Accuracy of genomic breeding values in multi-breed dairy cattle populations

<p>Abstract</p> <p>Background</p> <p>Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.</p> <p>Methods</p> <p>Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.</p> <p>Results</p> <p>When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.</p> <p>Conclusion</p> <p>Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.</p

Tools for a comprehensive assessment of public health risks associated with limited sanitation services provision

Three water, sanitation and hygiene (WASH) support tools were applied to Kampala city, Uganda, to evaluate areas with the highest health hazard due to poor wastewater and faecal sludge management and to develop interventions to improve sanitation and reduce exposure. The Pathogen Flow and Mapping Tool (PFMT) assessed how different sanitation management interventions influence pathogen emissions to surface water using rotavirus as the indicator pathogen, while the HyCRISTAL health hazard tool evaluated how flooding and drainage infrastructure influence the presence of human excreta in the environment. The SaniPath tool identified common high-risk pathways of exposure to faecal contamination in food, open drains and floodwater. An overlap in high health hazard hotspot areas was identified by the PFMT and the HyCRISTAL tools. Across the city, the most important hazard sources were the indiscriminate disposal of faecal waste into open stormwater drains from onsite sanitation technologies, open defecation and the insufficient treatment of wastewater. The SaniPath tool identified drain water, floodwater, street food and uncooked produce as the dominant faecal exposure pathways for selected parishes in the city, demonstrating the presence of excreta in the environment. Together, the tools provide collective evidence guiding household, community, and city-wide sanitation, hygiene and infrastructure management interventions from a richer assessment than when a single tool is applied. For areas with high spatial risks, those practising open defecation, and for low-lying areas, these interventions include the provision of watertight pit latrines or septic tanks that are safely managed and regularly emptied. Faecal sludge should be emptied before flood events, direct connections of latrines to open storm drains should be prevented, and the safe handling of food and water promoted. The tools enhance decision making for local authorities, and the assessments can be replicated in other cities

Accuracy of multi-trait genomic selection using different methods

<p>Abstract</p> <p>Background</p> <p>Genomic selection has become a very important tool in animal genetics and is rapidly emerging in plant genetics. It holds the promise to be particularly beneficial to select for traits that are difficult or expensive to measure, such as traits that are measured in one environment and selected for in another environment. The objective of this paper was to develop three models that would permit multi-trait genomic selection by combining scarcely recorded traits with genetically correlated indicator traits, and to compare their performance to single-trait models, using simulated datasets.</p> <p>Methods</p> <p>Three (SNP) Single Nucleotide Polymorphism based models were used. Model G and BCπ0 assumed that contributed (co)variances of all SNP are equal. Model BSSVS sampled SNP effects from a distribution with large (or small) effects to model SNP that are (or not) associated with a quantitative trait locus. For reasons of comparison, model A including pedigree but not SNP information was fitted as well.</p> <p>Results</p> <p>In terms of accuracies for animals without phenotypes, the models generally ranked as follows: BSSVS > BCπ0 > G > > A. Using multi-trait SNP-based models, the accuracy for juvenile animals without any phenotypes increased up to 0.10. For animals with phenotypes on an indicator trait only, accuracy increased up to 0.03 and 0.14, for genetic correlations with the evaluated trait of 0.25 and 0.75, respectively.</p> <p>Conclusions</p> <p>When the indicator trait had a genetic correlation lower than 0.5 with the trait of interest in our simulated data, the accuracy was higher if genotypes rather than phenotypes were obtained for the indicator trait. However, when genetic correlations were higher than 0.5, using an indicator trait led to higher accuracies for selection candidates. For different combinations of traits, the level of genetic correlation below which genotyping selection candidates is more effective than obtaining phenotypes for an indicator trait, needs to be derived considering at least the heritabilities and the numbers of animals recorded for the traits involved.</p