Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning

Objective: There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Design/methods: Six candidate methods – including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Results: Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Conclusions: Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates

Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef

BACKGROUND: Africa is home to numerous cattle breeds whose diversity has been shaped by subtle combinations of human and natural selection. African Sanga cattle are an intermediate type of cattle resulting from interbreeding between Bos taurus and Bos indicus subspecies. Recently, research has asserted the potential of Sanga breeds for commercial beef production with better meat quality as compared to Bos indicus breeds. Here, we identified meat quality related gene regions that are positively selected in Ankole (Sanga) cattle breeds as compared to indicus (Boran, Ogaden, and Kenana) breeds using cross-population (XP-EHH and XP-CLR) statistical methods. RESULTS: We identified 238 (XP-EHH) and 213 (XP-CLR) positively selected genes, of which 97 were detected from both statistics. Among the genes obtained, we primarily reported those involved in different biological process and pathways associated with meat quality traits. Genes (CAPZB, COL9A2, PDGFRA, MAP3K5, ZNF410, and PKM2) involved in muscle structure and metabolism affect meat tenderness. Genes (PLA2G2A, PARK2, ZNF410, MAP2K3, PLCD3, PLCD1, and ROCK1) related to intramuscular fat (IMF) are involved in adipose metabolism and adipogenesis. MB and SLC48A1 affect meat color. In addition, we identified genes (TIMP2, PKM2, PRKG1, MAP3K5, and ATP8A1) related to feeding efficiency. Among the enriched Gene Ontology Biological Process (GO BP) terms, actin cytoskeleton organization, actin filament-based process, and protein ubiquitination are associated with meat tenderness whereas cellular component organization, negative regulation of actin filament depolymerization and negative regulation of protein complex disassembly are involved in adipocyte regulation. The MAPK pathway is responsible for cell proliferation and plays an important role in hyperplastic growth, which has a positive effect on meat tenderness. CONCLUSION: Results revealed several candidate genes positively selected in Ankole cattle in relation to meat quality characteristics. The genes identified are involved in muscle structure and metabolism, and adipose metabolism and adipogenesis. These genes help in the understanding of the biological mechanisms controlling beef quality characteristics in African Ankole cattle. These results provide a basis for further research on the genomic characteristics of Ankole and other Sanga cattle breeds for quality beef. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12863-016-0467-1) contains supplementary material, which is available to authorized users

Evaluation of geospatial methods to generate subnational HIV prevalence estimates for local level planning

Objective: There is evidence of substantial subnational variation in the HIV epidemic. However, robust spatial HIV data are often only available at high levels of geographic aggregation and not at the finer resolution needed for decision making. Therefore, spatial analysis methods that leverage available data to provide local estimates of HIV prevalence may be useful. Such methods exist but have not been formally compared when applied to HIV. Design/methods: Six candidate methods – including those used by the Joint United Nations Programme on HIV/AIDS to generate maps and a Bayesian geostatistical approach applied to other diseases – were used to generate maps and subnational estimates of HIV prevalence across three countries using cluster level data from household surveys. Two approaches were used to assess the accuracy of predictions: internal validation, whereby a proportion of input data is held back (test dataset) to challenge predictions; and comparison with location-specific data from household surveys in earlier years. Results: Each of the methods can generate usefully accurate predictions of prevalence at unsampled locations, with the magnitude of the error in predictions similar across approaches. However, the Bayesian geostatistical approach consistently gave marginally the strongest statistical performance across countries and validation procedures. Conclusions: Available methods may be able to furnish estimates of HIV prevalence at finer spatial scales than the data currently allow. The subnational variation revealed can be integrated into planning to ensure responsiveness to the spatial features of the epidemic. The Bayesian geostatistical approach is a promising strategy for integrating HIV data to generate robust local estimates.</p