Selection of neutralizing antibody escape mutants with type A influenza virus HA-specific polyclonal antisera: possible significance for antigenic drift

Ten antisera were produced in rabbits by two or three intravenous injections of inactivated whole influenza type A virions. All contained haemagglutination-inhibition (HI) antibody directed predominantly to an epitope in antigenic site B and, in addition, various amounts of antibodies to an epitope in site A and in site D. The ability of untreated antisera to select neutralization escape mutants was investigated by incubating virus possessing the homologous haemagglutinin with antiserum adjusted to contain anti-B epitope HI titres of 100, 1000 and 10000 HIU/ml. Virus-antiserum mixtures were inoculated into embryonated hen's eggs, and progeny virus examined without further selection. Forty percent of the antisera at a titre of 1000 HIU/ml selected neutralizing antibody escape mutants as defined by their lack of reactivity to Mab HC10 (site B), and unchanged reactivity to other Mabs to site A and site D epitopes. All escape mutant-selecting antisera had a ratio of anti-site B (HC10)-epitope antibody[ratio]other antibodies of [gt-or-equal, slanted]2·0[ratio]1. The antiserum with the highest ratio (7·4[ratio]1) selected escape mutants in all eggs tested in four different experiments. No antiserum used at a titre of 10000 HIU/ml allowed multiplication of any virus. All antisera used at a titre of 100 HIU/ml permitted virus growth, but this was wild-type (wt) virus. We conclude that a predominant epitope-specific antibody response, a titre of [gt-or-equal, slanted]1000 HIU/ml, and a low absolute titre of other antibodies ([less-than-or-eq, slant]500 HIU/ml) are three requirements for the selection of escape mutants. None of the antisera in this study could have selected escape mutants without an appropriate dilution factor, so the occurrence of an escape mutant-selecting antiserum in nature is likely to be a rare event

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

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

Statistical coherence of primary schooling in population census microdata: IPUMS-International integrated samples compared for fifteen African countries

The IPUMS-International project, now in its fifteenth year, integrates and disseminates population microdata for twenty-two African countries (82 countries world-wide) and the number continues to increase as more National Statistical Offices cooperate with the initiative. Statistical quality is a serious concern both for the producers of the microdata as well as the researchers who use them. This paper applies the intra-cohort comparison method to pairs of integrated (harmonized) samples for fifteen African countries to assess statistical coherence using as a benchmark the proportion completing primary school by single years of birth. Samples for six countries show near perfect coherence (R2 > .9, and regression coefficients ~1.0 +/- <0.08). For a second group of five countries, coefficients are only slightly larger (R2 > 0.6 <0.9). Large deviations from 1.0 characterize samples for only four countries. On the whole, the results suggest that samples for the fifteen countries have considerable utility for socio-demographic analysis

Genotype imputation for the prediction of genomic breeding values in non-genotyped and low-density genotyped individuals

<p>Abstract</p> <p>Background</p> <p>There is wide interest in calculating genomic breeding values (GEBVs) in livestock using dense, genome-wide SNP data. The general framework for genomic selection assumes all individuals are genotyped at high-density, which may not be true in practice. Methods to add additional genotypes for individuals not genotyped at high density have the potential to increase GEBV accuracy with little or no additional cost. In this study a long haplotype library was created using a long range phasing algorithm and used in combination with segregation analysis to impute dense genotypes for non-genotyped dams in the training dataset (S1) and for non-genotyped or low-density genotyped individuals in the prediction dataset (S2), using the 14^th QTL-MAS Workshop dataset. Alternative low-density scenarios were evaluated for accuracy of imputed genotypes and prediction of GEBVs.</p> <p>Results</p> <p>In S1, females in the training population were not genotyped and prediction individuals were either not genotyped or genotyped at low-density (evenly spaced at 2, 5 or 10 Mb). The proportion of correctly imputed genotypes for training females did not change when genotypes were added for individuals in the prediction set whereas the number of correctly imputed genotypes in the prediction set increased slightly (S1). The S2 scenario assumed the complete training set was genotyped for all SNPs and the prediction set was not genotyped or genotyped at low-density. The number of correctly imputed genotypes increased with genotyping density in the prediction set. Accuracy of genomic breeding values for the prediction set in each scenario were the correlation of GEBVs with true breeding values and were used to evaluate the potential loss in accuracy with reduced genotyping. For both S1 and S2 the GEBV accuracies were similar when the prediction set was not genotyped and increased with the addition of low-density genotypes, with the increase larger for S2 than S1.</p> <p>Conclusions</p> <p>Genotype imputation using a long haplotype library and segregation analysis is promising for application in sparsely-genotyped pedigrees. The results of this study suggest that dense genotypes can be imputed for selection candidates with some loss in genomic breeding value accuracy, but with levels of accuracy higher than traditional BLUP estimated breeding values. Accurate genotype imputation would allow for a single low-density SNP panel to be used across traits.</p

“I’m Not Gonna Pull the Rug out from under You”: Patient-Provider Communication about Opioid Tapering

In response to increases in harms associated with prescription opioids, opioid prescribing has come under greater scrutiny, leading many healthcare organizations and providers to consider or mandate opioid dose reductions (tapering) for patients with chronic pain. Communicating about tapering can be difficult, particularly for patients on long-term opioids who perceive benefits and are using their medications as prescribed. Given the importance of effective patient-provider communication for pain management and recent health system-level initiatives and provider practices to taper opioids, this study used qualitative methods to understand communication processes related to opioid tapering, to identify best practices and opportunities for improvement. Up to 3 clinic visits per patient were audio-recorded, and individual interviews were conducted with patients and their providers. Four major themes emerged: 1) Explaining—Patients needed to understand individualized reasons for tapering, beyond general, population-level concerns such as addiction potential; 2) Negotiating—Patients needed to have input, even if it was simply the rate of tapering; 3) Managing difficult conversations—When patients and providers did not reach a shared understanding, difficulties and misunderstandings arose; 4) Non-abandonment—Patients needed to know that their providers would not abandon them throughout the tapering process

Bovine host genome acts on rumen microbiome function linked to methane emissions

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH(4)), highlighting the strength of a common host genomic control of specific microbial processes and CH(4). Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH(4) emissions per generation, which is higher than for selection based on measured CH(4) using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH(4) emissions and mitigate climate change