Evidence map›Paper›PMID 37526650›Full record

ArticleMolecular ecology resources2025

Easy-to-use R functions to separate reduced-representation genomic datasets into sex-linked and autosomal loci, and conduct sex assignment.

Diana A Robledo-Ruiz, Lana Austin, J Nevil Amos, Jesús Castrejón-Figueroa, Daniel K P Harley, Michael J L Magrath, Paul Sunnucks, Alexandra Pavlova

Abstract read
In one paragraph

Article in Molecular ecology resources, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

8 authors.

Diana A Robledo-RuizSchool of Biological Sciences, Monash University, Clayton, Victoria, Australia.ORCID https://orcid.org/0000-0001-9752-9602
Lana AustinSchool of Biological Sciences, Monash University, Clayton, Victoria, Australia.ORCID https://orcid.org/0000-0002-8204-9873
J Nevil AmosSchool of Biological Sciences, Monash University, Clayton, Victoria, Australia.ORCID https://orcid.org/0000-0003-1271-4767
Jesús Castrejón-FigueroaSchool of Biological Sciences, Monash University, Clayton, Victoria, Australia.ORCID https://orcid.org/0000-0001-5067-508X
Daniel K P HarleyDepartment of Wildlife Conservation and Science, Zoos Victoria, Parkville, Victoria, Australia.ORCID https://orcid.org/0000-0002-9664-1624
Michael J L MagrathDepartment of Wildlife Conservation and Science, Zoos Victoria, Parkville, Victoria, Australia.ORCID https://orcid.org/0000-0002-8022-9627
Paul SunnucksSchool of Biological Sciences, Monash University, Clayton, Victoria, Australia.ORCID https://orcid.org/0000-0002-8139-7059
Alexandra PavlovaSchool of Biological Sciences, Monash University, Clayton, Victoria, Australia.ORCID https://orcid.org/0000-0001-9455-4124

Funding

Australian Government Department of EducationAustralian Research Council DP180102359Australian Research Council DP210102275Australian Research Council LP160100482Department of Biodiversity, Conservation and Attractions (Western Australia)Department of Environment, Land, Water and Planning (Victoria)Diversity Arrays TechnologyEcological Society of Australia IncorporatedEnvironment, Planning & Sustainable Development Directorate (ACT)Monash University Faculty of ScienceRevive & RestoreZoos Victoria
6 · The paper itself

Abstract

Identifying sex-linked markers in genomic datasets is important because their presence in supposedly neutral autosomal datasets can result in incorrect estimates of genetic diversity, population structure and parentage. However, detecting sex-linked loci can be challenging, and available scripts neglect some categories of sex-linked variation. Here, we present new R functions to (1) identify and separate sex-linked loci in ZW and XY sex determination systems and (2) infer the genetic sex of individuals based on these loci. We tested these functions on genomic data for two bird and one mammal species and compared the biological inferences made before and after removing sex-linked loci using our function. We found that our function identified autosomal loci with ≥98.8% accuracy and sex-linked loci with an average accuracy of 87.8%. We showed that standard filters, such as low read depth and call rate, failed to remove up to 54.7% of sex-linked loci. This led to (i) overestimation of population F

Indexed as

Computational BiologyGenomicsSex Determination ProcessesSoftwareAnimalsBirdsFemaleMaleMammalsSex Chromosomesbioinformatic filteringCOLONYmolecular sexingmultilocus contigssex chromosomessex‐linked loci

Identifiers

PMID37526650
PMCPMC12142712

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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.