A Genomic Portrait of Haplotype Diversity and Signatures of Selection in Indigenous Southern African Populations

We report a study of genome-wide, dense SNP (∼900K) and copy number polymorphism data of indigenous southern Africans. We demonstrate the genetic contribution to southern and eastern African populations, which involved admixture between indigenous San, Niger-Congo-speaking and populations of Eurasian ancestry. This finding illustrates the need to account for stratification in genome-wide association studies, and that admixture mapping would likely be a successful approach in these populations. We developed a strategy to detect the signature of selection prior to and following putative admixture events. Several genomic regions show an unusual excess of Niger-Kordofanian, and unusual deficiency of both San and Eurasian ancestry, which were considered the footprints of selection after population admixture. Several SNPs with strong allele frequency differences were observed predominantly between the admixed indigenous southern African populations, and their ancestral Eurasian populations. Interestingly, many candidate genes, which were identified within the genomic regions showing signals for selection, were associated with southern African-specific high-risk, mostly communicable diseases, such as malaria, influenza, tuberculosis, and human immunodeficiency virus/AIDs. This observation suggests a potentially important role that these genes might have played in adapting to the environment. Additionally, our analyses of haplotype structure, linkage disequilibrium, recombination, copy number variation and genome-wide admixture highlight, and support the unique position of San relative to both African and non-African populations. This study contributes to a better understanding of population ancestry and selection in south-eastern African populations; and the data and results obtained will support research into the genetic contributions to infectious as well as non-communicable diseases in the region

PRETICTIVE BIOINFORMATIC METHODS FOR ANALYZING GENES AND PROTEINS

Since large amounts of biological data are generated using various high-throughput technologies, efficient computational methods are important for understanding the biological meanings behind the complex data. Machine learning is particularly appealing for biological knowledge discovery. Tissue-specific gene expression and protein sumoylation play essential roles in the cell and are implicated in many human diseases. Protein destabilization is a common mechanism by which mutations cause human diseases. In this study, machine learning approaches were developed for predicting human tissue-specific genes, protein sumoylation sites and protein stability changes upon single amino acid substitutions. Relevant biological features were selected for input vector encoding, and machine learning algorithms, including Random Forests and Support Vector Machines, were used for classifier construction. The results suggest that the approaches give rise to more accurate predictions than previous studies and can provide valuable information for further experimental studies. Moreover, seeSUMO and MuStab web servers were developed to make the classifiers accessible to the biological research community. Structure-based methods can be used to predict the effects of amino acid substitutions on protein function and stability. The nonsynonymous Single Nucleotide Polymorphisms (nsSNPs) located at the protein binding interface have dramatic effects on protein-protein interactions. To model the effects, the nsSNPs at the interfaces of 264 protein-protein complexes were mapped on the protein structures using homology-based methods. The results suggest that disease-causing nsSNPs tend to destabilize the electrostatic component of the binding energy and nsSNPs at conserved positions have significant effects on binding energy changes. The structure-based approach was developed to quantitatively assess the effects of amino acid substitutions on protein stability and protein-protein interaction. It was shown that the structure-based analysis could help elucidate the mechanisms by which mutations cause human genetic disorders. These new bioinformatic methods can be used to analyze some interesting genes and proteins for human genetic research and improve our understanding of their molecular mechanisms underlying human diseases

Patients with basal ganglia damage show preserved learning in an economic game.

Both basal ganglia (BG) and orbitofrontal cortex (OFC) have been widely implicated in social and non-social decision-making. However, unlike OFC damage, BG pathology is not typically associated with disturbances in social functioning. Here we studied the behavior of patients with focal lesions to either BG or OFC in a multi-strategy competitive game known to engage these regions. We find that whereas OFC patients are significantly impaired, BG patients show intact learning in the economic game. By contrast, when information about the strategic context is absent, both cohorts are significantly impaired. Computational modeling further shows a preserved ability in BG patients to learn by anticipating and responding to the behavior of others using the strategic context. These results suggest that apparently divergent findings on BG contribution to social decision-making may instead reflect a model where higher-order learning processes are dissociable from trial-and-error learning, and can be preserved despite BG damage

The N-Glycosylation of immunoglobulin G as a novel biomarker of Parkinson’s disease

For neurodegenerative diseases, interventions during the early stages of the disease, before significant neurodegeneration has occurred, are associated with an increased probability of slowing or halting the disease process. In order to intervene early, it is essential that an accurate diagnosis is obtained and that disease progression can be monitored. This is particularly relevant for Parkinson’s disease (PD; International Classification of Diseases version 10) because significant neurodegeneration has already occurred by the time the clinical motor symptoms are present. Therefore, the development of translatable, high-throughput biomarkers for large scale population screening is a crucial area of research. Of promise are the emerging “omics” technologies, which enable the detection of preclinical biomolecule fluctuations associated with the development of different diseases. One such field is glycomics which is the study of the set of sugar structures, hereon in known as glycans, in a given protein, cell or tissue. Notably, the functional diversity of proteins is increased by several magnitudes with the addition of glycans, a process known as glycosylation. The glycosylation of certain proteins, including immunoglobulin G (IgG), has been found to remain fairly stable over short periods of time, with modifications thought to result from changes in the cellular environment or disease presence. Indeed, IgG has the ability to exert both anti-inflammatory and pro-inflammatory effects throughout the body and these properties are controlled by the N-glycosylation of the fragment crystallisable (Fc) portion. To our knowledge, this was the first time that the potential of using IgG glycomic biomarkers to identify people with PD, as well as identify people with PD who are at risk of cognitive decline, was investigated. It was demonstrated that the peripheral IgG glycome in the PD cases was indicative of an increased capacity to biologically age. While advancing age has previously been associated with modifications to the glycosylation of IgG, making them more pro-inflammatory, advancing age was only associated with significant increases in modifications to the peripheral IgG glycome that infer more pro-inflammatory IgG in the PD cases but not the controls. In PD, the severity of the underlying pathology increases as the individual ages and, therefore, is a confounder of the effect of advancing age on pro-inflammation. Consequently, the peripheral IgG in people with PD have a propensity to become more pro-inflammatory at a faster rate as they age, and this may be linked to the severity of pathology during the course of the disease. PD has a heterogeneous presentation of clinical symptoms, and many factors contribute to the development of the disease. While this is true, it was demonstrated that the peripheral IgG glycome does not have utility in identifying risk of cognitive decline, which would result from progression of PD pathology in the central nervous system (CNS). These results are indicative of the peripheral IgG interacting with PD pathology in the enteric nervous system (ENS) as well as when it propagates from the ENS to the CNS along the vagal nerve. Inflammation may facilitate the neuron-to-neuron propagation of PD inclusions along this pathway and thus be a contributor to PD development during the prodromal phase. Hence, the peripheral IgG glycome may be useful as a novel biomarker of PD presence in the prodromal phase of the disease

LRRK2 genetics and expression in the Parkinsonian brain

Mutations in LRRK2 have been established as a common genetic cause of Parkinson’s disease (PD). Variation in gene expression of PARK loci has previously been demonstrated in PD pathogenesis, although it has not been described in detail for LRRK2 expression in the human brain. This study further elucidates the role of LRRK2 in development of PD by describing an investigation into the role of LRRK2 genetics and expression in the human brain. The G2019S mutation is a common LRRK2 mutation that exhibits a clinical and pathological phenotype indistinguishable from idiopathic PD. Thus, the study of G2019S mutation is a recurrent theme. The frequency of G2019S was estimated in unaffected subjects that lived or shared a cultural heritage to the predicted founding populations of the mutation, and was found not to be common in these populations. Morphological analysis revealed a ubiquitous expression for LRRK2 mRNA and protein in the human brain. In-situ hybridisation data suggests that LRRK2 mRNA is present as a low copy number mRNA in the human brain. A semi-quantitative analysis of LRRK2 immunohistochemistry revealed extensive regional variation in the LRRK2 protein levels, although the weakest immunoreactivity was consistently identified in the nigrostriatal dopamine region. No difference was observed in the morphological localisation of LRRK2 mRNA and protein in unaffected, IPD or G2019S positive PD subjects. Dysregulation of LRRK2 mRNA expression and the effects of cis- acting genetic variation on these levels were demonstrated. A widespread decrease of LRRK2 mRNA was observed in IPD and G2019S positive PD subjects in comparison to unaffected controls. Furthermore, non-coding genetic variation was also demonstrated to have an effect on the LRRK2 transcriptional activity in PD subjects. Collectively, these findings suggest that LRRK2 has an important physiological role, and a dysregulation in its levels could affect auxiliary mechanisms that contribute to PD pathogenesis. This data also supports the possibility of a shared mechanism contributing to the identical phenotype of IPD and G2019S linked PD

Expression quantitative trait loci in human brain tissues

To what extent genetic variability influences gene expression in human primary tissues is a critical question in molecular genetics. Work investigating this phenomenon is not only interesting biologically, but also has the potential to provide mechanistic insight into traits, including disease. The past decade has seen tremendous progress in this field, and this thesis includes a description of work that spanned from the relatively early stages of this type of work, to current, more refined efforts. This work sought to ask three questions: first, are eQTL detectable in brain tissues using whole genome methods; second, are eQTL measurably different in different parts of the brain; and third, does the investigation of eQTL in a particular neuronal cell type offer significant advantages over similar studies in tissue with a mixed cellular composition. In the first part of this work, I present a pilot study aimed at assessing the feasibility of eQTL detection in brain tissue. This study showed that the use of genome wide genotyping and expression arrays revealed a number of significant eQTL, and that in general, when genetic variability was associated with expression, the genetic locus and the expressed transcript were physically close. This work was then expanded to assess eQTL in multiple brain regions, with an attempt to assess whether eQTL were measurably different between distinct brain regions. In this work, tissue from cerebral frontal cortex, cerebral temporal cortex, caudal pons, and cerebellum was used. The analysis showed that there are region-specific eQTL, but that many of the strongest eQTL were present in multiple tissues. Lastly, I show using data from laser capture microdissected Purkinje cells that additional cell-type specific eQTL may be found that are not revealed when performing eQTL in heterogeneous tissue containing this cell type. In summary this work initially revealed the feasibility of eQTL work in human brain, showed that eQTL were measurably different, but generally similar across varied brain tissues, and showed that there are likely several advantages in pursuing single cell type work in tandem with whole tissue efforts

Genetic Characterisation of Neurodegenerative disorders

Our global population is ageing and an ever increasing number of elderly are affected with neurodegenerative diseases, including the subjects of the studies in this work, Alzheimer's disease (AD), Parkinson's disease (PD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). On strong evidence that several genes may influence the development of sporadic neurodegenerative diseases, the genetic association approach was used in the work of this thesis to identify the multiple variants of small effect that may modulate susceptibility to common, complex neurodegenerative diseases. It has been shown that the common genetic variation of one of these susceptibility genes, MAPT, that of the microtubule associated protein, tau, is an important genetic risk factor for neurodegenerative diseases. There are two major MAPT haplotypes at 17q21.31 designated as H1 and H2. In order to dissect the relationship between MAPT variants and the pathogenesis of neurodegenerative diseases, the architecture and distribution the major haplotypes of MAPT have been assessed. The distribution of H2 haplotype is almost exclusively in the Caucasian population, with other populations having H2 allele frequencies of essentially zero. A series of association studies of common variation of MAPT in PSP, CBD, AD and PD in different populations were performed in this work with the hypothesis that common molecular pathways are involved in these disorders. Multiple common variants of the H1 haplotypes were identified and one common haplotype, H1c, showed preferential association with PSP and AD. A whole-genome association study of PD was also undertaken in this study in order to detect if common genetic variability exerts a large effect in risk for disease in idiopathic PD. Twenty six candidate loci have been found in this whole-genome association study and they provide the basis for our investigation of disease causing genetic variants in idiopathic PD