Genome-Wide Association and Genomic Prediction for Host Response to Porcine Reproductive and Respiratory Syndrome Virus Infection

Host genetics has been shown to play a role in porcine reproductive and respiratory syndrome (PRRS), which is the most economically important disease in the swine industry. A region on Sus scrofa chromosome (SSC) 4 has been previously reported to have a strong association with serum viremia and weight gain in pigs experimentally infected with the PRRS virus (PRRSV). The objective here was to identify haplotypes associated with the favorable phenotype, investigate additional genomic regions associated with host response to PRRSV, and to determine the predictive ability of genomic estimated breeding values (GEBV) based on the SSC4 region and based on the rest of the genome. Phenotypic data and 60 K SNP genotypes from eight trials of ~200 pigs from different commercial crosses were used to address these objectives. Across the eight trials, heritability estimates were 0.44 and 0.29 for viral load (VL, area under the curve of log-transformed serum viremia from 0 to 21 days post infection) and weight gain to 42 days post infection (WG), respectively. Genomic regions associated with VL were identified on chromosomes 4, X, and 1. Genomic regions associated with WG were identified on chromosomes 4, 5, and 7. Apart from the SSC4 region, the regions associated with these two traits each explained less than 3% of the genetic variance. Due to the strong linkage disequilibrium in the SSC4 region, only 19 unique haplotypes were identified across all populations, of which four were associated with the favorable phenotype. Through cross-validation, accuracies of EBV based on the SSC4 region were high (0.55), while the rest of the genome had little predictive ability across populations (0.09). Traits associated with response to PRRSV infection in growing pigs are largely controlled by genomic regions with relatively small effects, with the exception of SSC4. Accuracies of EBV based on the SSC4 region were high compared to the rest of the genome. These results show that selection for the SSC4 region could potentially reduce the effects of PRRS in growing pigs, ultimately reducing the economic impact of this disease

Quantitative Trait Locus on Sus scrofa Chromosome 4 Associated with Host Response to Experimental Infection with Porcine Reproductive and Respiratory Syndrome Virus

The objective of this study was to conduct a genomewide association study to discover the genetic basis of host response to PRRS virus using data from the PRRS Host Genetics Consortium NPB and PRRS-CAP project. Approximately 1,600 commercial crossbred piglets were experimentally infected with the Porcine Reproductive and Respiratory Syndrome (PRRS) virus. Blood samples and body weights were collected up to 42 days post infection (dpi). Experimental pigs and their parents were genotyped with Illumina’s Porcine 60k BeadChip. Phenotypes analyzed were viral load (VL = area under the curve for log-transformed qRT-PCR based serum virus from 0-21 dpi) and weight gain from 0-42 dpi (WG). Heritabilities estimated using pedigree information were moderate at 0.41 for VL and 0.29 for WG. A 1 Mb region on Sus scrofa chromosome (SSC) 4 was found to be associated with VL and WG and explained a substantial amount of genetic variation. The frequency of the favorable allele for the most significant single nucleotide polymorphism (SNP) was 0.15. These results show that there is a host genetic component to PRRS virus infection and that there is room for genetic improvement

Fixed-order H∞ filtering for discrete-time markovian jump linear systems with unobservable jump modes

In practical applications, it is often encountered that the jump modes of a Markovian jump linear system may not be fully accessible to the filter, and thus designing a filter which partially or totally independent of the jump modes becomes significant. In this paper, by virtue of a new stability and H ∞ performance characterization, a novel necessary and sufficient condition for the existence of mode-independent H∞ filters is established in terms of a set of nonlinear matrix inequalities that possess special properties for computation. Then, two com putational approaches are developed to solve the condition. One is based on the solution of a set of linear matrix inequalities (LMIs), and the other is based on the sequential LMI optimization with more computational effort but less conservatism. In addition, a specific property of the feasible solutions enables one to further improve the solvability of these two computational approaches. ©2009 ACA.published_or_final_versionThe 7th Asian Control Conference (ASCC 2009), Hong Kong, China, 27-29 August 2009. In Proceedings of the Asian Control Conference, 2009, p. 424-42

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks