Evidence map›Paper›PMID 32487519›Full record

ArticleGenetics2020

The Impact of Recessive Deleterious Variation on Signals of Adaptive Introgression in Human Populations.

Xinjun Zhang, Bernard Kim, Kirk E Lohmueller, Emilia Huerta-Sánchez

Open access · bronzeAbstract read
In one paragraph

Article in Genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
5.9field-weighted citation impact, top 3% 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

27 citing papers in PubMed, 45 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. TheScience (New York, N.Y.) · 2025
    Article
  5. Accessible, realistic genome simulation with selection using stdpopsim.bioRxiv : the preprint server for biology · 2025
    Article
  6. Article
  7. Article
  8. Review
  9. Revisiting Dominance in Population Genetics.Genome biology and evolution · 2024
    Review
  10. Article
  11. Article
  12. Ghost admixture in eastern gorillas.Nature ecology & evolution · 2023
    Article
  13. Article
  14. Article
  15. Article
  16. Apportioning archaic variants among modern populations.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Article
  17. Article
  18. Review
  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

4 authors at 2 institutions in 1 country.

Xinjun ZhangDepartment of Ecology and Evolutionary Biology, University of California Los Angeles, California 90095-7246.ORCID 0000-0003-1298-3545
Bernard KimDepartment of Biology, Stanford University, Stanford, California 94305.ORCID 0000-0002-5025-1292
Kirk E Lohmueller *Department of Ecology and Evolutionary Biology, University of California Los Angeles, California 90095-7246 klohmueller@g.ucla.edu emilia_huerta-sanchez@brown.edu.ORCID 0000-0002-3874-369X
Emilia Huerta-Sánchez *Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912 klohmueller@g.ucla.edu emilia_huerta-sanchez@brown.edu.ORCID 0000-0002-1506-5494
University of California, Los Angeles · USStanford University · US

Funding

Population genomics of the selective effects of new mutationsR35GM119856 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LOHMUELLER, KIRK · 2016 to 2025
$3.4M
Characterizing modes of natural selection via diverse ancient and modern samplesR35GM128946 · NIGMS · BROWN UNIVERSITY · PI Emilia Huerta-Sanchez · 2018 to 2026
$3.1M
NIGMS NIH HHS R35 GM119856NIGMS NIH HHS R35 GM128946
6 · The paper itself

Abstract

Admixture with archaic hominins has altered the landscape of genomic variation in modern human populations. Several gene regions have been identified previously as candidates of adaptive introgression (AI) that facilitated human adaptation to specific environments. However, simulation-based studies have suggested that population genetic processes other than adaptive mutations, such as heterosis from recessive deleterious variants private to populations before admixture, can also lead to patterns in genomic data that resemble AI. The extent to which the presence of deleterious variants affect the false-positive rate and the power of current methods to detect AI has not been fully assessed. Here, we used extensive simulations under parameters relevant for human evolution to show that recessive deleterious mutations can increase the false positive rates of tests for AI compared to models without deleterious variants, especially when the recombination rates are low. We next examined candidates of AI in modern humans identified from previous studies, and show that 24 out of 26 candidate regions remain significant, even when deleterious variants are included in the null model. However, two AI candidate genes,

Indexed as

Genes, RecessiveGenetic IntrogressionModels, GeneticMutationAdaptation, PhysiologicalCell Adhesion MoleculesComputer SimulationEvolution, MolecularGenome, HumanGPI-Linked ProteinsHLA AntigensHumansHyaluronoglucosaminidasePopulationCell Adhesion MoleculesGPI-Linked ProteinsHLA AntigensHyal2 protein, humanHyaluronoglucosaminidaseadaptive introgressionarchaic humansexon densityheterosisrecessive deleterious mutations

Identifiers

PMID32487519
PMCPMC7337073
OpenAlexW3032299993

What OpenQuestion holds

Textmetadata
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.