Population Structure and Genetic Diversity of Sheep Breeds in the Kyrgyzstan

Sheep are a main livestock species of Kyrgyzstan, a Central Asian country with predominating mountain terrain. The current gene pool of local sheep resources has been forming under diverse climate conditions from the era of the trading caravans of the Great Silk Road, through the Soviet period of large-scale livestock improvements, which was followed by the deep crisis at the end of the 20th century, up to now. However, not much is known about the genetic background and variability of the local sheep populations. Therefore, our aims were to provide a characterization of the population structure and genetic relations within the Kyrgyz sheep breeds and to study their genetic connections with the global sheep breeds using SNP analysis. Samples of the Alai (n = 31), Gissar (n = 30), Kyrgyz coarse wool (n = 13), Aykol (n = 31), and Tien-Shan (n = 24) breeds were genotyped with the OvineSNP50 BeadChip or the Ovine Infinium HD BeadChip (Illumina Inc., USA). The measure of inbreeding based on runs of homozygosity showed a minimum value in the Aykol breed (FROH = 0.034), while the maximum was found in the Alai breed (FROH = 0.071). Short ROH segments (ROH ≤ 4 Mb) were predominant in all breeds. Long ROH segments (ROH > 16 Mb) were absent in the Gissar breed. The Gissar and Aykol breeds had the highest values of the effective population sizes estimated for five generations ago (Ne5 = 660 and 563), whereas the Alai and Kyrgyz coarse wool displayed lower values (Ne5 = 176 and 128, respectively). The synthetic origin of the Aykol breed was clearly evidenced by all analyses applied. Based on the network and admixture analyses of the Kyrgyz and global sheep breeds, the Tien-Shan and the Russian semi-fine wool breeds demonstrated a common ancestry that most likely is due to a contribution of the Lincoln breed. The Gissar, Aykol, and Kyrgyz coarse wool breeds showed a genetic background predominating in sheep populations from Iran and China whereas the Alai demonstrated the different ancestry type. The revealed admixture patterns probably resulted from the exchange and trade during the era of the Great Silk Road, which partly overlapped with historical and archeological findings

Novas fontes de financiamento para a educação : o caso dos royalties do petróleo e a expectativa nos municípios brasileiros

Faculdade de Educação (FE)Departamento de Políticas Públicas e Gestão da Educação (FE PGE

Remapping of the belted phenotype in cattle on BTA3 identifies a multiplication event as the candidate causal mutation

Background: It has been known for almost a century that the belted phenotype in cattle follows a pattern of dominant inheritance. In 2009, the approximate position of the belt locus in Brown Swiss cattle was mapped to a 922-kb interval on bovine chromosome 3 and, subsequently, assigned to a 336-kb haplotype block based on an animal set that included, Brown Swiss, Dutch Belted (Lakenvelder) and Belted Galloway individuals. A possible candidate gene in this region i.e. HES6 was investigated but the causal mutation remains unknown. Thus, to elucidate the causal mutation of this prominent coat color phenotype, we decided to remap the belted phenotype in an independent animal set of several European bovine breeds, i.e. Gurtenvieh (belted Brown Swiss), Dutch Belted and Belted Galloway and to systematically scan the candidate region. We also checked the presence of the detected causal mutation in the genome of belted individuals from a Siberian cattle breed. Results: A combined linkage disequilibrium and linkage analysis based on 110 belted and non-belted animals identified a candidate interval of 2.5 Mb. Manual inspection of the haplotypes in this region identified four candidate haplotypes that consisted of five to eight consecutive SNPs. One of these haplotypes overlapped with the initial 922-kb interval, whereas two were positioned proximal and one was positioned distal to this region. Next-generation sequencing of one heterozygous and two homozygous belted animals identified only one private belted candidate allele, i.e. a multiplication event that is located between 118,608,000 and 118,614,000 bp. Targeted locus amplification and quantitative real-time PCR confirmed an increase in copy number of this region in the genomes of both European (Belted Galloway, Dutch Belted and Gurtenvieh) and Siberian (Yakutian cattle) breeds. Finally, using nanopore sequencing, the exact breakpoints were determined at 118,608,362 and 118,614,132 bp. The closest gene to the candidate causal mutation (16 kb distal) is TWIST2. Conclusions: Based on our findings and those of a previously published study that identified the same multiplication event, a quadruplication on bovine chromosome 3 between positions 118,608,362 and 118,614,132 bp is the most likely candidate causal mutation for the belted phenotype in cattle

Population structure and genetic diversity of 25 Russian sheep breeds based on whole-genome genotyping

Background: Russia has a diverse variety of native and locally developed sheep breeds with coarse, fine, and semi-fine wool, which inhabit different climate zones and landscapes that range from hot deserts to harsh northern areas. To date, no genome-wide information has been used to investigate the history and genetic characteristics of the extant local Russian sheep populations. To infer the population structure and genome-wide diversity of Russian sheep, 25 local breeds were genotyped with the OvineSNP50 BeadChip. Furthermore, to evaluate admixture contributions from foreign breeds in Russian sheep, a set of 58 worldwide breeds from publicly available genotypes was added to our data. Results: We recorded similar observed heterozygosity (0.354-0.395) and allelic richness (1.890-1.955) levels across the analyzed breeds and they are comparable with those observed in the worldwide breeds. Recent effective population sizes estimated from linkage disequilibrium five generations ago ranged from 65 to 543. Multi-dimensional scaling, admixture, and neighbor-net analyses consistently identified a two-step subdivision of the Russian local sheep breeds. A first split clustered the Russian sheep populations according to their wool type (fine wool, semi-fine wool and coarse wool). The Dagestan Mountain and Baikal fine-fleeced breeds differ from the other Merino-derived local breeds. The semi-fine wool cluster combined a breed of Romanian origin, Tsigai, with its derivative Altai Mountain, the two Romney-introgressed breeds Kuibyshev and North Caucasian, and the Lincoln-introgressed Russian longhaired breed. The coarse-wool group comprised the Nordic short-tailed Romanov, the long-fat-tailed outlier Kuchugur and two clusters of fat-tailed sheep: the Caucasian Mountain breeds and the Buubei, Karakul, Edilbai, Kalmyk and Tuva breeds. The Russian fat-tailed breeds shared co-ancestry with sheep from China and Southwestern Asia (Iran). Conclusions: In this study, we derived the genetic characteristics of the major Russian local sheep breeds, which are moderately diverse and have a strong population structure. Pooling our data with a worldwide genotyping set gave deeper insight into the history and origin of the Russian sheep populations

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution