Evidence map›Paper›PMID 40436020›Full record

ArticleCell reports methods2025

Precise estimation of in-depth relatedness in biobank-scale datasets using deepKin.

Qi-Xin Zhang, Dovini Jayasinghe, Zhe Zhang, Sang Hong Lee, Hai-Ming Xu, Guo-Bo Chen

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PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
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  3. 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

6 authors.

Qi-Xin ZhangInstitute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang 310058, China; Center for Laboratory Medicine, Department of Genetic and Genomic Medicine, and Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang 310014, China; Australian Centre for Precision Health and UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA 5000, Australia. Electronic address: zhangqx@zju.edu.cn.
Dovini JayasingheAustralian Centre for Precision Health and UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA 5000, Australia; South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA 5000, Australia.
Zhe ZhangDepartment of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China.
Sang Hong LeeAustralian Centre for Precision Health and UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA 5000, Australia; South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA 5000, Australia.
Hai-Ming XuInstitute of Bioinformatics, Zhejiang University, Hangzhou, Zhejiang 310058, China.
Guo-Bo ChenCenter for Laboratory Medicine, Department of Genetic and Genomic Medicine, and Clinical Research Institute, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang 310014, China; Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, Zhejiang 310014, China. Electronic address: chenguobo@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate relatedness estimation is essential in biobank-scale genetic studies. We present deepKin, a method-of-moments framework that accounts for sampling variance to enable statistical inference and classification of relatedness. Unlike traditional methods using fixed thresholds, deepKin computes data-specific significance thresholds, determines the minimum effective number of markers, and estimates the statistical power to detect distant relatives. Through simulations, we demonstrate that deepKin accurately infers both unrelated pairs and relatives by leveraging sampling variance. In the UK Biobank (UKB), analysis of the 3K Oxford subset showed that SNP sets with a larger effective number of markers provided greater power for detecting distant relatives. In the White British subset, deepKin identified over 212,000 significant relative pairs, categorized into six degrees, and revealed their geographic patterns across 19 UKB assessment centers through within-cohort and cross-cohort relatedness estimation. An R package (deepKin) is available at GitHub.

Indexed as

Biological Specimen BanksComputer SimulationHumansPedigreePolymorphism, Single NucleotideUnited KingdomCP: geneticsCP: systems biologydeepKineffective number of markersgeo-geno patterns of UK Biobanklinkage disequilibriumrelatedness estimationrelatedness inferencesampling variance of KINGUK Biobank

Identifiers

PMID40436020
PMCPMC12272256

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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.