ArticleEvolutionary bioinformatics online2015
Discovering All Transcriptome Single-Nucleotide Polymorphisms and Scanning for Selection Signatures in Ducks (Anas platyrhynchos).
Article in Evolutionary bioinformatics online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Selection signature analysis in chickens divergently selected for growth rate reveals novel candidate genes regulating fat deposition.BMC genomics · 2025Article
- Genome-wide association study for the primary feather color trait in a native Chinese duck.Frontiers in genetics · 2023Article
- Comparison of village dog and wolf genomes highlights the role of the neural crest in dog domestication.BMC biology · 2018Article
- Detecting the Population Structure and Scanning for Signatures of Selection in Horses (Evolutionary bioinformatics online · 2018Article
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7 authors.
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Abstract
The duck is one of the most economically important waterfowl as a source of meat, eggs, and feathers. Characterizing the genetic variation in duck species is an important step toward linking genes or genomic regions with phenotypes. Human-driven selection during duck domestication and subsequent breed formation has likely left detectable signatures in duck genome. In this study, we employed a panel of >1.4 million single-nucleotide polymorphisms (SNPs) identified from the RNA sequencing (RNA-seq) data of 15 duck individuals. The density of the resulting SNPs is significantly positively correlated with the density of genes across the duck genome, which demonstrates that the usage of the RNA-seq data allowed us to enrich variant functional categories, such as coding exons, untranslated regions (UTRs), introns, and downstream/upstream. We performed a complete scan of selection signatures in the ducks using the composite likelihood ratio (CLR) and found 76 candidate regions of selection, many of which harbor genes related to phenotypes relevant to the function of the digestive system and fat metabolism, including TCF7L2, EIF2AK3, ELOVL2, and fatty acid-binding protein family. This study illustrates the potential of population genetic approaches for identifying genomic regions affecting domestication-related phenotypes and further helps to increase the known genetic information about this economically important animal.
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