Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating

Genealogical networks, also known as family trees or population pedigrees, are commonly studied by genealogists wanting to know about their ancestry, but they also provide a valuable resource for disciplines such as digital demography, genetics, and computational social science. These networks are typically constructed by hand through a very time-consuming process, which requires comparing large numbers of historical records manually. We develop computational methods for automatically inferring large-scale genealogical networks. A comparison with human-constructed networks attests to the accuracy of the proposed methods. To demonstrate the applicability of the inferred large-scale genealogical networks, we present a longitudinal analysis on the mating patterns observed in a network. This analysis shows a consistent tendency of people choosing a spouse with a similar socioeconomic status, a phenomenon known as assortative mating. Interestingly, we do not observe this tendency to consistently decrease (nor increase) over our study period of 150 years.Comment: This is a pre-print of an article appearing in the proceedings of the Web Conference (WWW 2018

Literature review on the information system for digitization of royal history and Waqf

There has been a significant increase in the study of the history and culture of historical artifacts, whether they take the form of cultural heritage or Waqf. A literature review of web-based information systems was conducted for digitizing historical preservation and Waqf. Papers were sourced from various databases, including Publish or Perish, which produced 1043 journals, 370 articles, and 673 items from reputable sources, Google Scholar, and Crossref, respectively. The focus of the literature review was the information system for digitizing history and Waqf and integrating ontology databases. This literature review study aims to trace the evolution of study objects related to history and endowments. The results showed that most studies emphasized the user-understanding aspect of digitization, while the technical aspect was focused on using cutting-edge technology, such as 3D and virtual reality

Darwin throws dice: modelling stochastic processes of molecular evolution

The availability of protein and DNA sequences in the second half of the 20th century revolutionised evolutionary biology. For the first time, it was possible to quantify genetic variation among individuals and populations. Using molecular data to understand past demography and natural selection became an attainable goal. In the era of whole-genome sequences, application of early theoretical results proved to be challenging. The stochastic nature of evolutionary processes acting on DNA sequences makes it hard to distinguish signal from noise. Although progress has been made, models of molecular evolution are still lagging behind the availability of sequence data. In this thesis I contribute to bridging this gap, even if slightly. My main result is the development of the integrated sequentially Markovian coalescent (iSMC) – a novel framework that jointly models the effects of ancestral demography, recombination heterogeneity (Chapter 1) and mutation heterogeneity (Chapter 2) in shaping genetic diversity along the genome. This principled approach represents a step towards more realistic models of Population Genetics. The consequences of intracellular stochasticity extend beyond DNA sequences, however. Due to randomness in the diffusion of key molecules, isogenic cells differ in their gene expression patterns – hence in their phenotypes – even in homogeneous environments. To avoid chaos, intracellular stochasticity must be tamed by natural selection. In Chapter 3, I leverage single-cell transcriptomics data to disentangle the factors that constrain gene expression noise. Although selection against elevated noise acts at different levels of organisation, I show that it responds primarily to the architecture of molecular networks. This result may impact our understanding of the genotype-phenotype-fitness map

Architecture of human complex trait variation

A complex trait is a trait or disease that is controlled by both genetic and environmental factors, along with their interactions. Trait architecture encompasses the genetic variants and environmental causes of variation in the trait or disease, their effects on the trait or disease and the mechanism by which these factors interact at molecular and organism levels. It is important to understand trait architecture both from a biological viewpoint and a health perspective. In this thesis, I laid emphasis on exploring the influence of familial environmental factors on complex trait architecture alongside the genetic components. I performed a variety of studies to explore the architecture of anthropometric and cardio-metabolic traits, such as height, body mass index, high density lipoprotein content of blood and blood pressure, using a cohort of 20,000 individuals of recent Scottish descent and their phenotype measurements, Single Nucleotide Polymorphism (SNP) data and genealogical information. I extended a method of variance component analysis that could simultaneously estimate SNP-associated heritability and total heritability whilst considering familial environmental effects shared among siblings, couples and nuclear family members. I found that most missing heritability could be explained by including closely related individuals in the analysis and accounting for these close relationships; and that, on top of genetics, couple and sibling environmental effects are additional significant contributors to the complex trait variation investigated. Subsequently, I accounted for couple and sibling environmental effects in Genome- Wide Association Study (GWAS) and prediction models. Results demonstrated that by adding additional couple and sibling information, both GWAS performance and prediction accuracy were boosted for most traits investigated, especially for traits related to obesity. Since couple environmental effects as modelled in my study might, in fact, reflect the combined effect of assortative mating and shared couple environment, I explored further the dissection of couple effects according to their origin. I extended assortative mating theory by deriving the expected resemblance between an individual and in-laws of his first-degree relatives. Using the expected resemblance derived, I developed a novel pedigree study which could jointly estimate the heritability and the degree of assortative mating. I have shown in this thesis that, for anthropometric and cardio-metabolic traits, environmental factors shared by siblings and couples seem to have important effects on trait variation and that appropriate modelling of such effects may improve the outcome of genetic analyses and our understanding of the causes of trait variation. My thesis also points out that future studies on exploring trait architecture should not be limited to genetics because environment, as well as mate choice, might be a major contributor to trait variation, although trait architecture varies from trait to trait

Statistical Population Genomics

This open access volume presents state-of-the-art inference methods in population genomics, focusing on data analysis based on rigorous statistical techniques. After introducing general concepts related to the biology of genomes and their evolution, the book covers state-of-the-art methods for the analysis of genomes in populations, including demography inference, population structure analysis and detection of selection, using both model-based inference and simulation procedures. Last but not least, it offers an overview of the current knowledge acquired by applying such methods to a large variety of eukaryotic organisms. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, pointers to the relevant literature, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Statistical Population Genomics aims to promote and ensure successful applications of population genomic methods to an increasing number of model systems and biological questions

The role of visual adaptation in cichlid fish speciation

D. Shane Wright (1) , Ole Seehausen (2), Ton G.G. Groothuis (1), Martine E. Maan (1) (1) University of Groningen; GELIFES; EGDB(2) Department of Fish Ecology & Evolution, EAWAG Centre for Ecology, Evolution and Biogeochemistry, Kastanienbaum AND Institute of Ecology and Evolution, Aquatic Ecology, University of Bern.In less than 15,000 years, Lake Victoria cichlid fishes have radiated into as many as 500 different species. Ecological and sexual sel ection are thought to contribute to this ongoing speciation process, but genetic differentiation remains low. However, recent work in visual pigment genes, opsins, has shown more diversity. Unlike neighboring Lakes Malawi and Tanganyika, Lake Victoria is highly turbid, resulting in a long wavelength shift in the light spectrum with increasing depth, providing an environmental gradient for exploring divergent coevolution in sensory systems and colour signals via sensory drive. Pundamilia pundamila and Pundamilia nyererei are two sympatric species found at rocky islands across southern portions of Lake Victoria, differing in male colouration and the depth they reside. Previous work has shown species differentiation in colour discrimination, corresponding to divergent female preferences for conspecific male colouration. A mechanistic link between colour vision and preference would provide a rapid route to reproductive isolation between divergently adapting populations. This link is tested by experimental manip ulation of colour vision - raising both species and their hybrids under light conditions mimicking shallow and deep habitats. We quantify the expression of retinal opsins and test behaviours important for speciation: mate choice, habitat preference, and fo raging performance

Estructura genética, filogeografía, variación adaptativa y especiación en árboles tropicales del género Symphonia

The genetic structure within a species is the result of the levels of the genetic diversity and its spatial distribution. Also, it depends significantly on the specific evolutionary history experienced by the species. Thus, to disentangle the overlapping evolutionary processes acting at different levels in a species or a taxon, it will be necessary to work at different spatial scales and at different taxonomic levels as complementary approaches. The study of the fine-scale spatial genetic structure in plants (the micro scale approach) will imply to work at the shortest spatial scales and to capture detailed information on the spatial distribution of genotypes at within-population scale. The analysis at this scale will help to detect mainly evolutionary and ecological processes more related to short‐term periods of time and/or smaller spatial scales such as habitat fragmentation and other disturbances, efficiency of dispersal mechanisms or gene dispersal distances. On the other side, the study of the genetic structure at wider scales (the macro scale approach), including both geographical and taxonomic (i.e., speciation) points of view, will usually imply to detect larger spatio-temporal processes and to work with deeper evolutionary timescales. In this sense, the spatial genetic structure within a species at this macro scale will be the result of different historical and contemporary influences such as connectivity across the range of the species and landscape barriers, environmental adaptation, demographic history or climatic events, among others. Finally, if we include the taxonomic perspective in the analysis of genetic structure in a group of closely related species, we will be able to analyse the processes leading to speciation, which also may involve those previously mentioned.La estructura genética que presenta una especie es el resultado de sus niveles de diversidad genética, así como de su distribución espacial. Además, depende significativamente de la historia evolutiva específica que ha sufrido la especie. Así, para desentrañar los procesos evolutivos que actúan simultáneamente a diferentes niveles en una especie o taxon, será necesario trabajar mediante enfoques complementarios, orientados a diferentes escalas espaciales y a diferentes niveles taxonómicos. El estudio de la estructura genética espacial a pequeña escala en plantas (un enfoque en escala micro) implicará trabajar en las escalas espaciales más pequeñas, así como recoger información detallada de la distribución espacial de genotipos dentro de las poblaciones. El análisis a esta escala ayudará a detectar principalmente procesos evolutivos y ecológicos relacionados principalmente con periodos de tiempo cortos y/o a escala espacial pequeña, tales como fragmentación de hábitats y otras perturbaciones, eficiencia de los mecanismos de dispersión o distancias de dispersión genética. Por otro lado, el estudio de la estructura genética a escala más amplia (un enfoque en escala macro), incluyendo los puntos de vista geográfico y taxonómico (es decir, de especiación), normalmente implicará detectar procesos espacio-temporales más amplios y trabajar con escalas evolutivas de tiempo más largas. En este sentido, la estructura genética espacial de una especie a escala macro será el resultado de diferentes influencias históricas y contemporáneas, tales como la conectividad a lo largo de la distribución de la especie, las barreras del paisaje, la adaptación al medio, la historia demográfica o los eventos climáticos, entre otros. Finalmente, si incluímos la perspectiva taxonómica en el análisis de la estructura genética de un grupo de especies muy relacionadas, podremos ser capaces de analizar los procesos que conducen a la especiación, entre los cuales se pueden encontrar también los ya previamente mencionados.Doctorado en Conservación y Uso Sostenible de Sistemas Forestale