Mapping quantitative trait loci in line cross with repeat records

<p>Abstract</p> <p>Background</p> <p>Phenotypes with repeat records from one individual or multiple individuals were often encountered in practices of mapping QTL in linecross. The current genetic mapping method for a trait with repeat records is adopted by simply replacing the phenotype by the average value of the repeat records. This simple treatment has not sufficiently utilized the information from the replication and ignored the impacts of the permanent environmental effects on the accuracy of the estimated QTL.</p> <p>Results</p> <p>We propose to map QTL by using the repeatability model to directly analyze the repeat records rather than simply analyze the mean phenotype, improving the efficiency of QTL detecting because of adequately utilizing the information from data and allowing for the permanent environmental effects. A maximum likelihood method implemented via the expectation-maximization (EM) algorithm is applied to perform the parameter estimation of the repeatability model. The superiority of the mapping method based on the repeatability model over simple analysis using the mean phenotype was demonstrated by a series of simulations.</p> <p>Conclusion</p> <p>Our results suggest that the proposed method can serve as a powerful alternative to existing methods. By mean of the repeatability model, utilizing the repeat records on individual may improve the efficiency of QTL detecting in line cross.</p

Defining the in vivo mechanism of air pollutant toxicity using murine stress response biomarkers.

Air pollution can cause a wide range of serious human diseases. For the informed instigation of interventions which prevent these outcomes there is an urgent need to develop robust in vivo biomarkers which provide insights into mechanisms of toxicity and relate pollutants to specific adverse outcomes. We exemplify for a first time the application of in vivo stress response reporters in establishing mechanisms of air pollution toxicity and the application of this knowledge in epidemiological studies. We first demonstrated the utility of reporter mice to understand toxicity mechanisms of air pollutants using diesel exhaust particles compounds. We observed that nitro-PAHs induced Hmox1 and CYP1a1 reporters in a time- and dose-dependent, cell- and tissue-specific manner. Using in vivo genetic and pharmacological approaches we confirmed that the NRF2 pathway mediated this Hmox1-reporter induction stress reporter activity. We then correlated the activation of stress-reporter models (oxidative stress/inflammation, DNA damage and Ah receptor -AhR- activity) with responses in primary human nasal cells exposed to chemicals present in particulate matter (PM; PM2.5-SRM2975, PM10-SRM1648b) or fresh roadside PM10. To exemplify their use in clinical studies, Pneumococcal adhesion was assessed in exposed primary human nasal epithelial cells (HPNEpC). The combined use of HPNEpC and in vivo reporters demonstrated that London roadside PM10 particles induced pneumococcal infection in HPNEpC mediated by oxidative stress responses. The combined use of in vivo reporter models with human data thus provides a robust approach to define the relationship between air pollutant exposure and health risks. Moreover, these models can be used in epidemiological studies to hazard ranking environmental pollutants by considering the complexity of mechanisms of toxicity. These data will facilitate the relationship between toxic potential and the level of pollutant exposure in populations to be established and potentially extremely valuable tools for intervention studies for disease prevention

A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images

Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability. While building models for template estimation, variability in sites and image acquisition protocols need to be accounted for. To account for such variability, we propose a generative template estimation model that makes simultaneous inference of both bias fields in individual images, deformations for image registration, and variance hyperparameters. In contrast, existing maximum a posterori based methods need to rely on either bias-invariant similarity measures or robust image normalization. Results on synthetic and real brain MRI images demonstrate the capability of the model to capture heterogeneity in intensities and provide a reliable template estimation from registration

Inclusion of genetically identical animals to a numerator relationship matrix and modification of its inverse

In the field of animal breeding, estimation of genetic parameters and prediction of breeding values are routinely conducted by analyzing quantitative traits. Using an animal model and including the direct inverse of a numerator relationship matrix (NRM) into a mixed model has made these analyses possible. However, a method including a genetically identical animal (GIA) in NRM if genetic relationships between pairs of GIAs are not perfect, is still lacking. Here, we describe a method to incorporate GIAs into NRM using a K matrix in which diagonal elements are set to 1.0, off-diagonal elements between pairs of GIAs to (1-x) and the other elements to 0, where x is a constant less than 0.05. The inverse of the K matrix is then calculated directly by a simple formula. Thus, the inverse of the NRM is calculated by the products of the lower triangular matrix that identifies the parents of each individual, its transpose matrix, the inverse of the K matrix and the inverse of diagonal matrix D, in which the diagonal elements comprise a number of known parents and their inbreeding coefficients. The computing method is adaptable to the analysis of a data set including pairs of GIAs with imperfect relationships

Genetic prediction of complex traits: integrating infinitesimal and marked genetic effects

Genetic prediction for complex traits is usually based on models including individual (infinitesimal) or marker effects. Here, we concentrate on models including both the individual and the marker effects. In particular, we develop a ''Mendelian segregation'' model combining infinitesimal effects for base individuals and realized Mendelian sampling in descendants described by the available DNA data. The model is illustrated with an example and the analyses of a public simulated data file. Further, the potential contribution of such models is assessed by simulation. Accuracy, measured as the correlation between true (simulated) and predicted genetic values, was similar for all models compared under different genetic backgrounds. As expected, the segregation model is worthwhile when markers capture a low fraction of total genetic variance. (Résumé d'auteur

Extension of the bayesian alphabet for genomic selection

<p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability <it>π </it>that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.</p> <p>Results</p> <p>Estimates of <it>π </it>from BayesC<it>π</it>, in contrast to BayesD<it>π</it>, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesC<it>π </it>than for BayesD<it>π</it>, and longest for our implementation of BayesA.</p> <p>Conclusions</p> <p>Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesC<it>π </it>has merit for routine applications.</p

‘Carbon-Monoxide-Releasing Molecule-2 (CORM-2)’ Is a Misnomer: Ruthenium Toxicity, Not CO Release, Accounts for Its Antimicrobial Effects

Carbon monoxide (CO)-releasing molecules (CORMs) are used to deliver CO, a biological ‘gasotransmitter’, in biological chemistry and biomedicine. CORMs kill bacteria in culture and in animal models, but are reportedly benign towards mammalian cells. CORM-2 (tricarbonyldichlororuthenium(II) dimer, Ru2Cl4(CO)6), the first widely used and commercially available CORM, displays numerous pharmacological, biochemical and microbiological activities, generally attributed to CO release. Here, we investigate the basis of its potent antibacterial activity against Escherichia coli and demonstrate, using three globin CO sensors, that CORM-2 releases negligible CO (<0.1 mol CO per mol CORM-2). A strong negative correlation between viability and cellular ruthenium accumulation implies that ruthenium toxicity underlies biocidal activity. Exogenous amino acids and thiols (especially cysteine, glutathione and N-acetyl cysteine) protected bacteria against inhibition of growth by CORM-2. Bacteria treated with 30 μM CORM-2, with added cysteine and histidine, exhibited no significant loss of viability, but were killed in the absence of these amino acids. Their prevention of toxicity correlates with their CORM-2-binding affinities (Cys, Kd 3 μM; His, Kd 130 μM) as determined by 1H-NMR. Glutathione is proposed to be an important intracellular target of CORM-2, with CORM-2 having a much higher affinity for reduced glutathione (GSH) than oxidised glutathione (GSSG) (GSH, Kd 2 μM; GSSG, Kd 25,000 μM). The toxicity of low, but potent, levels (15 μM) of CORM-2 was accompanied by cell lysis, as judged by the release of cytoplasmic ATP pools. The biological effects of CORM-2 and related CORMs, and the design of biological experiments, must be re-examined in the light of these data

Retrospective harm benefit analysis of pre-clinical animal research for six treatment interventions

The harm benefit analysis (HBA) is the cornerstone of animal research regulation and is considered to be a key ethical safeguard for animals. The HBA involves weighing the anticipated benefits of animal research against its predicted harms to animals but there are doubts about how objective and accountable this process is.i. To explore the harms to animals involved in pre-clinical animal studies and to assess these against the benefits for humans accruing from these studies; ii. To test the feasibility of conducting this type of retrospective HBA.Data on harms were systematically extracted from a sample of pre-clinical animal studies whose clinical relevance had already been investigated by comparing systematic reviews of the animal studies with systematic reviews of human studies for the same interventions (antifibrinolytics for haemorrhage, bisphosphonates for osteoporosis, corticosteroids for brain injury, Tirilazad for stroke, antenatal corticosteroids for neonatal respiratory distress and thrombolytics for stroke). Clinical relevance was also explored in terms of current clinical practice. Harms were categorised for severity using an expert panel. The quality of the research and its impact were considered. Bateson's Cube was used to conduct the HBA.The most common assessment of animal harms by the expert panel was 'severe'. Reported use of analgesia was rare and some animals (including most neonates) endured significant procedures with no, or only light, anaesthesia reported. Some animals suffered iatrogenic harms. Many were kept alive for long periods post-experimentally but only 1% of studies reported post-operative care. A third of studies reported that some animals died prior to endpoints. All the studies were of poor quality. Having weighed the actual harms to animals against the actual clinical benefits accruing from these studies, and taking into account the quality of the research and its impact, less than 7% of the studies were permissible according to Bateson's Cube: only the moderate bisphosphonate studies appeared to minimise harms to animals whilst being associated with benefit for humans.This is the first time the accountability of the HBA has been systematically explored across a range of pre-clinical animal studies. The regulatory systems in place when these studies were conducted failed to safeguard animals from severe suffering or to ensure that only beneficial, scientifically rigorous research was conducted. Our findings indicate a pressing need to: i. review regulations, particularly those that permit animals to suffer severe harms; ii. reform the processes of prospectively assessing pre-clinical animal studies to make them fit for purpose; and iii. systematically evaluate the benefits of pre-clinical animal research to permit a more realistic assessment of its likely future benefits