303 research outputs found
Investigating the relationship between mitochondrial genetic variation and cardiovascular-related traits to develop a framework for mitochondrial phenome-wide association studies
BACKGROUND: Mitochondria play a critical role in the cell and have DNA independent of the nuclear genome. There is much evidence that mitochondrial DNA (mtDNA) variation plays a role in human health and disease, however, this area of investigation has lagged behind research into the role of nuclear genetic variation on complex traits and phenotypic outcomes. Phenome-wide association studies (PheWAS) investigate the association between a wide range of traits and genetic variation. To date, this approach has not been used to investigate the relationship between mtDNA variants and phenotypic variation. Herein, we describe the development of a PheWAS framework for mtDNA variants (mt-PheWAS). Using the Metabochip custom genotyping array, nuclear and mitochondrial DNA variants were genotyped in 11,519 African Americans from the Vanderbilt University biorepository, BioVU. We employed both polygenic modeling and association testing with mitochondrial single nucleotide polymorphisms (mtSNPs) to explore the relationship between mtDNA variants and a group of eight cardiovascular-related traits obtained from de-identified electronic medical records within BioVU. RESULTS: Using polygenic modeling we found evidence for an effect of mtDNA variation on total cholesterol and type 2 diabetes (T2D). After performing comprehensive mitochondrial single SNP associations, we identified an increased number of single mtSNP associations with total cholesterol and T2D compared to the other phenotypes examined, which did not have more significantly associated SNPs than would be expected by chance. Among the mtSNPs significantly associated with T2D we identified variant mt16189, an association previously reported only in Asian and European-descent populations. CONCLUSIONS: Our replication of previous findings and identification of novel associations from this initial study suggest that our mt-PheWAS approach is robust for investigating the relationship between mitochondrial genetic variation and a range of phenotypes, providing a framework for future mt-PheWAS
Role of mitochondria in early molecular diagnosis and prognosis of cancer
Background:Earlier clinical detection of cancer may improve survival as well as offer opportunities for less invasive treatment options. This thesis explores whether the mitochondria and its related genes in the nuclear genome can be used as novel methods for the diagnosis and prognosis of cancers.Aims and Methods:Paper I: To investigate if mitochondrial dysfunction (characterized by mtDNA copy number variations) is associated with prevalent, incident cancer and cancer mortality – droplet digital PCR (ddPCR).Paper II: To investigate the potential causal relationship between mitochondrial dysfunction (characterized by genetic predispositions in all mitochondrial-related genes) and common cancer risks – Mendelian randomization, colocalization.Paper III: To investigate mitochondrial mutations as potential biomarkers for the early diagnosis of breast cancer – whole mitochondrial genome sequencing, bioinformatics, ddPCR.Paper IV: To investigate the mitochondrial-related gene expression signature as a prognostic model to predict the clinical outcome for breast cancer patients – machine learning.Results and conclusions:Paper I: We found that mtDNA-CN was significantly associated with prevalent and incident cancer as well as cancer mortality. However, these associations were cancer-type specific and need further investigation.Paper II: We identified potential causal relationships between mitochondrial-related genes and breast, prostate and lung cancer. Furthermore, this study identified candidate genes that can be the targets of potential pharmacological agents for cancer prevention.Paper III: We comprehensively characterized the mtDNA mutation landscape of breast cancer biopsies and matched baseline whole blood samples. Notably, we have identified and validated mt.16093T>C mutation, which was associated with a 67% increased risk of developing breast cancer, and could potentially be used as early breast cancer diagnostic biomarkers.Paper IV: We built a novel 14 genes mitochondrial signature model that could be an independent prognostic predictor and together with clinical variables as an improved model for predicting overall earlystage of breast cancer survival
Hi-MC: a novel method for high-throughput mitochondrial haplogroup classification
Effective approaches for assessing mitochondrial DNA (mtDNA) variation are important to multiple scientific disciplines. Mitochondrial haplogroups characterize branch points in the phylogeny of mtDNA. Several tools exist for mitochondrial haplogroup classification. However, most require full or partial mtDNA sequence which is often cost prohibitive for studies with large sample sizes. The purpose of this study was to develop Hi-MC, a high-throughput method for mitochondrial haplogroup classification that is cost effective and applicable to large sample sizes making mitochondrial analysis more accessible in genetic studies. Using rigorous selection criteria, we defined and validated a custom panel of mtDNA single nucleotide polymorphisms that allows for accurate classification of European, African, and Native American mitochondrial haplogroups at broad resolution with minimal genotyping and cost. We demonstrate that Hi-MC performs well in samples of European, African, and Native American ancestries, and that Hi-MC performs comparably to a commonly used classifier. Implementation as a software package in R enables users to download and run the program locally, grants greater flexibility in the number of samples that can be run, and allows for easy expansion in future revisions. Hi-MC is available in the CRAN repository and the source code is freely available at https://github.com/vserch/himc
Rapporteur summaries of plenary, symposia, and oral sessions from the XXIIIrd World Congress of Psychiatric Genetics Meeting in Toronto, Canada, 16-20 October 2015
The XXIIIrd World Congress of Psychiatric Genetics meeting, sponsored by the International Society of Psychiatric Genetics, was held in Toronto, ON, Canada, on 16-20 October 2015. Approximately 700 participants attended to discuss the latest state-of-the-art findings in this rapidly advancing and evolving field. The following report was written by trainee travel awardees. Each was assigned one session as a rapporteur. This manuscript represents the highlights and topics that were covered in the plenary sessions, symposia, and oral sessions during the conference, and contains major notable and new findings. © 2016 Wolters Kluwer Health, Inc
A Genome-First Approach To Investigating The Biological And Clinical Relevance Of Exome-Wide Rare Coding Variation Using Electronic Health Record Phenotypes
Genome-wide association studies (GWAS) have successfully described the roles of common genetic variation on human diseases by analyzing large populations recruited based on a shared phenotype, but the biological and clinical relevance of numerous genes remain incompletely described through these ‘phenotype-first’ methodologies. Much of the unexplained genetic contribution to disease risk and variability in complex traits may belong to the very rare and private spectrum of alleles, a range traditionally ignored by GWAS. Furthermore, the phenotype-first approach is likely to miss unexpected phenotypic consequences of genetic variants, such as those that may not be feasible to study in a phenotype-first approach due to rarity of the condition. The Penn Medicine BioBank, a healthcare system-based database of genotype, whole-exome sequencing, and electronic health record data, allows for an unbiased, ‘genome-first’ approach to describing the relationships between genetic variants and human disease traits captured in the clinical setting. Through ‘gene burden’ tests that interrogate the cumulative effects of multiple rare and private variants in a gene that are predicted to affect gene function, this dissertation aims to characterize the clinical manifestations of diseases and traits caused by rare, predicted loss-of-function and predicted deleterious missense variants on an exome-wide and/or phenome-wide scale. These analyses uncover previously unsuspected medical and biological consequences of loss-of-function variants in multiple genes. In summary, this dissertation will investigate the biological and clinical relevance of disease-associated genes by investigating the association of rare coding variation found in whole-exome sequencing with phenotypes derived from the EHR
The role of common genetic variants for predicting the modulation of cardiovascular outcomes
Attrition is a major issue in the drug development process with 79% of clinical failures due to safety and efficacy concerns. Genetic research can provide supporting evidence of a clear causal relationship between the drug target and disease or reveal unintended effects through associations with non-relevant phenotypes informing on potential drug safety. However, due to the underlying genetic architecture, it is often unclear which gene or variant in the loci identified through genetic analyses is driving the association. Due to recent advancements in CRISPR-Cas9 gene-editing, it is now possible to relatively easily perform whole gene knock-out studies and single base-edits to validate genetic findings of the most likely causal variant and gene. Utilising a combination of genetic approaches and functional studies can provide supporting evidence of the therapeutic profile and potential effects of drug therapies and improve our overall understanding of biological pathways and disease mechanisms.
The primary aim of this thesis is to provide genetic data to support the ongoing clinical development of hypoxia-inducible factor (HIF)-prolyl hydroxylase inhibitors (PHIs) for treating anaemia of chronic kidney disease (CKD). Genome-wide association studies (GWAS) were used to identify genetic variants lying within or nearby genes encoding the drug target (prolyl hydroxylase [PHD] enzymes). These identified variants were used in Mendelian Randomisation analysis and phenome-wide association studies to genetically mirror the pharmaceutical effects of PHIs and investigate cardiovascular safety. Functional validation studies were employed to functionally validate a genetic variant for use as a proxy and to obtain a better understanding of the downstream causal pathways and biological mechanisms of the drug target.
In summary, this thesis demonstrates how a combination of genetic analyses and functional validation studies is a powerful approach to validate GWAS results and further characterise therapeutic effects. This PhD project identified relevant genetic markers to genetically proxy therapeutic modulation of biomarker levels through PHD inhibition and could potentially inform further research using patient-level clinical data from Phase III trials
Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach
Background
The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain.
Methods and Findings
We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination.
Conclusions
We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases
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Common 'Inborn Errors' of Metabolism in the General Population
Inborn errors of metabolism (IEMs) are a group of disorders characterised by the toxic accumulation or deficiency of circulating molecules (‘metabolites’) caused by rare genetic mutations. Previous studies have identified select examples where common variants at genes known to cause rare Mendelian diseases, including IEMs (e.g. LPL, DBH, PPM1K), are linked to phenotypic consequences in the general population that also occur in patients with the corresponding rare disease. Advances in genetic and metabolic profiling at an epidemiological scale now provide an opportunity to systematically identify such examples in the population and characterise their downstream effects on health.
To assess the value of untargeted metabolomic profiling for the study of common complex diseases, I identified candidate mediators of the association between weight gain and future type 2 diabetes risk based on untargeted, large-scale metabolomic profiling of a large prospective cohort. Integration of metabolomics, genetic profiling and comprehensive longitudinal follow up for a range of diseases together with the application of Bayesian and genetic epidemiological methods enabled the identification of 20 candidate mediators. These reflected genetic susceptibility to adiposity and insulin resistance and explained most of the increased T2D risk associated with weight gain.
To systematically characterise the phenotypic effects of variation at IEM-causing genes, I identified sentinel variants at these genes associated with plasma metabolites affected in the corresponding IEM across the genome. Of the 202 ‘IEM familiar’ variants (IFVs) detected, 187 at 89 loci were not previously reported as pathogenic for the corresponding IEM in ClinVar and 51 of these were associated with extreme metabolite levels (97.5th percentile) or had non-additive effects on metabolite levels. Phenome-wide assessment identified 1,553 IFV-phenotype associations at 108 loci. Of the detected associations, 703 at 54 loci were of particular interest as the phenotype related to a symptom of the corresponding IEM. At 24 of these 54 loci, genetic colocalisation detected shared genetic signals for IEM-related metabolites and phenotypes. For example, in line with norepinephrine deficiency causing dizziness on standing in severe cases of rare orthostatic hypotension (OMIM #223360), I identified a genetic signal at the dopamine beta hydroxylase (DBH) locus associated with decreased levels of the downstream catecholamine vanillylmandelate in the general population (IFV EAF=0.074). This signal was shared with that for lower risk of hypertension (based on 462,933 participants in UK Biobank) and other blood pressure-related phenotypes with high posterior probability of colocalisation (PPcolocalisation=0.94, with >99% of the probability explained by the IFV).
This work uses untargeted metabolomic profiling to identify underlying disease mechanisms and demonstrate the proof-of-principle that common variants can have similar health consequences to those caused by rare mutations at the same IEM gene.Wellcome Trust, Cambridge Trus
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