The evolution of lactase persistence in Europe. A synthesis of archaeological and genetic evidence

Lactase persistence, the ability to digest the milk sugar lactose in adulthood, is highly associated with a T allele situated 13,910 bp upstream from the actual lactase gene in Europeans. The frequency of this allele rose rapidly in Europe after transition from hunter–gatherer to agriculturalist lifestyles and the introduction of milkable domestic species from Anatolia some 8000 years ago. Here we first introduce the archaeological and historic background of early farming life in Europe, then summarize what is known of the physiological and genetic mechanisms of lactase persistence. Finally, we compile the evidence for a co-evolutionary process between dairying culture and lactase persistence. We describe the different hypotheses on how this allele spread over Europe and the main evolutionary forces shaping this process. We also summarize three different computer simulation approaches, which offer a means of developing a coherent and integrated understanding of the process of spread of lactase persistence and dairying.

Forward-in-Time, Spatially Explicit Modeling Software to Simulate Genetic Lineages Under Selection

SELECTOR is a software package for studying the evolution of multiallelic genes under balancing or positive selection while simulating complex evolutionary scenarios that integrate demographic growth and migration in a spatially explicit population framework. Parameters can be varied both in space and time to account for geographical, environmental, and cultural heterogeneity. SELECTOR can be used within an approximate Bayesian computation estimation framework. We first describe the principles of SELECTOR and validate the algorithms by comparing its outputs for simple models with theoretical expectations. Then, we show how it can be used to investigate genetic differentiation of loci under balancing selection in interconnected demes with spatially heterogeneous gene flow. We identify situations in which balancing selection reduces genetic differentiation between population groups compared with neutrality and explain conflicting outcomes observed for human leukocyte antigen loci. These results and three previously published applications demonstrate that SELECTOR is efficient and robust for building insight into human settlement history and evolution.

Directional genetic differentiation and relative migration

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum-likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user-friendly web application called divMigrate-online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.

Contemporary population genetic structure and phylogeography of pink salmon

Thesis (Master's)--University of Washington, 2016-06

Development and application of sensitive genome-wide platforms to study the genetic and epigenetic (DNA methylation) makeup of gametes and early bovine embryos

Pour ce projet, nous avons développé une plateforme pour l’analyse pangénomique de la méthylation de l’ADN chez le bovin qui est compatible avec des échantillons de petites tailles. Cet outil est utilisé pour étudier les caractéristiques génétiques et épigénétiques (méthylation de l’ADN) des gamètes soumis aux procédures de procréation médicalement assisitée et des embryons précoces. Dans un premier temps, une plateforme d’analyse de biopuces spécifiques pour l’étude de la méthylation de l’ADN chez l’espèce bovine a été développée. Cette plateforme a ensuite été optimisée pour produire des analyses pangénomiques de méthylation de l’ADN fiables et reproductibles à partir d’échantillons de très petites tailles telle que les embryons précoces (≥ 10 ng d’ADN a été utilisé, ce qui correspond à 10 blastocystes en expansion). En outre, cet outil a permis d’évaluer de façon simultanée la méthylation de l’ADN et le transcriptome dans le même échantillon, fournissant ainsi une image complète des profils génétiques et épigénétiques (méthylation de l’ADN). Comme preuve de concept, les profils comparatifs de méthylation de l’ADN spermatique et de blastocystes bovins ont été analysés au niveau de l’ensemble du génome. Dans un deuxième temps, grâce à cette plateforme, les profils globaux de méthylation de l’ADN de taureaux jumeaux monozygotes (MZ) ont été analysés. Malgré qu’ils sont génétiquement identiques, les taureaux jumeaux MZ ont des descendants avec des performances différentes. Par conséquent, l’hypothèse que le profil de méthylation de l’ADN spermatique de taureaux jumeaux MZ est différent a été émise. Dans notre étude, des différences significatives entre les jumeaux MZ au niveau des caractéristiques de la semence ainsi que de la méthylation de l’ADN ont été trouvées, chacune pouvant contribuer à l’obtention de performances divergentes incongrues des filles engendrées par ces jumeaux MZ. Dans la troisième partie de ce projet, la même plateforme a été utilisée pour découvrir les impacts d’une supplémentation à forte concentration en donneur de méthyle universel sur les embryons précoces bovins. La supplémentation avec de grandes quantités d’acide folique (AF) a été largement utilisée et recommandée chez les femmes enceintes pour sa capacité bien établie à prévenir les malformations du tube neural chez les enfants. Cependant, plus récemment, plusieurs études ont rapporté des effets indésirables de l’AF utilisé à des concentrations élevées, non seulement sur le développement de l’embryon, mais aussi chez les adultes. Au niveau cellulaire, l’AF entre dans le métabolisme monocarboné, la seule voie de production de S-adénosyl méthionine (SAM), un donneur universel de groupements méthyles pour une grande variété de biomolécules, y compris l’ADN. Par conséquent, pour résoudre cette controverse, une forte dose de SAM a été utilisée pour traiter des embryons produits in vitro chez le bovin. Ceci a non seulement permis d’influencer le phénotype des embryons précoces, mais aussi d’avoir un impact sur le transcriptome et le méthylome de l’ADN. En somme, le projet en cours a permis le développement d’une plateforme d’analyse de la méthylation de l’ADN à l’échelle du génome entier chez le bovin à coût raisonnable et facile à utiliser qui est compatible avec les embryons précoces. De plus, puisque c’est l’une des premières études de ce genre en biologie de la reproduction bovine, ce projet avait trois objectifs qui a donné plusieurs nouveaux résultats, incluant les profils comparatifs de méthylation de l’ADN au niveau : i) blastocystes versus spermatozoïdes ; ii) semence de taureaux jumeaux MZ et iii) embryons précoces traités à de fortes doses de SAM versus des embryons précoces non traités.

The Visual Arts as a Canvas for Teaching Computer Programming

We explore the relationships between the construction of a work of art and the crafting of a computer program in Java and suggest that the structure of paintings and drawings may be used to teach the fundamental concepts of computer programming. This movement "from Art to Science", using art to drive computing, complements the common use of computing to inform art. We report on initial experiences using this approach with undergraduate and postgraduate students. An embryonic theory of the correspondence between art and computing is presented and a methodology proposed to develop this project further.

Ecological and evolutionary effects of dispersal on freshwater zooplankton

A recent focus on contemporary evolution and the connections between communities has sought to more closely integrate the fields of ecology and evolutionary biology. Studies of coevolutionary dynamics, life history evolution, and rapid local adaptation demonstrate that ecological circumstances can dictate evolutionary trajectories. Thus, variation in species identity, trait distributions, and genetic composition may be maintained among ecologically divergent habitats. New theories and hypotheses (e.g., metacommunity theory and the Monopolization hypothesis) have been developed to understand better the processes occurring in spatially structured environments and how the movement of individuals among habitats contributes to ecology and evolution at broader scales. As few empirical studies of these theories exist, this work seeks to further test these concepts. Spatial and temporal dispersal are the mechanisms that connect habitats to one another. Both processes allow organisms to leave conditions that are suboptimal or unfavorable, and enable colonization and invasion, species range expansion, and gene flow among populations. Freshwater zooplankton are aquatic crustaceans that typically develop resting stages as part of their life cycle. Their dormant propagules allow organisms to disperse both temporally and among habitats. Additionally, because a number of species are cyclically parthenogenetic, they make excellent model organisms for studying evolutionary questions in a controlled environment. Here, I use freshwater zooplankton communities as model systems to explore the mechanisms and consequences of dispersal and to test these nascent theories on the influence of spatial structure in natural systems. In Chapter one, I use field experiments and mathematical models to determine the range of adult zooplankton dispersal over land and what vectors are moving zooplankton. Chapter two focuses on prolonged dormancy of one aquatic zooplankter, Daphnia pulex. Using statistical models with field and mesocosm experiments, I show that variation in Daphnia dormant egg hatching is substantial among populations in nature, and some of that variation can be attributed to genetic differences among the populations. Chapters three and four explore the consequences of dispersal at multiple levels of biological organization. Chapter three seeks to understand the population level consequences of dispersal over evolutionary time on current patterns of population genetic differentiation. Nearby populations of D. pulex often exhibit high population genetic differentiation characteristic of very low dispersal. I explore two alternative hypotheses that seek to explain this pattern. Finally, chapter four is a case study of how dispersal has influenced patterns of variation at the community, trait and genetic levels of biodiversity in a lake metacommunity.

Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity.

'Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem'

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

Predicting the metabolic energy costs of bipedalism using evolutionary robotics

To understand the evolution of bipedalism among the homnoids in an ecological context we need to be able to estimate theenerrgetic cost of locomotion in fossil forms. Ideally such an estimate would be based entirely on morphology since, except for the rare instances where footprints are preserved, this is hte only primary source of evidence available. In this paper we use evolutionary robotics techniques (genetic algoritms, pattern generators and mechanical modeling) to produce a biomimentic simulation of bipedalism based on human body dimensions. The mechnaical simulation is a seven-segment, two-dimensional model with motive force provided by tension generators representing the major muscle groups acting around the lower-limb joints. Metabolic energy costs are calculated from the muscel model, and bipedal gait is generated using a finite-state pattern generator whose parameters are produced using a genetic algorithm with locomotor economy (maximum distance for a fixed energy cost) as the fitness criterion. The model is validated by comparing the values it generates with those for modern humans. The result (maximum efficiency of 200 J m-1) is within 15% of the experimentally derived value, which is very encouraging and suggests that this is a useful analytic technique for investigating the locomotor behaviour of fossil forms. Initial work suggests that in the future this technique could be used to estimate other locomotor parameters such as top speed. In addition, the animations produced by this technique are qualitatively very convincing, which suggests that this may also be a useful technique for visualizing bipedal locomotion.

A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.

An Indirect Genetic Algorithm for Set Covering Problems

This paper presents a new type of genetic algorithm for the set covering problem. It differs from previous evolutionary approaches first because it is an indirect algorithm, i.e. the actual solutions are found by an external decoder function. The genetic algorithm itself provides this decoder with permutations of the solution variables and other parameters. Second, it will be shown that results can be further improved by adding another indirect optimisation layer. The decoder will not directly seek out low cost solutions but instead aims for good exploitable solutions. These are then post optimised by another hill-climbing algorithm. Although seemingly more complicated, we will show that this three-stage approach has advantages in terms of solution quality, speed and adaptability to new types of problems over more direct approaches. Extensive computational results are presented and compared to the latest evolutionary and other heuristic approaches to the same data instances.

Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem.

Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity.

A Pyramidal Evolutionary Algorithm with Different Inter-Agent Partnering Strategies for Scheduling Problems

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the agents on solution quality are examined for two multiple-choice optimisation problems. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements.