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Genetic sex assignment in wild populations using genotyping‐by‐sequencing data: A statistical threshold approach

Overview of attention for article published in Molecular Ecology Resources, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

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1 blog
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1 Facebook page

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66 Mendeley
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Article details
Title
Genetic sex assignment in wild populations using genotyping‐by‐sequencing data: A statistical threshold approach
Published in
Molecular Ecology Resources, March 2018
DOI 10.1111/1755-0998.12767
Pubmed ID
Authors
Abstract

Establishing the sex of individuals in wild systems can be challenging and often requires genetic testing. Genotyping-by-sequencing (GBS) and other reduced-representation DNA sequencing (RRS) protocols (e.g., RADseq, ddRAD) have enabled the analysis of genetic data on an unprecedented scale. Here, we present a novel approach for the discovery and statistical validation of sex-specific loci in GBS datasets. We used GBS to genotype 166 New Zealand fur seals (NZFS, Arctocephalus forsteri) of known sex. We retained monomorphic loci as potential sex-specific markers in the locus discovery phase. We then used (i) a sex-specific locus threshold (SSLT) to identify significantly male-specific loci within our dataset and (ii) a significant sex-assignment threshold (SSAT) to confidently assign sex in silico the presence or absence of significantly male-specific loci to individuals in our dataset treated as unknowns (98.9% accuracy for females; 95.8% for males, estimated via cross-validation). Furthermore, we assigned sex to 86 individuals of true unknown sex using our SSAT, and assessed the effect of SSLT adjustments on these assignments. From 90 verified sex-specific loci, we developed a panel of three sex-specific PCR primers that we used to ascertain sex independently of our GBS data, which we show amplify reliably in at least two other pinniped species. Using monomorphic loci normally discarded from large SNP datasets is an effective way to identify robust sex-linked markers for non-model species. Our novel pipeline can be used to identify and statistically validate monomorphic and polymorphic sex-specific markers across a range of species and RRS datasets. This article is protected by copyright. All rights reserved.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 66 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 22 33%
Student > Ph. D. Student 11 17%
Student > Master 7 11%
Student > Bachelor 6 9%
Student > Doctoral Student 3 5%
Other 7 11%
Unknown 10 15%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 33 50%
Biochemistry, Genetics and Molecular Biology 16 24%
Environmental Science 4 6%
Computer Science 1 2%
Unknown 12 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 September 2018.
All research outputs
#2,766,370
of 28,815,917 outputs
Outputs from Molecular Ecology Resources
#345
of 1,951 outputs
Outputs of similar age
#50,161
of 352,979 outputs
Outputs of similar age from Molecular Ecology Resources
#5
of 38 outputs
Altmetric has tracked 28,815,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,951 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 82% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 352,979 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.