Computational analysis of the genetic and environmental contributions to disease-related human phenotypes: From predicting adverse lipid response of HIV patients receiving antiretroviral therapy to genome-wide association studies of cardiovascular and lipid related disorders

Genetic variants influence the risk to develop certain diseases or give rise to differences in drug response. Recent progresses in cost-effective, high-throughput genome-wide techniques, such as microarrays measuring Single Nucleotide Polymorphisms (SNPs), have facilitated genotyping of large clinical and population cohorts. Combining the massive genotypic data with measurements of phenotypic traits allows for the determination of genetic differences that explain, at least in part, the phenotypic variations within a population. So far, models combining the most significant variants can only explain a small fraction of the variance, indicating the limitations of current models. In particular, researchers have only begun to address the possibility of interactions between genotypes and the environment. Elucidating the contributions of such interactions is a difficult task because of the large number of genetic as well as possible environmental factors.In this thesis, I worked on several projects within this context. My first and main project was the identification of possible SNP-environment interactions, where the phenotypes were serum lipid levels of patients from the Swiss HIV Cohort Study (SHCS) treated with antiretroviral therapy. Here the genotypes consisted of a limited set of SNPs in candidate genes relevant for lipid transport and metabolism. The environmental variables were the specific combinations of drugs given to each patient over the treatment period. My work explored bioinformatic and statistical approaches to relate patients' lipid responses to these SNPs, drugs and, importantly, their interactions. The goal of this project was to improve our understanding and to explore the possibility of predicting dyslipidemia, a well-known adverse drug reaction of antiretroviral therapy. Specifically, I quantified how much of the variance in lipid profiles could be explained by the host genetic variants, the administered drugs and SNP-drug interactions and assessed the predictive power of these features on lipid responses. Using cross-validation stratified by patients, we could not validate our hypothesis that models that select a subset of SNP-drug interactions in a principled way have better predictive power than the control models using "random" subsets. Nevertheless, all models tested containing SNP and/or drug terms, exhibited significant predictive power (as compared to a random predictor) and explained a sizable proportion of variance, in the patient stratified cross-validation context. Importantly, the model containing stepwise selected SNP terms showed higher capacity to predict triglyceride levels than a model containing randomly selected SNPs. Dyslipidemia is a complex trait for which many factors remain to be discovered, thus missing from the data, and possibly explaining the limitations of our analysis. In particular, the interactions of drugs with SNPs selected from the set of candidate genes likely have small effect sizes which we were unable to detect in a sample of the present size (<800 patients).In the second part of my thesis, I performed genome-wide association studies within the Cohorte Lausannoise (CoLaus). I have been involved in several international projects to identify SNPs that are associated with various traits, such as serum calcium, body mass index, two-hour glucose levels, as well as metabolic syndrome and its components. These phenotypes are all related to major human health issues, such as cardiovascular disease. I applied statistical methods to detect new variants associated with these phenotypes, contributing to the identification of new genetic loci that may lead to new insights into the genetic basis of these traits. This kind of research will lead to a better understanding of the mechanisms underlying these pathologies, a better evaluation of disease risk, the identification of new therapeutic leads and may ultimately lead to the realization of "personalized" medicine.

Genetic associations in the detection of QTLs for wheat spike-related traits

The objective of this work was to assess the genetic diversity and population structure of wheat genotypes, to detect significant and stable genetic associations, as well as to evaluate the efficiency of statistical models to identify chromosome regions responsible for the expression of spike-related traits. Eight important spike characteristics were measured during five growing seasons in Serbia. A set of 30 microsatellite markers positioned near important agronomic loci was used to evaluate genetic diversity, resulting in a total of 349 alleles. The marker-trait associations were analyzed using the general linear and mixed linear models. The results obtained for number of allelic variants per locus (11.5), average polymorphic information content value (0.68), and average gene diversity (0.722) showed that the exceptional level of polymorphism in the genotypes is the main requirement for association studies. The population structure estimated by model-based clustering distributed the genotypes into six subpopulations according to log probability of data. Significant and stable associations were detected on chromosomes 1B, 2A, 2B, 2D, and 6D, which explained from 4.7 to 40.7% of total phenotypic variations. The general linear model identified a significantly larger number of marker-trait associations (192) than the mixed linear model (76). The mixed linear model identified nine markers associated to six traits.

Developmental critical period in genetic redox dysregulation: animal and human studies in schizophrenia

Converging evidence favors an abnormal susceptibility to oxidative stress in schizophrenia. Decreased levels of glutathione (GSH), the major cellular antioxidant and redox regulator, was observed in cerebrospinal-fluid and prefrontal cortex of patients. Importantly, abnormal GSH synthesis of genetic origin was observed: Two case-control studies showed an association with a GAG trinucleotide repeat (TNR) polymorphism in the GSH key synthesizing enzyme glutamate-cysteine-ligase (GCL) catalytic subunit (GCLC) gene. The most common TNR genotype 7/7 was more frequent in controls, whereas the rarest TNR genotype 8/8 was three times more frequent in patients. The disease associated genotypes (35% of patients) correlated with decreased GCLC protein, GCL activity and GSH content. Similar GSH system anomalies were observed in early psychosis patients. Such redox dysregulation combined with environmental stressors at specific developmental stages could underlie structural and functional connectivity anomalies. In pharmacological and knock-out (KO) models, GSH deficit induces anomalies analogous to those reported in patients. (a) morphology: spine density and GABA-parvalbumine immunoreactivity (PV-I) were decreased in anterior cingulate cortex. KO mice showed delayed cortical PV-I at PD10. This effect is exacerbated in mice with increased DA from PD5-10. KO mice exhibit cortical impairment in myelin and perineuronal net known to modulate PV connectivity. (b) physiology: In cultured neurons, NMDA response are depressed by D2 activation. In hippocampus, NMDA-dependent synaptic plasticity is impaired and kainate induced g-oscillations are reduced in parallel to PV-I. (c) cognition: low GSH models show increased sensitivity to stress, hyperactivity, abnormal object recognition, olfactory integration and social behavior. In a clinical study, GSH precursor N-acetyl cysteine (NAC) as add on therapy, improves the negative symptoms and decreases the side effects of antipsychotics. In an auditory oddball paradigm, NAC improves the mismatched negativity, an evoked potential related to pre-attention and to NMDA receptors function. In summary, clinical and experimental evidence converge to demonstrate that a genetically induced dysregulation of GSH synthesis combined with environmental insults in early development represent a major risk factor contributing to the development of schizophrenia

A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

BACKGROUND: Obesity is strongly associated with major depressive disorder (MDD) and various other diseases. Genome-wide association studies have identified multiple risk loci robustly associated with body mass index (BMI). In this study, we aimed to investigate whether a genetic risk score (GRS) combining multiple BMI risk loci might have utility in prediction of obesity in patients with MDD. METHODS: Linear and logistic regression models were conducted to predict BMI and obesity, respectively, in three independent large case-control studies of major depression (Radiant, GSK-Munich, PsyCoLaus). The analyses were first performed in the whole sample and then separately in depressed cases and controls. An unweighted GRS was calculated by summation of the number of risk alleles. A weighted GRS was calculated as the sum of risk alleles at each locus multiplied by their effect sizes. Receiver operating characteristic (ROC) analysis was used to compare the discriminatory ability of predictors of obesity. RESULTS: In the discovery phase, a total of 2,521 participants (1,895 depressed patients and 626 controls) were included from the Radiant study. Both unweighted and weighted GRS were highly associated with BMI (P <0.001) but explained only a modest amount of variance. Adding 'traditional' risk factors to GRS significantly improved the predictive ability with the area under the curve (AUC) in the ROC analysis, increasing from 0.58 to 0.66 (95% CI, 0.62-0.68; χ(2) = 27.68; P <0.0001). Although there was no formal evidence of interaction between depression status and GRS, there was further improvement in AUC in the ROC analysis when depression status was added to the model (AUC = 0.71; 95% CI, 0.68-0.73; χ(2) = 28.64; P <0.0001). We further found that the GRS accounted for more variance of BMI in depressed patients than in healthy controls. Again, GRS discriminated obesity better in depressed patients compared to healthy controls. We later replicated these analyses in two independent samples (GSK-Munich and PsyCoLaus) and found similar results. CONCLUSIONS: A GRS proved to be a highly significant predictor of obesity in people with MDD but accounted for only modest amount of variance. Nevertheless, as more risk loci are identified, combining a GRS approach with information on non-genetic risk factors could become a useful strategy in identifying MDD patients at higher risk of developing obesity.

Sex-linked inheritance, genetic correlations and sexual dimorphism in three melanin-based colour traits in the barn owl.

Theory states that genes on the sex chromosomes have stronger effects on sexual dimorphism than genes on the autosomes. Although empirical data are not necessarily consistent with this theory, this situation may prevail because the relative role of sex-linked and autosomally inherited genes on sexual dimorphism has rarely been evaluated. We estimated the quantitative genetics of three sexually dimorphic melanin-based traits in the barn owl (Tyto alba), in which females are on average darker reddish pheomelanic and display more and larger black eumelanic feather spots than males. The plumage traits with higher sex-linked inheritance showed lower heritability and genetic correlations, but contrary to prediction, these traits showed less pronounced sexual dimorphism. Strong offspring sexual dimorphism primarily resulted from daughters not expressing malelike melanin-based traits and from sons expressing femalelike traits to similar degrees as their sisters. We conclude that in the barn owl, polymorphism at autosomal genes rather than at sex-linked genes generate variation in sexual dimorphism in melanin-based traits.

Persistence of Transmitted HIV-1 Drug Resistance Mutations Associated with Fitness Costs and Viral Genetic Backgrounds.

Transmission of drug-resistant pathogens presents an almost-universal challenge for fighting infectious diseases. Transmitted drug resistance mutations (TDRM) can persist in the absence of drugs for considerable time. It is generally believed that differential TDRM-persistence is caused, at least partially, by variations in TDRM-fitness-costs. However, in vivo epidemiological evidence for the impact of fitness costs on TDRM-persistence is rare. Here, we studied the persistence of TDRM in HIV-1 using longitudinally-sampled nucleotide sequences from the Swiss-HIV-Cohort-Study (SHCS). All treatment-naïve individuals with TDRM at baseline were included. Persistence of TDRM was quantified via reversion rates (RR) determined with interval-censored survival models. Fitness costs of TDRM were estimated in the genetic background in which they occurred using a previously published and validated machine-learning algorithm (based on in vitro replicative capacities) and were included in the survival models as explanatory variables. In 857 sequential samples from 168 treatment-naïve patients, 17 TDRM were analyzed. RR varied substantially and ranged from 174.0/100-person-years;CI=[51.4, 588.8] (for 184V) to 2.7/100-person-years;[0.7, 10.9] (for 215D). RR increased significantly with fitness cost (increase by 1.6[1.3,2.0] per standard deviation of fitness costs). When subdividing fitness costs into the average fitness cost of a given mutation and the deviation from the average fitness cost of a mutation in a given genetic background, we found that both components were significantly associated with reversion-rates. Our results show that the substantial variations of TDRM persistence in the absence of drugs are associated with fitness-cost differences both among mutations and among different genetic backgrounds for the same mutation.

Genetic dissection of sleep homeostasis.

Sleep is a complex behavior both in its manifestation and regulation, that is common to almost all animal species studied thus far. Sleep is not a unitary behavior and has many different aspects, each of which is tightly regulated and influenced by both genetic and environmental factors. Despite its essential role for performance, health, and well-being, genetic mechanisms underlying this complex behavior remain poorly understood. One important aspect of sleep concerns its homeostatic regulation, which ensures that levels of sleep need are kept within a range still allowing optimal functioning during wakefulness. Uncovering the genetic pathways underlying the homeostatic aspect of sleep is of particular importance because it could lead to insights concerning sleep's still elusive function and is therefore a main focus of current sleep research. In this chapter, we first give a definition of sleep homeostasis and describe the molecular genetics techniques that are used to examine it. We then provide a conceptual discussion on the problem of assessing a sleep homeostatic phenotype in various animal models. We finally highlight some of the studies with a focus on clock genes and adenosine signaling molecules.

Past climate-driven range shifts and population genetic diversity in arctic plants

AimHigh intra-specific genetic diversity is necessary for species adaptation to novel environments under climate change, but species tracking suitable conditions are losing alleles through successive founder events during range shift. Here, we investigated the relationship between range shift since the Last Glacial Maximum (LGM) and extant population genetic diversity across multiple plant species to understand variability in species responses. LocationThe circumpolar Arctic and northern temperate alpine ranges. MethodsWe estimated the climatic niches of 30 cold-adapted plant species using range maps coupled with species distribution models and hindcasted species suitable areas to reconstructions of the mid-Holocene and LGM climates. We computed the species-specific migration distances from the species glacial refugia to their current distribution and correlated distances to extant genetic diversity in 1295 populations. Differential responses among species were related to life-history traits. ResultsWe found a negative association between inferred migration distances from refugia and genetic diversities in 25 species, but only 11 had statistically significant negative slopes. The relationships between inferred distance and population genetic diversity were steeper for insect-pollinated species than wind-pollinated species, but the difference among pollination system was marginally independent from phylogenetic autocorrelation. Main conclusionThe relationships between inferred migration distances and genetic diversities in 11 species, independent from current isolation, indicate that past range shifts were associated with a genetic bottleneck effect with an average of 21% loss of genetic diversity per 1000km(-1). In contrast, the absence of relationship in many species also indicates that the response is species specific and may be modulated by plant pollination strategies or result from more complex historical contingencies than those modelled here.

Reciprocal Effects on Neurocognitive and Metabolic Phenotypes in Mouse Models of 16p11.2 Deletion and Duplication Syndromes.

The 16p11.2 600 kb BP4-BP5 deletion and duplication syndromes have been associated with developmental delay; autism spectrum disorders; and reciprocal effects on the body mass index, head circumference and brain volumes. Here, we explored these relationships using novel engineered mouse models carrying a deletion (Del/+) or a duplication (Dup/+) of the Sult1a1-Spn region homologous to the human 16p11.2 BP4-BP5 locus. On a C57BL/6N inbred genetic background, Del/+ mice exhibited reduced weight and impaired adipogenesis, hyperactivity, repetitive behaviors, and recognition memory deficits. In contrast, Dup/+ mice showed largely opposite phenotypes. On a F1 C57BL/6N × C3B hybrid genetic background, we also observed alterations in social interaction in the Del/+ and the Dup/+ animals, with other robust phenotypes affecting recognition memory and weight. To explore the dosage effect of the 16p11.2 genes on metabolism, Del/+ and Dup/+ models were challenged with high fat and high sugar diet, which revealed opposite energy imbalance. Transcriptomic analysis revealed that the majority of the genes located in the Sult1a1-Spn region were sensitive to dosage with a major effect on several pathways associated with neurocognitive and metabolic phenotypes. Whereas the behavioral consequence of the 16p11 region genetic dosage was similar in mice and humans with activity and memory alterations, the metabolic defects were opposite: adult Del/+ mice are lean in comparison to the human obese phenotype and the Dup/+ mice are overweight in comparison to the human underweight phenotype. Together, these data indicate that the dosage imbalance at the 16p11.2 locus perturbs the expression of modifiers outside the CNV that can modulate the penetrance, expressivity and direction of effects in both humans and mice.

Cultural practices and genetic resistance as factors affecting soybean stem canker and plant yield in the Cerrado

Field experiments were conducted in the 1995-96 soybean (Glycine max) growing season to evaluate the effects of cultural practices and host genetic resistance on the intensity of soybean stem canker, caused by Diaporthe phaseolorum f.sp. meridionalis (Dpm). Experiments were conducted in a commercial field severely infected in the previous (1994-95) season. In one study, minimum tillage (MT) and no-tillage (NT) cropping systems were investigated for their effects on disease development and on plant yields in cvs. FT-Cristalina (susceptible) and FT-Seriema (moderately resistant). Another study evaluated the effects of plant densities (8, 15, 21 and 36 plants/m) on disease development in cvs. FT-Cristalina, FT-101 (moderately resistant) and FT-104 (resistant). Disease incidence and severity were consistently lower in NT than in MT, and plant yields were increased by 23% and 14% in the NT system for the susceptible and moderately resistant cultivars, respectively, compared to the yields in the MT system. The Gompertz and Logistic models described well the disease progress curves in all situations. For both susceptible and moderately resistant cultivars, disease severity increased proportionately to the increase in plant densities. At the end of the season, 100% of the plants of cv. FT-Cristalina were infected by Dpm, at all plant densities. Disease levels on cv. FT-101 were intermediate while only very low disease levels were recorded on cv. FT-104. There was a consistent negative correlation between stem canker severity and yield. Some practices demonstrated potential for direct application in disease control, and could be combined considering their additive effects.

Transgenic animal models for the functional analysis of vasoactive peptides

The interplay of vasoactive peptide systems is an essential determinant of blood pressure regulation in mammals. While the endothelin and the renin-angiotensin systems raise blood pressure by inducing vasoconstriction and sodium retention, the kallikrein-kinin and the natriuretic-peptide systems reduce arterial pressure by eliciting vasodilatation and natriuresis. Transgenic technology has proven to be very useful for the functional analysis of vasoactive peptide systems. As an outstanding example, transgenic rats overexpressing the mouse Ren-2 renin gene in several tissues become extremely hypertensive. Several other transgenic rat and mouse strains with genetic modifications of components of the renin-angiotensin system have been developed in the past decade. Moreover, in recent years gene-targeting technology was employed to produce mouse strains lacking these proteins. The established animal models as well as the main insights gained by their analysis are summarized in this review.

Sex chromosome translocation and speciation : a Drosophila genetic model

Although exceptions may be readily identified, two generalizations concerning genetic differences among species may be drawn from the available allozyme and chromosome data. First, structural gene differences among species vary widely. In many cases, species pairs do not differ more than intraspecific populations. This suggests that either very few or no gene substitutions are required to produce barriers to reproduction (Avise 1976). Second, chromosome form and/or number differs among even closely related species (White 1963; 1978; Fredga 1977; Wright 1970). Many of the observed chromosomal differences involve translocational rearrangements; these produce severe fitness depression in heterozygotes and were, thus, long considered unlikely candidates for the fixation required of genetic changes leading to speciation (Wright 1977). Nonetheless, the fact that species differences are frequently translocational argues convincingly for their fixation despite prejudices to the contrary. Haldane's rule states that in the F of interspecific crosses, the heterogametic sex is absent or sterile in the preponderance of cases (Haldane 1932). This rule definitely applies in the genus Dr°sophila (Ehrman 1962). Sex chromosome translocations do not impose a fitness depression as severe as that imposed by autosomal translocations, and X-Y translocations may account for Haldane's rule (Haldane 1932). Consequently a study of the fit ness parameters of an X·yL and a yS chromosome in Drosophila melanogaster populations was initiated by Tracey (1972). Preliminary results suggested that x.yL//YSmales enjoyed a mating advantage with X·yL//X·yL females, that this advantage was frequency dependent, that the translocation produced sexual isolation and that interactions between the yL, yS and a yellow marker contributed to the observed isolation (Tracey and Espinet 1976; Espinet and Tracey 1976). Encouraged by the results of these prelimimary studies, further experiments were performed to clarify the genetic nature of the observed sexual isolation, S the reality of the y frequency dependent fitness .and the behavioural changes, if any, produced by the translocation. The results of this work are reported herein. Although the marker genes used in earlier studies, sparkling poliert an d yellow have both been found to affect activity,but only yellow effects asymmetric sexual isolation. In addition yellow effects isolation through an interaction with the T(X-y) chromosomes, yS also effects isolation, and translocational strains are isolated from those of normal karyotype in the absence of marker gene differences. When yS chromosomes are in competition with y chromosomes on an X.yL background, yS males are at a distinct advantage only when their frequency is less than 97%. The sex chromosome translocation alters the normal courtship pattern by the incorporation of circling between vibration and licking in the male repertoire. Finally a model of speciation base on the fixation of this sex chromosome translocation in a geographically isolated gene pool is proposed.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.