Evidence map›Paper›PMID 40393068›Full record

ArticleGenetics2025

A rapid accurate approach to inferring pedigrees in endogamous populations.

Cole M Williams, Brooke A Scelza, Sarah D Slack, Neus Font-Porterias, Dana R Al-Hindi, Rasika A Mathias, Harold Watson, Kathleen C Barnes, Ethan Lange, Randi K Johnson and 3 more

Abstract read
In one paragraph

Article in Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Article
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  9. Increased homozygosity due to endogamy results in fitness consequences in a human population.Proceedings of the National Academy of Sciences of the United States of America · 2023
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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

13 authors.

Cole M WilliamsCenter for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.ORCID 0000-0002-5888-5994
Brooke A ScelzaDepartment of Anthropology, University of California, Los Angeles, CA 90095, USA.
Sarah D SlackDepartment of Biomedical Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO 80045, USA.
Neus Font-PorteriasDepartment of Anthropology and the UCD Genome Center, University of California, Davis, CA 95616, USA.
Dana R Al-HindiDepartment of Anthropology and the UCD Genome Center, University of California, Davis, CA 95616, USA.
Rasika A MathiasGenomics and Precision Health Section, Laboratory of Allergic Diseases, National Institute of Allergy and Infection Disease, Bethesda, MD 20892, USA.
Harold WatsonFaculty of Medical Sciences, The University of the West Indies, Queen Elizabeth Hospital, Bridgetown, St. Michael, Barbados.
Kathleen C BarnesColorado Center for Personalized Medicine, University of Colorado-Anschutz, Aurora, CO 80045, USA.
Ethan LangeColorado Center for Personalized Medicine, University of Colorado-Anschutz, Aurora, CO 80045, USA.
Randi K JohnsonColorado Center for Personalized Medicine, University of Colorado-Anschutz, Aurora, CO 80045, USA.
Christopher R GignouxColorado Center for Personalized Medicine, University of Colorado-Anschutz, Aurora, CO 80045, USA.
Sohini RamachandranCenter for Computational Molecular Biology, Brown University, Providence, RI 02912, USA.ORCID 0000-0002-9588-7964
Brenna M HennDepartment of Anthropology and the UCD Genome Center, University of California, Davis, CA 95616, USA.ORCID 0000-0003-4998-287X

Funding

Supplement: Improving Inference of Genetic Architecture and Selection with African GenomesR35GM133531 · NIGMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI HENN, BRENNA M · 2019 to 2023
$2.3M
Population-genetic methods for inferring recent histories and targets of selection in the biobank eraR35GM139628 · NIGMS · BROWN UNIVERSITY · PI Sohini Ramachandran · 2021 to 2026
$2.2M
Predoctoral Training Program in Biological Data Science at Brown UniversityT32GM128596 · NIGMS · BROWN UNIVERSITY · PI RAMACHANDRAN, SOHINI, SANDSTEDE, BJORN · 2018 to 2022
$1.5M
NIGMS NIH HHS R35 GM133531NIGMS NIH HHS R35 GM139628NIGMS NIH HHS T32 GM128596NIH HHS R35GM133531NIH HHS R35GM139628NIH HHS T32 GM128596NSF BCS-1534682
6 · The paper itself

Abstract

Accurate reconstruction of pedigrees from genetic data remains a challenging problem. Many relationship categories (e.g. half-sibships vs avuncular) can be difficult to distinguish without external information. Pedigree inference algorithms are often trained on European-descent families in urban locations. Thus, existing methods tend to perform poorly in endogamous populations for which there may be reticulations within the pedigrees and elevated haplotype sharing. We present a simple, rapid algorithm which initially uses only high-confidence first-degree relationships to seed a machine learning step based on summary statistics of identity-by-descent sharing. One of these statistics, our "haplotype score," is novel and can be used to: (1) distinguish half-sibling pairs from avuncular or grandparent-grandchildren pairs; and (2) assign individuals to ancestor vs descendant generation. We test our approach in a sample of 700 individuals from northern Namibia, sampled from an endogamous population called the Himba. Due to a culture of concurrent relationships in the Himba, there is a high proportion of half-sibships. We accurately identify first through fourth-degree relationships and distinguish between various second-degree relationships: half-sibships, avuncular pairs, and grandparent-grandchildren. We further validate our approach in a second African-descent dataset, the Barbados Asthma Genetics Study, and a European-descent founder population from Quebec. Accurate reconstruction of relatives facilitates estimation of allele frequencies, tracing allele trajectories, improved phasing, heritability and other population genomic questions.

Indexed as

Genetics, PopulationPedigreeAlgorithmsFemaleHaplotypesHumansMachine LearningMalegenealogyidentity-by-descentpedigree

Identifiers

PMID40393068
PMCPMC12341879

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.