Accuracy of breeding values for production traits in turkeys (Meleagris gallopavo) using recursive models with or without genomics.

BACKGROUND Knowledge about potential functional relationships among traits of interest offers a unique opportunity to understand causal mechanisms and to optimize breeding goals, management practices, and prediction accuracy. In this study, we inferred the phenotypic causal networks among five traits in a turkey population and assessed the effect of the use of such causal structures on the accuracy of predictions of breeding values. METHODS Phenotypic data on feed conversion ratio, residual feed intake, body weight, breast meat yield, and walking score in addition to genotype data from a commercial breeding population were used. Causal links between the traits were detected using the inductive causation algorithm based on the joint distribution of genetic effects obtained from a standard Bayesian multiple trait model. Then, a structural equation model was implemented to infer the magnitude of causal structure coefficients among the phenotypes. Accuracies of predictions of breeding values derived using pedigree- and blending-based multiple trait models were compared to those obtained with the pedigree- and blending-based structural equation models. RESULTS In contrast to the two unconditioned traits (i.e., feed conversion ratio and breast meat yield) in the causal structures, the three conditioned traits (i.e., residual feed intake, body weight, and walking score) showed noticeable changes in estimates of genetic and residual variances between the structural equation model and the multiple trait model. The analysis revealed interesting functional associations and indirect genetic effects. For example, the structural coefficient for the path from body weight to walking score indicated that a 1-unit genetic improvement in body weight is expected to result in a 0.27-unit decline in walking score. Both structural equation models outperformed their counterpart multiple trait models for the conditioned traits. Applying the causal structures led to an increase in accuracy of estimated breeding values of approximately 7, 6, and 20% for residual feed intake, body weight, and walking score, respectively, and different rankings of selection candidates for the conditioned traits. CONCLUSIONS Our results suggest that structural equation models can improve genetic selection decisions and increase the prediction accuracy of breeding values of selection candidates. The identified causal relationships between the studied traits should be carefully considered in future turkey breeding programs

Photoionization of High Altitude Gas in a Supernova-Driven Turbulent Interstellar Medium

We investigate models for the photoionization of the widespread diffuse ionized gas in galaxies. In particular we address the long standing question of the penetration of Lyman continuum photons from sources close to the galactic midplane to large heights in the galactic halo. We find that recent hydrodynamical simulations of a supernova-driven interstellar medium have low density paths and voids that allow for ionizing photons from midplane OB stars to reach and ionize gas many kiloparsecs above the midplane. We find ionizing fluxes throughout our simulation grids are larger than predicted by one dimensional slab models, thus allowing for photoionization by O stars of low altitude neutral clouds in the Galaxy that are also detected in Halpha. In previous studies of such clouds the photoionization scenario had been rejected and the Halpha had been attributed to enhanced cosmic ray ionization or scattered light from midplane H II regions. We do find that the emission measure distributions in our simulations are wider than those derived from Halpha observations in the Milky Way. In addition, the horizontally averaged height dependence of the gas density in the hydrodynamical models is lower than inferred in the Galaxy. These discrepancies are likely due to the absence of magnetic fields in the hydrodynamic simulations and we discuss how magnetohydrodynamic effects may reconcile models and observations. Nevertheless, we anticipate that the inclusion of magnetic fields in the dynamical simulations will not alter our primary finding that midplane OB stars are capable of producing high altitude diffuse ionized gas in a realistic three-dimensional interstellar medium.Comment: ApJ accepted. 17 pages, 7 figure

Infrared Spectroscopy of the Diffuse Ionized Halo of NGC 891

We present infrared spectroscopy from the Spitzer Space Telescope at one disk position and two positions at a height of 1 kpc from the disk in the edge-on spiral NGC 891, with the primary goal of studying halo ionization. Our main result is that the [Ne III]/[Ne II] ratio, which provides a measure of the hardness of the ionizing spectrum free from the major problems plaguing optical line ratios, is enhanced in the extraplanar pointings relative to the disk pointing. Using a 2D Monte Carlo-based photo-ionization code which accounts for the effects of radiation field hardening, we find that this trend cannot be reproduced by any plausible photo-ionization model, and that a secondary source of ionization must therefore operate in gaseous halos. We also present the first spectroscopic detections of extraplanar PAH features in an external normal galaxy. If they are in an exponential layer, very rough emission scale-heights of 330-530 pc are implied for the various features. Extinction may be non-negligible in the midplane and reduce these scale-heights significantly. There is little significant variation in the relative emission from the various features between disk and extraplanar environment. Only the 17.4 micron feature is significantly enhanced in the extraplanar gas compared to the other features, possibly indicating a preference for larger PAHs in the halo.Comment: 35 pages in ApJ preprint format, 8 figures, accepted for publication in ApJ. Minor change to Introduction to give appropriate credit to earlier, related wor

Dynamic association between perfusion and white matter integrity across time since injury in Veterans with history of TBI.

ObjectiveCerebral blood flow (CBF) plays a critical role in the maintenance of neuronal integrity, and CBF alterations have been linked to deleterious white matter changes. Although both CBF and white matter microstructural alterations have been observed within the context of traumatic brain injury (TBI), the degree to which these pathological changes relate to one another and whether this association is altered by time since injury have not been examined. The current study therefore sought to clarify associations between resting CBF and white matter microstructure post-TBI.Methods37 veterans with history of mild or moderate TBI (mmTBI) underwent neuroimaging and completed health and psychiatric symptom questionnaires. Resting CBF was measured with multiphase pseudocontinuous arterial spin labeling (MPPCASL), and white matter microstructural integrity was measured with diffusion tensor imaging (DTI). The cingulate cortex and cingulum bundle were selected as a priori regions of interest for the ASL and DTI data, respectively, given the known vulnerability of these regions to TBI.ResultsRegression analyses controlling for age, sex, and posttraumatic stress disorder (PTSD) symptoms revealed a significant time since injury × resting CBF interaction for the left cingulum (p < 0.005). Decreased CBF was significantly associated with reduced cingulum fractional anisotropy (FA) in the chronic phase; however, no such association was observed for participants with less remote TBI.ConclusionsOur results showed that reduced CBF was associated with poorer white matter integrity in those who were further removed from their brain injury. Findings provide preliminary evidence of a possible dynamic association between CBF and white matter microstructure that warrants additional consideration within the context of the negative long-term clinical outcomes frequently observed in those with history of TBI. Additional cross-disciplinary studies integrating multiple imaging modalities (e.g., DTI, ASL) and refined neuropsychiatric assessment are needed to better understand the nature, temporal course, and dynamic association between brain changes and clinical outcomes post-injury

Oceanic sediment accumulation rates predicted via machine learning algorithm: towards sediment characterization on a global scale

AbstractObserved vertical sediment accumulation rates (n = 1031) were gathered from ~ 55 years of peer reviewed literature. Original methods of rate calculation include long-term isotope geochronology (14C,210Pb, and137Cs), pollen analysis, horizon markers, and box coring. These observations are used to create a database of global, contemporary vertical sediment accumulation rates. Rates were converted to cm year−1, paired with the observation's longitude and latitude, and placed into a machine learning–based Global Predictive Seabed Model (GPSM). GPSM finds correlations between the data and established global "predictors" (quantities known or estimable everywhere, e.g., distance from coastline and river mouths). The result, using a k-nearest neighbor (k-NN) algorithm, is a 5-arc-minute global map of predicted benthic vertical sediment accumulation rates. The map generated provides a global reference for vertical sedimentation from coastal to abyssal depths. Areas of highest sedimentation, ~ 3–8 cm year−1, are generally river mouth proximal coastal zones draining relatively large areas with high maximum elevations and with wide, shallow continental shelves (e.g., the Gulf of Mexico and the Amazon Delta), with rates falling exponentially towards the deepest parts of the oceans. The exception is Oceania, which displays significant vertical sedimentation over a large area without draining the large drainage basins seen in other regions. Coastal zones with relatively small drainage basins and steep shelves display vertical sedimentation of ~ 1 cm year−1, which is limited to the near shore when compared with shallow, wide margins (e.g., the western coasts of North and South America). Abyssal depth rates are functionally zero at the time scale examined (~ 10−4 cm year−1) and increase one order of magnitude near the Mid-Atlantic Ridge and at the Galapagos Triple Junction

Applicability of single-step genomic evaluation with a random regression model for reproductive traits in turkeys (Meleagris gallopavo).

Fertility and hatchability are economically important traits due to their effect on poult output coming from the turkey hatchery. Traditionally, fertility is recorded as the number of fertile eggs set in the incubator (FERT), defined at a time point during incubation by the identification of a developing embryo. Hatchability is recorded as either the number of fertile eggs that hatched (hatch of fertile, HOF) or the number hatched from all the eggs set (hatch of set, HOS). These traits are collected throughout the productive life of the bird and are conventionally cumulated, resulting in each bird having a single record per trait. Genetic evaluations of these traits have been estimated using pedigree relationships. However, the longitudinal nature of the traits and the availability of genomic information have renewed interest in using random regression (RR) to capture the differences in repeatedly recorded traits, as well as in the incorporation of genomic relationships. Therefore, the objectives of this study were: 1) to compare the applicability of a RR model with a cumulative model (CUM) using both pedigree and genomic information for genetic evaluation of FERT, HOF, and HOS and 2) to estimate and compare predictability from the models. For this study, a total of 63,935 biweekly FERT, HOF, and HOS records from 7,211 hens mated to 1,524 toms were available for a maternal turkey line. In total, 4,832 animals had genotypic records, and pedigree information on 11,191 animals was available. Estimated heritability from the CUM model using pedigree information was 0.11 0.02, 0.24 0.02, and 0.24 0.02 for FERT, HOF, and HOS, respectively. With random regression using pedigree relationships, heritability estimates were in the range of 0.04-0.09, 0.11-0.17, and 0.09-0.18 for FERT, HOF, and HOS, respectively. The incorporation of genomic information increased the heritability by an average of 28 and 23% for CUM and RR models, respectively. In addition, the incorporation of genomic information caused predictability to increase by approximately 11 and 7% for HOF and HOS, respectively; however, a decrease in predictability of about 12% was observed for FERT. Our findings suggest that RR models using pedigree and genomic relationships simultaneously will achieve a higher predictability than the traditional CUM model

Genome-wide association study reveals candidate genes relevant to body weight in female turkeys (Meleagris gallopavo).

The underlying genetic mechanisms affecting turkey growth traits have not been widely investigated. Genome-wide association studies (GWAS) is a powerful approach to identify candidate regions associated with complex phenotypes and diseases in livestock. In the present study, we performed GWAS to identify regions associated with 18-week body weight in a turkey population. The data included body weight observations for 24,989 female turkeys genotyped based on a 65K SNP panel. The analysis was carried out using a univariate mixed linear model with hatch-week-year and the 2 top principal components fitted as fixed effects and the accumulated polygenic effect of all markers captured by the genomic relationship matrix as random. Thirty-three significant markers were observed on 1, 2, 3, 4, 7 and 12 chromosomes, while 26 showed strong linkage disequilibrium extending up to 410 kb. These significant markers were mapped to 37 genes, of which 13 were novel. Interestingly, many of the investigated genes are known to be involved in growth and body weight. For instance, genes AKR1D1, PARP12, BOC, NCOA1, ADCY3 and CHCHD7 regulate growth, body weight, metabolism, digestion, bile acid biosynthetic and development of muscle cells. In summary, the results of our study revealed novel candidate genomic regions and candidate genes that could be managed within a turkey breeding program and adapted in fine mapping of quantitative trait loci to enhance genetic improvement in this species