in medicine : official journal of the American College of Medical Genetics · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors at 6 institutions in 2 countries.

Yasmin BylstraSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Weng Khong LimSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Sylvia KamSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Koei Wan ThamSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
R Ryanne WuCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Jing Xian TeoSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Sonia DavilaSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Jyn Ling KuanSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Sock Hoai ChanCancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore.
Nicolas BertinCentre for Big Data and Integrative Genomics, Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore.
Cheng Xi YangNational Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore.
Steve RozenSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Bin Tean TehSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Khung Keong YeoCardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, Singapore.
Stuart Alexander CookSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Saumya Shekhar JamuarSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.
Geoffrey S GinsburgCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
Lori A OrlandoCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA. orlan002@duke.edu.
Patrick TanSingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore. gmstanp@duke-nus.edu.sg.ORCID 0000-0002-0179-8048
SingHealth · SGDuke University · USAgency for Science, Technology and Research · SGDuke-NUS Medical School · SGNational Cancer Centre Singapore · SGNational Heart Centre Singapore · SG

Funding

Women's Cancer Research ProgramP30CA014236 · NCI · DUKE UNIVERSITY · PI JOHN N. NORTON · 1985 to 2026
$174.8M
NCI NIH HHS P30 CA014236
6 · The paper itself

Abstract

backgroundFamily history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection.

methodsTo ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes.

resultsAmongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant.

conclusionsThese findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs.

trial registrationClinicalTrials.gov NCT02791152 . Retrospectively registered on May 31, 2016.

Indexed as

Delivery of Health CareGenomicsMedical History TakingPrecision MedicineAdolescentAdultAgedAged, 80 and overCohort StudiesFemaleGenome, HumanHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedRisk FactorsCancerClinically actionable variantsFamily historyPopulation genomics screening

Identifiers

PMID33413596
PMCPMC7791763
OpenAlexW3119812814

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.