Galibert, F., Quignon, P., Hitte, C. & Andre, C. Toward understanding dog evolutionary and domestication history. C R Biol. 334, 190–196. https://doi.org/10.1016/j.crvi.2010.12.011 (2011).
Club, A. K. The Complete Dog Book 20th edn. (Random House Publishing Group, 2007).
American Kennel Club, <https://www.akc.org/> (2022).
Australian National Kennel, C. Illustrated breed standards / Australian National Kennel Council. (Royal NSW Canine Council], 1998).
Dogs Australia, <https://dogsaustralia.org.au/> (2022).
Club, K. The Kennel Club’s Illustrated Breed Standards: The Official Guide to Registered Breeds (Ebury, 2011).
The Kennel Club, <https://www.thekennelclub.org.uk/> (2022).
Boyko, A. R. et al. A simple genetic architecture underlies morphological variation in dogs. PLoS Biol. 8, e1000451. https://doi.org/10.1371/journal.pbio.1000451 (2010).
Rimbault, M. et al. Derived variants at six genes explain nearly half of size reduction in dog breeds. Gen. Res. 23, 1985–1995. https://doi.org/10.1101/gr.157339.113 (2013).
Akey, J. M. et al. Tracking footprints of artificial selection in the dog genome. Proc. Nat. Acad Sci. U.S.A. 107, 1160–1165. https://doi.org/10.1073/pnas.0909918107 (2010).
Yang, Q. et al. Genetic diversity and signatures of selection in 15 chinese indigenous dog breeds revealed by genome-wide SNPs. Front. Genet. 10, 1174. https://doi.org/10.3389/fgene.2019.01174 (2019).
Akey, J. M., Zhang, G., Zhang, K., Jin, L. & Shriver, M. D. Interrogating a high-density SNP map for signatures of natural selection. Gen. Res. https://doi.org/10.1101/gr.631202 (2002).
Freedman, A. H. et al. Demographically-based evaluation of genomic regions under selection in domestic dogs. Plos Genet. 12, e1005851. https://doi.org/10.1371/journal.pgen.1005851 (2016).
Cagan, A. & Blass, T. Identification of genomic variants putatively targeted by selection during dog domestication. BMC Evol. Biol. 16, 10. https://doi.org/10.1186/s12862-015-0579-7 (2016).
Kim, J. et al. Genetic selection of athletic success in sport-hunting dogs. Proc. Nat. Acad. Sci. U.S.A. 115, E7212–E7221. https://doi.org/10.1073/pnas.1800455115 (2018).
Sabeti, P. C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918. https://doi.org/10.1038/nature06250 (2007).
Vaysse, A. et al. Identification of genomic regions associated with phenotypic variation between dog breeds using selection mapping. Plos Genet. 7, e1002316. https://doi.org/10.1371/journal.pgen.1002316 (2011).
Grossman, S. R. et al. A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327, 883–886. https://doi.org/10.1126/science.1183863 (2010).
Utsunomiya, Y. T. et al. Detecting loci under recent positive selection in dairy and beef cattle by combining different genome-wide scan methods. PLoS ONE 8, e64280. https://doi.org/10.1371/journal.pone.0064280 (2013).
Verity, R. et al. minotaur: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny. Mol. Ecol. Resour. 17, 33–43. https://doi.org/10.1111/1755-0998.12579 (2017).
Randhawa, I. A. S., Khatkar, M. S., Thomson, P. C. & Raadsma, H. W. Composite selection signals can localize the trait specific genomic regions in multi-breed populations of cattle and sheep. BMC Genet. 15, 34–34. https://doi.org/10.1186/1471-2156-15-34 (2014).
Randhawa, I. A., Khatkar, M. S., Thomson, P. C. & Raadsma, H. W. Composite selection signals for complex traits exemplified through bovine stature using multibreed cohorts of European and African bos taurus. G3(Bethesda) 5, 1391–1401. https://doi.org/10.1534/g3.115.017772 (2015).
Gutierrez, L. S. & Gutierrez, J. Thrombospondin 1 in metabolic diseases. Front. Endocrinol. (Lausanne) 12, 638536. https://doi.org/10.3389/fendo.2021.638536 (2021).
Deschenes, M. R. et al. Effects of exercise training on neuromuscular junctions and their active zones in young and aged muscles. Neurobiol. Aging 95, 1–8. https://doi.org/10.1016/j.neurobiolaging.2020.07.001 (2020).
Patterson, E. E. et al. A canine DNM1 mutation is highly associated with the syndrome of exercise-induced collapse. Nat. Genet. 40, 1235–1239. https://doi.org/10.1038/ng.224 (2008).
Shelton, G. D. Myasthenia gravis and disorders of neuromuscular transmission. Vet. Clin. North Am. Small Anim. Pract. 32(189–206), vii. https://doi.org/10.1016/s0195-5616(03)00085-8 (2002).
Buroker, N. E. et al. EPAS1 and EGLN1 associations with high altitude sickness in Han and Tibetan Chinese at the QINGHAI-Tibetan plateau. Blood. Cells Mol. Dis. 49, 67–73. https://doi.org/10.1016/j.bcmd.2012.04.004 (2012).
vonHoldt, B., Fan, Z., Ortega-Del Vecchyo, D. & Wayne, R. K. EPAS1 variants in high altitude Tibetan wolves were selectively introgressed into highland dogs. Peer J. 5, e3522. https://doi.org/10.7717/peerj.3522 (2017).
Edea, Z., Dadi, H., Dessie, T. & Kim, K. S. Genomic signatures of high-altitude adaptation in Ethiopian sheep populations. Gen. Genom. 41, 973–981. https://doi.org/10.1007/s13258-019-00820-y (2019).
Zhang, J. et al. P4HB, a novel hypoxia target gene related to gastric cancer invasion and metastasis. Biomed. Res. Int. 2019, 9749751. https://doi.org/10.1155/2019/9749751 (2019).
Patterson, A. J., Xiao, D., Xiong, F., Dixon, B. & Zhang, L. Hypoxia-derived oxidative stress mediates epigenetic repression of PK Cepsilon gene in foetal rat hearts. Cardiovasc. Res. 93, 302–310. https://doi.org/10.1093/cvr/cvr322 (2012).
Pham, K., Parikh, K. & Heinrich, E. C. Hypoxia and inflammation: insights from high-altitude physiology. Front. Physiol. https://doi.org/10.3389/fphys.2021.676782 (2021).
Günter, J., Ruiz-Serrano, A., Pickel, C., Wenger, R. H. & Scholz, C. C. The functional interplay between the HIF pathway and the ubiquitin system—more than a one-way road. Exp. Cell Res. 356, 152–159. https://doi.org/10.1016/j.yexcr.2017.03.027 (2017).
Fan, R. et al. A positive correlation between elevated altitude and frequency of mutant alleles at the EPAS1 and HBB Loci in Chinese indigenous dogs. J. Genet. Genom. 42, 173–177. https://doi.org/10.1016/j.jgg.2015.02.006 (2015).
Li, Y. et al. Population variation revealed high-altitude adaptation of Tibetan mastiffs. Mol. Biol. Evol. 31, 1200–1205. https://doi.org/10.1093/molbev/msu070 (2014).
Simonson, T. S. et al. Genetic evidence for high-altitude adaptation in Tibet. Science 329, 72–75. https://doi.org/10.1126/science.1189406 (2010).
Quan, C. et al. Characterization of structural variation in Tibetans reveals new evidence of high-altitude adaptation and introgression. Genom. Biol. 22, 159. https://doi.org/10.1186/s13059-021-02382-3 (2021).
Kuhn, H., Banthiya, S. & van Leyen, K. Mammalian lipoxygenases and their biological relevance. Biochem. Biophys. Acta. 308–330, 2015. https://doi.org/10.1016/j.bbalip.2014.10.002 (1851).
Truog, W. E. et al. Chronic hypoxia and rat lung development: Analysis by morphometry and directed microarray. Pediatr. Res. 64, 56–62. https://doi.org/10.1203/PDR.0b013e31817289f2 (2008).
Sharma, K. et al. High-altitude pulmonary edema is aggravated by risk loci and associated transcription factors in HIF-prolyl hydroxylases. Hum. Mol. Genet. 30, 1734–1749. https://doi.org/10.1093/hmg/ddab139 (2021).
Cortesi, E. E. et al. Increased LGR6 expression sustains long-term wnt activation and acquisition of senescence in epithelial progenitors in chronic lung diseases. Cells 10, 3437 (2021).
Zhao, M. et al. Non-proteolytic ubiquitination of OTULIN regulates NF-κB signaling pathway. J. Mol. Cell Biol. 12, 163–175. https://doi.org/10.1093/jmcb/mjz081 (2019).
Choi, J. H., Jeong, S. Y., Oh, M. R., Allen, P. D. & Lee, E. H. TRPCs: Influential mediators in skeletal muscle. Cells https://doi.org/10.3390/cells9040850 (2020).
Conte, E. et al. Alteration of STIM1/Orai1-mediated SOCE in skeletal muscle: Impact in genetic muscle diseases and beyond. Cells https://doi.org/10.3390/cells10102722 (2021).
Pfeffer, G. et al. Mutations in the SPG7 gene cause chronic progressive external ophthalmoplegia through disordered mitochondrial DNA maintenance. Brain 137, 1323–1336. https://doi.org/10.1093/brain/awu060 (2014).
Sacco, T. et al. Mouse brain expression patterns of Spg7, Afg3l1, and Afg3l2 transcripts, encoding for the mitochondrial m-AAA protease. BMC Neurosci. 11, 55. https://doi.org/10.1186/1471-2202-11-55 (2010).
Axelsson, E. et al. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature 495, 360–364. https://doi.org/10.1038/nature11837 (2013).
Braz, C. U. et al. Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle. BMC Genet. 20, 1 (2019).
Guo, Y., Li, J., Bonham, A. J., Wang, Y. & Deng, H. Gains in power for exhaustive analyses of haplotypes using variable-sized sliding window strategy: A comparison of association-mapping strategies. Eur. J. Hum. Genet. 17, 785–792. https://doi.org/10.1038/ejhg.2008.244 (2009).
Beissinger, T. M., Rosa, G. J., Kaeppler, S. M., Gianola, D. & de Leon, N. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genet. Sel. Evol. 47, 30. https://doi.org/10.1186/s12711-015-0105-9 (2015).
Dai, J. Y., Leblanc, M., Smith, N. L., Psaty, B. & Kooperberg, C. Share: An adaptive algorithm to select the most informative set of SNPs for candidate genetic association. Biostatistics 10, 680–693. https://doi.org/10.1093/biostatistics/kxp023 (2009).
Biswas, S. & Akey, J. M. Genomic insights into positive selection. Trends Genet. 22, 437–446. https://doi.org/10.1016/j.tig.2006.06.005 (2006).
Morrill, K. et al. Ancestry-inclusive dog genomics challenges popular breed stereotypes. Science 376, eabk0639. https://doi.org/10.1126/science.abk0639 (2022).
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575. https://doi.org/10.1086/519795 (2007).
Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097. https://doi.org/10.1086/521987 (2007).
Browning, B. L., Zhou, Y. & Browning, S. R. A one-penny imputed genome from next-generation reference panels. Am. J. Hum. Genet. 103, 338–348. https://doi.org/10.1016/j.ajhg.2018.07.015 (2018).
Browning, S. R. & Browning, B. L. Haplotype phasing: Existing methods and new developments. Nat. Rev. Genet. 12, 703–714 (2011).
Arouisse, B., Korte, A., van Eeuwijk, F. & Kruijer, W. Imputation of 3 million SNPs in the arabidopsis regional mapping population. Plant J. 102, 872–882. https://doi.org/10.1111/tpj.14659 (2020).
Szpiech, Z. A. & Hernandez, R. D. selscan: An efficient multithreaded program to perform EHH-based scans for positive selection. Mol. Biol. Evol. 31, 2824–2827. https://doi.org/10.1093/molbev/msu211 (2014).
Porto-Neto, L. R., Lee, S. H., Lee, H. K. & Gondro, C. Detection of signatures of selection using Fst. Method Mol. Biol. 1019, 423–436. https://doi.org/10.1007/978-1-62703-447-0_19 (2013).
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370. https://doi.org/10.2307/2408641 (1984).
Weir, B. S., Cardon, L. R., Anderson, A. D., Nielsen, D. M. & Hill, W. G. Measures of human population structure show heterogeneity among genomic regions. Genome Res. 15, 1468–1476. https://doi.org/10.1101/gr.4398405 (2005).
Fitak, R. R., Rinkevich, S. E. & Culver, M. Genome-wide analysis of SNPs Is consistent with no domestic dog ancestry in the endangered mexican wolf (Canis lupus baileyi). J. Hered. 109, 372–383. https://doi.org/10.1093/jhered/esy009 (2018).
da Huang, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57. https://doi.org/10.1038/nprot.2008.211 (2009).
da Huang, W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic. Acids Res. 37, 1–13. https://doi.org/10.1093/nar/gkn923 (2009).
Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498–2504. https://doi.org/10.1101/gr.1239303 (2003).
Bindea, G., Galon, J. & Mlecnik, B. CluePedia Cytoscape plugin: Pathway insights using integrated experimental and in silico data. Bioinformatics 29, 661–663. https://doi.org/10.1093/bioinformatics/btt019 (2013).
Bindea, G. et al. ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093. https://doi.org/10.1093/bioinformatics/btp101 (2009).
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