Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Methods Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. Results In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Conclusions Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available

Computer-aided rational design of the phosphotransferase system for enhanced glucose uptake in Escherichia coli

The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer-aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate

Genomic evaluations with many more genotypes

<p>Abstract</p> <p>Background</p> <p>Genomic evaluations in Holstein dairy cattle have quickly become more reliable over the last two years in many countries as more animals have been genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as the 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. The overall cost of selection is reduced by genotyping most animals at less than the highest density and imputing their missing genotypes using haplotypes. Algorithms to combine different densities need to be efficient because numbers of genotyped animals and markers may continue to grow quickly.</p> <p>Methods</p> <p>Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage disequilibrium was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations using linear and nonlinear methods were compared.</p> <p>Results</p> <p>Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations with 50,000 markers and 1.6% lower with 500,000 markers.</p> <p>Conclusions</p> <p>Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can be modified to reflect success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.</p

Studies on changes of estimated breeding values of U.S. Holstein bulls for final score from the first to second crop of daughters

The purpose of this study was to find ways of reducing changes of sire predicted transmitting ability for type’s final scores (PTATs) from the first to second crop of daughters. The PTATs were estimated from two datasets: D01 (scores recorded up to 2001) and D05 (scores recorded up to 2005). The PTAT changes were calculated as the difference between the evaluations based on D01 and D05. The PTATs were adjusted to a common genetic base of all evaluated cows born in 1995. The single-trait (ST) animal model included the fixed effects of the herd–year–season–classifier, age by year group at classification, stage of lactation at classification, registry status of animals, and additive genetic and permanent environment random effects. Unknown parent groups (UPGs) were defined based on every other birth year starting from 1972. Modifications to the ST model included the usage of a single record per cow, separate UPGs for first and second crop daughters, separate UPGs for sires and dams, and deepened pedigrees for dams with missing phenotypic records. Also, the multiple-trait (MT) model treated records of registered and grade cows as correlated traits. The mean PTAT change, for all of the sires, was close to zero in all of the models analyzed. The estimated mean PTAT change for 145 sires with 40 to 100 first crop and ≥200 second crop daughters was −0.33, −0.20, −0.13, −0.28, and −0.12 with ST, only first records, only last records, updated pedigrees, and allowing separate parent groups (PGs) for sires and dams after updating the pedigrees, respectively. The percentages of sires showing PTAT decline were reduced from 74.5 (with ST) to 57.3 by using only the last records of cows, and to 56.4 by allowing separate UPGs for sires and dams after updating the pedigrees. Though updating of the pedigrees alone was not effective, separate UPGs for sires together with additional pedigree was helpful in reducing the bias

Cubic-spline interpolation to estimate effects of inbreeding on milk yield in first lactation Holstein cows

Milk yield records (305d, 2X, actual milk yield) of 123,639 registered first lactation Holstein cows were used to compare linear regression (y = β0 + β1X + e), quadratic regression, (y = β0 + β1X + β2X2 + e) cubic regression (y = β0 + β1X + β2X2 + β3X3 +e) and fixed factor models, with cubic-spline interpolation models, for estimating the effects of inbreeding on milk yield. Ten animal models, all with herd-year-season of calving as fixed effect, were compared using the Akaike corrected-Information Criterion (AICc). The cubic-spline interpolation model with seven knots had the lowest AICc, whereas for all those labeled as “traditional”, AICc was higher than the best model. Results from fitting inbreeding using a cubic-spline with seven knots were compared to results from fitting inbreeding as a linear covariate or as a fixed factor with seven levels. Estimates of inbreeding effects were not significantly different between the cubic-spline model and the fixed factor model, but were significantly different from the linear regression model. Milk yield decreased significantly at inbreeding levels greater than 9%. Variance component estimates were similar for the three models. Ranking of the top 100 sires with daughter records remained unaffected by the model used

A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes

<p>Abstract</p> <p>Background</p> <p>Knowing the phase of marker genotype data can be useful in genome-wide association studies, because it makes it possible to use analysis frameworks that account for identity by descent or parent of origin of alleles and it can lead to a large increase in data quantities via genotype or sequence imputation. Long-range phasing and haplotype library imputation constitute a fast and accurate method to impute phase for SNP data.</p> <p>Methods</p> <p>A long-range phasing and haplotype library imputation algorithm was developed. It combines information from surrogate parents and long haplotypes to resolve phase in a manner that is not dependent on the family structure of a dataset or on the presence of pedigree information.</p> <p>Results</p> <p>The algorithm performed well in both simulated and real livestock and human datasets in terms of both phasing accuracy and computation efficiency. The percentage of alleles that could be phased in both simulated and real datasets of varying size generally exceeded 98% while the percentage of alleles incorrectly phased in simulated data was generally less than 0.5%. The accuracy of phasing was affected by dataset size, with lower accuracy for dataset sizes less than 1000, but was not affected by effective population size, family data structure, presence or absence of pedigree information, and SNP density. The method was computationally fast. In comparison to a commonly used statistical method (fastPHASE), the current method made about 8% less phasing mistakes and ran about 26 times faster for a small dataset. For larger datasets, the differences in computational time are expected to be even greater. A computer program implementing these methods has been made available.</p> <p>Conclusions</p> <p>The algorithm and software developed in this study make feasible the routine phasing of high-density SNP chips in large datasets.</p

Productive Parvovirus B19 Infection of Primary Human Erythroid Progenitor Cells at Hypoxia Is Regulated by STAT5A and MEK Signaling but not HIFα

Human parvovirus B19 (B19V) causes a variety of human diseases. Disease outcomes of bone marrow failure in patients with high turnover of red blood cells and immunocompromised conditions, and fetal hydrops in pregnant women are resulted from the targeting and destruction of specifically erythroid progenitors of the human bone marrow by B19V. Although the ex vivo expanded erythroid progenitor cells recently used for studies of B19V infection are highly permissive, they produce progeny viruses inefficiently. In the current study, we aimed to identify the mechanism that underlies productive B19V infection of erythroid progenitor cells cultured in a physiologically relevant environment. Here, we demonstrate an effective reverse genetic system of B19V, and that B19V infection of ex vivo expanded erythroid progenitor cells at 1% O2 (hypoxia) produces progeny viruses continuously and efficiently at a level of approximately 10 times higher than that seen in the context of normoxia. With regard to mechanism, we show that hypoxia promotes replication of the B19V genome within the nucleus, and that this is independent of the canonical PHD/HIFα pathway, but dependent on STAT5A and MEK/ERK signaling. We further show that simultaneous upregulation of STAT5A signaling and down-regulation of MEK/ERK signaling boosts the level of B19V infection in erythroid progenitor cells under normoxia to that in cells under hypoxia. We conclude that B19V infection of ex vivo expanded erythroid progenitor cells at hypoxia closely mimics native infection of erythroid progenitors in human bone marrow, maintains erythroid progenitors at a stage conducive to efficient production of progeny viruses, and is regulated by the STAT5A and MEK/ERK pathways

In vitro evaluation of various bioabsorbable and nonresorbable barrier membranes for guided tissue regeneration

<p>Abstract</p> <p>Background</p> <p>Different types of bioabsorbable and nonresorbable membranes have been widely used for guided tissue regeneration (GTR) with its ultimate goal of regenerating lost periodontal structures. The purpose of the present study was to evaluate the biological effects of various bioabsorbable and nonresorbable membranes in cultures of primary human gingival fibroblasts (HGF), periodontal ligament fibroblasts (PDLF) and human osteoblast-like (HOB) cells <it>in vitro</it>.</p> <p>Methods</p> <p>Three commercially available collagen membranes [TutoDent^®(TD), Resodont^®(RD) and BioGide^®(BG)] as well as three nonresorbable polytetrafluoroethylene (PTFE) membranes [ACE (AC), Cytoplast^®(CT) and TefGen-FD^®(TG)] were tested. Cells plated on culture dishes (CD) served as positive controls. The effect of the barrier membranes on HGF, PDLF as well as HOB cells was assessed by the Alamar Blue fluorometric proliferation assay after 1, 2.5, 4, 24 and 48 h time periods. The structural and morphological properties of the membranes were evaluated by scanning electron microscopy (SEM).</p> <p>Results</p> <p>The results showed that of the six barriers tested, TD and RD demonstrated the highest rate of HGF proliferation at both earlier (1 h) and later (48 h) time periods (<it>P </it>< 0.001) compared to all other tested barriers and CD. Similarly, TD, RD and BG had significantly higher numbers of cells at all time periods when compared with the positive control in PDLF culture (<it>P </it>≤ 0.001). In HOB cell culture, the highest rate of cell proliferation was also calculated for TD at all time periods (<it>P </it>< 0.001). SEM observations demonstrated a microporous structure of all collagen membranes, with a compact top surface and a porous bottom surface, whereas the nonresorbable PTFE membranes demonstrated a homogenous structure with a symmetric dense skin layer.</p> <p>Conclusion</p> <p>Results from the present study suggested that GTR membrane materials, per se, may influence cell proliferation in the process of periodontal tissue/bone regeneration. Among the six membranes examined, the bioabsorbable membranes demonstrated to be more suitable to stimulate cellular proliferation compared to nonresorbable PTFE membranes.</p

Modeling relationships between calving traits: a comparison between standard and recursive mixed models

<p>Abstract</p> <p>Background</p> <p>The use of structural equation models for the analysis of recursive and simultaneous relationships between phenotypes has become more popular recently. The aim of this paper is to illustrate how these models can be applied in animal breeding to achieve parameterizations of different levels of complexity and, more specifically, to model phenotypic recursion between three calving traits: gestation length (GL), calving difficulty (CD) and stillbirth (SB). All recursive models considered here postulate heterogeneous recursive relationships between GL and liabilities to CD and SB, and between liability to CD and liability to SB, depending on categories of GL phenotype.</p> <p>Methods</p> <p>Four models were compared in terms of goodness of fit and predictive ability: 1) standard mixed model (SMM), a model with unstructured (co)variance matrices; 2) recursive mixed model 1 (RMM1), assuming that residual correlations are due to the recursive relationships between phenotypes; 3) RMM2, assuming that correlations between residuals and contemporary groups are due to recursive relationships between phenotypes; and 4) RMM3, postulating that the correlations between genetic effects, contemporary groups and residuals are due to recursive relationships between phenotypes.</p> <p>Results</p> <p>For all the RMM considered, the estimates of the structural coefficients were similar. Results revealed a nonlinear relationship between GL and the liabilities both to CD and to SB, and a linear relationship between the liabilities to CD and SB.</p> <p>Differences in terms of goodness of fit and predictive ability of the models considered were negligible, suggesting that RMM3 is plausible.</p> <p>Conclusions</p> <p>The applications examined in this study suggest the plausibility of a nonlinear recursive effect from GL onto CD and SB. Also, the fact that the most restrictive model RMM3, which assumes that the only cause of correlation is phenotypic recursion, performs as well as the others indicates that the phenotypic recursion may be an important cause of the observed patterns of genetic and environmental correlations.</p