Dissertations / Theses on the topic 'Complex traits'

The availability of cheap genotyping technologies has enabled to collection of very large samples with both genetic and phenotypic information, enabling the interrogation of the genetic architecture of complex traits in humans and other organisms. The role of interactions between genetic variants and between genetic variants and environmental factors in complex traits is not well characterised, especially in humans. This is in part due to a lack of theory and methods designed for powerful investigation of interactions in complex traits in large-scale datasets. This thesis develops both theory and methods relating to interactions between genetic variants and between genetic and environmental factors, complemented by empirical analyses aimed at discovering the influence of interactions on complex traits. The effect of genetic variation on trait variation can be decomposed into components reflecting interactions involving different numbers of genetic variants. The first part of this thesis generalises classical theory on the decomposition of the genetic variance into components arising from different types of interaction to finite populations, where the influence of interactions is more easily detected. The theory is applied to determine the proportion of growth variance from pairwise and third and higher order interactions in a yeast cross. The subsequent parts of the thesis are more directly concerned with interactions between genetic variants and environmental factors. It is first demonstrated that multiple lifestyle factors modify the effect of variants in the FTO gene on body mass index (BMI). This motivates the development of the heteroskedastic linear mixed model (HLMM), which exploits changes in variability with genotype to aid discovery of genetic variants involved in interactions. An efficient algorithm for application of the HLMM to large scale datasets is developed and applied to discover genetic variants likely to be involved in interactions on BMI.

Of central importance in the dissection of the components that govern complex traits is understanding the architecture of natural genetic variation. Genetic interaction, or epistasis, constitutes one aspect of this, but epistatic analysis has been largely avoided in genome wide association studies because of statistical and computational difficulties. This thesis explores both issues in the context of two-locus interactions. Initially, through simulation and deterministic calculations it was demonstrated that not only can epistasis maintain deleterious mutations at intermediate frequencies when under selection, but that it may also have a role in the maintenance of additive variance. Based on the epistatic patterns that are evolutionarily persistent, and the frequencies at which they are maintained, it was shown that exhaustive two dimensional search strategies are the most powerful approaches for uncovering both additive variance and the other genetic variance components that are co-precipitated. However, while these simulations demonstrate encouraging statistical benefits, two dimensional searches are often computationally prohibitive, particularly with the marker densities and sample sizes that are typical of genome wide association studies. To address this issue different software implementations were developed to parallelise the two dimensional triangular search grid across various types of high performance computing hardware. Of these, particularly effective was using the massively-multi-core architecture of consumer level graphics cards. While the performance will continue to improve as hardware improves, at the time of testing the speed was 2-3 orders of magnitude faster than CPU based software solutions that are in current use. Not only does this software enable epistatic scans to be performed routinely at minimal cost, but it is now feasible to empirically explore the false discovery rates introduced by the high dimensionality of multiple testing. Through permutation analysis it was shown that the significance threshold for epistatic searches is a function of both marker density and population sample size, and that because of the correlation structure that exists between tests the threshold estimates currently used are overly stringent. Although the relaxed threshold estimates constitute an improvement in the power of two dimensional searches, detection is still most likely limited to relatively large genetic effects. Through direct calculation it was shown that, in contrast to the additive case where the decay of estimated genetic variance was proportional to falling linkage disequilibrium between causal variants and observed markers, for epistasis this decay was exponential. One way to rescue poorly captured causal variants is to parameterise association tests using haplotypes rather than single markers. A novel statistical method that uses a regularised parameter selection procedure on two locus haplotypes was developed, and through extensive simulations it can be shown that it delivers a substantial gain in power over single marker based tests. Ultimately, this thesis seeks to demonstrate that many of the obstacles in epistatic analysis can be ameliorated, and with the current abundance of genomic data gathered by the scientific community direct search may be a viable method to qualify the importance of epistasis.

Thousands of genetic and epigenetic variants have been identified for many common diseases including cancer through genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS). To advance the complex interpretation of both GWAS and EWAS results, I developed new software tools (FORGE2 and eFORGE) for the analysis and interpretation of GWAS and EWAS data, respectively. Both tools determine the cell type-specific regulatory component of a set of target regions (either GWAS-identified genetic variants or EWAS-identified differentially methylated positions). This is achieved by detecting enrichment of overlap with histone mark peaks or DNase I hypersensitive sites across hundreds of tissues, primary cell types, and cell lines from the ENCODE, Roadmap Epigenomics, and BLUEPRINT projects. Application of both tools to publicly available datasets identified novel disease-relevant cell types for many common diseases, a stem cell-like signature in cancer EWAS, and also demonstrated the ability to detect cell-composition effects for EWAS performed on heterogeneous tissues. To complement these bioinformatics efforts and validate selected variants predicted by FORGE2, eFORGE and additional analyses, I performed conformation capture using 4C-seq to fine-map the 3D context of the genomic regions involved, uncovering novel interactions for autoimmunity-associated variants and IKZF3.

The first section of this thesis addresses the problem of simultaneously identifying multiple loci that are associated with a trait, using a Bayesian Markov Chain Monte Carlo method. It is applicable to both case/control and quantitative data. I present simulations comparing the methods to standard frequentist methods in human case/control and mouse QTL datasets, and show that in the case/control simulations the standard frequentist method out performs my model for all but the highest effect simulations and that for the mouse QTL simulations my method performs as well as the frequentist method in some cases and worse in others. I also present analysis of real data and simulations applying my method to a simulated epistasis data set. The next section was inspired by the challenges involved in applying a Markov Chain Monte Carlo method to genetic data. It is an investigation into the performance and benefits of the Matlab parallel computing toolbox, specifically its implementation of the Cuda programing language to Matlab's higher level language. Cuda is a language which allows computational calculations to be carried out on the computer's graphics processing unit (GPU) rather than its central processing unit (CPU). The appeal of this tool box is its ease of use as few code adaptions are needed. The final project of this thesis was to develop an HMM for reconstructing the founders of sparsely sequenced inbred populations. The motivation here, that whilst sequencing costs are rapidly decreasing, it is still prohibitively expensive to fully sequence a large number of individuals. It was proposed that, for populations descended from a known number of founders, it would be possible to sequence these individuals with a very low coverage, use a hidden Markov model (HMM) to represent the chromosomes as mosaics of the founders, then use these states to impute the missing data. For this I developed a Viterbi algorithm with a transition probability matrix based on recombination rate which changes for each observed state.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2017
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 201-245).
In this thesis, I introduce new methods for learning about diseases and traits from genetic data. First, I introduce a method for partitioning heritability by functional annotation from genome-wide association summary statistics, and I apply it to 17 diseases and traits and many different functional annotations. Next, I show how to apply this method to use gene expression data to identify diseaserelevant tissues and cell types. I next introduce a method for estimating genetic correlation from genome-wide association summary statistics and apply it to estimate genetic correlations between all pairs of 24 diseases and traits. Finally, I consider a model of disease subtypes and I show how to determine a lower bound on the sample size required to distinguish between two disease subtypes as a function of several parameters.
by Hilary Kiyo Finucane.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Mathematics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references.
A fundamental goal in biology is to understand how the information stored in DNA results in a cellular function. However, it is insufficient to study one variant of a particular DNA sequence because most people do not share identical genome sequences, and the differences in DNA sequence have functional consequences. In this thesis, I examine how natural variation in the Saccharomyces cerevisiae genome can affect cellular processes. This is done using deletion libraries to examine how mutations in the same gene but in two different genetic backgrounds of S. cerevisiae, S288c and [summation]1278b, can lead different phenotypes for two traits: gene essentiality and agar adhesion. We found that the genomes of the S288c and [summation]1278b strains are only as divergent as two humans in the population. However, analyses of deletion libraries in each strain revealed 57 genes have functions that are essential in one strain but not the other. Strain specific phenotypes are more pronounced for the trait of agar adhesion where 553 deletions have phenotypes that are specific to one strain or the other. Part of the difference is because the [summation]1278b strain requires the filamentation mitogen activated kinase pathway (fMAPK) for agar adhesion but the S288c strain does not. I found that S288c is able to bypass the fMAPK pathway because it contains an allele of the transcription factor RPI1 that promotes transcription of the gene FLO11. Characterization of the sequence differences between the S288c and 11278b alleles of RPIJ revealed that they differ in the number of intragenic tandem repeats. Examination of the genomes of both strains uncovered the possibility that expansions and contractions of intragenic repeats may be a general mechanism to quickly introduce genomic and phenotypic variation.
by Brian L. Chin.
Ph.D.

Variation is a universal property of life, and much of contemporary genetics research is directed towards understanding the causes of variation in traits. Here I present the results of my investigations into the genetic and other causes of trait variation in humans and mice. I address these questions in the context of two distinct research projects, which use pre-existing data to investigate the causes of trait variation, through a range of analytic techniques. I first extract trait data from historic breeding records from the incipient Collaborative Cross (a genetic reference population of recombinant inbred mice) and use them to map genetic factors affecting litter size and other reproductive traits. Mapping reveals significant quantitative trait loci associated with litter size and time between litters, as well as a number of suggestive loci. I characterise the genetic effects at these loci and investigate candidate genes. The most robust finding, a litter size locus on chromosome 5, explains around 3% of observed variation and 24% of the variation attributable to genetics. Using data obtained from the Netherlands Twin Registry - a longitudinal database of Dutch twins - I investigate the prevalence of parent of origin effects on gene expression traits in peripheral blood in humans. I first phase individuals' genotypes by parental origin and use these genotypes to calculate the heritability of over 44,000 gene expression traits partitioned into that attributable to matching and nonmatching parent of origin. I replicate prior genomewide heritability estimates for many traits, but I find little evidence of widespread parent-of-origin effects on human gene expression in blood. On further examination, the small sample size severely limits the power to detect such effects. Nonetheless, I identify approximately 200 genes enriched for immune system processes that show evidence of parent-of-origin-specific effects on heritability.

Thesis (Ph. D.)--University of Oregon, 2002.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 87-99). Also available for download via the World Wide Web; free to University of Oregon users.

The fundamental theme my thesis explores is the relationship between genetic variation and phenotypic variation. It addresses three main questions. What is the genetic architecture of traits in the HS? How can sequence information help identifying the sequence variants and genes responsible for phenotypic variation? Are the genetic factors contributing to phenotypic variation in the rat homologous to those contributing to variation in the same phenotype in the mouse? To address these questions, I analysed data collected by the EURATRANS consortium on 1,407 Heterogeneous Stock (HS) rats descended from eight inbred strains through sixty generations of outbreeding. The HS rats were genotyped at 803,485 SNPs and 160 measures relevant to a number of models of disease (e.g. anxiety, type 2 diabetes, multiple sclerosis) were collected. The eight founders of the Stock were genotyped and sequenced. I identified loci in the genome that contribute to phenotypic variation (Quantitative Trait Loci, QTLs), and integrated sequence information with the mapping results to identify the genetic variants underlying the QTLs. I made some important observations about the nature of genetic architecture in rats, and how this compares to mice and humans. I also showed how sequence information can be used to improve mapping resolution, and in some cases to identify causal variants. However, I report an unexpected observation: at the majority of QTLs, the genetic effect cannot be accounted for by a single variant. This finding suggests that genetic variation cannot be reduced to sequence variation. This complexity will need to be taken into account by studies that aim at unravelling the genetic basis of complex traits.

I explore the genetic and environmental basis of inheritance using modern techniques, in particular high-density genotyping arrays, and older techniques, in particular family history, to explore some longstanding questions about the way we inherit complex traits. Using pedigree data and the parent-offspring regression technique, I estimate narrow sense heritability (h2) of human lifespan in 20th Century Scotland as 0.16, lower than commonly cited studies in other populations. I also observe similar concordance between spouses as between parents and offspring - suggesting my estimate of heritability may include significant within-family environment effects and thus should be considered an upper bound. Using genome-wide array data to identify runs of homozygosity, from 150 cohorts across the world and up to 350,000 subjects per trait, I show that cognitive function and body size are associated with the total length of genome-wide runs of homozygosity. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of homozygosity on blood pressure and low-density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. An association between genome-wide homozygosity and complex traits arises due to directional dominance. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases have not. The analysis of less common single nucleotide polymorphism (SNP) variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association, whilst avoiding false positives. I show that addition of 100 population-specific exome sequences to 1,000 genomes global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%). The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%. Inheritance of complex traits remains a field wide open for discovery, both in determining the balance between nature and nurture and discovery of the specific mechanisms by which DNA causes variation in these traits, with the prospect of such discoveries illuminating biological pathways involved and, as knowledge deepens, facilitating prediction.

Melanin pigmentation is a complex trait governed by many genes. Variation in melanin pigmentation within, and between, populations makes it an important trait for assisting in physical identification of an individual in forensic investigations. Utilizing a training sample (n=789) comprised of various ethnicities and SNPs (75) in 24 genes previously implicated in human or animal pigmentation studies, I determined three-SNP multiple linear regression models that accounted for large proportions of pigmentation variation in skin (45.7%), eye color (76.4%), and hair [eumelanin-to-pheomelanin (43.2%) and total melanin (76.3%)], independent of ethnic origin. Rather than implementing stepwise regression, to ascertain the three-SNP predictive models, I devised an algorithm that is likely more robust than stepwise regression. The algorithm consisted of two steps: the first step reduced the pool of 75 SNPs to a pool of 40 by selection of SNPs that were significant (p<0.05) by one-way ANOVA; the second step enabled selection of SNPs for model incorporation based on their frequency in the best-fitted models of all possible combinations of three-SNP models (i.e., 40 choose 3).Prediction models were validated utilizing an independent cohort (n=242, test sample) that was very similar in ethnic composition to the training sample. Relative shrinkage was moderate for skin reflectance (23.4%), eye color (19.4%), and eumelanin-to-pheomelanin (37.3%) of hair, and largest for total melanin (67%) of hair. Additionally, we refined our model-building algorithm, enabling visual comparison of the frequency and co-linearity due to linkage or co-inheritance of SNPs of the best-fitted models. Application of our algorithm to the test sample yielded the same or similar models as the training sample. Two of the three SNPs composing the models were the same, with some variability in the third SNP of the model.

Etiological models of complex disease are elusive[46, 33, 9], as are consistently replicable findings for major genetic susceptibility loci[54, 14, 15, 24]. Commonly-cited explanations invoke low-frequency genomic variation[41], allelic heterogeneity at susceptibility loci[33, 30], variable etiological trajectories[18, 17], and epistatic effects between multiple loci; these represent among the most methodologically-challenging issues in molecular genetic studies of complex traits. The response has been con- sistently reactionary—hypotheses regarding the relative contributions of known func- tional elements, or emphasizing a greater role of rare variation[46, 33] have undergone periodic revision, driving increasingly collaborative efforts to ascertain greater numbers of participants and which assay a rapidly-expanding catalogue of human genetic variation. Major deep-sequencing initiatives, such as the 1,000 Genomes Project, are currently identifying human polymorphic sites at frequencies previously unassailable and, not ten years after publication of the first major genome-wide association find- ings, re-sequencing has already begun to displace GWAS as the standard for genetic analysis of complex traits. With studies of complex disease primed for an unprecedented survey of human genetic variation, it is essential that human geneticists address several prominent, problematic aspects of this research. Realizations regarding the boundaries of human traits previously considered to be effectively disparate in presentation[44, 39, 35, 27, 25, 12, 4, 13], as well as profound insight into the extent of human genetic diversity[23, 22] are not without consequence. Whereas the resolution of fine-mapping studies have undergone persistent refinement, recent polygenic findings suggest a less discriminant basis of genetic liability, raising the question of what a given, unitary association finding actually represents. Furthermore, realistic expectations regarding the pattern of findings for a particular genetic factor between or even within populations remain unclear. Of interest herein are methodologies which exploit the finite extent of genomic variability within human populations to distinguish single-point and cumulative group differences in liability to complex traits, the range of allele frequencies for which common association tests are appropriate, and the relevant dimensionality of common genetic variation within ethnically-concordant but differentially ascertained populations. Using high-density SNP genotype data, we consider both hypothesis-driven and agnostic (genome-wide) approaches to association analysis, and address specific issues pertaining to empirical significance and the statistical properties of commonly- applied tests. Lastly, we demonstrate a novel perspective of genome-wide genetic “background” through exhaustive evaluation of fundamental, stochastic genetic processes in a sample of matched affected and unaffected siblings selected from high- density schizophrenia families.

The goal of this thesis is to explore, improve and implement some advanced modern computational methods in statistics, focusing on applications in genetics. The thesis has three major directions. First, we study likelihoods for genetics analysis of experimental populations. Here, the maximum likelihood can be viewed as a computational global optimization problem. We introduce a faster optimization algorithm called PruneDIRECT, and explain how it can be parallelized for permutation testing using the Map-Reduce framework. We have implemented PruneDIRECT as an open source R package, and also Software as a Service for cloud infrastructures (QTLaaS). The second part of the thesis focusses on using sparse matrix methods for solving linear mixed models with large correlation matrices. For populations with known pedigrees, we show that the inverse of covariance matrix is sparse. We describe how to use this sparsity to develop a new method to maximize the likelihood and calculate the variance components. In the final part of the thesis we study computational challenges of psychiatric genetics, using only pedigree information. The aim is to investigate existence of maternal effects in obsessive compulsive behavior. We add the maternal effects to the linear mixed model, used in the second part of this thesis, and we describe the computational challenges of working with binary traits.
eSSENCE

Recent events have probably lead us to wonder why people make decisions that seem to be irrational, and that go against any easily understandable logic. The fact that these decisions are emotionally driven often explains what, at first glance, does not have a plausible explanation. Evidence has been found that proves that emotions and other affective characteristics guide decisions beyond a purely rational deliberation. Understanding the way emotions take place, the way emotions change, and/or the way emotions influence behavior, has traditionally been a concern of several fields including psychology and neurology. Moreover, other sciences such as behavioral economics, artificial intelligence, and in general, all sciences that aim to understand, explain, or simulate human behavior, acknowledge the important role of affective characteristics in this task. Specifically, artificial intelligence uses psychological findings in order to create agents that simulate human behavior. Nevertheless, individual research efforts in modeling affective characteristics are often overlapped, short of integration, and they lack of a common conceptual system. This deprives individual researches of the exchange and cooperation's inherent benefits, and makes the task of computationally simulating affective characteristics more difficult. Although much individual effort has been put in classifying, formalizing and modeling emotions and emotion theories on some fields, recognized researchers of emotions' and affective processes' modeling report that a common formal language, an informal conceptual system, and a general purpose affective agent architecture will greatly improve the interdisciplinary exchange and the intradisciplinary coordination. The research literature proposes a wide amount of affective models that deal with some of: relationship between emotions and cognition, relationship between emotions and behavior, emotions and their evolutionary account, emotions for appraising situations, emotion regulation, etc. These models are useful tools for addressing particular emotion-related issues. Furthermore, computational approaches that are based on particular psychological theories have also been proposed. They often address domain specific issues starting from a specific psychological theory. In such solutions, the absence of a common conceptual system and/or platform, makes difficult the feedback between psychological theories and computational approaches. This thesis systematizes and formalizes affect-related theories, what can benefit the interdisciplinary exchange, the intradisciplinary coordination, and hence, allows the improvement of involved disciplines. Specifically this thesis makes the following contributions: (1) a theoretical framework that includes the main processes and concepts that a model of an affective agent with practical reasoning should have; (2) a general-purpose affective agent architecture that shares the concepts of the proposed theoretical framework; (3) an implementation-independent formal language for designing affective agents that have the proposed architecture; and (4) a specific agent language for implementing affective agents which is an extension of a BDI language. Some studies with human participants have helped to validate the contributions of this thesis. They include classical games of game theory, and an study with 300 participants, which have provided the necessary information to evaluate the contributions. The validation has been performed in three directions: determine whether the proposed computational approach represents better the human behavior than traditional computational approaches; determine whether this approach allows to improve psychological theories used by default; and determine whether the proposed affective agents' behavior is closer to human behavior than the behavior of a purely rational agent.
Probablemente algunos eventos recientes nos han conducido a preguntarnos por qué las personas toman decisiones aparentemente irracionales y en contra de alguna lógica fácilmente comprensible. El hecho de que estas decisiones estén bajo la influencia de las emociones a menudo explica lo que, a primera vista, parece no tener una explicación aceptable. En este sentido, se han encontrado evidencias que prueban que las emociones y otras características afectivas condicionan las decisiones más allá de una deliberación meramente racional. Entender cómo las emociones tienen lugar, cómo cambian y cómo influyen en el comportamiento, ha sido tradicionalmente de interés para muchos campos de investigación, incluyendo la psicología y la neurología. Además, otras ciencias como la economía conductual o la inteligencia artificial reconocen el importante papel de las características afectivas en esta tarea. Específicamente, la inteligencia artificial utiliza los resultados obtenidos en psicología para crear agentes que simulan el comportamiento humano. Sin embargo, a menudo los esfuerzos individuales de investigación en el modelado del afecto se solapan, carecen de la suficiente integración y de un sistema conceptual común. Esto limita a las investigaciones individuales para disponer de los beneficios que ofrecen el intercambio y la cooperación, y hace más compleja la tarea de simular los procesos afectivos. Las emociones y teorías relacionadas han sido clasificadas, formalizadas y modeladas. No obstante, reconocidos investigadores argumentan que un lenguaje formal común, un sistema conceptual informal y una arquitectura de agentes de propósito general, mejorarán significativamente el intercambio interdisciplinar y la coordinación intradisciplinar. En la literatura se propone una amplia cantidad de modelos afectivos que modelan: la relación entre las emociones y la cognición, la relación entre las emociones y el comportamiento, las emociones para evaluar las situaciones, la regulación de emociones, etc. Estos modelos son herramientas útiles para abordar aspectos particulares relacionados con las emociones. Además, se han realizado propuestas computacionales que abordan aspectos específicos sobre la base de teorías psicológicas específicas. En éstas soluciones, la ausencia de una plataforma y/o sistema conceptual dificulta la retroalimentación entre las teorías psicológicas y las propuestas computacionales. Esta tesis sistematiza y formaliza teorías relacionadas con el afecto, lo cual beneficia el intercambio interdisciplinar y la coordinación intradisciplinar, y por tanto, permite el desarrollo de las disciplinas correspondientes. Específicamente esta tesis realiza las siguientes contribuciones: (1) una plataforma teórica que incluye los conceptos y procesos principales que debería poseer un modelo de agentes afectivos con razonamiento práctico; (2) una arquitectura de agentes de propósito general que comparte los conceptos de la plataforma teórica propuesta; (3) un lenguaje formal independiente de la implementación, para diseñar agentes afectivos que poseen la arquitectura propuesta; y (4) un lenguaje de agentes específico para implementar agentes afectivos el cual es un extensión de un lenguaje BDI. Algunos estudios con participantes humanos han ayudado a validar las contribuciones de esta tesis. Estos incluyen juegos clásicos de teoría de juegos y un estudio con 300 participantes, los cuales han proporcionado la información necesaria para evaluar las contribuciones. La validación se ha realizado en tres direcciones: determinar si la propuesta computacional que se ha realizado representa mejor el comportamiento humano que propuestas computacionales tradicionales; determinar si esta propuesta permite mejorar las teorías psicológicas empleadas por defecto; y determinar si el comportamiento de los agentes afectivos propuestos se acerca más al comportamiento humano que el compor
Probablement alguns esdeveniments recents ens han conduït a preguntar-nos per què les persones prenen decisions que aparentment són irracionals i que van en contra d'algun tipus de lògica fàcilment comprensible. El fet que aquestes decisions estiguin sota la influència de les emocions sovint explica el que, a primera vista, sembla no tenir una explicació acceptable. En aquest sentit, s'han trobat evidències que proven que les emocions i altres característiques afectives condicionen les decisions més enllà d'una deliberació merament racional. Entendre com les emocions tenen lloc, com canvien i com influeixen en el comportament, ha estat tradicionalment d'interès per a molts camps d'investigació, incloent la psicologia i la neurologia. A més, altres ciències com l'economia conductual, la intel·ligència artificial i, en general, totes les ciències que intenten entendre, explicar o simular el comportament humà, reconeixen l'important paper de les característiques afectives en aquesta tasca. Específicament, la intel·ligència artificial utilitza els resultats obtinguts en psicologia per crear agents que simulen el comportament humà. No obstant això, sovint els esforços individuals d'investigació en el modelatge de l'afecte es solapen, no tenen la suficient integració ni compten amb un sistema conceptual comú. Això limita a les investigacions individuals, que no poden disposar dels beneficis que ofereixen l'intercanvi i la cooperació, i fa més complexa la tasca de simular els processos afectius. Les emocions i teories relacionades han estat classificades, formalitzades i modelades. No obstant això reconeguts investigadors argumenten que un llenguatge formal comú, un sistema conceptual informal i una arquitectura d'agents de propòsit general, milloraran significativament l'intercanvi interdisciplinar i la coordinació intradisciplinar. En la literatura es proposa una àmplia quantitat de models afectius que modelen: la relació entre les emocions i la cognició, la relació entre les emocions i el comportament, les emocions per avaluar les situacions, la regulació d'emocions, etc. Aquests models són eines útils per abordar aspectes particulars relacionats amb les emocions. A més, s'han realitzat propostes computacionals que aborden aspectes específics sobre la base de teories psicològiques específiques. En aquestes solucions, l'absència d'una plataforma i/o sistema conceptual dificulta la retroalimentació entre les teories psicològiques i les propostes computacionals. Aquesta tesi sistematitza i formalitza teories relacionades amb l'afecte, la qual cosa beneficia l'intercanvi interdisciplinar i la coordinació intradisciplinar, i per tant, permet el desenvolupament de les disciplines corresponents. Específicament aquesta tesi realitza les següents contribucions: (1) una plataforma teòrica que inclou els conceptes i processos principals que hauria de posseir un model d'agents afectius amb raonament pràctic; (2) una arquitectura d'agents de propòsit general que comparteix els conceptes de la plataforma teòrica proposta; (3) un llenguatge formal independent de la implementació, per dissenyar agents afectius que posseeixen l'arquitectura proposada; i (4) un llenguatge d'agents específic per implementar agents afectius el qual és un extensió d'un llenguatge BDI. Alguns estudis amb participants humans han ajudat a validar les contribucions d'aquesta tesi. Aquests inclouen jocs clàssics de teoria de jocs i un estudi amb 300 participants, els quals han proporcionat la informació necessària per avaluar les contribucions. La validació s'ha realitzat en tres direccions: determinar si la proposta computacional que s'ha realitzat representa millor el comportament humà que propostes computacionals tradicionals; determinar si aquesta proposta permet millorar les teories psicològiques emprades per defecte; i determinar si el comportament dels agents afectius proposats s'acosta més al
Alfonso Espinosa, B. (2017). Agents with Affective Traits for Decision-Making in Complex Environments [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90497
TESIS

The ageing process in humans is affected by lifestyle and by our genetics, the mechanisms of which we understand to varying degrees. Unpicking the phenotypic and genetic architecture of ageing traits will explain why there is such variability in the ageing phenotype. I begin with a study of physical and cognitive capability in middle to older aged individuals (Chapter 2). I use a SNP shown to associate with nicotine dependence in a Mendelian Randomization meta-analysis to explore the causality of the association of smoking with ageing, demonstrating how genetic information can be used to improve our understanding of the causal association of lifestyle and ageing traits. In Chapter 3, I meta-analyse the association of carrier status for four Mendelian diseases and physical capability, cognitive capability and lung function in ageing individuals to understand whether presumed asymptomatic heterozygotes present with a characteristic phenotype in later life. This study generated a novel finding: PI-MZ carrier status for alpha 1-antitrypsin deficiency is associated with increased height and increased respiratory capacity. In Chapter 4, I conduct the first metabochip-wide association scan of objective physical capability and self-reported disability in middle to older aged individuals, with the aim of identifying candidates for ageing genes. Lastly (Chapter 5), I conduct a simulation of evolution to test a pleiotropic model developed by Eyre-Walker (2010) regarding the genetic architecture of complex traits. I find good concordance between simulation and theory, although discrepancies arise due to the assumptions of the diffusion model. I discuss the potential portability of the pleiotropic model to the context of complex ageing traits. This thesis applies contrasting approaches to exploring phenotypic and genetic influences on complex ageing trais, enhancing our current understanding of how lifestyle and genetics shape our ageing phenome. In a world of increasing geriatric morbidity, such progress is of burgeoning importance.

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.

This thesis presents several developments on Variance component (VC) approach for Quantitative Trait Locus (QTL) mapping. The first part consists of methodological improvements: a new fast and efficient method for estimating IBD matrices, have been developed. The new method makes a better use of the computer resources in terms of computational power and storage memory, facilitating further improvements by resolving methodological bottlenecks in algorithms to scan multiple QTL. A new VC model have also been developed in order to consider and evaluate the correlation of the allelic effects within parental lines origin in experimental outbred crosses. The method was tested on simulated and experimental data and revealed a higher or similar power to detect QTL than linear regression based QTL mapping. The second part focused on the prospect to analyze multi-generational pedigrees by VC approach. The IBD estimation algorithm was extended to include haplotype information in addition to genotype and pedigree to improve the accuracy of the IBD estimates, and a new haplotyping algorithm was developed for limiting the risk of haplotyping errors in multigenerational pedigrees. Those newly developed methods where subsequently applied for the analysis of a nine generations AIL pedigree obtained after crossing two chicken lines divergently selected for body weight. Nine QTL described in a F2 population were replicated in the AIL pedigree, and our strategy to use both genotype and phenotype information from all individuals in the entire pedigree clearly made efficient use of the available genotype information provided in AIL.

Thesis (Ph. D.)--University of California, San Diego, 2008.
Title from first page of PDF file (viewed June 3, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 222-236).

Since 2005, genome-wide association (GWA) studies have dominated the field of complex traits. Genetic and environmental factors play a role in causing disease and influencing the variance of a quantitative trait. GWA is a hypothesis-free approach that follows on from candidate gene and linkage studies and has markedly increased the number of loci associated with complex traits. Despite the relative success of GWA studies in identifying several hundreds of phenotypic associations, the genetic component of most complex traits remains largely unaccounted for. The field has now begun to focus its, efforts on the "missing heritability" to enhance the understanding of genetics and the associated biological pathways that underlie the aetiology of complex phenotypes. This thesis presents a series of studies that attempt to address this issue by exploring other sources of variation and statistical models that have not been extensively addressed in GWA studies to date. Chapter 1 is an introduction to genome-wide association studies. In particular it describes the origins of these studies, what we have learnt from them as well as their limitations. Chapter 2 describes a study that shows how multiple signals within a single locus can explain more of the genetic component of a complex trait, using gene expression as a model trait. 2 Chapter 3 describes a study that tests for deviation from additivity (additivity is an assumption of most GWA studies to date) through dominant, recessive and gene-gene interaction analyses using height, body mass index, and waist-hip ratio (adjusted for BMI) as model phenotypes. Chapter 4 describes a study that examines how more signals may be identified by increasing the density of variants through 1000 Genomes based imputation compared to HapMap based imputation. I use 93 phenotypes, all circulating factors, including proteins, ions and vitamins. Chapter 5 describes a study that tests whether more association signals can be discovered through low-coverage whole-genome sequencing. In particular, I compare association testing based on 1000 Genomes based imputation and sequencing. I use gene expression as a model trait. Chapter 6 discusses the research findings from the previous chapters, presents conclusions, and describes future research plans in the field of complex traits for a fuller understanding of the role of genetics. 3

Evolutionary forces such as recombination, demography, and selection can shape patterns of genetic diversity within populations and contribute to phenotypic variation. While theoretical models exist for each of these forces independently, mathematically modeling their joint impact on patterns of genetic diversity remains very challenging. Fortunately, it is possible to perform forward-in-time computer simulations of DNA sequences that incorporate all of these forces simultaneously. Here, I show that there are trade-offs between computational efficiency and accuracy for simulations of a widely investigated model of recurrent positive selection. I develop a theoretical model to explain this trade-off, and a simple algorithm that obtains the best possible computational performance for a given error tolerance. I then pivot to develop a framework for simulations of human DNA sequences and genetically complex phenotypes, incorporating recently inferred demographic models of human continental groups and selection on genes and non-coding elements. I use these simulations to investigate the power of rare variant association tests in the context of rampant selection and non-equilibrium demography. I show that the power of rare variant association tests is in some cases quite sensitive to underlying assumptions about the relationship between selection and effect sizes. This work highlights both the challenge and the promise of applying forward simulations in genetic studies that seek to infer the parameters of evolutionary models and detect statistical associations.

This thesis explores the genetic characterization of the mechanisms underlying complex traits in chicken through the use and development of bioinformatics tools. The characterization of quantitative trait loci controlling complex traits has proven to be very challenging. This thesis comprises the study of experimental designs, annotation procedures and functional analyses. These represent some of the main ‘bottlenecks’ involved in the integration of QTLs with the biological interpretation of high-throughput technologies. The thesis begins with an investigation of the bioinformatics tools and procedures available for genome research, briefly reviewing microarray technology and commonly applied experimental designs. A targeted experimental design based on the concept of genetical genomics is then presented and applied in order to study a known functional QTL responsible for chicken body weight. This approach contrasts the gene expression levels of two alternative QTL genotypes, hence narrowing the QTL-phenotype gap, and, giving a direct quantification of the link between the genotypes and the genetic responses. Potential candidate genes responsible for the chicken body weight QTL are identified by using the location of the genes, their expression and biological significance. In order to deal with the multiple sources of information and exploit the data effectively, a systematic approach and a relational database were developed to improve the annotation of the probes of the ARK-Genomics G. gallus 13K v4.0 cDNA array utilized on the experiment. To follow up the investigation of the targeted genetical genomics study, a detailed functional analysis is performed on the dataset. The aim is to identify the downstream effects through the identification of functional variation found in pathways, and secondly to achieve a further characterization of potential candidate genes by using comparative genomics and sequence analyses. Finally the investigation of the body weight QTL syntenic regions and their reported QTLs are presented.

Mon projet de thèse vise à exploiter le potentiel des isolats de population pour étudier la composante génétique des maladies multifactorielles. En effet, les isolats peuvent faciliter l'identification des facteurs génétiques habituellement trop rares en population générale. Cette thèse est composée de deux études principalement : l'imputation génétique et l'analyse de l'héritabilité. Chacune de ces études ont été abordée sous deux angles : l’un théorique, s’appuyant sur une vaste étude de simulations basée sur les caractéristiques de la population isolée du Cilento, permettant d’évaluer des stratégies d’analyse et de déterminer la plus adéquate ; l’autre appliqué, s’appuyant sur l’analyse de données génétiques réelles issues de la même population.L'imputation génétique est une étape cruciale pour effectuer des analyses d'association dans un isolat et représente une méthode peu couteuse pour obtenir les séquences complètes du génome ou de l’exome des individus de la population. L'efficacité de cette approche dépend de la précision de l’imputation ; nous avons donc étudié plusieurs stratégies pour obtenir une précision d'imputation maximale dans un isolat. Nous avons montré que les logiciels utilisant des algorithmes qui s’appuient sur les caractéristiques particulières des isolats n’étaient pas, de façon inattendue, aussi performants que ceux conçus pour les populations générales. De plus, malgré la disponibilité de panels de référence publics contenant plusieurs milliers de chromosomes, nous avons confirmé qu’un panel de référence spécifique de la population d’étude, même de taille très réduite, était essentiel pour la qualité de l’imputation. Ceci était d’autant plus vrai pour les variantes rares.Pour de nombreux traits, il existe des discordances entre les estimations de l'héritabilité obtenues à partir d’individus apparentés et à partir d’individus non apparentés. En particulier, la plupart des chercheurs considère que les effets dominants (non additifs) ne jouent pas un rôle majeur malgré les résultats contrastés des études sur les isolats. Notre deuxième analyse a révélé des mécanismes possibles pour expliquer la disparité de ces estimations publiées entre populations isolées et populations générales. Cela nous a permis de faire des déductions intéressantes pour nos propres analyses dans le Cilento. En particulier, nous avons identifié la possibilité d'une composante de dominance non nulle pour les niveaux de lipoprotéines de basse densité (LDL). Cela nous a amenés à effectuer des analyses d'association pan-génomique des composantes additives et non-additives pour LDL dans le Cilento et nous avons pu identifier des gènes qui avaient déjà été liés au trait dans d'autres études.Dans le contexte de nos deux études, nous avons observé l'importance de conserver l'incertitude génotypique (dosage pour l’imputation, vraisemblance des génotypes pour les données de séquençage). Dans la perspective de cette thèse, nous avons proposé des moyens d’incorporer cette incertitude à certaines méthodes utilisées dans ce projet.Nos résultats concernant les stratégies d'imputation et l'analyse de l'héritabilité seront très utiles pour la poursuite de l'étude de l'isolat de Cilento. Mais, ils seront également instructifs pour les chercheurs travaillant sur d'autres populations isolées et également applicables plus généralement à l'étude des maladies complexes
My thesis project is concerned with tapping the potential of population isolates for the dissection of complex trait architecture. Specifically, isolates can aid the identification of variants that are usually rare in other populations. This thesis principally contains in depth investigations into genetic imputation and heritability analysis in isolates. We approached both of these studies from two main angles; first from a methodological standpoint where we created extensive simulation datasets in order to investigate how the specificities of an isolate should determine strategies for analyses. Secondly, we demonstrated such concepts through analysis of genetic data in the known isolate of Cilento. Imputation is a crucial step to performing association analyses in an isolate and represents a cost-efficient method for gaining dense genetic data for the population. The effectiveness of imputation is of course dependent on its accuracy. Hence, we investigated the wide range of possible strategies to gain maximal imputation accuracy in an isolate. We showed that software using algorithms which specifically evoke known characteristics of isolates were, unexpectedly, not as successful as those designed for general populations. We also demonstrated a very small study specific imputation reference panel performing very strongly in an isolate; particularly for rare variants. For many complex traits, there exist discordances between estimates of heritabilities from studies in closely related individuals and from studies on unrelated individuals. In particular, we noted that most researchers consider dominant (non-additive) genetic effects as unlikely to play a significant role despite contrasting results from previous studies on isolates. Our second analysis revealed possible mechanisms to explain such disparate published heritability estimates between isolated populations and general populations. This allowed us to make interesting deductions from our own heritability analyses of the Cilento dataset, including an indication of a non-null dominance component involved in the distribution of low-density lipoprotein level measurements (LDL). This led us to perform genome-wide association analyses of additive and non-additive components for LDL in Cilento and we were able to identify genes that had been previously linked to the trait in other studies. In the contexts of both of our studies, we observed the importance of retaining genotype uncertainty (genotype dosage following imputation or genotype likelihoods from sequencing data). As a prospective of this thesis, we have proposed ways to incorporate this uncertainty into certain methods used in this project. Our findings for imputation strategies and heritability analysis will be highly valuable for the continued study of the isolate of Cilento but will also be instructive to researchers working on other isolated populations and also applicable to the study of complex diseases in general

This study focuses on integrating and applying computational techniques for modelling quantitative traits and complex diseases, such as hypertension and diabetes, using the rat model system and translating the findings to humans. Complex disease traits are heritable, highly polygenic, and influenced by environmental factors. Human studies, like Genome Wide Association Studies (GWAS), have identified many genetic determinants underlying these traits but have provided little information about the functional effects of these variants and mechanisms regulating the disease. This study takes a systems-level approach for looking at the genetic regulation of complex traits in the rat by analysing multiple phenotypes, genomewide genetic variation and gene expression data in multiple tissues. I integrated these multi-modality datasets in the BXH/HXB rat Recombinant Inbred (RI) lines, an established model of the human metabolic syndrome, to identify candidate genes, pathways and networks associated with complex disease phenotypes. I evaluated methods for Expression Quantitative Trait Locus (eQTL) analysis and used sparse Bayesian regression approaches to map eQTLs in the RI lines, delineating a new, large eQTL data resource for the rat genetic community. I have also developed and applied signal processing and time series analysis methods to physiological traits to extract more detailed indices of blood pressure, and integrated these with genetic, expression and eQTL data to inform on the regulation of these traits. Then, using publicly available data, I used comparative genomics approaches to elucidate a set of genes and pathways that can play a role in human diseases. This study has provided a valuable resource for future work in the rat, by means of new eQTLs in multiple tissues, and physiological time series phenotypes and approaches. This has enabled an integrative analysis of these data to give new insights into the regulation of complex traits in rats and humans.

Nowadays, there are many statistical methods available for genetic association analyses with data various designs. However, it is usually ignored in these analyses that an analytical method must be appropriate for an experimental design from which data is collected. In addition, association study is a population-based analysis and, thus its inference is highly vulnerable to many population-oriented confounding factors. This thesis starts with a comprehensive survey and comparison of those methods commonly used in the literature of genetic association study in order to obtain insights into the statistical aspects and problem of the methods. On the basis of these reviews, we managed to calculate the optimal trend set for the Armitage’s trend test for different penetrance models with a high level of genetic heterogeneity. We introduced two new strategies to adjust for the population stratification in association analyses. We proposed a maximum likelihood estimation method to adjust for biases in statistical inference of linkage disequilibrium (LD) between pairs of polymorphic loci by using non-random samples. In the process of the analysis, we derived a more sophisticated but robust likelihood-based statistical framework, accounting properly for the non-random nature of case and control samples. Finally, we developed a multi-point likelihood-based statistical approach for a genome-wide search for the genetic variants that contribute to phenotypic variation of complex quantitative traits. We tested these methods through intensive simulation studies and demonstrated their application in analyses with large case and control SNP datasets of the Parkinson’s disease. Despite that we have mainly focused on SNP data scored from microarray techniques, the theory and methodology presented here paved a useful stepping stone approach to the modeling and analysis of data depicting genome structure and function from the new generation sequencing techniques.

A central problem in evolutionary quantitative genetics has been to attempt to dissect the genetic basis of complex traits. A variety of inferential methods have been developed to probe this issue. Here, I use experimental evolution, next generation sequencing and standing genetic variation in the nematode Caenorhabditis remanei to dissect the genetic basis of two model complex traits: oxidative and heat stress response. Pleiotropy, when one gene affects more than one trait, is an important phenomenon to understand when attempting to understand the genetic architecture of a complex trait. Previous work in the nematode C. elegans found that abiotic stress response is controlled by a handful of genes of major effect, and that mutations in one gene can affect the ability of the organism to respond to multiple types of stressors. I used experimental evolution to probe the extent of pleiotropy between the genes selected for resistance to one of two abiotic stressors: acute heat and oxidative. In contrast to expectations, I find that acute heat stress response and acute oxidative response are polygenic, complex traits. Additionally, I find that the evolved responses do not share a genetic basis. This lack of correlation is reflected at the levels of phenotype, gene expression and genomic response to selection. In addition to the complex interactions within an organism, the genetic architecture of complex traits and response to selection are affected by population dynamics. Here, I investigate the effect of gene flow on patterns and extent of phenotypic and genetic divergence between populations in distinct environments – a standard lab environment and a chronic heat stress environment. Gene flow of lab-adapted individuals into chronic heat stress adapted populations did not affect phenotypic adaptation, but greatly decreased the number of genomic sites that responded to selection. These results fit predictions that gene flow of non-locally adapted individuals will create an additional barrier for local adaptation, and the strength of selection of locally adapted alleles must not only be greater than the strength of random effects, but also be stronger than the effects of gene flow. This work includes unpublished co-authored material.
2019-01-27

Infectious diseases are a major cause of morbidity and mortality worldwide. Streptococcus pneumoniae and Neisseria meningitidis are major causes of severe bacterial disease which can manifest as invasive disease such as bacteraemia and meningitis. Exposure to these pathogens is relatively widespread, yet only a minority of individuals develop invasive disease. A host genetic component to infectious disease susceptibility has been implied from twin and adoptee studies. A role for rare large effect genetic variants in predisposition to infection has been demonstrated through the study of individuals with primary immunodeficiencies. However, a majority of these studies have been undertaken in individuals with a history of recurrent disease or in multi-case families. The relative role of rare genetic variants of moderate to large effect at the population level has not been widely explored. This thesis presents effort made using next generation sequencing methods to identify rare genetic variants that lead to increased susceptibility to bacterial disease focussing on meningococcal disease, pleural infection(empyema), pneumococcal disease and sepsis phenotypes. Using an exome sequencing approach in 13 cases with invasive meningococcal disease, a novel mutation leading to a complement deficiency and increased risk of meningococcal infection was identified and functionally validated in one individual. This mutation in the CFP gene was demonstrated as leading to impaired properdin secretion. Further analysis implicated loss of function mutations in CD4 and ZAP70 as novel loci for meningococcal disease susceptibility. A case control association analysis for sepsis susceptibility highlighted the possible role for small Rho GTPases in sepsis pathology. By aggregating all rare predicted deleterious mutations in a gene, four genes in this pathway, (ROCK2, ARHGAP18, FYN and CDC42BPG) were implicated as having an excess of rare deleterious variants in sepsis samples compared to population controls. A similar approach identified low frequency genetic variants in the CD109 gene as predisposing to empyema susceptibility in children. Finally, preliminary evidence from adult individuals with invasive pneumococcal disease points to a potential role of the RNASE7 gene in invasive pneumococcal disease susceptibility. This association was primarily due to a predicted deleterious missense mutation present in cases and absent in controls. Taken together, these results have identified a number of potential loci with rare variants associated with susceptibility to severe phenotypes of bacterial diseases.

Local adaptation to climate in temperate forest trees involves the integration of multiple physiological, morphological, and phenological traits. Latitudinal clines for the relevant component traits are frequently observed for species that have a north-south distribution, but these relationships do not account for climatic variation within a given latitudinal band, which may be reflected in adaptive traits. We used black cottonwood (Populus trichocarpa) as a model to characterize the interplay between geography, climate, and adaptation to abiotic factors. Twelve traits (height, diameter, volume index, crown diameter, number of branches, number of sylleptic branches, relative number of branches, Relative canopy depth, Bud set, Bud flush, cold index of injury, carbon isotope ratio) were measured in a range-wide sample of 124 P. trichocarpa genotypes grown in a common garden. Heritability's were moderate to high (0.24 to 0.55) and significant population differentiation (QST > 0.3) suggested adaptive divergence. When climate variables were taken as predictors and the 12 traits as response variables in a multivariate regression tree analysis, aridity (Eref) explained the most variation, with subsequent splits grouping individuals according to mean temperature of the warmest month, frost-free period (FFP), and mean annual precipitation (MAP). This grouping matches relatively well the splits using geographic variables as predictors: the northernmost groups (short FFP and low Eref) had the lowest growth performance, and the highest cold hardiness. The groups spanning the south of British Columbia (low Eref and intermediate temperatures) displayed an average growth and cold hardiness. The group from the coast of California and Oregon (high Eref and FFP) had the best growth performance and the lowest cold hardiness. The southernmost and high-elevated group (with High Eref and low FFP) performed poorly, had a low cold hardiness and a significantly lower WUE.
Master of Science

Malaria parasites of the species infecting humans and animal hosts exhibit genetic and phenotypic diversity. Some of this diversity, including the responses to anti-malarial drugs, growth rate and virulence and antigenic variability, is medically significant. This is because these phenotypes may determine the existence and survival of the parasites in the host and, in turn, contribute to the clinical outcome of infection. Understanding of the biological characteristics and the genetic basis underlying these complex phenotypes can thus lead to the development of effective control strategies against the disease, such as anti-malarial drugs and vaccines. Genetic studies in rodent malaria parasites have proved useful in providing insights into the genetic determinants of these complex traits and thus can be used to complement the study of human malaria. The present studies aim to investigate genetic determinants underlying two major medically important phenotypes, Strain Specific Protective Immunity (SSPI) and Growth rate, using the newly devised genetic method of Linkage Group Selection (LGS). The results presented here relate to the accomplishment of these aims. LGS analysis of SSPI using a genetic cross between clones AJ and CB-pyr10 of Plasmodium chabaudi chabaudi has identified a single region on chromosome 8 containing the gene for the Merozoite Surface Protein-1 as encoding a major target of SSPI. A similar finding was also obtained in a previous LGS study using a different genetic cross between clones AS-pyr1 and CB of P. c. chabaudi (Martinelli et al., 2005). Hence, the results of two independent studies strongly indicate that a single locus within the parasite genome contains a major target antigen, or antigens, of SSPI against P. c. chabaudi malaria. These results have particular relevance for research on SSPI in human malaria and the choice of candidate antigens for malaria vaccine development. LGS analysis of growth rate conducted upon a genetic cross between a fast-growing line, 17XYM, and a slow-growing line, 33XC, of Plasmodium yoelii yoelii has identified a ~ 1 megabase pair region on P. y. yoelii chromosome 13 as containing a major genetic determinant(s) of growth rate in these malaria parasites. This is consistent with the finding of the classical linkage analysis by Walliker et al., (1976), that growth rate in P. y. yoelii is mainly determined at a single genetic locus. Because the fast-growing line 17XYM arose spontaneously during infection with a mild strain of P. y. yoelii 17X, identification of parasites with a slow growth rate phenotype derived from the same genetic stock as 17XYM can be useful in determining genes underlying growth rate in these malaria parasites. It has been shown here that parasites of the P. y. yoelii lines 17X consist of two completely distinct genotypes. One is represented by the fast-growing line, 17XYM, and a slow-growing line of P. y. yoelii, 17XNIMR. The other is represented by another slow-growing line 17XA. Comparing the region of P. y. yoelii chromosome 13 under strong growth selection between the two congenic lines, 17XYM and 17XNIMR, could lead to the identification of the gene(s) controlling growth rate differences in these two parasite lines. Such findings could be relevant to the location of genetic determinants of growth rate in human malaria.

Genetic associations have been discovered for many human complex traits, and yet for most associated loci the causal variants and molecular mechanisms remain unknown. Studies mapping quantitative trait loci (QTLs) for molecular phenotypes, such as gene expression, RNA splicing, and chromatin accessibility, provide rich data that can link variant effects in specific cell types with complex traits. These genetic effects can also now be modeled in vitro by differentiating human induced pluripotent stem cells (iPSCs) into specific cell types, including inaccessible cell types such as those of the brain. In this thesis, I explore a range of approaches for using QTLs to identify causal variants and to link these with molecular functions and complex traits. In Chapter 2, I describe QTL mapping in 123 sensory neuronal cell lines differentiated from human iPSCs. I observed that gene expression was highly variable across iPSC-derived neuronal cultures in specific gene categories, and that a portion of this variability was explained by commonly used iPSC culture conditions, which influenced differentiation efficiency. A number of QTLs overlapped with common disease associations; however, using simulations I showed that identifying causal regulatory variants with a recall-by- genotype approach in iPSC-derived neurons is likely to require large sample sizes, even for variants with moderately large effect sizes. In Chapter 3, I developed a computational model that uses publicly available gene expression QTL data, along with molecular annotations, to generate cell type-specific probability of regulatory function (PRF) scores for each variant. I found that predictive power was improved when the model was modified to use the quantitative value of annotations. PRF scores outperformed other genome-wide scores, including CADD and GWAVA, in identifying likely causal eQTL variants. In Chapter 4, I used PRF scores to identify relevant cell types and to fine map potential causal variants using summary association statistics in six complex traits. By examining individual loci in detail, I showed how the enrichments contributing to a high PRF score are transparent, which can help to distinguish plausible causal variant predictions from model misspecification.

Depression is the most common psychiatric disorder and the leading cause of disability worldwide. Despite evidence for a genetic component, the genetic aetiology of this disorder remains elusive. To date, only one association study has identified and replicated risk loci for depression. This thesis focuses on aiding genetic discovery by revisiting the depressed phenotype and developing a quantitative trait, using data from Generation Scotland: The Scottish Family Health Study. These analyses aim to test whether this derived quantitative trait has improved statistical power to identify genetic risk variants for depression, relative to the binary classification of case/control. Measures of genetic covariation were used to evaluate and rank ten measures of mood, personality and cognitive ability as endophenotypes for depression. The highest ranking traits were subjected to principal component analysis, and the first principal component used as a quantitative measure of depression. This composite trait was compared to the binary classification of depression in terms of ability to identify risk loci in a genome-wide association study, and phenotypic variance explained by polygenic profile scores for psychiatric disorders. I also compared the composite trait to the univariate traits in terms of their ability to fulfill the endophenotype criteria as described by Gottesman and Gould, namely: being heritable, genetically and phenotypically correlated with depression, state independent, co-segregating with illness in families, and observed at a higher rate in unaffected relatives than in unrelated controls. Four out of ten traits fulfilled most endophenotype criteria, however, only two traits - neuroticism and the general health questionnaire (a measure of current psychological distress) - consistently ranked highest across all analyses. As such, three composite traits were derived incorporating two, three, or four traits. Association analyses of binary depression, univariate traits and composite traits yielded no genome-wide significant results, with most traits performing equivalently. However, composite traits were more heritable and more highly correlated with depression than their constituent traits, suggesting that analyzing these traits in combination was capturing more of the heritable component of depression. Polygenic scores for psychiatric disorders explained more trait variance for the composite traits than the univariate traits, and depression itself. Overall, whilst the composite traits generally obtained more significant results, they did not identify any further insight into the genetic aetiology of depression. This work therefore provides support for the urgent need to redefine the depressed phenotype based on objective and quantitative measures. This is essential for risk stratification, better diagnoses, novel target identification and improved treatment.

Quantitative traits and disease risk in humans are affected by both genetic and environmental factors. Using genome-wide association studies (GWAS) over the past decade, researchers have been successful in finding common genetic polymorphisms that explain a proportion of the variation in many common phenotypes. Despite these significant leaps forward in our understanding, the heritable components of many traits remain largely unaccounted for. A number of explanations as to the “missing heritability” of complex traits and disease risk have been postulated. This thesis addresses some of the unexplained potential sources of heritable trait variation and explores two of its potential causes: low frequency and structural genetic variation. Chapter 1 provides a background to GWAS, what we have learned from them, discusses the different mechanisms of heritability and reviews the potential explanations for “missing heritability” in complex traits. The chapter then describes low frequency and structural genetic variation and how they fit into the spectrum of genetic variation. Chapter 2 describes a study that tests the extent to which low frequency association signals can be discovered through low pass whole genome sequencing when using well-powered gene expression and biomarker phenotypes as model traits. The study then compares these association signals to 1000 Genomes based imputation in the same individuals. Chapter 3 uses methods to detect the structural forms of the human amylase locus with whole-genome sequencing data. The study detects and validates multi-allelic copy number within this region and finds a lack of evidence of a previous association between structural variation of the amylase locus and obesity and body mass index. Chapter 4 scans for rare copy-number variation (CNV) using SNP microarray data from over 120 thousand individuals at 69 sites that were previously identified as being associated with developmental delay. The chapter aims to refine their prevalence in the general population and attempts to understand their relationship with developmental delay and complex traits. Chapter 5 aims to detect large deletions and duplications genome-wide using SNP microarray data in a sample of over 120 thousand individuals where we have power to detect rare copy number events. I used novel approaches to test their association with 204 clinically relevant complex traits to determine their role in the heritability of complex traits. Chapter 6 discusses the findings from the previous chapters within this thesis. I then continue by describing some limitations of this work and explore the potential further directions for future work in this area of study.