Surgical approach for cardiac surgery in a patient with tracheostoma

The thoracic approach for cardiac surgery in a patient with a tracheostoma can result in difficult problems, such as mediastinitis, stoma necrosis or inadequate operative exposure. We present a distinct approach consisting of an incision at the second intercostal space, transverse sternum transection and longitudinal median sternotomy to the xiphoid process, performed for coronary artery bypass grafting and aortic valve replacement, in a patient with previous tracheotomy. This approach permitted adequate surgical exposure for cardiopulmonary bypass, aortic valve replacement and coronary revascularization procedures

Slow oscillatory activity and levodopa-induced dyskinesias in Parkinson’s disease

The pathophysiology of levodopa-induced dyskinesias (LID) in Parkinson’s disease is not well understood. We have recorded local field potentials (LFP) from macroelectrodes implanted in the subthalamic nucleus (STN) of 14 patients with Parkinson’s disease following surgical treatment with deep brain stimulation. Patients were studied in the ‘Off’ medication state and in the ‘On’ motor state after administration of levodopa– carbidopa (po) or apomorphine (sc) that elicited dyskinesias in 11 patients. The logarithm of the power spectrum of the LFP in selected frequency bands (4–10, 11–30 and 60–80 Hz) was compared between the ‘Off’ and ‘On’ medication states. A peak in the 11–30 Hz band was recorded in the ‘Off’ medication state and reduced by 45.2% (P < 0.001) in the ‘On’ state. The ‘On’ was also associated with an increment of 77. 6% (P < 0.001) in the 4–10 Hz band in all patients who showed dyskinesias and of 17.8% (P < 0.001) in the 60–80 Hz band in the majority of patients. When dyskinesias were only present in one limb (n = 2), the 4–10 Hz peak was only recorded in the contralateralSTN. These findings suggest that the 4–10 Hz oscillation is associated with the expression of LID in Parkinson’s disease

Involvement of the subthalamic nucleus in impulse control disorders associated with Parkinson’s disease

Behavioural abnormalities such as impulse control disorders may develop when patients with Parkinson’s disease receive dopaminergic therapy, although they can be controlled by deep brain stimulation of the subthalamic nucleus. We have recorded local field potentials in the subthalamic nucleus of 28 patients with surgically implanted subthalamic electrodes. According to the predominant clinical features of each patient, their Parkinson’s disease was associated with impulse control disorders (n = 10), dyskinesias (n = 9) or no dopaminergic mediated motor or behavioural complications (n = 9). Recordings were obtained during the OFF and ON dopaminergic states and the power spectrum of the subthalamic activity as well as the subthalamocortical coherence were analysed using Fourier transform-based techniques. The position of each electrode contact was determined in the postoperative magnetic resonance image to define the topography of the oscillatory activity recorded in each patient. In the OFF state, the three groups of patients had similar oscillatory activity. By contrast, in the ON state, the patients with impulse control disorders displayed theta-alpha (4–10 Hz) activity (mean peak: 6.71 Hz) that was generated 2–8mm below the intercommissural line. Similarly, the patients with dyskinesia showed theta-alpha activity that peaked at a higher frequency (mean: 8.38 Hz) and was generated 0–2mm below the intercommissural line. No such activity was detected in patients that displayed no dopaminergic side effects. Cortico-subthalamic coherence was more frequent in the impulsive patients in the 4–7.5 Hz range in scalp electrodes placed on the frontal regions anterior to the primary motor cortex, while in patients with dyskinesia it was in the 7.5–10 Hz range in the leads overlying the primary motor and supplementary motor area. Thus, dopaminergic side effects in Parkinson’s disease are associated with oscillatory activity in the theta-alpha band, but at different frequencies and with different topography for the motor (dyskinesias) and behavioural (abnormal impulsivity) manifestations. These findings suggest that the activity recorded in parkinsonian patients with impulse control disorders stems from the associative-limbic area (ventral subthalamic area), which is coherent with premotor frontal cortical activity. Conversely, in patients with L-dopa-induced dyskinesias such activity is recorded in the motor area (dorsal subthalamic area) and it is coherent with cortical motor activity. Consequently, the subthalamic nucleus appears to be implicated in the motor and behavioural complications associated with dopaminergic drugs in Parkinson’s disease, specifically engaging different anatomo-functional territories

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

publishedVersio

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

The Dimensionality of Genomic Information and Its Effect on Genomic Prediction

The genomic relationship matrix (GRM) can be inverted by the algorithm for proven and young (APY) based on recursion on a random subset of animals. While a regular inverse has a cubic cost, the cost of the APY inverse can be close to linear. Theory for the APY assumes that the optimal size of the subset (maximizing accuracy of genomic predictions) is due to a limited dimensionality of the GRM, which is a function of the effective population size (N(e)). The objective of this study was to evaluate these assumptions by simulation. Six populations were simulated with approximate effective population size (N(e)) from 20 to 200. Each population consisted of 10 nonoverlapping generations, with 25,000 animals per generation and phenotypes available for generations 1–9. The last 3 generations were fully genotyped assuming genome length L = 30. The GRM was constructed for each population and analyzed for distribution of eigenvalues. Genomic estimated breeding values (GEBV) were computed by single-step GBLUP, using either a direct or an APY inverse of GRM. The sizes of the subset in APY were set to the number of the largest eigenvalues explaining x% of variation (EIGx, x = 90, 95, 98, 99) in GRM. Accuracies of GEBV for the last generation with the APY inverse peaked at EIG98 and were slightly lower with EIG95, EIG99, or the direct inverse. Most information in the GRM is contained in ∼N(e)L largest eigenvalues, with no information beyond 4N(e)L. Genomic predictions with the APY inverse of the GRM are more accurate than by the regular inverse

Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait,-1,900,000 animals, and-5,900 genotyped elite artificial insemination rams. For the-8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare "partial" and "whole" predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the "official" (routine) evalua-tion, which is a mixture of both single and multiple trait, and "deletion" of data before 1990; and (2) sev-eral models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relation-ship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies esti-mated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdisper-sion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gam-ma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball ef-fect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records

Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs

Background: Most developments in quantitative genetics theory focus on the study of intra-breed/line concepts. With the availability of massive genomic information, it becomes necessary to revisit the theory for crossbred populations. We propose methods to construct genomic covariances with additive and non-additive (dominance) inheritance in the case of pure lines and crossbred populations. Results: We describe substitution effects and dominant deviations across two pure parental populations and the crossbred population. Gene effects are assumed to be independent of the origin of alleles and allelic frequencies can differ between parental populations. Based on these assumptions, the theoretical variance components (additive and dominant) are obtained as a function of marker effects and allelic frequencies. The additive genetic variance in the crossbred population includes the biological additive and dominant effects of a gene and a covariance term. Dominance variance in the crossbred population is proportional to the product of the heterozygosity coefficients of both parental populations. A genomic BLUP (best linear unbiased prediction) equivalent model is presented. We illustrate this approach by using pig data (two pure lines and their cross, including 8265 phenotyped and genotyped sows). For the total number of piglets born, the dominance variance in the crossbred population represented about 13 % of the total genetic variance. Dominance variation is only marginally important for litter size in the crossbred population. Conclusions: We present a coherent marker-based model that includes purebred and crossbred data and additive and dominant actions. Using this model, it is possible to estimate breeding values, dominant deviations and variance components in a dataset that comprises data on purebred and crossbred individuals. These methods can be exploited to plan assortative mating in pig, maize or other species, in order to generate superior crossbred individuals in terms of performance

Evaluation of methods and marker systems in genomic selection of oil palm (Elaeis guineensis Jacq.)

Background Genomic selection (GS) uses genome-wide markers as an attempt to accelerate genetic gain in breeding programs of both animals and plants. This approach is particularly useful for perennial crops such as oil palm, which have long breeding cycles, and for which the optimal method for GS is still under debate. In this study, we evaluated the effect of different marker systems and modeling methods for implementing GS in an introgressed dura family derived from a Deli dura x Nigerian dura (Deli x Nigerian) with 112 individuals. This family is an important breeding source for developing new mother palms for superior oil yield and bunch characters. The traits of interest selected for this study were fruit-to-bunch (F/B), shell-to-fruit (S/F), kernel-to-fruit (K/F), mesocarp-to-fruit (M/F), oil per palm (O/P) and oil-to-dry mesocarp (O/DM). The marker systems evaluated were simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). RR-BLUP, Bayesian A, B, Cπ, LASSO, Ridge Regression and two machine learning methods (SVM and Random Forest) were used to evaluate GS accuracy of the traits. Results The kinship coefficient between individuals in this family ranged from 0.35 to 0.62. S/F and O/DM had the highest genomic heritability, whereas F/B and O/P had the lowest. The accuracies using 135 SSRs were low, with accuracies of the traits around 0.20. The average accuracy of machine learning methods was 0.24, as compared to 0.20 achieved by other methods. The trait with the highest mean accuracy was F/B (0.28), while the lowest were both M/F and O/P (0.18). By using whole genomic SNPs, the accuracies for all traits, especially for O/DM (0.43), S/F (0.39) and M/F (0.30) were improved. The average accuracy of machine learning methods was 0.32, compared to 0.31 achieved by other methods. Conclusion Due to high genomic resolution, the use of whole-genome SNPs improved the efficiency of GS dramatically for oil palm and is recommended for dura breeding programs. Machine learning slightly outperformed other methods, but required parameters optimization for GS implementation