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Chen Y, Li R, Sun J, Li C, Xiao H, Chen S. Genome-Wide Population Structure and Selection Signatures of Yunling Goat Based on RAD-seq. Animals (Basel) 2022; 12:ani12182401. [PMID: 36139261 PMCID: PMC9495202 DOI: 10.3390/ani12182401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/07/2022] [Accepted: 09/10/2022] [Indexed: 12/04/2022] Open
Abstract
Simple Summary Goats are important domestic animals that provide meat, milk, fur, and other products for humans. The demand for these products has increased in recent years. Disease resistance among goat breeds is different, but the genetic basis of the differences in resistance to diseases is still unclear and needs to be further studied. In this study, many genes and pathways related to immunity and diseases were identified to be under positive selection between Yunling and Nubian goats using RAD-seq technology. This study on the selection signatures of Yunling goats provides the scientific basis and technical support for the breeding of domestic goats for disease resistance, which has important social and economic significance. Abstract Animal diseases impose a huge burden on the countries where diseases are endemic. Conventional control strategies of vaccines and veterinary drugs are to control diseases from a pharmaceutical perspective. Another alternative approach is using pre-existing genetic disease resistance or tolerance. We know that the Yunling goat is an excellent local breed from Yunnan, southwestern China, which has characteristics of strong disease resistance and remarkable adaptability. However, genetic information about the selection signatures of Yunling goats is limited. We reasoned that the genes underlying the observed difference in disease resistance might be identified by investigating selection signatures between two different goat breeds. Herein, we selected the Nubian goat as the reference group to perform the population structure and selection signature analysis by using RAD-seq technology. The results showed that two goat breeds were divided into two clusters, but there also existed gene flow. We used Fst (F-statistics) and π (pi/θπ) methods to carry out selection signature analysis. Eight selected regions and 91 candidate genes were identified, in which some genes such as DOK2, TIMM17A, MAVS, and DOCK8 related to disease and immunity and some genes such as SPEFI, CDC25B, and MIR103 were associated with reproduction. Four GO (Gene Ontology) terms (GO:0010591, GO:001601, GO:0038023, and GO:0017166) were associated with cell migration, signal transduction, and immune responses. The KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathways were mainly associated with immune responses, inflammatory responses, and stress reactions. This study preliminarily revealed the genetic basis of strong disease resistance and adaptability of Yunling goats. It provides a theoretical basis for the subsequent genetic breeding of disease resistance of goats.
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Affiliation(s)
- Yuming Chen
- School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China; (Y.C.); (R.L.); (C.L.); (H.X.)
- School of Life Sciences, Yunnan University, Kunming 650500, China;
| | - Rong Li
- School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China; (Y.C.); (R.L.); (C.L.); (H.X.)
- College of Life Science, Yunnan Normal University, Kunming 650500, China
| | - Jianshu Sun
- School of Life Sciences, Yunnan University, Kunming 650500, China;
| | - Chunqing Li
- School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China; (Y.C.); (R.L.); (C.L.); (H.X.)
| | - Heng Xiao
- School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China; (Y.C.); (R.L.); (C.L.); (H.X.)
| | - Shanyuan Chen
- School of Ecology and Environmental Science, Yunnan University, Kunming 650500, China; (Y.C.); (R.L.); (C.L.); (H.X.)
- Correspondence: ; Tel.: +86-18687122260
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D'Ercole J, Dincă V, Opler PA, Kondla N, Schmidt C, Phillips JD, Robbins R, Burns JM, Miller SE, Grishin N, Zakharov EV, DeWaard JR, Ratnasingham S, Hebert PDN. A DNA barcode library for the butterflies of North America. PeerJ 2021; 9:e11157. [PMID: 33976967 PMCID: PMC8061581 DOI: 10.7717/peerj.11157] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 03/04/2021] [Indexed: 12/14/2022] Open
Abstract
Although the butterflies of North America have received considerable taxonomic attention, overlooked species and instances of hybridization continue to be revealed. The present study assembles a DNA barcode reference library for this fauna to identify groups whose patterns of sequence variation suggest the need for further taxonomic study. Based on 14,626 records from 814 species, DNA barcodes were obtained for 96% of the fauna. The maximum intraspecific distance averaged 1/4 the minimum distance to the nearest neighbor, producing a barcode gap in 76% of the species. Most species (80%) were monophyletic, the others were para- or polyphyletic. Although 15% of currently recognized species shared barcodes, the incidence of such taxa was far higher in regions exposed to Pleistocene glaciations than in those that were ice-free. Nearly 10% of species displayed high intraspecific variation (>2.5%), suggesting the need for further investigation to assess potential cryptic diversity. Aside from aiding the identification of all life stages of North American butterflies, the reference library has provided new perspectives on the incidence of both cryptic and potentially over-split species, setting the stage for future studies that can further explore the evolutionary dynamics of this group.
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Affiliation(s)
- Jacopo D'Ercole
- Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada.,Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada
| | - Vlad Dincă
- Ecology and Genetics Research Unit, University of Oulu, Oulu, Finland
| | - Paul A Opler
- Colorado State University, Fort Collins, CO, United States of America
| | | | - Christian Schmidt
- Canadian National Collection of Insects, Arachnids and Nematodes, Agriculture and Agri-Food, Guelph, Ontario, Canada
| | - Jarrett D Phillips
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada.,School of Computer Science, University of Guelph, Guelph, Ontario, Canada
| | - Robert Robbins
- Department of Entomology, Smithsonian Institution, Washington DC, United States of America
| | - John M Burns
- Department of Entomology, Smithsonian Institution, Washington DC, United States of America
| | - Scott E Miller
- Department of Entomology, Smithsonian Institution, Washington DC, United States of America
| | - Nick Grishin
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, United States of America.,Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, United States of America
| | - Evgeny V Zakharov
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada
| | - Jeremy R DeWaard
- Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada
| | | | - Paul D N Hebert
- Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada.,Centre for Biodiversity Genomics, University of Guelph, Guelph, Ontario, Canada
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