Short and long term outcome of bilateral pallidal stimulation in chorea-acanthocytosis

BACKGROUND: Chorea-acanthocytosis (ChAc) is a neuroacanthocytosis syndrome presenting with severe movement disorders poorly responsive to drug therapy. Case reports suggest that bilateral deep brain stimulation (DBS) of the ventro-postero-lateral internal globus pallidus (GPi) may benefit these patients. To explore this issue, the present multicentre (n=12) retrospective study collected the short and long term outcome of 15 patients who underwent DBS. METHODS: Data were collected in a standardized way 2-6 months preoperatively, 1-5 months (early) and 6 months or more (late) after surgery at the last follow-up visit (mean follow-up: 29.5 months). RESULTS: Motor severity, assessed by the Unified Huntington's Disease Rating Scale-Motor Score, UHDRS-MS), was significantly reduced at both early and late post-surgery time points (mean improvement 54.3% and 44.1%, respectively). Functional capacity (UHDRS-Functional Capacity Score) was also significantly improved at both post-surgery time points (mean 75.5% and 73.3%, respectively), whereas incapacity (UHDRS-Independence Score) improvement reached significance at early post-surgery only (mean 37.3%). Long term significant improvement of motor symptom severity (≥ 20 % from baseline) was observed in 61.5 % of the patients. Chorea and dystonia improved, whereas effects on dysarthria and swallowing were variable. Parkinsonism did not improve. Linear regression analysis showed that preoperative motor severity predicted motor improvement at both post-surgery time points. The most serious adverse event was device infection and cerebral abscess, and one patient died suddenly of unclear cause, 4 years after surgery. CONCLUSION: This study shows that bilateral DBS of the GPi effectively reduces the severity of drug-resistant hyperkinetic movement disorders such as present in ChAc

How to remove bias in genomic predictions ?

International audienc

Validation of models for analysis of ranks in horse breeding evaluation

Background: Ranks have been used as phenotypes in the genetic evaluation of horses for a long time through the use of earnings, normal score or raw ranks. A model, ("underlying model" of an unobservable underlying variable responsible for ranks) exists. Recently, a full Bayesian analysis using this model was developed. In addition, in reality, competitions are structured into categories according to the technical level of difficulty linked to the technical ability of horses (horses considered to be the "best" meet their peers). The aim of this article was to validate the underlying model through simulations and to propose a more appropriate model with a mixture distribution of horses in the case of a structured competition. The simulations involved 1000 horses with 10 to 50 performances per horse and 4 to 20 horses per event with unstructured and structured competitions.[br/] Results: The underlying model responsible for ranks performed well with unstructured competitions by drawing liabilities in the Gibbs sampler according to the following rule: the liability of each horse must be drawn in the interval formed by the liabilities of horses ranked before and after the particular horse. The estimated repeatability was the simulated one (0.25) and regression between estimated competing ability of horses and true ability was close to 1. Underestimations of repeatability (0.07 to 0.22) were obtained with other traditional criteria (normal score or raw ranks), but in the case of a structured competition, repeatability was underestimated (0.18 to 0.22). Our results show that the effect of an event, or category of event, is irrelevant in such a situation because ranks are independent of such an effect. The proposed mixture model pools horses according to their participation in different categories of competition during the period observed. This last model gave better results (repeatability 0.25), in particular, it provided an improved estimation of average values of competing ability of the horses in the different categories of events.[br/] Conclusions: The underlying model was validated. A correct drawing of liabilities for the Gibbs sampler was provided. For a structured competition, the mixture model with a group effect assigned to horses gave the best results

Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method

BackgroundCross-validation tools are used increasingly to validate and compare genetic evaluation methods but analytical properties of cross-validation methods are rarely described. There is also a lack of cross-validation tools for complex problems such as prediction of indirect effects (e.g. maternal effects) or for breeding schemes with small progeny group sizes.ResultsWe derive the expected value of several quadratic forms by comparing genetic evaluations including partial and whole data. We propose statistics that compare genetic evaluations including partial and whole data based on differences in means, covariance, and correlation, and term the use of these statistics method LR (from linear regression). Contrary to common belief, the regression of true on estimated breeding values is (on expectation) lower than 1 for small or related validation sets, due to family structures. For validation sets that are sufficiently large, we show that these statistics yield estimators of bias, slope or dispersion, and population accuracy for estimated breeding values. Similar results hold for prediction of future phenotypes although we show that estimates of bias, slope or dispersion using prediction of future phenotypes are sensitive to incorrect heritabilities or precorrection for fixed effects. We present an example for a set of 2111 Brahman beef cattle for which, in repeated partitioning of the data into training and validation sets, there is very good agreement of statistics of method LR with prediction of future phenotypes.ConclusionsAnalytical properties of cross-validation measures are presented. We present a new method named LR for cross-validation that is automatic, easy to use, and which yields the quantities of interest. The method compares predictions based on partial and whole data, which results in estimates of accuracy and biases. Prediction of observed records may yield biased results due to precorrection or use of incorrect heritabilities

Computational strategies for national integration of phenotypic, genomic, and pedigree data in a single-step best linear unbiased prediction

The single-step genomic BLUP (SSGBLUP) is a method that can integrate pedigree and genotypes at molecular markers in an optimal way. However, its present form (regular SSGBLUP) has a high computational cost (cubic in the number of genotyped animals) and may need extensive rewriting of genetic evaluation software. In this work, we propose several strategies to implement the single step in a simpler manner. The first one expands the single-step mixed-model equations to obtain equivalent equations from which the regular (including pedigree and records only) mixed-model equations are a subset. These new equations (unsymmetric extended SSGBLUP) have low computational cost, but require a nonsymmetric solver such as the biconjugate gradient stabilized method or successive underrelaxation, which is a variant of successive overrelaxation, with a relaxation factor lower than 1. In addition, we show a new derivation of the single-step method, which includes, as an extra effect, deviations from strictly polygenic breeding values. As a result, the same set of equations as above is obtained. We show that, whereas the new derivation shows apparent problems of nonpositive definiteness for certain covariance matrices, a proper equivalent model including imaginary effects always exists, leading always to the regular SSGBLUP mixed model equations. The system of equations can be solved (iterative SSGBLUP) by iterating between a pedigree and records evaluation and a genomic evaluation (each one solved by any iterative or direct method), whereas global iteration can use a block version of successive underrelaxation, which ensures convergence. The genomic evaluation can explicitly include marker or haplotype effects and possibly involve nonlinear (e.g., Bayesian by Markov chain Monte Carlo) methods. In a simulated example with 28,800 individuals and 1,800 genotyped individuals, all methods converged quickly to the same solutions. Using existing efficient methods with limited memory requirements to compute the products Gt and A22t for any t (where G and A22 are genomic and pedigree relationships for genotyped animals, and t is a vector), all strategies can be converted to iteration on data procedures for which the total number of operations is linear in the number of animals + number of genotyped animals × number of markers