Noise and Nonlinearity in Measles Epidemics: Combining Mechanistic and Statistical Approaches to Population Modeling

We present and evaluate an approach to analyzing population dynamics data using semimechanistic models. These models incorporate reliable information on population structure and underlying dynamic mechanisms but use nonparametric surface-fitting methods to avoid unsupported assumptions about the precise form of rate equations. Using historical data on measles epidemics as a case study, we show how this approach can lead to better forecasts, better characterizations of the dynamics, and better understanding of the factors causing complex population dynamics relative to either mechanistic models or purely descriptive statistical time-series models. The semimechanistic models are found to have better forecasting accuracy than either of the model types used in previous analyses when tested on data not used to fit the models. The dynamics are characterized as being both nonlinear and noisy, and the global dynamics are clustered very tightly near the border of stability (dominant Lyapunov exponent λ < 0). However, locally in state space the dynamics oscillate between strong short-term stability and strong short-term chaos (i.e., between negative and positive local Lyapunov exponents). There is statistically significant evidence for short-term chaos in all data sets examined. Thus the nonlinearity in these systems is characterized by the variance over state space in local measures of chaos versus stability rather than a single summary measure of the overall dynamics as either chaotic or nonchaotic

Genomisk prediksjon ved bruk av høy tetthets- og hel-genom sekvens genotyper

The main objective of this thesis was to investigate genomic prediction methods for high-density and whole-genome sequence genotypes, with emphasis on traits that may have difficulties achieving a high prediction accuracy with pedigree-based predictions, such as disease resistance and maternal traits. A Bayesian variable selection method that combines a polygenic term through a G-matrix and a BayesC term (BayesGC) was compared with Genomic Best Linear Unbiased Prediction (GBLUP), and for Paper I and II, it was also compared to BayesC. Paper I aimed to investigate genomic prediction accuracy for the trait host resistance to salmon lice in Atlantic salmon (Salmo salar). Three genomic prediction methods (GBLUP, BayesC and BayesGC) were compared using 215K and 750K SNP genotypes through both within-family and across-family prediction scenarios. The data consisted of 1385 fish with both phenotype- and genotype, and the prediction accuracy was determined through five-fold cross-validation. The results showed an accuracy of ~0.6 and ~0.61 for across-family prediction with 215K and 750K genotypes and ~0.67 for within-family prediction for both genotypes. BayesGC showed a slightly higher prediction accuracy than GBLUP and BayesC, especially for the across-family predictions, but the differences were insignificant. Paper II investigated the prediction accuracy of GBLUP, BayesC and BayesGC for six maternal traits in Landrace sows. The data consisted of between 10,000 and 15,000 sows, all genotyped and imputed to a genotype density of 660K SNPs. The effects of different priors for the Bayesian variable selection methods were also investigated. The ~1,000 youngest sows were used as validation animals to validate the prediction accuracy. Results showed a variation in genomic prediction accuracy between 0.31 to 0.61 for the different traits. The accuracy did not vary much between the different methods and priors within traits. BayesGC had a 9.8 and 3% higher accuracy than GBLUP for traits M3W and BCS. However, for the other traits, there were minor differences. For within-breed prediction marker density and sizes of reference populations are often sufficient. However, when predicting across breeds, one might need a higher density, such as Whole Genome Sequence (WGS), or one could benefit from functional markers derived from WGS. Paper III investigates prediction accuracy for four maternal traits in two pig populations, a pure-bred Landrace (L) and a Synthetic (S) Yorkshire/Large White line. Prediction accuracy was tested with three different marker data sets: High-Density (HD), Whole Genome Sequence (WGS) and markers derived from WGS based on their pig Combined Annotation Dependent Depletion (pCADD) score. Two genomic prediction methods (GBLUP and BayesGC) were investigated for across- within- and multi-line predictions. For across- and within-line prediction, reference population sizes between 1K and 30K animals were analysed for prediction accuracy. In addition, multi-line reference population consisting of 1K, 3K or 6K animals for each line in different ratios were tested. The results showed that a reference population of 3K-6K animals for within-line prediction was usually sufficient to achieve a high prediction accuracy. However, increasing to 30K animals in the reference population further increased prediction accuracy for two of the traits. A reference population of 30K across-line animals achieved a similar accuracy to 1K within-line animals. For multi-line prediction, the accuracy was most dependent on the number of within-line animals in the reference data. The S-line provided a generally higher prediction accuracy than the L-line. Using pCADD scores to reduce the number of markers from WGS data in combination with the GBLUP method generally reduced prediction accuracies relative to GBLUP_HD analyses. When using BayesGC, prediction accuracies were generally similar when using HD, pCADD, or WGS marker data, suggesting that the Bayesian method selects a suitable set of markers irrespective of the markers provided (HD, pCADD, or WGS). Overall, these three studies showed that BayesGC seemed to have a slight advantage over GBLUP, especially with large datasets, high-density genotypes, and when relationships between the reference and validation animals were lower. They also showed that the relationship between the animals in the reference and validation population, and the size of the reference population, had a more significant impact on the prediction accuracy than the prediction method

Exploring the importance of cell-type-specific gene expression regulation and splicing in Parkinson’s disease

Parkinson’s disease (PD) is defined primarily as a movement disorder, but its symptoms extend beyond the diagnosis-defining motor symptoms. Among non-motor symptoms, dementia is one of the most common and debilitating, yet it remains relatively understudied in comparison to motor symptoms, in part due to the considerable clinical, genetic and pathologic overlap between Parkinson’s disease with dementia (PDD) and dementia with Lewy bodies (DLB). Common to all three diseases is a lack of disease-modifying therapies, the development of which requires knowledge of the genes, cell types and biological pathways affected in disease. In this thesis, publicly available brain-relevant functional genomic annotations were used to identify PD-relevant pathways and cell types in silico. PD heritability was not found enriched in a specific cell type or state; however, PD heritability was found significantly enriched in a lysosomal and loss-of-function-intolerant gene set, with the former highly expressed in astrocytic, microglial, and oligodendrocyte subtypes and the latter highly expressed in almost all tested cellular subtypes. In addition, new annotations were generated by applying bulk-tissue and single-nucleus RNA-sequencing to anterior cingulate cortex samples derived from individuals with PD, PDD and DLB. This pairing permitted cellular deconvolution of bulk-tissue gene expression; estimation of bulk-tissue cell-type abundances; and in-depth splicing analyses. These analyses found that PD, PDD and DLB were associated not just with one, but several cell types, including neuronal, glial and vascular cell types, suggesting that these are disorders of global pathways working across various cell types. Furthermore, these analyses illustrated the commonalities and differences between the three diseases in terms of associated pathways, cell types, and upstream regulators of splicing, observations that can be used to begin building a biological basis on which to distinguish these disorders

Systems biological approach to Parkinson’s disease

Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the Western world. It shows a high degree of genetic and phenotypic complexity with many implicated factors, various disease manifestations but few clear causal links. Ongoing research has identified a growing number of molecular alterations linked to the disease. Dopaminergic neurons in the substantia nigra, specifically their synapses, are the key-affected region in PD. Therefore, this work focuses on understanding the disease effects on the synapse, aiming to identify potential genetic triggers and synaptic PD associated mechanisms. Currently, one of the main challenges in this area is data quality and accessibility. In order to study PD, publicly available data were systematically retrieved and analysed. 418 PD associated genes could be identified, based on mutations and curated annotations. I curated an up-to-date and complete synaptic proteome map containing a total of 6,706 proteins. Region specific datasets describing the presynapse, postsynapse and synaptosome were also delimited. These datasets were analysed, investigating similarities and differences, including reproducibility and functional interpretations. The use of Protein-Protein-Interaction Network (PPIN) analysis was chosen to gain deeper knowledge regarding specific effects of PD on the synapse. Thus I generated a customised, filtered, human specific Protein-Protein Interaction (PPI) dataset, containing 211,824 direct interactions, from four public databases. Proteomics data and PPI information allowed the construction of PPINs. These were analysed and a set of low level statistics, including modularity, clustering coefficient and node degree, explaining the network’s topology from a mathematical point of view were obtained. Apart from low-level network statistics, high-level topology of the PPINs was studied. To identify functional network subgroups, different clustering algorithms were investigated. In the context of biological networks, the underlying hypothesis is that proteins in a structural community are more likely to share common functions. Therefore I attempted to identify PD enriched communities of synaptic proteins. Once identified, they were compared amongst each other. Three community clusters could be identified as containing largely overlapping gene sets. These contain 24 PD associated genes. Apart from the known disease associated genes in these communities, a total of 322 genes was identified. Each of the three clusters is specifically enriched for specific biological processes and cellular components, which include neurotransmitter secretion, positive regulation of synapse assembly, pre- and post-synaptic membrane, scaffolding proteins, neuromuscular junction development and complement activation (classical pathway) amongst others. The presented approach combined a curated set of PD associated genes, filtered PPI information and synaptic proteomes. Various small- and large-scale analytical approaches, including PPIN topology analysis, clustering algorithms and enrichment studies identified highly PD affected synaptic proteins and subregions. Specific disease associated functions confirmed known research insights and allowed me to propose a new list of so far unknown potential disease associated genes. Due to the open design, this approach can be used to answer similar research questions regarding other complex diseases amongst others

Evolutionary genomics : statistical and computational methods

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Population genomics of intrapatient HIV-1 evolution

Many microbial populations rapidly adapt to changing environments with multiple variants competing for survival. To quantify such complex evolutionary dynamics in vivo, time resolved and genome wide data including rare variants are essential. We performed whole-genome deep sequencing of HIV-1 populations in 9 untreated patients, with 6-12 longitudinal samples per patient spanning 5-8 years of infection. The data can be accessed and explored via an interactive web application. We show that patterns of minor diversity are reproducible between patients and mirror global HIV-1 diversity, suggesting a universal landscape of fitness costs that control diversity. Reversions towards the ancestral HIV-1 sequence are observed throughout infection and account for almost one third of all sequence changes. Reversion rates depend strongly on conservation. Frequent recombination limits linkage disequilibrium to about 100 bp in most of the genome, but strong hitch-hiking due to short range linkage limits diversity

Evolutionary Genomics

This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward

Africa, the Cradle of Human Diversity

This book brings together experts from several disciplines, reviewing and discussing our current knowledge of the complex history, biological diversity and behavioral evolution of African populations. The collection provides a valuable resource for students and researchers from various fields.; Readership: This book offers accessible knowledge from a multidisciplinary perspective to students, scholars and researchers interested in human evolutionary history, population genetics, archaeology, paleogenomics, anthropology, and related disciplines

Africa, the Cradle of Human Diversity

This book brings together experts from several disciplines, reviewing and discussing our current knowledge of the complex history, biological diversity and behavioral evolution of African populations. The collection provides a valuable resource for students and researchers from various fields.; Readership: This book offers accessible knowledge from a multidisciplinary perspective to students, scholars and researchers interested in human evolutionary history, population genetics, archaeology, paleogenomics, anthropology, and related disciplines