Evidence map›Paper›PMID 39749474›Full record

ArticleGenetic epidemiology2025

Using Family History Data to Improve the Power of Association Studies: Application to Cancer in UK Biobank.

Naomi Wilcox, Jonathan P Tyrer, Joe Dennis, Xin Yang, John R B Perry, Eugene J Gardner, Douglas F Easton

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In one paragraph

Article in Genetic epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

1 citing paper in PubMed.

  1. 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

7 authors.

Naomi WilcoxCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0002-7709-7029
Jonathan P TyrerCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Joe DennisCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID http://orcid.org/0000-0003-4591-1214
Xin YangCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
John R B PerryMetabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
Eugene J GardnerMRC Epidemiology Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
Douglas F EastonCentre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.

Funding

N.W. was supported by the International Alliance for Cancer Early Detection, an alliance between Cancer Research UK (C14478/A29329), Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester. J.D. was supported by core funding from the NIHR Cambridge Biomedical Research Centre (NIHR203312). X.W. and J.P.T. were supported by Cancer Research UK (PPRPGM-Nov20\100002, PRCPJT-May21\100006 and G110748).QC of the data has been funded by the Medical Research Council (unit programmes: MC_UU_12015/2, MC_UU_00006/2).
6 · The paper itself

Abstract

In large cohort studies the number of unaffected individuals outnumbers the number of affected individuals, and the power can be low to detect associations for outcomes with low prevalence. We consider how including recorded family history in regression models increases the power to detect associations between genetic variants and disease risk. We show theoretically and using Monte-Carlo simulations that including a family history of the disease, with a weighting of 0.5 compared with true cases, increases the power to detect associations. This is a powerful approach for detecting variants with moderate effects, but for larger effect sizes a weighting of > 0.5 can be more powerful. We illustrate this both for common variants and for exome sequencing data for over 400,000 individuals in UK Biobank to evaluate the association between the burden of protein-truncating variants in genes and risk for four cancer types.

Indexed as

Biological Specimen BanksGenetic Predisposition to DiseaseNeoplasmsExome SequencingGenetic VariationHumansMonte Carlo MethodUK BiobankUnited Kingdomcancerexome sequencingfamily historypower calculationsprotein‐truncating variantsUK Biobank

Identifiers

PMID39749474
PMCPMC11867219

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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.