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

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

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

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

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

In vitro synthesis of heparosan using recombinant Pasteurella multocida heparosan synthase PmHS2

In vertebrates and bacteria, heparosan the precursor of heparin is synthesized by glycosyltransferases via the stepwise addition of UDP-N-acetylglucosamine and UDP-glucuronic acid. As heparin-like molecules represent a great interest in the pharmaceutical area, the cryptic Pasteurella multocida heparosan synthase PmHS2 found to catalyze heparosan synthesis using substrate analogs has been studied. In this paper, we report an efficient way to purify PmHS2 and to maintain its activity stable during 6 months storage at −80 °C using His-tag purification and a desalting step. In the presence of 1 mM of each nucleotide sugar, purified PmHS2 synthesized polymers up to an average molecular weight of 130 kDa. With 5 mM of UDP-GlcUA and 5 mM of UDP-GlcNAc, an optimal specific activity, from 3 to 6 h of incubation, was found to be about 0.145 nmol/μg/min, and polymers up to an average of 102 kDa were synthesized in 24 h. In this study, we show that the chain length distribution of heparosan polymers can be controlled by change of the initial nucleotide sugar concentration. It was observed that low substrate concentration favors the formation of high molecular weight heparosan polymer with a low polydispersity while high substrate concentration did the opposite. Similarities in the polymerization mechanism between PmHS2, PmHS1, and PmHAS are discussed

Design of a Bovine Low-Density SNP Array Optimized for Imputation

The Illumina BovineLD BeadChip was designed to support imputation to higher density genotypes in dairy and beef breeds by including single-nucleotide polymorphisms (SNPs) that had a high minor allele frequency as well as uniform spacing across the genome except at the ends of the chromosome where densities were increased. The chip also includes SNPs on the Y chromosome and mitochondrial DNA loci that are useful for determining subspecies classification and certain paternal and maternal breed lineages. The total number of SNPs was 6,909. Accuracy of imputation to Illumina BovineSNP50 genotypes using the BovineLD chip was over 97% for most dairy and beef populations. The BovineLD imputations were about 3 percentage points more accurate than those from the Illumina GoldenGate Bovine3K BeadChip across multiple populations. The improvement was greatest when neither parent was genotyped. The minor allele frequencies were similar across taurine beef and dairy breeds as was the proportion of SNPs that were polymorphic. The new BovineLD chip should facilitate low-cost genomic selection in taurine beef and dairy cattle

Impact of monopolar radiofrequency energy on subchondral bone viability

The purpose of this study was to analyze the impact of monopolar radiofrequency energy treatment on subchondral bone viability. The femoral grooves of six chinchilla bastard rabbits were exposed bilaterally to monopolar radiofrequency energy for 2, 4 and 8 s, creating a total of 36 defects. An intravital fluorescence bone-labeling technique characterized the process of subchondral bone mineralization within the 3 months following exposure to radiofrequency energy and was analyzed by widefield epifluorescence optical sectioning microscopy using an ApoTome. After 2 s of radiofrequency energy exposure, regular fluorescence staining of the subchondral bone was evident in all samples when compared to untreated areas. The depth of osteonecrosis after 4 and 8 s of radiofrequency energy treatment averaged 126 and 942 µm at 22 days (P < .05; P < .01). The 4 s treatment group showed no osteonecrosis after 44 days whereas the depth of osteonecrosis extended from 519 µm at 44 days (P < .01), to 281 µm at 66 days (P < .01) and to 133 µm at 88 days (P < .05) after 8 s of radiofrequency energy application. Though radiofrequency energy may induce transient osteonecrosis in the superficial zone of the subchondral bone, the results of this study suggest that post-arthroscopic osteonecrosis appears to be of only modest risk given the current clinical application in humans

Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers

Background: At the current price, the use of high-density single nucleotide polymorphisms (SNP) genotyping assays in genomic selection of dairy cattle is limited to applications involving elite sires and dams. The objective of this study was to evaluate the use of low-density assays to predict direct genomic value (DGV) on five milk production traits, an overall conformation trait, a survival index, and two profit index traits (APR, ASI). Methods. Dense SNP genotypes were available for 42,576 SNP for 2,114 Holstein bulls and 510 cows. A subset of 1,847 bulls born between 1955 and 2004 was used as a training set to fit models with various sets of pre-selected SNP. A group of 297 bulls born between 2001 and 2004 and all cows born between 1992 and 2004 were used to evaluate the accuracy of DGV prediction. Ridge regression (RR) and partial least squares regression (PLSR) were used to derive prediction equations and to rank SNP based on the absolute value of the regression coefficients. Four alternative strategies were applied to select subset of SNP, namely: subsets of the highest ranked SNP for each individual trait, or a single subset of evenly spaced SNP, where SNP were selected based on their rank for ASI, APR or minor allele frequency within intervals of approximately equal length. Results: RR and PLSR performed very similarly to predict DGV, with PLSR performing better for low-density assays and RR for higher-density SNP sets. When using all SNP, DGV predictions for production traits, which have a higher heritability, were more accurate (0.52-0.64) than for survival (0.19-0.20), which has a low heritability. The gain in accuracy using subsets that included the highest ranked SNP for each trait was marginal (5-6%) over a common set of evenly spaced SNP when at least 3,000 SNP were used. Subsets containing 3,000 SNP provided more than 90% of the accuracy that could be achieved with a high-density assay for cows, and 80% of the high-density assay for young bulls. Conclusions: Accurate genomic evaluation of the broader bull and cow population can be achieved with a single genotyping assays containing ∼ 3,000 to 5,000 evenly spaced SNP