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

Bone Degeneration and Recovery after Early and Late Bisphosphonate Treatment of Ovariectomized Wistar Rats Assessed by In Vivo Micro-Computed Tomography

Bisphosphonates are antiresorptive drugs commonly used to treat osteoporosis. It is not clear, however, what the influence of the time point of treatment is. Recently developed in vivo micro-computed tomographic (CT) scanners offer the possibility to study such effects on bone microstructure in rats. The aim of this study was to determine the influence of early and late zoledronic acid treatment on bone in ovariectomized rats, using in vivo micro-CT. Twenty-nine female Wistar rats were divided into the following groups: ovariectomy (OVX, n = 5), OVX and zoledronic acid (ZOL) at week 0 (n = 8), OVX and ZOL at week 8 (n = 7), and sham (n = 9). CT scans were made of the proximal tibia at weeks 0, 2, 4, 8, 12, and 16; and bone structural parameters were determined in the metaphysis. Two fluorescent labels were administered to calculate dynamic histomorphometric parameters. At week 16, all groups were significantly different from each other in bone volume fraction (BV/TV), connectivity density, and trabecular number (Tb.N), except for the early ZOL and control groups which were not significantly different for any structural parameter. After ZOL treatment at week 8, BV/TV, structure model index, Tb.N, and trabecular thickness significantly improved in the late ZOL group. The OVX and ZOL groups showed, respectively, higher and lower bone formation rates than the control group. Early ZOL treatment inhibited all bone microstructural changes seen after OVX. Late ZOL treatment significantly improved bone microstructure, although the structure did not recover to original levels. Early ZOL treatment resulted in a significantly better microstructure than late treatment. However, late treatment was still significantly better than no treatment

Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling

Background: Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences. Results: We present a mixture of experts approach to biomolecular sequence labeling that takes into account the global similarity between biomolecular sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian techniques to combine the predictions of the experts. We evaluate our approach on two biomolecular sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biomolecular sequence data. Conclusion: The mixture of experts model helps improve the performance of machine learning methods for identifying functionally important sites in biomolecular sequences.This is a proceeding from IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 10 (2009): S4, doi: 10.1186/1471-2105-10-S4-S4. Posted with permission.</p

Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

BACKGROUND: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level probe intensities, then deriving significance by comparing these expression levels between conditions. The proposed PL-LM (Probe-Level Linear Model) method implements a linear model applied on the probe-level data to directly estimate the treatment effect. A finite mixture of Gaussian components is then used to identify DEGs using the coefficients estimated by the linear model. This approach can readily be applied to experimental design with or without replication. RESULTS: On a wholly defined dataset, the PL-LM method was able to identify 75% of the differentially expressed genes within 10% of false positives. This accuracy was achieved both using the three replicates per conditions available in the dataset and using only one replicate per condition. CONCLUSION: The method achieves, on this dataset, a higher accuracy than the best set of tools identified by the authors of the dataset, and does so using only one replicate per condition

Identification of monetary policy in SVAR models: A data-oriented perspective

In the literature using short-run timing restrictions to identify monetary policy shocks in vector-auto-regressions (VAR) there is a debate on whether (i) contemporaneous real activity and prices or (ii) only data typically observed with high frequency should be assumed to be in the information set of the central bank when the interest rate decision is taken. This paper applies graphical modeling theory, a data-based tool, in a small-scale VAR of the US economy to shed light on this issue. Results corroborate the second type of assumption

The contribution of genetic variants to disease depends on the ruler

Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures-specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction-and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease

Effects of protein–carbohydrate supplementation on immunity and resistance training outcomes: a double-blind, randomized, controlled clinical trial

Purpose: To examine the impact of ingesting hydrolyzed beef protein, whey protein, and carbohydrate on resistance training outcomes, body composition, muscle thickness, blood indices of health and salivary human neutrophil peptides (HNP1-3), as reference of humoral immunity followed an 8-week resistance training program in college athletes. Methods: Twenty-seven recreationally physically active males and females (n = 9 per treatment) were randomly assigned to one of the three groups: hydrolyzed beef protein, whey protein, or non-protein isoenergetic carbohydrate. Treatment consisted of ingesting 20 g of supplement, mixed with orange juice, once a day immediately post-workout or before breakfast on non-training days. Measurements were performed pre- and post-intervention on total load (kg) lifted at the first and last workout, body composition (via plethysmography) vastus medialis thickness (mm) (via ultrasonography), and blood indices of health. Salivary HNP1-3 were determined before and after performing the first and last workout. Results: Salivary concentration and secretion rates of the HNP1-3 decreased in the beef condition only from pre-first-workout (1.90 ± 0.83 μg/mL; 2.95 ± 2.83 μg/min, respectively) to pre-last-workout (0.92 ± 0.63 μg/mL, p = 0.025, d = 1.03; 0.76 ± 0.74 μg/min, p = 0.049, d = 0.95), and post-last-workout (0.95 ± 0.60 μg/mL, p = 0.032, d = 1.00; 0.59 ± 0.52 μg/min, p = 0.027, d = 1.02). No other significant differences between groups were observed. Conclusions: Supplementation with a carbohydrate–protein beverage may support resistance training outcomes in a comparable way as the ingestion of only carbohydrate. Furthermore, the ingestion of 20 g of hydrolyzed beef protein resulted in a decreased level and secretion rates of the HNP1-3 from baseline with no negative effect on blood indices of health

Modeling Boundary Vector Cell Firing Given Optic Flow as a Cue

Boundary vector cells in entorhinal cortex fire when a rat is in locations at a specific distance from walls of an environment. This firing may originate from memory of the barrier location combined with path integration, or the firing may depend upon the apparent visual input image stream. The modeling work presented here investigates the role of optic flow, the apparent change of patterns of light on the retina, as input for boundary vector cell firing. Analytical spherical flow is used by a template model to segment walls from the ground, to estimate self-motion and the distance and allocentric direction of walls, and to detect drop-offs. Distance estimates of walls in an empty circular or rectangular box have a mean error of less than or equal to two centimeters. Integrating these estimates into a visually driven boundary vector cell model leads to the firing patterns characteristic for boundary vector cells. This suggests that optic flow can influence the firing of boundary vector cells