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Molecular sexing of birds using quantitative PCR (qPCR) of sex-linked genes and logistic regression models

March 4, 2024

The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.

Publication Year 2024
Title Molecular sexing of birds using quantitative PCR (qPCR) of sex-linked genes and logistic regression models
DOI 10.1111/1755-0998.13946
Authors Eleni Leto Petrou, Laura Celeste Scott, Cherie Marie Mckeeman, Andrew M. Ramey
Publication Type Article
Publication Subtype Journal Article
Series Title Molecular Ecology Resources
Index ID 70252458
Record Source USGS Publications Warehouse
USGS Organization Alaska Science Center Ecosystems