Evidence map›Paper›PMID 30010715›Full record

ArticleBriefings in bioinformatics2019

A comprehensive survey of models for dissecting local ancestry deconvolution in human genome.

Ephifania Geza, Jacquiline Mugo, Nicola J Mulder, Ambroise Wonkam, Emile R Chimusa, Gaston K Mazandu

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Review
  2. Recomb-Mix: fast and accurate local ancestry inference.Bioinformatics (Oxford, England) · 2025
    Article
  3. Article
  4. Article
  5. Fast and accurate local ancestry inference with Recomb-Mix.bioRxiv : the preprint server for biology · 2024
    Article
  6. Article
  7. Genome evolution is surprisingly predictable after initial hybridization.bioRxiv : the preprint server for biology · 2023
    Article
  8. Strategies for the Genomic Analysis of Admixed Populations.Annual review of biomedical data science · 2023
    Review
  9. Article
  10. GeMo: a web-based platform for the visualization and curation of genome ancestry mosaics.Database : the journal of biological databases and curation · 2022
    Article
  11. Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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.

Ephifania GezaAfrican Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa.
Jacquiline MugoAfrican Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa.
Nicola J MulderComputational Biology Division, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, IDM, University of Cape Town, Cape Town 7925, South Africa.
Ambroise WonkamDivision of Human Genetics, Department of Pathology, University of Cape Town, Cape Town 7925, South Africa.
Emile R ChimusaDivision of Human Genetics, Department of Pathology, University of Cape Town, Cape Town 7925, South Africa.
Gaston K MazanduAfrican Institute for Mathematical Sciences, Muizenberg, Cape Town 7945, South Africa.

Funding

H3ABioNet: informatics solutions for H3AfricaU24HG006941 · NHGRI · UNIVERSITY OF CAPE TOWN · PI MULDER, NICOLA · 2017 to 2021
$12.9M
Sickle Africa Data Coordinating CenterU24HL135600 · NHLBI · UNIVERSITY OF CAPE TOWN · PI MULDER, NICOLA, WONKAM, AMBROISE · 2017 to 2025
$8.6M
Return of a Fragile X Syndrome genetic result: exploring the feedback of individual genetic findings and their relation to traditional knowledge in a village in Cameroon.U54HG009790 · NHGRI · UNIVERSITY OF CAPE TOWN · PI WONKAM, AMBROISE · 2017 to 2021
$2.5M
Hearing Impairment Genetics Studies in Africa (HI-GENES Africa)U01HG009716 · NHGRI · UNIVERSITY OF CAPE TOWN · PI WONKAM, AMBROISE · 2017 to 2021
$1.2M
NHGRI NIH HHS U01 HG009716NHGRI NIH HHS U24 HG006941NHGRI NIH HHS U54 HG009790NHLBI NIH HHS U24 HL135600Wellcome TrustWellcome Trust H3A/18/001
6 · The paper itself

Abstract

Over the past decade, studies of admixed populations have increasingly gained interest in both medical and population genetics. These studies have so far shed light on the patterns of genetic variation throughout modern human evolution and have improved our understanding of the demographics and adaptive processes of human populations. To date, there exist about 20 methods or tools to deconvolve local ancestry. These methods have merits and drawbacks in estimating local ancestry in multiway admixed populations. In this article, we survey existing ancestry deconvolution methods, with special emphasis on multiway admixture, and compare these methods based on simulation results reported by different studies, computational approaches used, including mathematical and statistical models, and biological challenges related to each method. This should orient users on the choice of an appropriate method or tool for given population admixture characteristics and update researchers on current advances, challenges and opportunities behind existing ancestry deconvolution methods.

Indexed as

Evolution, MolecularGenome, HumanModels, GeneticHumansadmixtureancestry informative markershidden Markov modelslinkage disequilibriumlocal ancestry

Identifiers

PMID30010715
PMCPMC7373186

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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.