Clinical Practice Recommendations on Genetic Testing of CYP2C9 and VKORC1 Variants in Warfarin Therapy

OBJECTIVE To systematically review evidence on genetic variants influencing outcomes during warfarin therapy and provide practice recommendations addressing the key questions: (1) Should genetic testing be performed in patients with an indication for warfarin therapy to improve achievement of stable anticoagulation and reduce adverse effects? (2) Are there subgroups of patients who may benefit more from genetic testing compared with others? (3) How should patients with an indication for warfarin therapy be managed based on their genetic test results? METHODS A systematic literature search was performed for VKORC1 and CYP2C9 and their association with warfarin therapy. Evidence was critically appraised, and clinical practice recommendations were developed based on expert group consensus. RESULTS Testing of VKORC1 (-1639G>A), CYP2C9*2, and CYP2C9*3 should be considered for all patients, including pediatric patients, within the first 2 weeks of therapy or after a bleeding event. Testing for CYP2C9*5, *6, *8, or *11 and CYP4F2 (V433M) is currently not recommended. Testing should also be considered for all patients who are at increased risk of bleeding complications, who consistently show out-of-range international normalized ratios, or suffer adverse events while receiving warfarin. Genotyping results should be interpreted using a pharmacogenetic dosing algorithm to estimate the required dose. SIGNIFICANCE This review provides the latest update on genetic markers for warfarin therapy, clinical practice recommendations as a basis for informed decision making regarding the use of genotype-guided dosing in patients with an indication for warfarin therapy, and identifies knowledge gaps to guide future research.

AN ASSESSMENT OF KNOWLEDGE AND ATTITUDES OF GENETIC COUNSELING SERVICES IN U.S. HTCs

Hemophilia is a hereditary bleeding disorder which requires lifelong specialized care. A network of Hemophilia Treatment Centers (HTCs) exists to meet the medical needs of patients affected by hemophilia. Genetic counseling services are an integral part of the HTC model of care; however, many HTCs do not have genetic counselors on staff. As a result, the duty to provide these services must fall to other healthcare providers within the HTC. To assess the knowledge and attitudes of these providers we developed a 49 question survey that was distributed electronically to hematologists and nurses at U.S. HTCs. The survey consisted of a three sections: demographic information, knowledge of hemophilia genetics, and attitudes towards genetic services. A total of 111 complete responses were received and analyzed. The average knowledge score among all participants was 74.8% with a total of 81 participants receiving a passing score of 70% or above. Thirty participants scored below 70% in the knowledge section. In general, attitude scores were high indicating that the majority of hematologists and nurses in HTCs feel confident in their ability to provide genetic counseling services. Over 90% of participants reported that they have some form of access to genetic counseling services at their center. Hematologists and nurses practicing in U.S. HTCs demonstrate sufficient knowledge of the genetics of hemophilia, and they generally feel confident in their ability to provide genetic counseling services to their patients. While their knowledge is sufficient, the average knowledge score was lower than 75%. Certain questions covering new genetic technologies and testing practices were more commonly missed than questions asking about more basic aspects of hemophilia genetics, such as inheritance and carrier testing. Finally, many clinics report having access to a counselor, but it is oftentimes a hematologist or nurse who is providing genetic counseling services to patients. Given the inconsistency in knowledge among providers coupled with the high confidence in one’s ability to counsel patients, it leaves room to question whether information about the genetics of hemophilia is being communicated to patients in the most appropriate and accurate manner.

Genetic diversity among alfalfa genotypes (Medicago sativa L.) of non-dormant cultivars using SSR markers and agronomic traits

The aim of this study was to assess genetic diversity among 40 alfalfa (Medicago sativa L.) genotypes of different non-dormant (FD=8) cultivars. Biomass yield, regrowth speed and reaction to spring black stem, lepto leaf spot, and rust were evaluated. Analyses of variances were performed using a mixed model to examine the agronomic variation among individuals. A principal component analysis on standardized agronomic data was performed. Agronomic data were also used to calculate Gower's distance and UPGMA algorithm. For the molecular analysis, six SSR markers were evaluated and 84 alleles were identified. The genetic distance was estimated using standard Nei's distance. Average standard genetic diversity was 0.843, indicating a high degree of variability among genotypes. Finally, a generalized procrustes analysis was performed to calculate the correlation between molecular and agronomic distance, indicating a 65.4% of consensus. This value is likely related to the low number of individuals included in the study, which might have underestimated the real phenotypic variability among genotypes. Despite the low number of individuals and SSR markers analyzed, this study provides a baseline for future diversity studies to identify genetically distant alfalfa individuals or cultivars.

Abstract interpretation with specialized definitions

The relationship between abstract interpretation and partial evaluation has received considerable attention and (partial) integrations have been proposed starting from both the partial evaluation and abstract interpretation perspectives. In this work we present what we argüe is the first generic algorithm for efñcient and precise integration of abstract interpretation and partial evaluation from an abstract interpretation perspective. Taking as starting point state-of-the-art algorithms for context-sensitive, polyvariant abstract interpretation and (abstract) partial evaluation of logic programs, we present an algorithm which combines the best of both worlds. Key ingredients include the accurate success propagation inherent to abstract interpretation and the powerful program transformations achievable by partial deduction. In our algorithm, the calis which appear in the analysis graph are not analyzed w.r.t. the original definition of the procedure but w.r.t. specialized definitions of these procedures. Such specialized definitions are obtained by applying both unfolding and abstract executability. Also, our framework is parametric w.r.t. different control strategies and abstract domains. Different combinations of these parameters correspond to existing algorithms for program analysis and specialization. Our approach efficiently computes strictly more precise results than those achievable by each of the individual techniques. The algorithm is one of the key components of CiaoPP, the analysis and specialization system of the Ciao compiler.

A Multi Ant Colony Optimization algorithm for a Mixed Car Assembly Line

This paper presents an ant colony optimization algorithm to sequence the mixed assembly lines considering the inventory and the replenishment of components. This is a NP-problem that cannot be solved to optimality by exact methods when the size of the problem growth. Groups of specialized ants are implemented to solve the different parts of the problem. This is intended to differentiate each part of the problem. Different types of pheromone structures are created to identify good car sequences, and good routes for the replenishment of components vehicle. The contribution of this paper is the collaborative approach of the ACO for the mixed assembly line and the replenishment of components and the jointly solution of the problem.

Genetic ablation of parietal cells in transgenic mice: a new model for analyzing cell lineage relationships in the gastric mucosa.

The gastric mucosa of mammalian stomach contains several differentiated cell types specialized for the secretion of acid, digestive enzymes, mucus, and hormones. Understanding whether each of these cell lineages is derived from a common stem cell has been a challenging problem. We have used a genetic approach to analyze the ontogeny of progenitor cells within mouse stomach. Herpes simplex virus 1 thymidine kinase was targeted to parietal cells within the gastric mucosa of transgenic mice, and parietal cells were ablated by treatment of animals with the antiherpetic drug ganciclovir. Ganciclovir treatment produced complete ablation of parietal cells, dissolution of gastric glands, and loss of chief and mucus-producing cells. Termination of drug treatment led to the reemergence of all major gastric epithelial cell types and restoration of glandular architecture. Our results imply the existence of a pluripotent stem cell for the gastric mucosa. Parietal cell ablation should provide a model for analyzing cell lineage relationships within the stomach as well as mechanisms underlying gastric injury and repair.

An unmodified heptadecapeptide pheromone induces competence for genetic transformation in Streptococcus pneumoniae.

Competence for genetic transformation in Streptococcus pneumoniae has been known for three decades to arise in growing cultures at a critical cell density, in response to a secreted protease-sensitive signal. We show that strain CP1200 produces a 17-residue peptide that induces cells of the species to develop competence. The sequence of the peptide was found to be H-Glu-Met-Arg-Leu-Ser-Lys-Phe-Phe-Arg-Asp-Phe-Ile-Leu-Gln-Arg- Lys-Lys-OH. A synthetic peptide of the same sequence was shown to be biologically active in small quantities and to extend the range of conditions suitable for development of competence. Cognate codons in the pneumococcal chromosome indicate that the peptide is made ribosomally. As the gene encodes a prepeptide containing the Gly-Gly consensus processing site found in peptide bacteriocins, the peptide is likely to be exported by a specialized ATP-binding cassette transport protein as is characteristic of these bacteriocins. The hypothesis is presented that this transport protein is encoded by comA, previously shown to be required for elaboration of the pneumococcal competence activator.

A maximum likelihood algorithm for genome mapping of cytogenetic loci from meiotic configuration data.

Frequencies of meiotic configurations in cytogenetic stocks are dependent on chiasma frequencies in segments defined by centromeres, breakpoints, and telomeres. The expectation maximization algorithm is proposed as a general method to perform maximum likelihood estimations of the chiasma frequencies in the intervals between such locations. The estimates can be translated via mapping functions into genetic maps of cytogenetic landmarks. One set of observational data was analyzed to exemplify application of these methods, results of which were largely concordant with other comparable data. The method was also tested by Monte Carlo simulation of frequencies of meiotic configurations from a monotelodisomic translocation heterozygote, assuming six different sample sizes. The estimate averages were always close to the values given initially to the parameters. The maximum likelihood estimation procedures can be extended readily to other kinds of cytogenetic stocks and allow the pooling of diverse cytogenetic data to collectively estimate lengths of segments, arms, and chromosomes.

A memetic algorithm for the delineation of local labour markets

Given a territory composed of basic geographical units, the delineation of local labour market areas (LLMAs) can be seen as a problem in which those units are grouped subject to multiple constraints. In previous research, standard genetic algorithms were not able to find valid solutions, and a specific evolutionary algorithm was developed. The inclusion of multiple ad hoc operators allowed the algorithm to find better solutions than those of a widely-used greedy method. However, the percentage of invalid solutions was still very high. In this paper we improve that evolutionary algorithm through the inclusion of (i) a reparation process, that allows every invalid individual to fulfil the constraints and contribute to the evolution, and (ii) a hillclimbing optimisation procedure for each generated individual by means of an appropriate reassignment of some of its constituent units. We compare the results of both techniques against the previous results and a greedy method.

Grouping genetic operators for the delineation of functional areas based on spatial interaction

The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time.

FPGA implementation of a multi-population PBIL algorithm

Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations.

Genetic analysis of complex demographic scenarios: Spatially expanding populations of the cane toad, Bufo marinus

Inferring the spatial expansion dynamics of invading species from molecular data is notoriously difficult due to the complexity of the processes involved. For these demographic scenarios, genetic data obtained from highly variable markers may be profitably combined with specific sampling schemes and information from other sources using a Bayesian approach. The geographic range of the introduced toad Bufo marinus is still expanding in eastern and northern Australia, in each case from isolates established around 1960. A large amount of demographic and historical information is available on both expansion areas. In each area, samples were collected along a transect representing populations of different ages and genotyped at 10 microsatellite loci. Five demographic models of expansion, differing in the dispersal pattern for migrants and founders and in the number of founders, were considered. Because the demographic history is complex, we used an approximate Bayesian method, based on a rejection-regression algorithm. to formally test the relative likelihoods of the five models of expansion and to infer demographic parameters. A stepwise migration-foundation model with founder events was statistically better supported than other four models in both expansion areas. Posterior distributions supported different dynamics of expansion in the studied areas. Populations in the eastern expansion area have a lower stable effective population size and have been founded by a smaller number of individuals than those in the northern expansion area. Once demographically stabilized, populations exchange a substantial number of effective migrants per generation in both expansion areas, and such exchanges are larger in northern than in eastern Australia. The effective number of migrants appears to be considerably lower than that of founders in both expansion areas. We found our inferences to be relatively robust to various assumptions on marker. demographic, and historical features. The method presented here is the only robust, model-based method available so far, which allows inferring complex population dynamics over a short time scale. It also provides the basis for investigating the interplay between population dynamics, drift, and selection in invasive species.

The parameterless self-organizing map algorithm

The parameterless self-organizing map (PLSOM) is a new neural network algorithm based on the self-organizing map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighborhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.

Stochastic delay differential equations for genetic regulatory networks

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

Genetic parameters and predicted response to selection for lean growth in an Australian commercial nucleus pig herd

Genetic parameters for performance traits in a pig population were estimated using a multi-trait derivative-free REML algorithm. The 2590 total data included 922 restrictively fed male and 1668 ad libitum fed female records. Estimates of heritability (standard error in parentheses) were 0.25 (0.03), 0.15 (0.03), and 0.30 (0.05) for lifetime daily gain, test daily gain, and P2-fat depth in males, respectively; and 0.27 (0.04) and 0.38 (0.05) for average daily gain and P2-fat depth in females, respectively. The genetic correlation between P2-fat depth and test daily gain in males was -0.17 (0.06) and between P2-fat and lifetime average daily gain in females 0.44 (0.09). Genetic correlations between sexes were 0.71 (0.11) for average daily gain and -0.30 (0.10) for P2-fat depth. Genetic response per standard deviation of selection on an index combining all traits was predicted at $AU120 per sow per year. Responses in daily gain and backfat were expected to be higher when using only male selection than when using only female selection. Selection for growth rate in males will improve growth rate and carcass leanness simultaneously.