Environmental and genetic risk factors for tinnitus

Tinnitus is a phantom auditory sensation, most often referred to as “ringing in the ears” with detrimental effect on quality of life. Between 4% and 37% of the global population has experienced tinnitus at some point in their life. For every 1 out of 10 individuals experiencing tinnitus, it becomes a severely impactful condition, affecting concentration, sleep, mood, and general quality of life. Despite its high prevalence and severe socio-economic burden, there is no successful treatment. The work presented in this thesis uses multiple scientific approaches to better understand the etiology of tinnitus, with the emphasis on the genetic landscape in order to gain insight into its molecular origins. First, we identify important gaps in knowledge on environmental risk factors associated with tinnitus. Second, we show using genetic epidemiology methods that severe tinnitus runs in families, which changes the current narrative that tinnitus would be generated purely due to environmental factors. Third, as tinnitus is commonly linked to hearing loss, we used a genome-wide biostatistical approach to reveal the genetic architecture of hearing loss, that will be further essential in distinguishing the two conditions. Fourth, we investigated the whole genome in relation to tinnitus to map correlated genomic regions and consequently, specific genes associated with tinnitus. Finally, we used a high-throughput sequencing of protein coding regions of the genome to identify disease-causing mutations impacting severe tinnitus. The work presented in this thesis provides insights from multiple aspects into the origins of tinnitus and will serve as a backbone to understanding the pathophysiology of the disorder

Grand Celebration: 10th Anniversary of the Human Genome Project

In 1990, scientists began working together on one of the largest biological research projects ever proposed. The project proposed to sequence the three billion nucleotides in the human genome. The Human Genome Project took 13 years and was completed in April 2003, at a cost of approximately three billion dollars. It was a major scientific achievement that forever changed the understanding of our own nature. The sequencing of the human genome was in many ways a triumph for technology as much as it was for science. From the Human Genome Project, powerful technologies have been developed (e.g., microarrays and next generation sequencing) and new branches of science have emerged (e.g., functional genomics and pharmacogenomics), paving new ways for advancing genomic research and medical applications of genomics in the 21st century. The investigations have provided new tests and drug targets, as well as insights into the basis of human development and diagnosis/treatment of cancer and several mysterious humans diseases. This genomic revolution is prompting a new era in medicine, which brings both challenges and opportunities. Parallel to the promising advances over the last decade, the study of the human genome has also revealed how complicated human biology is, and how much remains to be understood. The legacy of the understanding of our genome has just begun. To celebrate the 10th anniversary of the essential completion of the Human Genome Project, in April 2013 Genes launched this Special Issue, which highlights the recent scientific breakthroughs in human genomics, with a collection of papers written by authors who are leading experts in the field

The Siblings With Ischemic Stroke Study (SWISS) Protocol

BACKGROUND: Family history and twins studies suggest an inherited component to ischemic stroke risk. Candidate gene association studies have been performed but have limited capacity to identify novel risk factor genes. The Siblings With Ischemic Stroke Study (SWISS) aims to conduct a genome-wide scan in sibling pairs concordant or discordant for ischemic stroke to identify novel genetic risk factors through linkage analysis. METHODS: Screening at multiple clinical centers identifies patients (probands) with radiographically confirmed ischemic stroke and a family history of at least 1 living full sibling with stroke. After giving informed consent, without violating privacy among other family members, the proband invites siblings concordant and discordant for stroke to participate. Siblings then contact the study coordinating center. The diagnosis of ischemic stroke in potentially concordant siblings is confirmed by systematic centralized review of medical records. The stroke-free status of potentially discordant siblings is confirmed by validated structured telephone interview. Blood samples for DNA analysis are taken from concordant sibling pairs and, if applicable, from 1 discordant sibling. Epstein-Barr virus-transformed lymphoblastoid cell lines are created, and a scan of the human genome is planned. DISCUSSION: Conducting adequately powered genomics studies of stroke in humans is challenging because of the heterogeneity of the stroke phenotype and the difficulty of obtaining DNA samples from clinically well-characterized members of a cohort of stroke pedigrees. The multicentered design of this study is intended to efficiently assemble a cohort of ischemic stroke pedigrees without invoking community consent or using cold-calling of pedigree members

Dissecting the genetic bases of severe malaria resistance using genome-wide and post genomewide study approaches

P. falciparum malaria remains one of the leading public health problems worldwide. The global tally of malaria in 2018 was estimated at 228 million cases and 405, 000 deaths worldwide. African countries disproportionately carry the global burden of malaria accounting for 93% and 94% of cases and deaths, respectively. Even though most infected children recover from P. falciparum malaria, a small subset (~1%) of cases progresses to severe disease and death. Over the last decade, several genome-wide association studies (GWASs) have been conducted in diverse malaria endemic populations to understand the natural host protective immunity against severe malaria that can provide clues for the development of new vaccines and therapeutics. However, beyond identifying association variants, conventional GWAS approaches can't inform the underpinning biological functions. To bridge this gap, we applied various contemporary statistical genetic analytic approaches to malaria GWAS datasets of diverse malaria endemic populations. First, we accessed malaria resistance GWAS datasets of three African populations (N=~11,000) including Kenya, Gambia and Malawi from European Genome Phenome Archive (EGA) through MalariaGEN consortium standard data accession procedures. We explored the challenges of GWAS approaches in the genetically diverse Africa populations and figured out how various advanced statistical genetic methods can be implemented to address these challenges. We investigated single nucleotide polymorphism (SNP) heritability (h2 g) of malaria resistance in the Gambian populations and determined appropriate quality (QC) thresholds to accurately estimate the h2 g in our dataset. Second, we estimated h2 g in the three populations and partitioned the h2 g into chromosomes, allele frequencies and annotations using the genetic relationship-matrix restricted maximum likelihood approaches. We further created African specific reference panel from African population datasets obtained from 1000 Genomes Project and African Genome Variation Project dataset and computed linkage disequilibrium (LD). We used LD information obtained from these reference panels to compute cell-type specific and none cell-type specific enrichments for GWAS-summary statistics meta-analyzed across the three populations. Our results showed for the first time that malaria resistance is polygenic trait with h2 g of ~20% and that the causal variants are overrepresented around protein coding regions of the genome. We further showed that the h2 g is disproportionately concentrated on three chromosomes (chr 5, 11 and 20), suggesting cost-effectiveness of targeting these chromosomes in future malaria genomic sequencing studies. Third, we systematically predicted plausible candidate genes and pathways from functional analysis of severe malaria resistance GWAS summary statistics (N = 17,000) meta-analyzed across eleven populations in malaria endemic regions in Africa, Asia and Oceania. We applied positional mapping, expression quantitative trait locus (eQTL), chromatin interaction mapping and gene-based association analyses to identify candidate severe malaria resistance genes. We performed network and pathway analyses to investigate their shared biological functions. We further applied rare variant analysis to raw GWAS datasets of three malaria endemic populations including Kenya, Malawi and Gambia and performed various population genetic structures of the identified genes in the three endemic populations and 20 world-wide ethnics. Our functional mapping analysis identified 57 genes located in the known malaria genomic loci while our gene-based GWAS analysis identified additional 125 genes across the genome. The identified genes were significantly enriched in malaria pathogenic pathways including multiple overlapping pathways in erythrocyte-related functions, blood coagulations, ion channels, adhesion molecules, membrane signaling elements and neuronal systems. Furthermore, our population genetic analysis revealed that the minor allele frequencies (MAF) of the SNPs residing in the identified genes are generally higher in the three malaria endemic populations compared to global populations. Overall, our results suggest that severe malaria resistance trait is attributed to multiple genes that are enriched in pathways linked to severe malaria pathogenesis. This highlights the possibility of harnessing new malaria therapeutics that can simultaneously target multiple malaria protective host molecular pathways. In conclusions, this project showed that malaria resistance trait is mainly a polygenic trait which is influenced by genes and pathways linked to blood stage lifecycle of P. falciparum. These findings constitute the foundations for future experimental studies that can potentially lead to translational medicine including development of new vaccines and therapeutics. However, ‘-omics' studies including those implemented in this study, are limited to single datatype analysis and lack adequate power to explain the complexity of molecular processes and usually lead to identification of correlations than causations. Thus, beyond singe locus analysis, the future direction of malaria resistance requires a paradigm shift from single-omics to multi-stage and multi-dimensional integrative multi-omics studies that combines multiple data types from the human host, the parasite, and the environment. The current biotechnological and statistical advances may eventually lead to the feasibility of systems biology studies and revolutionize malaria research

Multivariate analysis of 1.5 million people identifies genetic associations with traits related to self-regulation and addiction

Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates

An investigation of genetic factors in Ebola virus disease

Introduction The West African Ebola epidemic was the largest Ebola epidemic to date with over 28,000 cases. The large number of cases permitted assessment of different disease phenotypes and outcomes of Ebola virus disease (EVD). Given the variety of disease phenotypes in EVD, a genetic predisposition to disease phenotype and outcome was hypothesised. Methods Samples from 325 deceased patients and 174 surviving patients were provided through the Sierra Leone Ministry of Health-Public Health England Ebola Biobank. Additionally, 1021 household contacts, 1004 community controls and 504 Ebola survivors were recruited in Sierra Leone. Participants provided a saliva sample for DNA extraction and an oral fluid sample for anti-EBOV IgG antibodies. Exome sequencing was undertaken on 250 extreme phenotype cases and genome wide genotyping was undertaken on 2153 Ebola patients, household contacts and community controls. Data analysis of the exome data included within family segregation studies, gene burden testing and pathway analysis. The genotyped data was interrogated through a genome wide association study comparing deceased and surviving cases. Results Of the household contacts, 3.5% were positive for anti-EBOV IgG. Seropositivity correlated with risk exposure level, with the highest risk level demonstrating seropositivity rates of 15.6%. Ebola survivors with more severe acute disease demonstrated lower levels of anti-EBOV IgG antibodies (p=0.01), as did those with more severe post-Ebola syndrome, although this was not significant. Exome sequencing revealed multiple protective mutations within cholesterol metabolism pathways. A key finding was a protective variant in the PCSK9 gene (p=0.002). Preliminary GWAS analysis of deceased versus surviving Ebola patients identified a genome wide significant (p=2.9x10-8) SNP in the Carbonic Anhydrase 5a gene. Conclusions The study established different extreme phenotypes of EVD, including highly exposed antibody negative and asymptomatic antibody positive individuals. Genetic factors affect both susceptibility to and severity of Ebola virus disease; with rare deleterious mutations in genes within cholesterol metabolism pathways, and common polymorphisms determining outcome of exposure to Ebola virus.Open Acces

Unraveling the Genetic Mysteries of the Norwegian Fjord-horse: Identifying Harmful Haplotypes for Improved Breeding Strategies

Fertiliteten til avlsdyrene er særdeles kritisk i små populasjoner med høy innavl. Fjordhest- populasjonen i Norge består av få stamfedre, og er blitt registrert til å ha en lavere føllings-rate enn flere andre hesteraser. Denne analysen er basert på høy-tetthets SNP genotyper fra ca. 330 Fjordhester. For å identifisere mulige resessive dødelige alleler undersøkes genomet for homozygote avvik i haplotyper. Signifikante avvik i haplotyper med én eller mindre observerte homozygoter ble karakterisert som kandidat-loci. Deretter, vil kandidat-loci analyseres i forhold til funksjonell kunnskap om genomet til hest, menneske og mus. Kandidatgenene som dette studiet kommer frem til er DEAF5L, DEFA22, KIA1109, og DPF2.The fertility in breeding-animals is especially critical in small populations in which there is a high degree of inbreeding. The Norwegian Fjord-horse population consists of few founder animals and is observed to underperform in foaling rate compared to several other horse breeds. The project is based on HD SNP genotypes from approximately 330 Fjord-horses. In order to identify potential recessive lethal alleles, the haplotype homozygote deficiencies were identified. Significant haplotypes with one or fewer homozygotes observed were characterized as the candidate loci. Further, the candidate loci were aligned with functional knowledge of equine, human, and mouse genomes. Candidate genes identified by this study were DEFA5L, DEFA22, KIA1109, and DPF2

Population analysis of Legionella pneumophila epidemiology and the genetic basis for human pathogenicity

Legionella are globally ubiquitous aquatic bacteria that cause both Pontiac Fever (a mild flu) and Legionnaires' disease, a severe form of pneumonia with a 5-10% mortality rate. They are natural parasites of freshwater protozoa that may also cause opportunistic human infections when inhaled from the environment via aerosols. Human infections are generally sporadic, although the last decade has seen a global increase in the number of infections, and large-scale outbreaks place an appreciable annual burden on public health worldwide. The species Legionella pneumophila causes around 90% of infections, a large number of which are caused by relatively few clonal lineages, each estimated to have emerged recently and independently. However, the factors leading to their pathogenic success still remain largely unknown. The growing abundance of whole genome sequence (WGS) data has revealed a new horizon for bacterial comparative genomics. Larger, more varied datasets enable more advanced statistical approaches to investigate bacterial evolution, epidemiology and pathogen emergence. In this project, I assembled a comprehensive WGS dataset to conduct population-scale genomic analysis of L. pneumophila. In addition to a historic Scottish reference isolate collection, I downloaded all publically available assemblies and sequence reads for Legionella species. A pipeline was then developed to assemble, filter, clean and curate these data based on a range of parameters, which was improved by visual inspection. I conducted a population-wide meta-analysis of the data to explore the global distribution of Sequence Types (STs) over time. Our results highlight the power of population-scale genomic analysis to monitor disease trends, although several major sources of spatial and temporal sampling bias were identified that should be accounted for in future work. I then used these data to conduct a nation-wide genomic epidemiological analysis of culture- positive clinical L. pneumophila isolates from Scotland over a 36 year timeframe in context with global isolates and epidemiological metadata. The analysis shed new light on the epidemiology of travel-associated infections and revealed widely disseminated endemic clones that were associated with repeated infections in Scotland over many years. In addition, specific clones were identified that were isolated from the water systems of individual hospitals over very long time periods, indicating either repeated re-colonisation or long-term environmental persistence. The results indicate that routine regular environmental sampling is required to support the identification of epidemiological links, attribution of outbreak sources and to inform public health measures targeting endemic clones that present an ongoing risk. Finally, I investigated the genomic features that differentiate clinical and environmental isolates of L. pneumophila and which may be important for human infection potential. I used PIRATE to calculate the L. pneumophila pangenome, which revealed that the number of genes was closely correlated with the population structure, and identified two major lineages in which clinical genomes contained significantly fewer genes. To identify specific genes or variants correlated with an environmental or clinical source, I mapped the hits from a machine learning-based association analysis to corresponding orthologous genes clusters, revealing a number of previously undetected associations with disease. Using a network visualisation approach, I identified strong linkage disequilibrium influencing the significance of hits in commonly syntenic genes throughout the pangenome. Taken together, the results demonstrate the value of high-resolution population-scale WGS data to monitor the distribution and spread of different Legionella pneumophila clones, including those posing a higher human health risk. Furthermore, it empowered the identification of genomic factors significantly associated with the isolation source, which may contribute towards human infection potential