Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost

BACKGROUND: Commercial breeding programs seek to maximise the rate of genetic gain while minimizing the costs of attaining that gain. Genomic information offers great potential to increase rates of genetic gain but it is expensive to generate. Low-cost genotyping strategies combined with genotype imputation offer dramatically reduced costs. However, both the costs and accuracy of imputation of these strategies are highly sensitive to several factors. The objective of this paper was to explore the cost and imputation accuracy of several alternative genotyping strategies in pedigreed populations. METHODS: Pedigree and genotype data from a commercial pig population were used. Several alternative genotyping strategies were explored. The strategies differed in the density of genotypes used for the ancestors and the individuals to be imputed. Parents, grandparents, and other relatives that were not descendants, were genotyped at high-density, low-density, or extremely low-density, and associated costs and imputation accuracies were evaluated. RESULTS: Imputation accuracy and cost were influenced by the alternative genotyping strategies. Given the mating ratios and the numbers of offspring produced by males and females, an optimized low-cost genotyping strategy for a commercial pig population could involve genotyping male parents at high-density, female parents at low-density (e.g. 3000 SNP), and selection candidates at very low-density (384 SNP). CONCLUSIONS: Among the selection candidates, 95.5 % and 93.5 % of the genotype variation contained in the high-density SNP panels were recovered using a genotyping strategy that costs respectively, $24.74 and$20.58 per candidate

The 3rd Fermi GBM Gamma-Ray Burst Catalog: The First Six Years

Since its launch in 2008, the Fermi Gamma-ray Burst Monitor (GBM) has triggered and located on average approximately two gamma-ray bursts (GRB) every three days. Here we present the third of a series of catalogs of GRBs detected by GBM, extending the second catalog by two more years, through the middle of July 2014. The resulting list includes 1405 triggers identified as GRBs. The intention of the GBM GRB catalog is to provide information to the community on the most important observables of the GBM detected GRBs. For each GRB the location and main characteristics of the prompt emission, the duration, peak flux and fluence are derived. The latter two quantities are calculated for the 50-300~keV energy band, where the maximum energy release of GRBs in the instrument reference system is observed, and also for a broader energy band from 10-1000 keV, exploiting the full energy range of GBM's low-energy NaI(Tl) detectors. Using statistical methods to assess clustering, we find that the hardness and duration of GRBs are better fitted by a two-component model with short-hard and long-soft bursts, than by a model with three components. Furthermore, information is provided on the settings and modifications of the triggering criteria and exceptional operational conditions during years five and six in the mission. This third catalog is an official product of the Fermi GBM science team, and the data files containing the complete results are available from the High-Energy Astrophysics Science Archive Research Center (HEASARC).Comment: 225 pages, 13 figures and 8 tables. Accepted for publication in Astrophysical Journal Supplement 201

Looking ahead: forecasting and planning for the longer-range future, April 1, 2, and 3, 2005

This repository item contains a single issue of the Pardee Conference Series, a publication series that began publishing in 2006 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. This was the Center's spring Conference that took place during April 1, 2, and 3, 2005.The conference allowed for many highly esteemed scholars and professionals from a broad range of fields to come together to discuss strategies designed for the 21st century and beyond. The speakers and discussants covered a broad range of subjects including: long-term policy analysis, forecasting for business and investment, the National Intelligence Council Global Trends 2020 report, Europe’s transition from the Marshal plan to the EU, forecasting global transitions, foreign policy planning, and forecasting for defense

A modeling-based design and assessment of an acousto-optic guided high-intensity focused ultrasound system

Real-time acousto-optic (AO) sensing has been shown to non-invasively detect changes in ex vivo tissue optical properties during high-intensity focused ultrasound (HIFU) exposures. The technique is particularly appropriate for monitoring non-cavitating lesions that offer minimal acoustic contrast. In this study, a numerical model is presented for an AO-guided HIFU system with an illumination wavelength of 1064 nm and an acoustic frequency of 1.1 MHz. To confirm the model’s accuracy, it is compared to previously published experimental data gathered during AO-guided HIFU in chicken breast. The model is used to determine an optimal design for an AO-guided HIFU system, to assess its robustness, and to predict its efficacy for the ablation of large volumes. It was found that a through transmission geometry results in the best performance, and an optical wavelength around 800 nm was optimal as it provided sufficient contrast with low absorption. Finally, it was shown that the strategy employed while treating large volumes with AO guidance has a major impact on the resulting necrotic volume and symmetry

A Common Dataset for Genomic Analysis of Livestock Populations

Although common datasets are an important resource for the scientific community and can be used to address important questions, genomic datasets of a meaningful size have not generally been available in livestock species. We describe a pig dataset that PIC (a Genus company) has made available for comparing genomic prediction methods. We also describe genomic evaluation of the data using methods that PIC considers best practice for predicting and validating genomic breeding values, and we discuss the impact of data structure on accuracy. The dataset contains 3534 individuals with high-density genotypes, phenotypes, and estimated breeding values for five traits. Genomic breeding values were calculated using BayesB, with phenotypes and de-regressed breeding values, and using a single-step genomic BLUP approach that combines information from genotyped and un-genotyped animals. The genomic breeding value accuracy increased with increased trait heritability and with increased relationship between training and validation. In nearly all cases, BayesB using de-regressed breeding values outperformed the other approaches, but the single-step evaluation performed only slightly worse. This dataset was useful for comparing methods for genomic prediction using real data. Our results indicate that validation approaches accounting for relatedness between populations can correct for potential overestimation of genomic breeding value accuracies, with implications for genotyping strategies to carry out genomic selection programs

The potential selection response of microbiome-driven breeding to mitigate methane emissions from beef cattle considering correlated production traits

ApplicationMicrobiome-driven breeding, as a cost-effective strategy to mitigate methane (CH4)emissions, is recommended to be used in a multiple trait model with correlated productiontraits, as it substantially increased the accuracy of estimation of breeding values (EBVs) andthus enhances selection response.IntroductionRoehe et al. (2016) found that rumen microbial gene abundances are closely linked to CH4emissions and highlighted these as a highly informative proxy for breeding low CH4 emittingcattle. Later, Martinez-Alvaro et al. (2022) demonstrated the effectiveness of usingmicrobial genes in microbiome-driven breeding to reduce CH4 yield (expressed as g ofCH4/kg of dry matter intake). In this study, we applied microbiome-driven breeding formitigating daily CH4 emissions (g of CH4 /day), and incorporated information from keyperformance traits genetically associated with CH4 emissions, such as daily feed intake (DFI),average daily gain (ADG) and carcass weight (CCW).Materials and MethodsThe experiment was conducted following the UK Animals Act 1986 and was approved by theAnimal Experiment Committee of SRUC. Three hundred sixty-three steers raised under thesame housing conditions on the same research farm were used in this project. The animalswere balanced for different breeds (Aberdeen Angus, Limousin, Charolais crosses andpurebred Luing) and basal diets (two diets of 520:480 and 920:80 forage:concentrate ratios).Blood and rumen fluid samples were collected at slaughter. Microbial DNA sequence readsfrom rumen fluid samples were aligned to the Kyoto Encyclopedia of Genes and Genomesdatabase, resulting in the identification of 3362 microbial genes. To account for thecompositionality of microbiome data, microbial gene abundance data were transformedusing the additive log-ratio method. CH4 production was measured individually for 285 ofthe 363 animals over a 48-hour period using six respiration chambers and expressed as CH4emissions per day (CH4p).Firstly, we conducted multiple bivariate genomic (37K SNPs) analyses to obtain geneticvariances and covariances between CH4p and microbial genes. Secondly, we identified themost informative microbial genes that yielded the largest correlated response in CH4p.Thirdly, we conducted genomic bivariate analyses between the identified microbial genesand the performance traits DFI, ADG, CCW to obtain the genomic (co)variances. Lastly, weused these genomic (co)variances for different breeding strategies to reduce CH4production: 1) univariate analyses, using measured CH4p only (CH4p measured), 2)multivariate analysis using only the most informative microbial gene abundances geneticallycorrelated with CH4p, i.e., microbiome-driven breeding (MDB.43), 3) multivariate analysis,including DFI, ADG, CCW, and measured CH4p (Four traits measures), and 4) multivariateanalysis, including DFI, ADG, CCW, and predicted CH4p using microbiome-driven breeding(Three traits & MDB.43). Three selection intensities (1.159, 1.400, and 1.755) wereconsidered for each strategy.ResultsWe identified 43 informative microbial genes, of which 17 were positively geneticallycorrelated with CH4p (rgCH4p, ranging from 0.45 to 0.80) and 26 microbial genes werenegatively correlated (rgCH4p ranged from -0.32 to -0.75) with CH4p. All correlations had morethan 80% probability of being greater or lower than zero (Pr0). The heritability of thesemicrobial genes ranged from 0.19 to 0.50.Of all performance traits, DFI showed strong positive genetic correlations (ranging from 0.84to 0.93, Pr0 = 100%) with ADG, CCW, and CH4p. CCW had a marginally higher geneticcorrelation with CH4p (0.61, Pr0 = 96%) than ADG (0.58, Pr0 = 96%).Selection using microbiome-driven breeding (MDB.43) resulted in similar selectionresponses to those based on measured CH4p using respiration chambers (Figure 1, -17.76 ±2.30% vs -16.76 ± 2.26% at highest selection intensity). Including measured CH4p in themultiple-trait model with ADG, DFI and CCW increased the accuracy of the EBVs from 0.63 ±0.15 to 0.81 ± 0.06 and the selection response to -20.59 ± 2.13%). Replacing measured CH4pby microbiome-driven breeding resulted in a further increase in response at -23.12 ± 2.88%.Figure 1. Methane mitigation using different selection strategies, considering threeselection intensities (1.159, 1.400, 1.755, equivalent to selection of the best 30%, 20% and10% of the population, respectively)ConclusionsMicrobiome-driven breeding for reduced CH4p was successfully integrated into a multipletrait model with production traits by considering all genetic and residual covariancesbetween microbial gene abundances and those traits. Since microbiome-driven breeding issubstantially more cost-effective than using measured CH4 emissions and provide at leastsimilar selection response to that obtained using the gold standard method of respirationchambers, this methodology provides large potential to effectively reduce this highly potentGHG gas in beef populations.ReferencesRoehe, R., Dewhurst, R.J., Duthie, C.A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J.,Waterhouse, A., Freeman, T.C., Watson, M. and Wallace, R.J., 2016. Bovine host geneticvariation influences rumen microbial methane production with best selection criterion forlow methane emitting and efficiently feed converting hosts based on metagenomic geneabundance. PLOS Genetics 12, e1005846.Martínez-Álvaro, M., Auffret, M.D., Duthie, C.A., Dewhurst, R.J., Cleveland, M.A., Watson,M. and Roehe, R., 2022. Bovine host genome acts on rumen microbiome function linked tomethane emissions. Communications Biology 5, 35

The potential selection response of microbiome-driven breeding to mitigate methane emissions from beef cattle considering correlated production traits

ApplicationMicrobiome-driven breeding, as a cost-effective strategy to mitigate methane (CH4)emissions, is recommended to be used in a multiple trait model with correlated productiontraits, as it substantially increased the accuracy of estimation of breeding values (EBVs) andthus enhances selection response.IntroductionRoehe et al. (2016) found that rumen microbial gene abundances are closely linked to CH4emissions and highlighted these as a highly informative proxy for breeding low CH4 emittingcattle. Later, Martinez-Alvaro et al. (2022) demonstrated the effectiveness of usingmicrobial genes in microbiome-driven breeding to reduce CH4 yield (expressed as g ofCH4/kg of dry matter intake). In this study, we applied microbiome-driven breeding formitigating daily CH4 emissions (g of CH4 /day), and incorporated information from keyperformance traits genetically associated with CH4 emissions, such as daily feed intake (DFI),average daily gain (ADG) and carcass weight (CCW).Materials and MethodsThe experiment was conducted following the UK Animals Act 1986 and was approved by theAnimal Experiment Committee of SRUC. Three hundred sixty-three steers raised under thesame housing conditions on the same research farm were used in this project. The animalswere balanced for different breeds (Aberdeen Angus, Limousin, Charolais crosses andpurebred Luing) and basal diets (two diets of 520:480 and 920:80 forage:concentrate ratios).Blood and rumen fluid samples were collected at slaughter. Microbial DNA sequence readsfrom rumen fluid samples were aligned to the Kyoto Encyclopedia of Genes and Genomesdatabase, resulting in the identification of 3362 microbial genes. To account for thecompositionality of microbiome data, microbial gene abundance data were transformedusing the additive log-ratio method. CH4 production was measured individually for 285 ofthe 363 animals over a 48-hour period using six respiration chambers and expressed as CH4emissions per day (CH4p).Firstly, we conducted multiple bivariate genomic (37K SNPs) analyses to obtain geneticvariances and covariances between CH4p and microbial genes. Secondly, we identified themost informative microbial genes that yielded the largest correlated response in CH4p.Thirdly, we conducted genomic bivariate analyses between the identified microbial genesand the performance traits DFI, ADG, CCW to obtain the genomic (co)variances. Lastly, weused these genomic (co)variances for different breeding strategies to reduce CH4production: 1) univariate analyses, using measured CH4p only (CH4p measured), 2)multivariate analysis using only the most informative microbial gene abundances geneticallycorrelated with CH4p, i.e., microbiome-driven breeding (MDB.43), 3) multivariate analysis,including DFI, ADG, CCW, and measured CH4p (Four traits measures), and 4) multivariateanalysis, including DFI, ADG, CCW, and predicted CH4p using microbiome-driven breeding(Three traits & MDB.43). Three selection intensities (1.159, 1.400, and 1.755) wereconsidered for each strategy.ResultsWe identified 43 informative microbial genes, of which 17 were positively geneticallycorrelated with CH4p (rgCH4p, ranging from 0.45 to 0.80) and 26 microbial genes werenegatively correlated (rgCH4p ranged from -0.32 to -0.75) with CH4p. All correlations had morethan 80% probability of being greater or lower than zero (Pr0). The heritability of thesemicrobial genes ranged from 0.19 to 0.50.Of all performance traits, DFI showed strong positive genetic correlations (ranging from 0.84to 0.93, Pr0 = 100%) with ADG, CCW, and CH4p. CCW had a marginally higher geneticcorrelation with CH4p (0.61, Pr0 = 96%) than ADG (0.58, Pr0 = 96%).Selection using microbiome-driven breeding (MDB.43) resulted in similar selectionresponses to those based on measured CH4p using respiration chambers (Figure 1, -17.76 ±2.30% vs -16.76 ± 2.26% at highest selection intensity). Including measured CH4p in themultiple-trait model with ADG, DFI and CCW increased the accuracy of the EBVs from 0.63 ±0.15 to 0.81 ± 0.06 and the selection response to -20.59 ± 2.13%). Replacing measured CH4pby microbiome-driven breeding resulted in a further increase in response at -23.12 ± 2.88%.Figure 1. Methane mitigation using different selection strategies, considering threeselection intensities (1.159, 1.400, 1.755, equivalent to selection of the best 30%, 20% and10% of the population, respectively)ConclusionsMicrobiome-driven breeding for reduced CH4p was successfully integrated into a multipletrait model with production traits by considering all genetic and residual covariancesbetween microbial gene abundances and those traits. Since microbiome-driven breeding issubstantially more cost-effective than using measured CH4 emissions and provide at leastsimilar selection response to that obtained using the gold standard method of respirationchambers, this methodology provides large potential to effectively reduce this highly potentGHG gas in beef populations.ReferencesRoehe, R., Dewhurst, R.J., Duthie, C.A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J.,Waterhouse, A., Freeman, T.C., Watson, M. and Wallace, R.J., 2016. Bovine host geneticvariation influences rumen microbial methane production with best selection criterion forlow methane emitting and efficiently feed converting hosts based on metagenomic geneabundance. PLOS Genetics 12, e1005846.Martínez-Álvaro, M., Auffret, M.D., Duthie, C.A., Dewhurst, R.J., Cleveland, M.A., Watson,M. and Roehe, R., 2022. Bovine host genome acts on rumen microbiome function linked tomethane emissions. Communications Biology 5, 35

The potential selection response of microbiome-driven breeding to mitigate methane emissions from beef cattle considering correlated production traits

ApplicationMicrobiome-driven breeding, as a cost-effective strategy to mitigate methane (CH4)emissions, is recommended to be used in a multiple trait model with correlated productiontraits, as it substantially increased the accuracy of estimation of breeding values (EBVs) andthus enhances selection response.IntroductionRoehe et al. (2016) found that rumen microbial gene abundances are closely linked to CH4emissions and highlighted these as a highly informative proxy for breeding low CH4 emittingcattle. Later, Martinez-Alvaro et al. (2022) demonstrated the effectiveness of usingmicrobial genes in microbiome-driven breeding to reduce CH4 yield (expressed as g ofCH4/kg of dry matter intake). In this study, we applied microbiome-driven breeding formitigating daily CH4 emissions (g of CH4 /day), and incorporated information from keyperformance traits genetically associated with CH4 emissions, such as daily feed intake (DFI),average daily gain (ADG) and carcass weight (CCW).Materials and MethodsThe experiment was conducted following the UK Animals Act 1986 and was approved by theAnimal Experiment Committee of SRUC. Three hundred sixty-three steers raised under thesame housing conditions on the same research farm were used in this project. The animalswere balanced for different breeds (Aberdeen Angus, Limousin, Charolais crosses andpurebred Luing) and basal diets (two diets of 520:480 and 920:80 forage:concentrate ratios).Blood and rumen fluid samples were collected at slaughter. Microbial DNA sequence readsfrom rumen fluid samples were aligned to the Kyoto Encyclopedia of Genes and Genomesdatabase, resulting in the identification of 3362 microbial genes. To account for thecompositionality of microbiome data, microbial gene abundance data were transformedusing the additive log-ratio method. CH4 production was measured individually for 285 ofthe 363 animals over a 48-hour period using six respiration chambers and expressed as CH4emissions per day (CH4p).Firstly, we conducted multiple bivariate genomic (37K SNPs) analyses to obtain geneticvariances and covariances between CH4p and microbial genes. Secondly, we identified themost informative microbial genes that yielded the largest correlated response in CH4p.Thirdly, we conducted genomic bivariate analyses between the identified microbial genesand the performance traits DFI, ADG, CCW to obtain the genomic (co)variances. Lastly, weused these genomic (co)variances for different breeding strategies to reduce CH4production: 1) univariate analyses, using measured CH4p only (CH4p measured), 2)multivariate analysis using only the most informative microbial gene abundances geneticallycorrelated with CH4p, i.e., microbiome-driven breeding (MDB.43), 3) multivariate analysis,including DFI, ADG, CCW, and measured CH4p (Four traits measures), and 4) multivariateanalysis, including DFI, ADG, CCW, and predicted CH4p using microbiome-driven breeding(Three traits & MDB.43). Three selection intensities (1.159, 1.400, and 1.755) wereconsidered for each strategy.ResultsWe identified 43 informative microbial genes, of which 17 were positively geneticallycorrelated with CH4p (rgCH4p, ranging from 0.45 to 0.80) and 26 microbial genes werenegatively correlated (rgCH4p ranged from -0.32 to -0.75) with CH4p. All correlations had morethan 80% probability of being greater or lower than zero (Pr0). The heritability of thesemicrobial genes ranged from 0.19 to 0.50.Of all performance traits, DFI showed strong positive genetic correlations (ranging from 0.84to 0.93, Pr0 = 100%) with ADG, CCW, and CH4p. CCW had a marginally higher geneticcorrelation with CH4p (0.61, Pr0 = 96%) than ADG (0.58, Pr0 = 96%).Selection using microbiome-driven breeding (MDB.43) resulted in similar selectionresponses to those based on measured CH4p using respiration chambers (Figure 1, -17.76 ±2.30% vs -16.76 ± 2.26% at highest selection intensity). Including measured CH4p in themultiple-trait model with ADG, DFI and CCW increased the accuracy of the EBVs from 0.63 ±0.15 to 0.81 ± 0.06 and the selection response to -20.59 ± 2.13%). Replacing measured CH4pby microbiome-driven breeding resulted in a further increase in response at -23.12 ± 2.88%.Figure 1. Methane mitigation using different selection strategies, considering threeselection intensities (1.159, 1.400, 1.755, equivalent to selection of the best 30%, 20% and10% of the population, respectively)ConclusionsMicrobiome-driven breeding for reduced CH4p was successfully integrated into a multipletrait model with production traits by considering all genetic and residual covariancesbetween microbial gene abundances and those traits. Since microbiome-driven breeding issubstantially more cost-effective than using measured CH4 emissions and provide at leastsimilar selection response to that obtained using the gold standard method of respirationchambers, this methodology provides large potential to effectively reduce this highly potentGHG gas in beef populations.ReferencesRoehe, R., Dewhurst, R.J., Duthie, C.A., Rooke, J.A., McKain, N., Ross, D.W., Hyslop, J.J.,Waterhouse, A., Freeman, T.C., Watson, M. and Wallace, R.J., 2016. Bovine host geneticvariation influences rumen microbial methane production with best selection criterion forlow methane emitting and efficiently feed converting hosts based on metagenomic geneabundance. PLOS Genetics 12, e1005846.Martínez-Álvaro, M., Auffret, M.D., Duthie, C.A., Dewhurst, R.J., Cleveland, M.A., Watson,M. and Roehe, R., 2022. Bovine host genome acts on rumen microbiome function linked tomethane emissions. Communications Biology 5, 35

KOunt: a reproducible KEGG orthologue abundance workflow

Accurate gene prediction is essential for successful metagenome analysis. We present KOunt, a Snakemake pipeline, that precisely quantifies KEGG orthologue abundance