Evidence map›Paper›PMID 36522764›Full record

ArticleGenome medicine2022

Calculating variant penetrance from family history of disease and average family size in population-scale data.

Thomas P Spargo, Sarah Opie-Martin, Harry Bowles, Cathryn M Lewis, Alfredo Iacoangeli, Ammar Al-Chalabi

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
5.6field-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.

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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Thomas P SpargoDepartment of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, SE5 9RX, UK.ORCID 0000-0003-4297-6418
Sarah Opie-MartinDepartment of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, SE5 9RX, UK.ORCID 0000-0003-0951-8589
Harry BowlesDepartment of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, SE5 9RX, UK.ORCID 0000-0002-4072-5255
Cathryn M LewisSocial, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, de Crespigny Park, London, SE5 8AF, UK.ORCID 0000-0002-8249-8476
Alfredo IacoangeliDepartment of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, SE5 9RX, UK. alfredo.iacoangeli@kcl.ac.uk.ORCID 0000-0002-5280-5017
Ammar Al-ChalabiDepartment of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, SE5 9RX, UK. ammar.al-chalabi@kcl.ac.uk.ORCID 0000-0002-4924-7712
King's College London · GB

Funding

Department of HealthMedical Research Council MR/L501529/1Medical Research Council MR/R024804/1Motor Neurone Disease Association ALCHALABI-DOBSON/APR14/829-791Motor Neurone Disease Association ALCHALABI-TALBOT/APR14/926-794
6 · The paper itself

Abstract

backgroundGenetic penetrance is the probability of a phenotype when harbouring a particular pathogenic variant. Accurate penetrance estimates are important across biomedical fields including genetic counselling, disease research, and gene therapy. However, existing approaches for penetrance estimation require, for instance, large family pedigrees or availability of large databases of people affected and not affected by a disease.

methodsWe present a method for penetrance estimation in autosomal dominant phenotypes. It examines the distribution of a variant among people affected (cases) and unaffected (controls) by a phenotype within population-scale data and can be operated using cases only by considering family disease history. It is validated through simulation studies and candidate variant-disease case studies.

resultsOur method yields penetrance estimates which align with those obtained via existing approaches in the Parkinson's disease LRRK2 gene and pulmonary arterial hypertension BMPR2 gene case studies. In the amyotrophic lateral sclerosis case studies, examining penetrance for variants in the SOD1 and C9orf72 genes, we make novel penetrance estimates which correspond closely to understanding of the disease.

conclusionsThe present approach broadens the spectrum of traits for which reliable penetrance estimates can be obtained. It has substantial utility for facilitating the characterisation of disease risks associated with rare variants with an autosomal dominant inheritance pattern. The yielded estimates avoid any kinship-specific effects and can circumvent ascertainment biases common when sampling rare variants among control populations.

Indexed as

Amyotrophic Lateral SclerosisFamily CharacteristicsHumansPedigreePenetrancePhenotypeAutosomal dominantDiseaseFamilialPenetranceSporadicVariant

Identifiers

PMID36522764
PMCPMC9753373
OpenAlexW4311611454

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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.