Snub-nosed monkeys (genus Rhinopithecus) are a group of endangered colobines 2 endemic to South Asia. Here, we re-sequenced the whole genomes of 38 snub-nosed monkeys representing four species within this genus. By conducting population 4 genomic analyses, we observed an similar load of deleterious variation in snub-nosed monkeys living in both smaller and larger populations and found that genomic 6 diversity was lower than that reported in other primates. Reconstruction of Rhinopithecus evolutionary history suggested that episodes of climatic variation over 8 the past 2 million years, associated with glacial advances and retreats and population isolation, have shaped snub-nosed monkey demography and evolution. We further 10 identified several hypoxia-related genes under selection in R. bieti (black snub-nosed monkey), a species that exploits habitats higher than any other nonhuman primate. 12These results provide the first detailed and comprehensive genomic insights into genetic diversity, demography, genetic burden and adaptation in this radiation of 14 endangered primates. 16

The domestication of wild vicuña and guanaco by early pre-Inca cultures is an iconic example of wildlife management and domestication in the Americas. Although domestic llamas and alpacas were clearly selected for key, yet distinct, phenotypic traits, the relative patterns and direction of selection and domestication have not been confirmed using genetic approaches. However, the detailed archaeological records from the region suggest that domestication was a process carried out under significant control and planning, which would have facilitated coordinated and thus extremely effective selective pressure to achieve and maintain desired phenotypic traits. Here we link patterns of sequence variation in two well-characterised genes coding for colour variation in vertebrates and interpret the results in the context of domestication in guanacos and vicuñas. We hypothesise that colour variation in wild populations of guanacos and vicunas were strongly selected against. In contrast, variation in coat colour variation in alpaca was strongly selected for and became rapidly fixed in alpacas. In contrast, coat colour variants in llamas were of less economic value, and thus were under less selective pressure. We report for the first time the full sequence of MC1R and 3 exons of ASIP in 171 wild specimens from throughout their distribution and which represented a range of commonly observed colour patterns. We found a significant difference in the number of non-synonymous substitutions, but not synonymous substitutions among wild and domestics species. The genetic variation in MC1R and ASIP did not differentiate alpaca from llama due to the high degree of reciprocal introgression, but the combination of 11 substitutions are sufficient to distinguish domestic from wild animals. Although there is gene flow among domestic and wild species, most of the non-synonymous variation in MC1R and ASIP was not observed in wild species, presumably because these substitutions and the associated colour phenotypes are not effectively transmitted back into wild populations. Therefore, this set of substitutions unequivocally differentiates wild from domestic animals, which will have important practical application in forensic cases involving the poaching of wild vicuñas and guanacos. These markers will also assist in identifying and studying archaeological remains pre- and post-domestication.

SummarySince the beginning of the genomic era, the number of available single nucleotide polymorphism (SNP) arrays has grown considerably. In the bovine species alone, 11 SNP chips not completely covered by intellectual property are currently available, and the number is growing. Genomic/genotype data are not standardized, and this hampers its exchange and integration. In addition, software used for the analyses of these data usually requires not standard (i.e. case specific) input files which, considering the large amount of data to be handled, require at least some programming skills in their production. In this work, we describe a software toolkit for SNP array data management, imputation, genomewide association studies, population genetics and genomic selection. However, this toolkit does not solve the critical need for standardization of the genotypic data and software input files. It only highlights the chaotic situation each researcher has to face on a daily basis and gives some helpful advice on the currently available tools in order to navigate the SNP array data complexity.

Geographic Information Systems (GIS) are becoming increasingly popular in the context of molecular ecology and conservation biology thanks to their display options efficiency, flexibility and management of geodata. Indeed, spatial data for wildlife and livestock species is becoming a trend with many researchers publishing genomic data that is specifically suitable for landscape studies. GIS uniquely reveal the possibility to overlay genetic information with environmental data and, as such, allow us to locate and analyze genetic boundaries of various plant and animal species or to study gene-environment associations (GEA). This means that, using GIS, we can potentially identify the genetic bases of species adaptation to particular geographic conditions or to climate change. However, many biologists are not familiar with the use of GIS and underlying concepts and thus experience difficulties in finding relevant information and instructions on how to use them. In this paper, we illustrate the power of free and open source GIS approaches and provide essential information for their successful application in molecular ecology. First, we introduce key concepts related to GIS that are too often overlooked in the literature, for example coordinate systems, GPS accuracy and scale. We then provide an overview of the most employed open-source GIS-related software, file formats and refer to major environmental databases. We also reconsider sampling strategies as high costs of Next Generation Sequencing (NGS) data currently diminish the number of samples that can be sequenced per location. Thereafter, we detail methods of data exploration and spatial statistics suited for the analysis of large genetic datasets. Finally, we provide suggestions to properly edit maps and to make them as comprehensive as possible, either manually or trough programming languages.

Regulating stem-cell therapies worldwide Japan's drive to regulate experimental stem-cell treatments is a welcome step (Nature 494, 5; 2013). However,