Vigne, J.-D. Early domestication and farming: What should we know or do for a better understanding?. Anthropozoologica 50(2), 123–150. https://doi.org/10.5252/az2015n2a5 (2015).
Google Scholar
Zeder, M. A. Animal domestication in the Zagros: An update and directions for future research. MOM Édit. 49(1), 243–277 (2008).
Sponenberg, D. P. & Bixby, D. E. Managing Breeds for a Secure Future: Strategies for Breeders and Breed Associations (ALBC, 2007).
Taberlet, P. et al. Are cattle, sheep, and goats endangered species?. Mol. Ecol. 17(1), 275–284. https://doi.org/10.1111/j.1365-294X.2007.03475.x (2008).
Google Scholar
Berihulay, H., Abied, A., He, X., Jiang, L. & Ma, Y. Adaptation mechanisms of small ruminants to environmental heat stress. Anim. Open Access J. MDPI 9(3), 75. https://doi.org/10.3390/ani9030075 (2019).
Google Scholar
Leroy, G., Baumung, R., Boettcher, P., Scherf, B. & Hoffmann, I. Review: Sustainability of crossbreeding in developing countries; definitely not like crossing a meadow…. Animal 10(2), 262–273. https://doi.org/10.1017/S175173111500213X (2016).
Google Scholar
Edea, Z., Dadi, H., Dessie, T. & Kim, K.-S. Genomic signatures of high-altitude adaptation in Ethiopian sheep populations. Genes Genomics 41(8), 973–981. https://doi.org/10.1007/s13258-019-00820-y (2019).
Google Scholar
Wei, C. et al. Genome-wide analysis reveals adaptation to high altitudes in Tibetan sheep. Sci. Rep. 6(1), 26770. https://doi.org/10.1038/srep26770 (2016).
Google Scholar
Yang, J. et al. Whole-genome sequencing of native sheep provides insights into rapid adaptations to extreme environments. Mol. Biol. Evol. 33(10), 2576–2592. https://doi.org/10.1093/molbev/msw129 (2016).
Google Scholar
Kim, E. S. et al. Multiple genomic signatures of selection in goats and sheep indigenous to a hot arid environment. Heredity 116(3), 255–264. https://doi.org/10.1038/hdy.2015.94 (2016).
Google Scholar
Ciani, E. et al. On the origin of European sheep as revealed by the diversity of the Balkan breeds and by optimizing population-genetic analysis tools. Genet. Sel. Evol. GSE 52, 1–14. https://doi.org/10.1186/s12711-020-00545-7 (2020).
Google Scholar
Colli, L. et al. Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes. Genet. Sel. Evol. GSE 50, 1–20. https://doi.org/10.1186/s12711-018-0422-x (2018).
Google Scholar
Kijas, J. W. et al. A genome wide survey of SNP variation reveals the genetic structure of sheep breeds. PLoS ONE 4(3), e4668. https://doi.org/10.1371/journal.pone.0004668 (2009).
Google Scholar
Brisebarre, A. Races ovines, systèmes d’élevage et représentations des éleveurs. in Développement rural, environnement et enjeux territoriaux. Regards croisés Oriental marocain et Sud-Est tunisien (dir. Bonte, P., Elloumi, M., Guillaume, H. & Mahdi, M.) 63–78 (Cérès Ed., 2009).
Hall, S. J. G. Livestock biodiversity as interface between people, landscapes and nature. People Nat. 1(3), 284–290. https://doi.org/10.1002/pan3.23 (2019).
Google Scholar
Caballero, R. et al. Grazing Systems and Biodiversity in Mediterranean Areas: Spain, Italy and Greece (Pastos, 2011).
Collantes, F. The demise of European Mountain Pastoralism: Spain 1500–2000. Nomadic People 13(2), 124–145 (2009).
Google Scholar
Luu, K., Bazin, E. & Blum, M. G. B. pcadapt: An R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17(1), 67–77. https://doi.org/10.1111/1755-0998.12592 (2017).
Google Scholar
Frichot, E., Schoville, S. D., Bouchard, G. & François, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30(7), 1687–1699. https://doi.org/10.1093/molbev/mst063 (2013).
Google Scholar
FAO. The State of the World’s Animal Genetic Resources for Food and Agriculture, edited by B. Rischkowsky & D. Pilling. Rome. (2007).
François, O. Running Structure-Like Population Genetic Analyses with R. R Tutorials in Population Genetics 1–9 (U. Grenoble-Alpes, 2016).
Dalongeville, A., Benestan, L., Mouillot, D., Lobreaux, S. & Manel, S. Combining six genome scan methods to detect candidate genes to salinity in the Mediterranean striped red mullet (Mullus surmuletus). BMC Genomics 19, 1–13. https://doi.org/10.1186/s12864-018-4579-z (2018).
Google Scholar
De Kort, H., Vandepitte, K., Mergeay, J., Mijnsbrugge, K. V. & Honnay, O. The population genomic signature of environmental selection in the widespread insect-pollinated tree species Frangula alnus at different geographical scales. Heredity 115(5), 415–425. https://doi.org/10.1038/hdy.2015.41 (2015).
Google Scholar
Capblancq, T., Luu, K., Blum, M. G. B. & Bazin, E. Evaluation of redundancy analysis to identify signatures of local adaptation. Mol. Ecol. Resour. 18(6), 1223–1233. https://doi.org/10.1111/1755-0998.12906 (2018).
Google Scholar
Bertolini, F. et al. Signatures of selection and environmental adaptation across the goat genome post-domestication. Genet. Sel. Evol. 50(1), 57. https://doi.org/10.1186/s12711-018-0421-y (2018).
Google Scholar
Fariello, M.-I. et al. Selection signatures in worldwide sheep populations. PLoS ONE 9(8), e103813. https://doi.org/10.1371/journal.pone.0103813 (2014).
Google Scholar
Manunza, A. et al. Population structure of eleven Spanish ovine breeds and detection of selective sweeps with BayeScan and hapFLK. Sci. Rep. 6(1), 1–10. https://doi.org/10.1038/srep27296 (2016).
Google Scholar
Oget, C., Servin, B. & Palhière, I. Genetic diversity analysis of French goat populations reveals selective sweeps involved in their differentiation. Anim. Genet. 50(1), 54–63. https://doi.org/10.1111/age.12752 (2019).
Google Scholar
Rochus, C. M. et al. Revealing the selection history of adaptive loci using genome-wide scans for selection: An example from domestic sheep. BMC Genomics 19(1), 71. https://doi.org/10.1186/s12864-018-4447-x (2018).
Google Scholar
Ruiz-Larrañaga, O. et al. Genomic selection signatures in sheep from the Western Pyrenees. Genet. Sel. Evol. GSE 50, 1–12. https://doi.org/10.1186/s12711-018-0378-x (2018).
Google Scholar
Wang, Q., Wang, D., Yan, G., Sun, L. & Tang, C. TRPC6 is required for hypoxia-induced basal intracellular calcium concentration elevation, and for the proliferation and migration of rat distal pulmonary venous smooth muscle cells. Mol. Med. Rep. 13(2), 1577–1585. https://doi.org/10.3892/mmr.2015.4750 (2016).
Google Scholar
Wang, X. et al. Whole-genome sequencing of eight goat populations for the detection of selection signatures underlying production and adaptive traits. Sci. Rep. 6, 38932. https://doi.org/10.1038/srep38932 (2016).
Google Scholar
Graae, B. et al. On the use of weather data in ecological studies along altitudinal and latitudinal gradients. Oikos 121, 3–19. https://doi.org/10.1111/j.1600-0706.2011.19694.x (2011).
Google Scholar
Rellstab, C., Gugerli, F., Eckert, A. J., Hancock, A. M. & Holderegger, R. A practical guide to environmental association analysis in landscape genomics. Mol. Ecol. 24(17), 4348–4370. https://doi.org/10.1111/mec.13322 (2015).
Google Scholar
Qi, X. et al. The transcriptomic landscape of yaks reveals molecular pathways for high altitude adaptation. Genome Biol. Evol. 11(1), 72–85. https://doi.org/10.1093/gbe/evy264 (2019).
Google Scholar
Yang, F., Wang, Q., Wang, M., He, K. & Pan, Y. Associations between gene polymorphisms in two crucial metabolic pathways and growth traits in pigs. Chin. Sci. Bull. 57(21), 2733–2740. https://doi.org/10.1007/s11434-012-5328-3 (2012).
Google Scholar
Schmidt, H. et al. Hypoxia tolerance, longevity and cancer-resistance in the mole rat Spalax—A liver transcriptomics approach. Sci. Rep. https://doi.org/10.1038/s41598-017-13905-z (2017).
Google Scholar
Tian, R. et al. Adaptive evolution of energy metabolism-related genes in hypoxia-tolerant mammals. Front. Genet. 8, 205. https://doi.org/10.3389/fgene.2017.00205 (2017).
Google Scholar
Cheng, A. H. et al. SOX2-dependent transcription in clock neurons promotes the robustness of the central circadian pacemaker. Cell Rep. 26(12), 3191-3202.e8. https://doi.org/10.1016/j.celrep.2019.02.068 (2019).
Google Scholar
Bai, L. et al. Hypoxic and cold adaptation insights from the Himalayan Marmot Genome. IScience 11, 519–530. https://doi.org/10.1016/j.isci.2018.11.034 (2019).
Google Scholar
Stronen, A. V., Pertoldi, C., Iacolina, L., Kadarmideen, H. N. & Kristensen, T. N. Genomic analyses suggest adaptive differentiation of northern European native cattle breeds. Evol. Appl. https://doi.org/10.1111/eva.12783 (2019).
Google Scholar
Lan, D. et al. Genetic diversity, molecular phylogeny, and selection evidence of Jinchuan Yak revealed by whole-genome resequencing. G3 (Bethesda, Md.) 8(3), 945–952. https://doi.org/10.1534/g3.118.300572 (2018).
Google Scholar
Chen, J. et al. Deletion of TRPC6 attenuates NMDA receptor-mediated Ca2+ entry and Ca2+-induced neurotoxicity following cerebral ischemia and oxygen-glucose deprivation. Front. Neurosci. 11, 138. https://doi.org/10.3389/fnins.2017.00138 (2017).
Google Scholar
Munsch, T., Freichel, M., Flockerzi, V. & Pape, H.-C. Contribution of transient receptor potential channels to the control of GABA release from dendrites. Proc. Natl. Acad. Sci. U. S. A. 100(26), 16065–16070. https://doi.org/10.1073/pnas.2535311100 (2003).
Google Scholar
Duan, J. et al. Structure of the mouse TRPC4 ion channel. Nat. Commun. 9, 1–10. https://doi.org/10.1101/282715 (2018).
Google Scholar
Malczyk, M. et al. The role of transient receptor potential channel 6 channels in the pulmonary vasculature. Front. Immunol. 8, 707. https://doi.org/10.3389/fimmu.2017.00707 (2017).
Google Scholar
Li, S. et al. Crucial role of TRPC6 in maintaining the stability of HIF-1α in glioma cells under hypoxia. J. Cell Sci. 128(17), 3317–3329. https://doi.org/10.1242/jcs.173161 (2015).
Google Scholar
Xu, L. et al. Chronic hypoxia increases TRPC6 expression and basal intracellular Ca2+ concentration in rat distal pulmonary venous smooth muscle. PLoS ONE 9(11), e112007. https://doi.org/10.1371/journal.pone.0112007 (2014).
Google Scholar
Deng, L. et al. Prioritizing natural-selection signals from the deep-sequencing genomic data suggests multi-variant adaptation in Tibetan highlanders. Natl. Sci. Rev. 6(6), 1201–1222. https://doi.org/10.1093/nsr/nwz108 (2019).
Google Scholar
Howard, J. T. et al. Beef cattle body temperature during climatic stress: A genome-wide association study. Int. J. Biometeorol. 58, 1665–1672. https://doi.org/10.1007/s00484-013-0773-5 (2013).
Google Scholar
Kijas, J. W. et al. Genome-wide analysis of the world’s sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biol. 10(2), e1001258. https://doi.org/10.1371/journal.pbio.1001258 (2012).
Google Scholar
Wei, C. et al. Genome-wide analysis reveals population structure and selection in Chinese indigenous sheep breeds. BMC Genomics 16(1), 1–12. https://doi.org/10.1186/s12864-015-1384-9 (2015).
Google Scholar
Chen, M. et al. Genome-wide detection of selection signatures in Chinese indigenous Laiwu pigs revealed candidate genes regulating fat deposition in muscle. BMC Genet. 19, 1–9. https://doi.org/10.1186/s12863-018-0622-y (2018).
Google Scholar
Chen, C. et al. Copy number variation in the MSRB3 gene enlarges porcine ear size through a mechanism involving miR-584-5p. Genet. Sel. Evol. GSE 50, 1–18. https://doi.org/10.1186/s12711-018-0442-6 (2018).
Google Scholar
Webster, M. T. et al. Linked genetic variants on chromosome 10 control ear morphology and body mass among dog breeds. BMC Genomics 16, 474. https://doi.org/10.1186/s12864-015-1702-2 (2015).
Google Scholar
Mastrangelo, S. et al. Novel and known signals of selection for fat deposition in domestic sheep breeds from Africa and Eurasia. PLoS ONE 14(6), e0209632. https://doi.org/10.1371/journal.pone.0209632 (2019).
Google Scholar
Xi, Y. et al. HMGA2 promotes adipogenesis by activating C/EBPβ-mediated expression of PPARγ. Biochem. Biophys. Res. Commun. 472(4), 617–623. https://doi.org/10.1016/j.bbrc.2016.03.015 (2016).
Google Scholar
Gou, X. et al. Whole-genome sequencing of six dog breeds from continuous altitudes reveals adaptation to high-altitude hypoxia. Genome Res. 24(8), 1308–1315. https://doi.org/10.1101/gr.171876.113 (2014).
Google Scholar
Yuan, Z. et al. Selection signature analysis reveals genes associated with tail type in Chinese indigenous sheep. Anim. Genet. 48(1), 55–66. https://doi.org/10.1111/age.12477 (2017).
Google Scholar
Zhu, C. et al. GWAS and Post-GWAS to Identification of Genes Associated with Sheep Tail Fat Deposition. Retrieved from https://www.preprints.org/manuscript/201906.0093/v1 (2019).
Allais-Bonnet, A. et al. Novel insights into the bovine polled phenotype and horn ontogenesis in Bovidae. PLoS ONE 8(5), e63512. https://doi.org/10.1371/journal.pone.0063512 (2013).
Google Scholar
Johnston, S. E. et al. Genome-wide association mapping identifies the genetic basis of discrete and quantitative variation in sexual weaponry in a wild sheep population. Mol. Ecol. 20(12), 2555–2566. https://doi.org/10.1111/j.1365-294X.2011.05076.x (2011).
Google Scholar
Oksenberg, N., Stevison, L., Wall, J. D. & Ahituv, N. Function and regulation of AUTS2, a gene implicated in autism and human evolution. PLoS Genet. 9(1), e1003221. https://doi.org/10.1371/journal.pgen.1003221 (2013).
Google Scholar
Hayashi, S. & Takeichi, M. Emerging roles of protocadherins: From self-avoidance to enhancement of motility. J. Cell Sci. 128(8), 1455–1464. https://doi.org/10.1242/jcs.166306 (2015).
Google Scholar
Seong, E., Yuan, L. & Arikkath, J. Cadherins and catenins in dendrite and synapse morphogenesis. Cell Adhes. Migr. 9(3), 202–213. https://doi.org/10.4161/19336918.2014.994919 (2015).
Google Scholar
Shin, D.-H. et al. Deleted copy number variation of Hanwoo and Holstein using next generation sequencing at the population level. BMC Genomics 15(1), 240. https://doi.org/10.1186/1471-2164-15-240 (2014).
Google Scholar
Zeng, X. Angus Cattle at High Altitude: Pulmonary Arterial Pressure, Estimated Breeding Value and Genome-Wide Association Study (PhD thesis). (Colorado State University, 2017).
Benjelloun, B. Diversité des génomes et adaptation locale des petits ruminants d’un pays méditerranéen : le Maroc (PhD thesis) (Université Grenoble Alpes, France, 2015).
Onzima, R. B. et al. Genome-wide characterization of selection signatures and runs of homozygosity in Ugandan Goat Breeds. Front. Genet. 9, 318. https://doi.org/10.3389/fgene.2018.00318 (2018).
Google Scholar
Farzana, F. et al. Neurobeachin regulates glutamate- and GABA-receptor targeting to synapses via distinct pathways. Mol. Neurobiol. 53(4), 2112–2123. https://doi.org/10.1007/s12035-015-9164-8 (2016).
Google Scholar
Nair, R. et al. Neurobeachin regulates neurotransmitter receptor trafficking to synapses. J. Cell Biol. 200(1), 61–80. https://doi.org/10.1083/jcb.201207113 (2013).
Google Scholar
Alberto, F. J. et al. Convergent genomic signatures of domestication in sheep and goats. Nat. Commun. 9, 1–9. https://doi.org/10.1038/s41467-018-03206-y (2018).
Google Scholar
Iranmehr, A. et al. Novel insight into the genetic basis of high-altitude pulmonary hypertension in Kyrgyz highlanders. Eur. J. Hum. Genet. EJHG 27(1), 150–159. https://doi.org/10.1038/s41431-018-0270-8 (2019).
Google Scholar
Newman, J. H. et al. High-altitude pulmonary hypertension in cattle (Brisket disease): Candidate genes and gene expression profiling of peripheral blood mononuclear cells. Pulmon. Circ. 1(4), 462–469. https://doi.org/10.4103/2045-8932.93545 (2011).
Google Scholar
Yang, X., Kong, Q., Zhao, C., Cai, Z., & Wang, M. New pathogenic variant of BMPR2 in pulmonary arterial hypertension. Cardiology in the Young, 29(4), 462–466. https://doi.org/10.1017/S1047951119000015 (2019).
Anderson, L. et al. Bmp2 and Bmp4 exert opposing effects in hypoxic pulmonary hypertension. Am. J. Physiol. Regul. Integr. Comp. Physiol. 298(3), R833–R842. https://doi.org/10.1152/ajpregu.00534.2009 (2009).
Google Scholar
Ciani, E. et al. Genome-wide analysis of Italian sheep diversity reveals a strong geographic pattern and cryptic relationships between breeds. Anim. Genet. 45(2), 256–266. https://doi.org/10.1111/age.12106 (2014).
Google Scholar
ESRI. ArcGIS Desktop: Release 10 (Environmental Systems Research Institute, 2011).
Ruiz, M. & Ruiz, J. P. Ecological history of transhumance in Spain. Biol. Conserv. 37, 73–86 (1986).
Google Scholar
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar
Jarvis, A., Reuter, H. I., Nelson, A. & Guevara, E. Hole-filled seamless SRTM dataV4, International Centre for Tropical Agriculture (CIAT). Available from https://srtm.csi.cgiar.org (2008).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018). https://www.R-project.org.
Brenning, A. Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models. In Hamburger Beitraege zur Physischen Geographie und Landschaftsoekologie (eds Böhner, J. et al.) 23–32 (SAGA, 2008).
Bivand, R. S., Pebesma, E. & Gomez-Rubio, V. Applied Spatial Data Analysis with R 2nd edn (Springer, 2013). http://www.asdar-book.org/.
Pebesma, E. J. & Bivand, R. S. Classes and methods for spatial data in R. R News 5(2), 9–13. https://CRAN.R-project.org/doc/Rnews/ (2005).
Keitt, T. H., Bivand, R., Pebesma, E. & Rowlingson, B. rgdal: Bindings for the geospatial data abstraction library. Copy at http://www.tinyurl.com/h8w8n29 (2010).
Le, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01 (2008).
Google Scholar
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27(15), 2156–2158. https://doi.org/10.1093/bioinformatics/btr330 (2011).
Google Scholar
Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81(3), 559–575. https://doi.org/10.1086/519795 (2007).
Google Scholar
Frichot, E. & François, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6(8), 925–929. https://doi.org/10.1111/2041-210X.12382 (2015).
Google Scholar
Cattell, R. B. The Scree plot test for the number of factors. Multivar. Behav. Res. 1, 140–161 (1966).
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U. S. A. 100, 9440–9445. https://doi.org/10.1073/pnas.1530509100 (2003).
Google Scholar
Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999).
Google Scholar
Ablondi, M., Viklund, Å., Lindgren, G., Eriksson, S. & Mikko, S. Signatures of selection in the genome of Swedish warmblood horses selected for sport performance. BMC Genomics 20(1), 717. https://doi.org/10.1186/s12864-019-6079-1 (2019).
Google Scholar
Avila, F., Mickelson, J. R., Schaefer, R. J. & McCue, M. E. Genome-wide signatures of selection reveal genes associated with performance in American Quarter Horse subpopulations. Front. Genet. 9, 249. https://doi.org/10.3389/fgene.2018.00249 (2018).
Google Scholar
Chen, M. et al. Identification of selective sweeps reveals divergent selection between Chinese Holstein and Simmental cattle populations. Genet. Sel. Evol. 48(1), 76. https://doi.org/10.1186/s12711-016-0254-5 (2016).
Google Scholar
Cheruiyot, E. K. et al. Signatures of selection in admixed dairy cattle in Tanzania. Front. Genet. 9, 607. https://doi.org/10.3389/fgene.2018.00607 (2018).
Google Scholar
López, M. E. et al. Multiple selection signatures in farmed Atlantic Salmon adapted to different environments across hemispheres. Front. Genet. 10, 901. https://doi.org/10.3389/fgene.2019.00901 (2019).
Google Scholar
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19(9), 1655–1664. https://doi.org/10.1101/gr.094052.109 (2009).
Google Scholar
Alexander, D. H. & Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinform. 12, 246 (2011).
Google Scholar
Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G. & Francois, O. Fast and efficient estimation of individual ancestry coefficients. Genetics 196, 973–983 (2014).
Google Scholar
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15(5), 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).
Google Scholar
Jombart, T. Adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).
Google Scholar
Source: Ecology - nature.com