Large Number Of Generations Research Articles

Impact of truncating historical data on prediction ability of dairy sheep selection candidates

Along the last decades, the genetic evaluation methodology has evolved, improving breeding value estimates. Many breeding programmes have historical phenotypic records and large number of generations, but to make use of them could result in more inconveniences than benefits. In this study, the prediction ability of genotyped young animals was assessed by simultaneously evaluating the removal of historical data, two pedigree deepness and two methodologies (traditional BLUP and single−step genomic BLUP or ssGBLUP), using milk yield records of 40 years of three Latxa dairy sheep populations. The linear regression method was used to compare predictions of young rams before and after progeny testing, with six cut-off points, by intervals of 4 years (from 1992 to 2012), and statistics of ratio of accuracies, bias, and dispersion were calculated. The prediction accuracy of selection candidates, when genomic information was included, was the highest in all Latxa populations (between 0.54 and 0.69 with full data set). Nevertheless, the deletion of historical phenotypic data resulted on moderate accuracy gain in the bigger data size populations (mean gain 2.5%), and the smaller population took advantage of a moderate data deletion (2.7% gain by removing data until 2004), reducing accuracy when more records were removed. The bias of validation individuals was lower when the breeding value was predicted based on genomic information (between 2.1 and 13.9), being lower when the biggest amount of data was deleted in the bigger data size populations (5.2% reduction), and the smaller population was benefited from data deletion between 1996 and 2008 (3.8% bias reduction). Meanwhile, the slope of estimated genetic trend was lower when less data were included, and an overestimation of the unknown parent group estimates was observed. The results indicated that ssGBLUP evaluations were outstanding, compared with traditional BLUP evaluations, while the depth of pedigree had a very small influence, and deletion of historical phenotypic data was beneficial. Thus, Latxa routine genetic evaluations would benefit from truncating phenotypic records between 2000 and 2004, the use of two pedigree generations and the implementation of ssGBLUP methodology.

Lisne buve štetočine kruške (Hemiptera: Psyllidae) u Srbiji

Pear psyllids or pear jumping plant-lice, are the most important pests of this fruit. In Serbia, four species of pear psyllids are harmful to pear. Ali four belong to the genus Cacopsylla: C. pyri (common pear psyllid), C. bidens, C. pyricola (small pear psyllid) and C. pvrisuga (large pear psyllid). The first three belong to the subgenus Hepatopsvlla, have a larger number of generations per year and usually overwinters on the host plant, and C. pvrisuga belongs to the subgenus Thamnopsvlla, has one generation per year and overwinters outside the host plant pear. They can damage the pear directly, through nutrition by sucking out the juices, leading to chlorosis, necrosis and defoliation, and indirectly, by secreting "honeydew". Added to this damage is the role of C. pyri and C. pyricola as vectors of the phytoplasma "Candidatus Phytoplasma pyri", the cause of the disease called "pear decline".

ARTIFICIAL INTELLIGENCE AND NATURE-INSPIRED OPTIMIZATION ON INTEGRATIVE CAPACITY OF RENEWABLE ENERGY IN THE WESTERN BALKAN

Two artificial intelligence models for the integration of renewable energy sources are developed within this research to contribute to the European Green Plan realization. The review of renewable energy natural potential, on one hand, and installed capacity, on the other hand, in the Western Balkans and twenty-eight European countries is done within this research, as well as emissions. The analyses show that the European countries, sometimes even with lower natural potential in renewables, have installed much more renewable capacities than the Balkans countries with much higher natural potential. According to this, the first artificial intelligence model is developed based on multi-criteria linear regression analysis. This model relies on the correlation between the relevant regressors, i.e. relevant input variables for twenty-eight European countries and the same regressors for a particular Balkans country. Its goal is to find the maximum possible integrative renewable capacity in a Balkans’s country within the real socio-economic environment. The second artificial intelligence model is developed based on multi-criteria evolution genetic algorithms. Its goal is to find the maximum possible integrative renewable capacity within a real electric power system. Nature-inspired optimization is applied. From the framework of a given large number of generations, technical combinations of the degree of renewable energy sources integration, the best populations, i.e. combinations are selected. As nature selects from many generations and allows the best to survive and punishes the „weakˮ, in our case, „weak” combinations are those failing to meet the given conditions and limitations of the real electric power system. A new methodology is offered. Theoretical general formulas are given for both models. Developed models are tested on a numerical experiment of solar energy integration in the Serbia case study. Analyses of sensitivity prove that both models are applicable for all renewable energy sources and countries or regions.

Genetic algorithms for the design and optimization of horizontal axis wind turbine (HAWT) blades: A continuous approach or a binary one?

The continuous and the binary techniques are the main types of genetic algorithms that could be employed for an optimization problem. The study investigates the robustness and accuracy of each technique for the wind turbine blades design and optimization problem. For that purpose, the geometry of the blade was designed for the maximization of the output power, the desirable goal of the blade design. The design variables consist of the distribution of the chord and twist along the blade. Results indicate that the continuous genetic algorithm outperforms the binary one from the standpoint of the accuracy and also the computational time. However, the use of uniform crossover could improve the convergence rate of the binary genetic algorithm. Moreover, the sensitivity analysis of the applied genetic algorithms with respect to the population size and the mutation rate was performed to find the appropriate values for those parameters. The outcomes emphasized that adopting a small number of population size together with a large number of generations could speed up the convergence rate of the problem. Also approved in the study was the efficiency of the so-called “Superblade” operator which improved the convergence rate of the algorithm and resulted in the powerful blades.

KOMPETENSI PENGETAHUAN PEMBINA GERAKAN KELUARGA SAKINAH

This study aims to determine the differences in knowledge competencies of the participants of the Diklat Pembina Keluarga Sakinah before and after learning. The research was conducted at BDK Jakarta on Monday - Saturday, 11-16 November 2019. This research approach is quantitative with the ex post facto method. Data were collected through pre-test and post-test using multiple choice questions. The study population was the participants of three classes training totaling 120 participants. The data were processed using descriptive statistics by comparing the pre-test and post-test scores. The results showed that there had been a significant increase in the knowledge competence of the training participants. It is recommended that the relevant training should be continued with a larger number of generations.

On the enumeration and asymptotic growth of free quasigroup words

This paper counts the number of reduced quasigroup words of a particular length in a certain number of generators. Taking account of the relationship with the Catalan numbers, counting words in a free magma, we introduce the term peri-Catalan number for the free quasigroup word counts. The main result of this paper is an exact recursive formula for the peri-Catalan numbers, structured by the Euclidean Algorithm. The Euclidean Algorithm structure does not readily lend itself to standard techniques of asymptotic analysis. However, conjectures for the asymptotic behavior of the peri-Catalan numbers, substantiated by numerical data, are presented. A remarkable aspect of the observed asymptotic behavior is the so-called asymptotic irrelevance of quasigroup identities, whereby cancelation resulting from quasigroup identities has a negligible effect on the asymptotic behavior of the peri-Catalan numbers for long words in a large number of generators.

An Embedded Internet of Energy Communication Platform for the Future Smart Microgrids Management

Microgrids (MGs) are moving toward electric power systems in a sort of an Internet of Energy (IoE) where a large number of generators can be connected anywhere. In this regard, to realize the envisioned IoE, information and communication technologies (ICTs) are crucial for developing innovative applications and services as well as achieving high levels of efficiency in MGs. However, due to the variety of ICT, there is not a de facto standard solution to implement IoE platforms. Moreover, standards for the current Internet of Things (IoT) platforms are not optimal for developing IoE platforms, which present more demanding challenges. In such context, this paper presents an embedded IoE platform for management of smart MGs. The performance of this platform has been tested in an experimental MG. Results show that the proposed platform fulfills the MGs requirements and it is able to manage the energy flows, the safety issues, etc. in MGs.

The natural selection of metabolism explains curvature in allometric scaling

I simulate the natural selection of metabolism and mass to explain the curvature in the metabolic allometry for placental and marsupial mammals. The simulation model starts with a single ancestor in each clade at the Cretaceous–Palaeogene boundary 65 million years ago. The release of inter‐specific competition by the extinction of dinosaurs make it possible for each clade to diversify into a multitude of species across a wide range of empty niches. The selection of mass in these species depends on the net assimilated energy that depends on 1) the handling of the resources in the different niches, and on 2) mass‐specific metabolism that defines the pace of the handling process. The model is fitted to explain the maximum observed body masses over time and the current inter‐specific allometry for metabolism. The selection of mass‐specific metabolism is found to bend the metabolic allometry over time, even when all species have the same selection on the per‐generation time‐scale of natural selection. This is because the smaller species evolve over a larger number of generations than the larger species. The strongest curvature is in the placental clade, where the estimated rate of exponential increase in mass‐specific metabolism is 9.3 × 10−9 (95% CI: 7.3 × 10−9 – 1.1 × 10−8) on the per‐generation time‐scale. This is an order of magnitude larger than the estimate for marsupials, in agreement with an average metabolism that is 30% larger in placentals relative to marsupials of similar size.

Real-Time Cascading Failures Prevention for Multiple Contingencies in Smart Grids Through a Multi-Agent System

Cascading failures (CF) is complex in nature and difficult to accurately model or solve mathematically. The current industry approach to preventing CF, which leads to blackout event, involves incurring losses. In this paper, a technique based on an adaptive multi-agent system algorithm is implemented to prevent CF without loss incurrence. The algorithm uses mathematical combinations heuristically selected through the use of sensitivities obtained from the economic dispatch history of the power system to redispatch the power from the generators. This approach enables the implementation of the algorithm on systems of any size. The algorithm is experimentally applied in real-time with the consideration of necessary constraints as it halts the occurrence of CF. The test system is an experimental set up of the generation and transmission side of the IEEE 30-bus system using a reconfigurable smart grid laboratory hardware developed for testing algorithms requiring two-way communication capabilities. It was first showed that the test system will experience CF if nothing is done to prevent CF after the occurrence of a contingency. A detailed experimental analysis of the ensuing blackout event is given. The algorithm was used to prevent CF in the system after the occurrence of N-1 and N-1-1 contingencies. The algorithm was also tested on a larger system, the IEEE 118-bus system, through simulation. The experimental and simulation results affirm the efficacy of the proposed algorithm for systems of any size. In fact, it was discovered from our results that the large number of generators in large systems helps the algorithm converge faster than it does for small systems, which have restricted resources and combinations.

PARAMETER VARIATION FOR LINEAR EQUATION SOLVER USING GENETIC ALGORITHM

Genetic Algorithm has been successfully applied for solving systems of Linear Equations; however the effects of varying the various Genetic Algorithms parameters on the GA systems of Linear Equations solver have not been investigated. Varying the GA parameters produces new and exciting information on the behaviour of the GA Linear Equation solver. In this paper, a general introduction on the Genetic Algorithm, its application on finding solutions to the Systems of Linear equation as well as the effects of varying the Population size and Number of Generation is presented. The genetic algorithm simultaneous linear equation solver program was run several times using different sets of simultaneous linear equation while varying the population sizes as well as the number of generations in order to observe their effects on the solution generation. It was observed that small population size does not produce perfect solutions as fast as when large population size is used and small or large number of generations did not really have much impact on the attainment of perfect solution as much as population size.

A novel approach to solve inverse heat conduction problems: Coupling scaled boundary finite element method to a hybrid optimization algorithm

Scaled boundary finite element method (SBFEM) has proved its abilities in problems with singularities successfully. In this work, the coupling of SBFEM and a hybrid optimization algorithm is employed to determine unknown heat flux in the transient heat conduction problems. The genetic algorithm (GA) is a stochastic method which solves problems considering a large number of generations, while the deterministic methods such as sequential quadratic programming (SQP), which are sensitive to the initial points, can solve problems faster. Combining GA, as the main optimizer, and SQP can lead to lower computational time. Herein, a square plate is considered as the case study. The inverse analysis is accomplished by utilizing the transient temperature data from direct solution. The difference between the calculated and the known values of temperature at four points within the plate is considered as the objective function, and the heat fluxes on the upper side of the plate are considered as the design variables. As a result, the exact value of the heat fluxes is obtained using this method. This new approach, in which the SBFEM as a meshless solver is combined with the hybrid GA-SQP as the optimizer, highlights its potentials in solving inverse problems.

First records of the bayberry whitefly, Parabemisia myricae (Kuwana) in Greece

Citrus groves of Corfu had been attacked during the last decade by citrus whitefly Dialeurodes citri (Ashmead). It presumably expanded later, and it was found to cause a serious infestation on lemon trees of Achaia region during summer of 1986, while it disappeared later existing today only in very small pockets on ornamental citrus species in Attica area. Late in August 1988, the leaves of citrus trees, and to a lesser extent of grapevines, in the region of Corinthia, NE Peloponnesos, in South Greece were covered by larvae, pupae and adults of a whitefly that was different from D. citri. In autumn of the same year, it was noticed that the infestation of this new whitefly had expanded to other parts of Peloponnese such as Argos, Patras and Ilia region. Samples of citrus and grapevine leaves with nymphs of the insect were sent for identification to British Museum of Natural History. Dr. J. H. Martin identified the species as Parabemisia myricae (Kuwana) (Homoptera: Aleyrodidae), making the first record of the species in Greece. The species is a native of Japan and possibly of other eastern asian countries such as Taiwan and West Malaysia. Today, the insect has been recorded in USA (California), Cyprus, Israel and Turkey, Egypt (Martin personal communication) and it is a pest of woody plants, especially citrus trees and grapevines. The damage caused to the plants by this insect, as a feeder, can be serious in cases of large populations accompanied with problems associated with the development of sooty mould on the excreted honeydew. In the autumn of 1988, infestation was high (more than 20 nymphs/leaf) in Achaia and Corinthia regions of N. Peloponnese, causing great concern to citrus growers. This species of whitefly was not included in the lists of known plant pests of Greece. It is probable that it invaded Greece from Israel. P. myricae is a newly introduced pest in citrus growing countries and there is little information available on its biology and control. Most of the published data comes from Israel and California on the biology and biological control of the insect. Additional data have been published on flight behaviour, oviposition behaviour, and survival of young nymphs on leaves of different age citrus leaves. The adults of P. myricae are smaller than D. citri adults and they have a dusty blue gray or lavender appearance. The females oviposit on very young leaves, often in circles. Preference for oviposition on very young leaves seems to be related to properties of the leaf cuticle. Leaf age is a critical factor for survival of nymphs. This gives a good explanation for the observed high rates of mortality in spring for nymphs growing on rapidly maturing leaves. It has been observed that on orange trees less eggs are layed on the lower surface of the leaves, while on lemon trees they tend to be layed equally on both surfaces. Initially eggs are off-white, while after about one day they gradually turn to brownish and finally black, before they hatch. First instar crawlers settle on the surface of the young leaves with a preference to the lower surface. First flights of adults were noticed in this area in late February of 1989. By end of March early April the emergence of adults of the first 1989 generation had been completed. It seems that it produces a quite large number of generations during the year, as it requires only 21 days at temperatures fluctuating between 210oC and 17.3oC and 65-100 percent relative humidity, to complete its cycle in glasshouse. Being a pest of Citrus and grapevines (Vitis vinifera) it has also been found on hosts like Ficus, Persea, Prunus, Psidium and Thea. As a matter of fact it has also been found in small colonies on Prunus cerasi in Northern Peloponnese in autumn 1988. Work on the control of Parabemisia myricae with the insect growth regulator Buprofezin has started in Greece in 1988 and is expected to be concluded in 1990.

Recommendations on the Implementation of Genetic Algorithms for the Directed Evolution of Enzymes for Industrial Purposes.

In directed evolution (DE) the assessment of candidate enzymes and their modification is essential. In this study we have investigated genetic algorithms (GAs) in this context and conducted a systematic study of the behavior of GAs on 20 fitness landscapes (FLs) of varying complexity. This has allowed the tuning of the GAs to be explored. On the basis of this study, recommendations for the best GA settings to use for a GA-directed high-throughput experimental program (in which populations and the number of generations is necessarily low) are reported. The FLs were based upon simple linear models and were characterized by the behavior of the GA on the landscape as demonstrated by stall plots and the footprints and adhesion of candidate solutions, which highlighted local optima (LOs). In order to maximize progress of the GA and to reduce the chances of becoming stuck in a LO it was best to use: 1) a large number of generations, 2) high populations, 3) removal of duplicate sequences (clones), 4) double mutation, and 5) high selection pressure (the two best individuals go to the next generation), and 6) to consider using a designed sequence as the starting point of the GA run. We believe that these recommendations might be appropriate starting points for studies employing GAs within DE experiments.

Pulmonary aerosol transport and deposition analysis in upper 17 generations of the human respiratory tract

The major problem in understanding the therapeutically targeted drug delivery system in the deeper airways of the human lung is the lack of adequate data of particle transport and deposition (TD) in the transitional and respiratory zones (deeper airways) of the human lung. An understanding of the morphometry of the pulmonary airways and the lungs forms the primary step in a study of pulmonary aerosol deposition. The present study is the first-ever approach to explore the pulmonary aerosol TD in a digital 17-generation human pulmonary airway model. The present numerical study achieved the lack of the particle TD data in the deeper airways of the human lung. This paper presents a 3-D (3-dimensional) CFD (Computational Fluid Dynamics) study of an anatomically realistic 17-generation lung bronchial tree model based on the high-resolution computer tomography (HRCT) data by Schmidt et al. (2004). Physical morphometry is necessary for sufficiently calculating air and particle dynamics in human pulmonary airways with available data on a large number of generations. A Lagrangian-based Discrete Phase Model (DPM) is used to study the particle TD in the 17-generation of the lung airways. The numerical results demonstrate that inertial impaction is dominant in the upper airways and a large percentage of particles is deposited in the upper airways. The numerical results also illustrate that a large percentage of smaller diameter particles leaves through the airway outlet boundary at the 17th generation irrespective of breathing patterns. The escaped particles are considered to continue to follow the airway flow field further downstream after the 17th generation till the 23rd generation and some of them will reach the alveolar sacs region. This computational model could potentially aid in overcoming the nanobiotechnology toxicity problem for drug delivery in the deeper airways.

Psychiatric Epigenetics: A Personal Perspective

Based on reading two popular science books by the noted Kenyan anthropologist Richard Leakey[1,2], I hypothesized in 2001 and 2002 that psychiatric disorders like schizophrenia (SZ), bipolar disorder (BD) and major depressive disorder (MDD), are due to epigenetic, rather than genetic, defects [3,4]. In these two books, Leakey discussed how humans evolved in Africa, widely thought to be the “cradle” of mankind. The emphasis of these books was on the evolution of the human brain and mind. The books suggested that during the history of life on earth, after the origin of life, and the origin of multicellular organisms, the origin of human consciousness, linked with the evolution of the human brain and mind, was the third major revolution. Leakey suggested that our ancestors, like other primates, were quintessentially social creatures. In primate societies, the challenge for individuals is to predict the mind and behaviour of others. If individuals were able to monitor their own behaviour, they would have developed a sense of what to do in different situations. This monitoring ability is one definition of consciousness, and would have conferred a great evolutionary advantage to those individuals who possessed it. Once consciousness was established, there would have been a positive selection of individuals well-endowed with it since it would have favoured survival. In due course, over a large number of generations, this would have led to an increase in size of the brain, development of the mind, increased intelligence, and sharpened self-awareness. Leakey suggested that these events probably occurred over the last 2.5 million years. At the time of reading these two books I was interested in the genetics of psychiatric disorders like SZ, BD, and MDD. On reading these two books, I intuitively gathered that such psychiatric disorders have a non-genetic, rather than a genetic, basis. On looking up the scientific literature I came across a concept I had not heard of before, namely, epigenetics. At that time epigenetics was commonly defined as “the study of heritable changes in gene expression not involving changes in DNA sequence”. I hypothesized that psychiatric disorders like SZ, BD, and MDD are epigenetic disorders due to heritable defects in gene expression not due to changes in DNA sequence, that is, they are due to epimutations [3,4]. Later on, based on epigenetic studies in rodents [5,6], I suggested that psychosocial factors act via epigenetic mechanisms in the pathogenesis of psychiatric disorders [7]. In retrospect, how has this theoretical research in psychiatric epigenetics fared in the light of experimental evidence?. At the turn of this century, when this theoretical work was initially published, there was very little experimental evidence to support it. However, over the past 15 years, the case for a role for epigenetics in the pathogenesis of psychiatric disorders like SZ, BD, and MDD has become increasingly solid. For example, in SZ and BD the promoters of the RELN and glutamic acid decarboxylase 1 (GAD1) genes are thought to be hypermethylated in the prefrontal cortex [8]. In MDD, the brain-derived neurotrophic factor (BDNF) gene is thought to be hypermethylated both in the prefrontal cortex and in peripheral blood [9]. In addition to DNA methylation, there is increasing evidence that noncoding RNAs like microRNAs (miRNAs) and histone modifications are dysregulated in these disorders [10,11]. The suggestion that psychosocial factors act via epigenetic mechanisms to lead to psychiatric disorders [7] has been supported by experimental evidence by numerous epigenetic studies on humans [12-14]. Compared to epigenetic studies on psychiatric disorders like SZ, BD, and MDD, genetic mapping studies of these disorders have been less successful, with no genetic mutation or polymorphism predisposing to these disorders having been definitively identified to date [15]. In addition to SZ, BD, and MDD, even other psychiatric disorders like Alzheimer’s disease, intellectual disability, and autism spectrum disorders are now known to have an epigenetic role in their pathogenesis [16, 17]. To conclude, as is well known, the study of the inheritance of psychiatric disorders involves the the most complicated aspects of the entire gamut of biology and medicine [18]. The study of the inheritance of psychiatric disorders cuts across scientific boundaries and can be approached from diametrically opposite vantage points. Psychiatric disorders have not readily revealed their secrets for more than a century and continue to conceal their secrets. In this context, epigenetics may well hold the keys to unlocking the mysteries of psychiatric disorders.

Why is factor V Leiden so rare in the Basques?

Factor V Leiden is the most common inherited trait in Caucasians that predisposes individuals to venous thrombosis. However, it is almost absent amongst the Basque people that live in the south western part of Europe. To explain this finding, we speculate upon the putative contribution of various evolutionary forces through which the Basque genome may have been shaped.

A directional mutation operator for differential evolution algorithms

Differential evolution (DE) is widely studied in the past decade. In its mutation operator, the random variations are derived from the difference of two randomly selected different individuals. Difference vector plays an important role in evolution. It is observed that the best fitness found so far by DE cannot be improved in every generation. In this article, a directional mutation operator is proposed. It attempts to recognize good variation directions and increase the number of generations having fitness improvement. The idea is to construct a pool of difference vectors calculated when fitness is improved at a generation. The difference vector pool will guide the mutation search in the next generation once only. The directional mutation operator can be applied into any DE mutation strategy. The purpose is to speed up the convergence of DE and improve its performance. The proposed method is evaluated experimentally on CEC 2005 test set with dimension 30 and on CEC 2008 test set with dimensions 100 and 1000. It is demonstrated that the proposed method can result in a larger number of generations having fitness improvement than classic DE. It is combined with eleven DE algorithms as examples of how to combine with other algorithms. After its incorporation, the performance of most of these DE algorithms is significantly improved. Moreover, simulation results show that the directional mutation operator is helpful for balancing the exploration and exploitation capacity of the tested DE algorithms. Furthermore, the directional mutation operator modifications can save computational time compared to the original algorithms. The proposed approach is compared with the proximity based mutation operator as both are claimed to be applicable to any DE mutation strategy. The directional mutation operator is shown to be better than the proximity based mutation operator on the five variants in the DE family. Finally, the applications of two real world engineering optimization problems verify the usefulness of the proposed method.

Usage of Partial Genome Fitness Evaluation Mechanism to Get Faster Results in Genetic Algorithms

The aim of the research presented in this paper is to test the efficiency of the proposed genetic algorithm in the fast or real-time optimizations. Proposed partial genome fitness evaluation mechanism was tested on the wireless network topology optimization and job shop scheduling problem (JSSP). Wireless network topology optimization requires frequent and very fast (sometimes real time) adjustments. Job shop scheduling problem is one of the most general and difficult of all traditional scheduling problems. Search based on traditional genetic algorithms usually are not suitable in solving these problems. Long computational time and high memory usage if a large population and/or a large number of generations are used, on the other hand, a larger population and a larger number of generations usually provide better results. In comparison with two other similar and commonly used genetic the proposed algorithms have significant improvements in speed and some case solution quality.

IPED: inheritance path-based pedigree reconstruction algorithm using genotype data.

The problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time-consuming and inaccurate for complicated pedigrees, such as pedigrees with inbreeding. In this work, we propose an efficient algorithm that is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation, backward in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path-based approach implemented with an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations, and therefore, it can reconstruct pedigrees that have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data.

Evolution at increased error rate leads to the coexistence of multiple adaptive pathways in an RNA virus

BackgroundWhen beneficial mutations present in different genomes spread simultaneously in an asexual population, their fixation can be delayed due to competition among them. This interference among mutations is mainly determined by the rate of beneficial mutations, which in turn depends on the population size, the total error rate, and the degree of adaptation of the population. RNA viruses, with their large population sizes and high error rates, are good candidates to present a great extent of interference. To test this hypothesis, in the current study we have investigated whether competition among beneficial mutations was responsible for the prolonged presence of polymorphisms in the mutant spectrum of an RNA virus, the bacteriophage Qβ, evolved during a large number of generations in the presence of the mutagenic nucleoside analogue 5-azacytidine.ResultsThe analysis of the mutant spectra of bacteriophage Qβ populations evolved at artificially increased error rate shows a large number of polymorphic mutations, some of them with demonstrated selective value. Polymorphisms distributed into several evolutionary lines that can compete among them, making it difficult the emergence of a defined consensus sequence. The presence of accompanying deleterious mutations, the high degree of recurrence of the polymorphic mutations, and the occurrence of epistatic interactions generate a highly complex interference dynamics.ConclusionsInterference among beneficial mutations in bacteriophage Qβ evolved at increased error rate permits the coexistence of multiple adaptive pathways that can provide selective advantages by different molecular mechanisms. In this way, interference can be seen as a positive factor that allows the exploration of the different local maxima that exist in rugged fitness landscapes.