124 resultados para genome editing
Resumo:
Mycobacterium bovis is the causal agent of bovine tuberculosis, one of the most important diseases currently facing the UK cattle industry. Here, we use high-density whole genome sequencing (WGS) in a defined sub-population of M. bovis in 145 cattle across 66 herd breakdowns to gain insights into local spread and persistence. We show that despite low divergence among isolates, WGS can in principle expose contributions of under-sampled host populations to M. bovis transmission. However, we demonstrate that in our data such a signal is due to molecular type switching, which had been previously undocumented for M. bovis. Isolates from farms with a known history of direct cattle movement between them did not show a statistical signal of higher genetic similarity. Despite an overall signal of genetic isolation by distance, genetic distances also showed no apparent relationship with spatial distance among affected farms over distances <5 km. Using simulations, we find that even over the brief evolutionary timescale covered by our data, Bayesian phylogeographic approaches are feasible. Applying such approaches showed that M. bovis dispersal in this system is heterogeneous but slow overall, averaging 2 km/year. These results confirm that widespread application of WGS to M. bovis will bring novel and important insights into the dynamics of M. bovis spread and persistence, but that the current questions most pertinent to control will be best addressed using approaches that more directly integrate WGS with additional epidemiological data.
Resumo:
The Neolithic and Bronze Age transitions were profound cultural shifts catalyzed in parts of Europe by migrations, first of early farmers from the Near East and then Bronze Age herders from the Pontic Steppe. However, a decades-long, unresolved controversy is whether population change or cultural adoption occurred at the Atlantic edge, within the British Isles. We address this issue by using the first whole genome data from prehistoric Irish individuals. A Neolithic woman (3343–3020 cal BC) from a megalithic burial (10.3× coverage) possessed a genome of predominantly Near Eastern origin. She had some hunter–gatherer ancestry but belonged to a population of large effective size, suggesting a substantial influx of early farmers to the island. Three Bronze Age individuals from Rathlin Island (2026–1534 cal BC), including one high coverage (10.5×) genome, showed substantial Steppe genetic heritage indicating that the European population upheavals of the third millennium manifested all of the way from southern Siberia to the western ocean. This turnover invites the possibility of accompanying introduction of Indo-European, perhaps early Celtic, language. Irish Bronze Age haplotypic similarity is strongest within modern Irish, Scottish, and Welsh populations, and several important genetic variants that today show maximal or very high frequencies in Ireland appear at this horizon. These include those coding for lactase persistence, blue eye color, Y chromosome R1b haplotypes, and the hemochromatosis C282Y allele; to our knowledge, the first detection of a known Mendelian disease variant in prehistory. These findings together suggest the establishment of central attributes of the Irish genome 4,000 y ago.
Resumo:
Rapid and affordable tumor molecular profiling has led to an explosion of clinical and genomic data poised to enhance the diagnosis, prognostication and treatment of cancer. A critical point has now been reached at which the analysis and storage of annotated clinical and genomic information in unconnected silos will stall the advancement of precision cancer care. Information systems must be harmonized to overcome the multiple technical and logistical barriers to data sharing. Against this backdrop, the Global Alliance for Genomic Health (GA4GH) was established in 2013 to create a common framework that enables responsible, voluntary and secure sharing of clinical and genomic data. This Perspective from the GA4GH Clinical Working Group Cancer Task Team highlights the data-aggregation challenges faced by the field, suggests potential collaborative solutions and describes how GA4GH can catalyze a harmonized data-sharing culture.