Showing papers on "Mutation (genetic algorithm) published in 2011"

Differential evolution algorithm with ensemble of parameters and mutation strategies

Abstract: Differential evolution (DE) has attracted much attention recently as an effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of the mutation strategy and associated control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Different mutation strategies with different parameter settings can be appropriate during different stages of the evolution. In this paper, we propose to employ an ensemble of mutation strategies and control parameters with the DE (EPSDE). In EPSDE, a pool of distinct mutation strategies along with a pool of values for each control parameter coexists throughout the evolution process and competes to produce offspring. The performance of EPSDE is evaluated on a set of bound-constrained problems and is compared with conventional DE and several state-of-the-art parameter adaptive DE variants.

Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators

Abstract: Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.

Initial Mutations Direct Alternative Pathways of Protein Evolution

Abstract: Whether evolution is erratic due to random historical details, or is repeatedly directed along similar paths by certain constraints, remains unclear. Epistasis (i.e. non-additive interaction between mutations that affect fitness) is a mechanism that can contribute to both scenarios. Epistasis can constrain the type and order of selected mutations, but it can also make adaptive trajectories contingent upon the first random substitution. This effect is particularly strong under sign epistasis, when the sign of the fitness effects of a mutation depends on its genetic background. In the current study, we examine how epistatic interactions between mutations determine alternative evolutionary pathways, using in vitro evolution of the antibiotic resistance enzyme TEM-1 β-lactamase. First, we describe the diversity of adaptive pathways among replicate lines during evolution for resistance to a novel antibiotic (cefotaxime). Consistent with the prediction of epistatic constraints, most lines increased resistance by acquiring three mutations in a fixed order. However, a few lines deviated from this pattern. Next, to test whether negative interactions between alternative initial substitutions drive this divergence, alleles containing initial substitutions from the deviating lines were evolved under identical conditions. Indeed, these alternative initial substitutions consistently led to lower adaptive peaks, involving more and other substitutions than those observed in the common pathway. We found that a combination of decreased enzymatic activity and lower folding cooperativity underlies negative sign epistasis in the clash between key mutations in the common and deviating lines (Gly238Ser and Arg164Ser, respectively). Our results demonstrate that epistasis contributes to contingency in protein evolution by amplifying the selective consequences of random mutations.

Amplification, mutation, and sequencing of a six-letter synthetic genetic system.

Abstract: The next goals in the development of a synthetic biology that uses artificial genetic systems will require chemistry–biology combinations that allow the amplification of DNA containing any number of sequential and nonsequential nonstandard nucleotides. This amplification must ensure that the nonstandard nucleotides are not unidirectionally lost during PCR amplification (unidirectional loss would cause the artificial system to revert to an all-natural genetic system). Further, technology is needed to sequence artificial genetic DNA molecules. The work reported here meets all three of these goals for a six-letter artificially expanded genetic information system (AEGIS) that comprises four standard nucleotides (G, A, C, and T) and two additional nonstandard nucleotides (Z and P). We report polymerases and PCR conditions that amplify a wide range of GACTZP DNA sequences having multiple consecutive unnatural synthetic genetic components with low (0.2% per theoretical cycle) levels of mutation. We demonstrate t...

Genetic Variation and the Fate of Beneficial Mutations in Asexual Populations

Abstract: The fate of a newly arising beneficial mutation depends on many factors, such as the population size and the availability and fitness effects of other mutations that accumulate in the population. It has proved difficult to understand how these factors influence the trajectories of particular mutations, since experiments have primarily focused on characterizing successful clones emerging from a small number of evolving populations. Here, we present the results of a massively parallel experiment designed to measure the full spectrum of possible fates of new beneficial mutations in hundreds of experimental yeast populations, whether these mutations are ultimately successful or not. Using strains in which a particular class of beneficial mutation is detectable by fluorescence, we followed the trajectories of these beneficial mutations across 592 independent populations for 1000 generations. We find that the fitness advantage provided by individual mutations plays a surprisingly small role. Rather, underlying “background” genetic variation is quickly generated in our initially clonal populations and plays a crucial role in determining the fate of each individual beneficial mutation in the evolving population.

Identifying cancer driver genes in tumor genome sequencing studies

Abstract: Motivation: Major tumor sequencing projects have been conducted in the past few years to identify genes that contain ‘driver’ somatic mutations in tumor samples. These genes have been defined as those for which the non-silent mutation rate is significantly greater than a background mutation rate estimated from silent mutations. Several methods have been used for estimating the background mutation rate. Results: We propose a new method for identifying cancer driver genes, which we believe provides improved accuracy. The new method accounts for the functional impact of mutations on proteins, variation in background mutation rate among tumors and the redundancy of the genetic code. We reanalyzed sequence data for 623 candidate genes in 188 non-small cell lung tumors using the new method. We found several important genes like PTEN, which were not deemed significant by the previous method. At the same time, we determined that some genes previously reported as drivers were not significant by the new analysis because mutations in these genes occurred mainly in tumors with large background mutation rates. Availability: The software is available at: http://linus.nci.nih.gov/Data/YounA/software.zip Contact: rsimon@mail.nih.gov Supplementary information:Supplementary data are available at Bioinformatics online.

Predicting mutation outcome from early stochastic variation in genetic interaction partners

Abstract: Many mutations, including those that cause disease, only have a detrimental effect in a subset of individuals. The reasons for this are usually unknown, but may include additional genetic variation and environmental risk factors. However, phenotypic discordance remains even in the absence of genetic variation, for example between monozygotic twins, and incomplete penetrance of mutations is frequent in isogenic model organisms in homogeneous environments. Here we propose a model for incomplete penetrance based on genetic interaction networks. Using Caenorhabditis elegans as a model system, we identify two compensation mechanisms that vary among individuals and influence mutation outcome. First, feedback induction of an ancestral gene duplicate differs across individuals, with high expression masking the effects of a mutation. This supports the hypothesis that redundancy is maintained in genomes to buffer stochastic developmental failure. Second, during normal embryonic development we find that there is substantial variation in the induction of molecular chaperones such as Hsp90 (DAF-21). Chaperones act as promiscuous buffers of genetic variation, and embryos with stronger induction of Hsp90 are less likely to be affected by an inherited mutation. Simultaneously quantifying the variation in these two independent responses allows the phenotypic outcome of a mutation to be more accurately predicted in individuals. Our model and methodology provide a framework for dissecting the causes of incomplete penetrance. Further, the results establish that inter-individual variation in both specific and more general buffering systems combine to determine the outcome inherited mutations in each individual.

Nucleic acid sequence design via efficient ensemble defect optimization.

Abstract: We describe an algorithm for designing the sequence of one or more interacting nucleic acid strands intended to adopt a target secondary structure at equilibrium. Sequence design is formulated as an optimization problem with the goal of reducing the ensemble defect below a user-specified stop condition. For a candidate sequence and a given target secondary structure, the ensemble defect is the average number of incorrectly paired nucleotides at equilibrium evaluated over the ensemble of unpseudoknotted secondary structures. To reduce the computational cost of accepting or rejecting mutations to a random initial sequence, candidate mutations are evaluated on the leaf nodes of a tree-decomposition of the target structure. During leaf optimization, defect-weighted mutation sampling is used to select each candidate mutation position with probability proportional to its contribution to the ensemble defect of the leaf. As subsequences are merged moving up the tree, emergent structural defects resulting from crosstalk between sibling sequences are eliminated via reoptimization within the defective subtree starting from new random subsequences. Using a Θ(N^3) dynamic program to evaluate the ensemble defect of a target structure with N nucleotides, this hierarchical approach implies an asymptotic optimality bound on design time: for sufficiently large N, the cost of sequence design is bounded below by 4/3 the cost of a single evaluation of the ensemble defect for the full sequence. Hence, the design algorithm has time complexity Ω(N^3). For target structures containing N ∈{100,200,400,800,1600,3200} nucleotides and duplex stems ranging from 1 to 30 base pairs, RNA sequence designs at 37°C typically succeed in satisfying a stop condition with ensemble defect less than N/100. Empirically, the sequence design algorithm exhibits asymptotic optimality and the exponent in the time complexity bound is sharp

Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms

Abstract: In this study, a new mutation operator has been developed to increase Genetic Algorithm (GA) performance to find the shortest distance in the known Traveling Salesman Problem (TSP). We called this method as Greedy Sub Tour Mutation (GSTM). There exist two different greedy search methods and a component that provides a distortion in this new operator. The developed GSTM operator was tested with simple GA mutation operators in 14 different TSP examples selected from TSPLIB. The application of this GSTM operator gives much more effective results regarding to the best and average error values. The GSTM operator used with simple GAs decreases the best error values according to the other mutation operators with the ratio of between 74.24% and 88.32% and average error values between 59.42% and 79.51%.

Tomato TILLING Technology: Development of a Reverse Genetics Tool for the Efficient Isolation of Mutants from Micro-Tom Mutant Libraries

Abstract: To accelerate functional genomic research in tomato, we developed a Micro-Tom TILLING (Targeting Induced Local Lesions In Genomes) platform. DNA pools were constructed from 3,052 ethyl methanesulfonate (EMS) mutant lines treated with 0.5 or 1.0% EMS. The mutation frequency was calculated by screening 10 genes. The 0.5% EMS population had a mild mutation frequency of one mutation per 1,710kb, whereas the 1.0% EMS population had a frequency of one mutation per 737kb, a frequency suitable for producing an allelic series of mutations in the target genes. The overall mutation frequency was one mutation per 1,237kb, which affected an average of three alleles per kilobase screened. To assess whether a Micro-Tom TILLING platform could be used for efficient mutant isolation, six ethylene receptor genes in tomato (SlETR1–SlETR6) were screened. Two allelic mutants of SlETR1 (Sletr1-1 and Sletr1-2) that resulted in reduced ethylene responses were identified, indicating that our Micro-Tom TILLING platform provides a powerful tool for the rapid detection of mutations in an EMS mutant library. This work provides a practical and publicly accessible tool for the study of fruit biology and for obtaining novel genetic material that can be used to improve important agronomic traits in tomato.

Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

Abstract: The genetic code is redundant with most amino acids using multiple codons. In many organisms, codon usage is biased toward particular codons. Understanding the adaptive and nonadaptive forces driving the evolution of codon usage bias (CUB) has been an area of intense focus and debate in the fields of molecular and evolutionary biology. However, their relative importance in shaping genomic patterns of CUB remains unsolved. Using a nested model of protein translation and population genetics, we show that observed gene level variation of CUB in Saccharomyces cerevisiae can be explained almost entirely by selection for efficient ribosomal usage, genetic drift, and biased mutation. The correlation between observed codon counts within individual genes and our model predictions is 0.96. Although a variety of factors shape patterns of CUB at the level of individual sites within genes, our results suggest that selection for efficient ribosome usage is a central force in shaping codon usage at the genomic scale. In addition, our model allows direct estimation of codon-specific mutation rates and elongation times and can be readily applied to any organism with high-throughput expression datasets. More generally, we have developed a natural framework for integrating models of molecular processes to population genetics models to quantitatively estimate parameters underlying fundamental biological processes, such a protein translation.

Self-adaptive differential evolution with multi-trajectory search for large-scale optimization

Abstract: In this paper, self-adaptive differential evolution (DE) is enhanced by incorporating the JADE mutation strategy and hybridized with modified multi-trajectory search (MMTS) algorithm (SaDE-MMTS) to solve large-scale continuous optimization problems. The JADE mutation strategy, the “DE/current-to-pbest” which is a variation of the classic “DE/current-to-best”, is used for generating mutant vectors. After the mutation phase, the binomial (uniform) crossover, the exponential crossover as well as no crossover option are used to generate each pair of target and trial vectors. By utilizing the self-adaptation in SaDE, both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, suitable offspring generation strategy along with associated parameter settings will be determined adaptively to match different phases of the search process. MMTS is applied frequently to refine several diversely distributed solutions at different search stages satisfying both the global and the local search requirement. The initialization of step sizes is also defined by a self-adaption during every MMTS step. The success rates of both SaDE and the MMTS are determined and compared; consequently, future function evaluations for both search algorithms are assigned proportionally to their recent past performance. The proposed SaDE-MMTS is employed to solve the 19 numerical optimization problems in special issue of soft computing on scalability of evolutionary algorithms for large-scale continuous optimization problems and competitive results are presented.

Comprehensive genetic analysis of 182 unrelated families with congenital adrenal hyperplasia due to 21-hydroxylase deficiency.

Abstract: Background: Genetic analysis is commonly performed in patients with congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency. Study Objective: The objective of the study was to describe comprehensive CYP21A2 mutation analysis in a large cohort of CAH patients. Methods: Targeted CYP21A2 mutation analysis was performed in 213 patients and 232 parents from 182 unrelated families. Complete exons of CYP21A2 were sequenced in patients in whom positive mutations were not identified by targeted mutation analysis. Copy number variation and deletions were determined using Southern blot analysis and PCR methods. Genotype was correlated with phenotype. Results: In our heterogeneous U.S. cohort, targeted CYP21A2 mutation analysis did not identify mutations on one allele in 19 probands (10.4%). Sequencing identified six novel mutations (p.Gln262fs, IVS8+1G>A, IVS9-1G>A, p.R408H, p.Gly424fs, p.R426P) and nine previously reported rare mutations. The majority of patients (79%) were compound heterozygotes and 69% of nonclassic (NC) patients were compound heterozygous for a classic and a NC mutation. Duplicated CYP21A2 haplotypes, de novo mutations and uniparental disomy were present in 2.7% of probands and 1.9 and 0.9% of patients from informative families, respectively. Genotype accurately predicted phenotype in 90.5, 85.1, and 97.8% of patients with salt-wasting, simple virilizing, and NC mutations, respectively. Conclusions: Extensive genetic analysis beyond targeted CYP21A2 mutational detection is often required to accurately determine genotype in patients with CAH due to the high frequency of complex genetic variation.

Strong higher order mutation-based test data generation

Abstract: This paper introduces SHOM, a mutation-based test data generation approach that combines Dynamic Symbolic Execution and Search Based Software Testing. SHOM targets strong mutation adequacy and is capable of killing both first and higher order mutants. We report the results of an empirical study using 17 programs, including production industrial code from ABB and Daimler and open source code as well as previously studied subjects. SHOM achieved higher strong mutation adequacy than two recent mutation-based test data generation approaches, killing between 8% and 38% of those mutants left unkilled by the best performing previous approach.

Mutation rates across budding yeast chromosome VI are correlated with replication timing.

Abstract: Previous experimental studies suggest that the mutation rate is nonuniform across the yeast genome. To characterize this variation across the genome more precisely, we measured the mutation rate of the URA3 gene integrated at 43 different locations tiled across Chromosome VI. We show that mutation rate varies 6-fold across a single chromosome, that this variation is correlated with replication timing, and we propose a model to explain this variation that relies on the temporal separation of two processes for replicating past damaged DNA: error-free DNA damage tolerance and translesion synthesis. This model is supported by the observation that eliminating translesion synthesis decreases this variation.

Loss-of-function mutation in GATA4 causes anomalies of human testicular development

Abstract: Approximately 1 of every 250 newborns has some abnormality of genital and/or gonadal development. However, a specific molecular cause is identified in only 20% of these cases of disorder of sex development (DSD). We identified a family of French origin presenting with 46,XY DSD and congenital heart disease. Sequencing of the ORF of GATA4 identified a heterozygous missense mutation (p.Gly221Arg) in the conserved N-terminal zinc finger of GATA4. This mutation was not observed in 450 ancestry-matched control individuals. The mutation compromised the ability of the protein to bind to and transactivate the anti-Mullerian hormone (AMH) promoter. The mutation does not interfere with the direct protein–protein interaction, but it disrupts synergistic activation of the AMH promoter by GATA4 and NR5A1. The p.Gly221Arg mutant protein also failed to bind to a known protein partner FOG2 that is essential for gonad formation. Our data demonstrate the key role of GATA4 in human testicular development.

Exome sequencing identifies MRPL3 mutation in mitochondrial cardiomyopathy.

Abstract: By combining exome sequencing in conjunction with genetic mapping, we have identified the first mutation in large mitochondrial ribosomal protein MRPL3 in a family of four sibs with hypertrophic cardiomyopathy, psychomotor retardation, and multiple respiratory chain deficiency. Affected sibs were compound heterozygotes for a missense MRPL3 mutation (P317R) and a large-scale deletion, inherited from the mother and the father, respectively. These mutations were shown to alter ribosome assembly and cause a mitochondrial translation deficiency in cultured skin fibroblasts resulting in an abnormal assembly of several complexes of the respiratory chain. This observation gives support to the view that exome sequencing combined with genetic mapping is a powerful approach for the identification of new genes of mitochondrial disorders. Hum Mutat 32:1225–1231, 2011. ©2011 Wiley Periodicals, Inc.

Virus Progeny of Murine Cytomegalovirus Bacterial Artificial Chromosome pSM3fr Show Reduced Growth in Salivary Glands due to a Fixed Mutation of MCK-2

Abstract: Murine cytomegalovirus (MCMV) Smith strain has been cloned as a bacterial artificial chromosome (BAC) named pSM3fr and used for analysis of virus gene functions in vitro and in vivo. When sequencing the complete BAC genome, we identified a frameshift mutation within the open reading frame (ORF) encoding MCMV chemokine homologue MCK-2. This mutation would result in a truncated MCK-2 protein. When mice were infected with pSM3fr-derived virus, we observed reduced virus production in salivary glands, which could be reverted by repair of the frameshift mutation. When looking for the source of the mutation, we consistently found that virus stocks of cell culture-passaged MCMV Smith strain are mixtures of viruses with or without the MCK-2 mutation. We conclude that the MCK-2 mutation in the pSM3fr BAC is the result of clonal selection during the BAC cloning procedure.

Proposed criteria for familial amyotrophic lateral sclerosis

Abstract: There is currently no consensus on the definition of familial ALS (FALS). We propose criteria for FALS that incorporate family history and genetic analysis. The aim is to increase the yield of gene...

Non-adaptive origins of interactome complexity

Abstract: The boundaries between prokaryotes, unicellular eukaryotes and multicellular eukaryotes are accompanied by orders-of-magnitude reductions in effective population size, with concurrent amplifications of the effects of random genetic drift and mutation. The resultant decline in the efficiency of selection seems to be sufficient to influence a wide range of attributes at the genomic level in a non-adaptive manner. A key remaining question concerns the extent to which variation in the power of random genetic drift is capable of influencing phylogenetic diversity at the subcellular and cellular levels. Should this be the case, population size would have to be considered as a potential determinant of the mechanistic pathways underlying long-term phenotypic evolution. Here we demonstrate a phylogenetically broad inverse relation between the power of drift and the structural integrity of protein subunits. This leads to the hypothesis that the accumulation of mildly deleterious mutations in populations of small size induces secondary selection for protein-protein interactions that stabilize key gene functions. By this means, the complex protein architectures and interactions essential to the genesis of phenotypic diversity may initially emerge by non-adaptive mechanisms.

The Repatterning of Eukaryotic Genomes by Random Genetic Drift

Abstract: Recent observations on rates of mutation, recombination, and random genetic drift highlight the dramatic ways in which fundamental evolutionary processes vary across the divide between unicellular microbes and multicellular eukaryotes. Moreover, population-genetic theory suggests that the range of variation in these parameters is sufficient to explain the evolutionary diversification of many aspects of genome size and gene structure found among phylogenetic lineages. Most notably, large eukaryotic organisms that experience elevated magnitudes of random genetic drift are susceptible to the passive accumulation of mutationally hazardous DNA that would otherwise be eliminated by efficient selection. Substantial evidence also suggests that variation in the population-genetic environment influences patterns of protein evolution, with the emergence of certain kinds of amino-acid substitutions and protein-protein complexes only being possible in populations with relatively small effective sizes. These observatio...

A New Method for Lower Bounds on the Running Time of Evolutionary Algorithms

Abstract: We present a new method for proving lower bounds on the expected running time of evolutionary algorithms. It is based on fitness-level partitions and an additional condition on transition probabilities between fitness levels. The method is versatile, intuitive, elegant, and very powerful. It yields exact or near-exact lower bounds for LO, OneMax, long k-paths, and all functions with a unique optimum. Most lower bounds are very general: they hold for all evolutionary algorithms that only use bit-flip mutation as variation operator---i.e. for all selection operators and population models. The lower bounds are stated with their dependence on the mutation rate. These results have very strong implications. They allow to determine the optimal mutation-based algorithm for LO and OneMax, i.e., which algorithm minimizes the expected number of fitness evaluations. This includes the choice of the optimal mutation rate.

Modified Genetic Algorithm for the Dynamic Identification of Structural Systems Using Incomplete Measurements

Abstract: : A modified real-coded genetic algorithm to identify the parameters of large structural systems subject to the dynamic loads is presented in this article. The proposed algorithm utilizes several subpopulations and a migration operator with a ring topology is periodically performed to allow the interaction between them. For each subpopulation, a specialized medley of recent genetic operators (crossover and mutation) has been adopted and is briefly discussed. The final algorithm includes a novel operator based on the auto-adaptive asexual reproduction of the best individual in the current subpopulation. This latter is introduced to avoid a long stagnation at the start of the evolutionary process due to insufficient exploration as well as to attempt an improved local exploration around the current best solution at the end of the search. Moreover, a search space reduction technique is performed to improve, both convergence speed and final accuracy, allowing a genetic-based search within a reduced region of the initial feasible domain. This numerical technique has been used to identify two shear-type mechanical systems with 10 and 30 degrees-of-freedom, assuming as unknown parameters the mass, the stiffness, and the damping coefficients. The identification will be conducted starting from some noisy acceleration signals to verify, both the computational effectiveness and the accuracy of the proposed optimizer in presence of high noise-to-signal ratio. A critical and detailed analysis of the results is presented to investigate the inner work of the optimizer. Finally, its performances are examined and compared to the most recent results documented in the current literature to demonstrate the numerical competitiveness of the proposed strategy.

Genome analyses substantiate male mutation bias in many species.

Abstract: In many species the mutation rate is higher in males than in females, a phenomenon denoted as male mutation bias. This is often observed in animals where males produce many more sperm than females produce eggs, and is thought to result from differences in the number of replication-associated mutations accumulated in each sex. Thus, studies of male mutation bias have the capacity to reveal information about the replication-dependent or replication-independent nature of different mutations. The availability of whole genome sequences for many species, as well as for multiple individuals within a species, has opened the door to studying factors, both sequence-specific and those acting on the genome globally, that affect differences in mutation rates between males and females. Here, we assess the advantages that genomic sequences provide for studies of male mutation bias and general mutation mechanisms, discuss major challenges left unresolved, and speculate about the direction of future studies.

Maintaining Healthy Population Diversity Using Adaptive Crossover, Mutation, and Selection

Abstract: This paper presents ACROMUSE, a novel genetic algorithm (GA) which adapts crossover, mutation, and selection parameters. ACROMUSEs objective is to create and maintain a diverse population of highly-fit (healthy) individuals, capable of adapting quickly to fitness landscape change and well-suited to the efficient optimization of multimodal fitness landscapes. A new methodology is introduced for determining standard population diversity (SPD) and an original measure of healthy population diversity (HPD) is proposed. The SPD measure is employed to adapt crossover and mutation, while selection pressure is controlled by adapting tournament size according to HPD. In addition to selection pressure control, ACROMUSE tournament selection selects individuals according to healthy diversity contribution rather than fitness. This proposed selection mechanism simultaneously promotes diversity and fitness within the population. The performance of ACROMUSE is evaluated using various multimodal benchmark functions. Statistically significant results are presented comparing ACROMUSEs fitness and diversity performance to that of several other GAs. By maintaining a diverse population of healthy individuals, ACROMUSE responds to fitness landscape change by restoring better fitness scores faster than other GAs. Analysis of the adaptive operators illustrates that the key benefit of ACROMUSE is the synergy of the operators working together to achieve an effective balance between exploration and exploitation.

On the origin and diffusion of BRCA1 c.5266dupC (5382insC) in European populations

Abstract: The BRCA1 mutation c.5266dupC was originally described as a founder mutation in the Ashkenazi Jewish (AJ) population. However, this mutation is also present at appreciable frequency in several European countries, which raises intriguing questions about the origins of the mutation. We genotyped 245 carrier families from 14 different population groups (Russian, Latvian, Ukrainian, Czech, Slovak, Polish, Danish, Dutch, French, German, Italian, Greek, Brazilian and AJ) for seven microsatellite markers and confirmed that all mutation carriers share a common haplotype from a single founder individual. Using a maximum likelihood method that allows for both recombination and mutational events of marker loci, we estimated that the mutation arose some 1800 years ago in either Scandinavia or what is now northern Russia and subsequently spread to the various populations we genotyped during the following centuries, including the AJ population. Age estimates and the molecular evolution profile of the most common linked haplotype in the carrier populations studied further suggest that c.5266dupC likely entered the AJ gene pool in Poland approximately 400-500 years ago. Our results illustrate that (1) BRCA1 c.5266dupC originated from a single common ancestor and was a common European mutation long before becoming an AJ founder mutation and (2) the mutation is likely present in many additional European countries where genetic screening of BRCA1 may not yet be common practice.