Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example.

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer

Whole Genome Interpretation for a Family of Five.

Although best practices have emerged on how to analyse and interpret personal genomes, the utility of whole genome screening remains underdeveloped. A large amount of information can be gathered from various types of analyses via whole genome sequencing including pathogenicity screening, genetic risk scoring, fitness, nutrition, and pharmacogenomic analysis. We recognize different levels of confidence when assessing the validity of genetic markers and apply rigorous standards for evaluation of phenotype associations. We illustrate the application of this approach on a family of five. By applying analyses of whole genomes from different methodological perspectives, we are able to build a more comprehensive picture to assist decision making in preventative healthcare and well-being management. Our interpretation and reporting outputs provide input for a clinician to develop a healthcare plan for the individual, based on genetic and other healthcare data

Genetic, magnetic resonance imaging and body fluid biomarker associations with severity of multiple sclerosis

Multiple sclerosis is a chronic and progressive neuroinflammatory disease that leads to demyelination and neurodegeneration in the central nervous system (CNS). Previous research has identified a wide range of environmental, lifestyle and genetic factors which increase MS susceptibility. However, the pathomechanisms that influence the severity of MS are largely unknown, and adequate biomarkers of disease severity are consequently lacking. Therefore, the aim of my thesis was to; 1) assess associations between the nerve injury biomarker neurofilament light (NfL) and brain atrophy and lesion volumes; 2) assess which brain/lesion volume measures show the strongest longitudinal association with clinical MS disability measures and to what degree these associations were affected by age; and to 3) identify genetic variants associated with brain atrophy, lesion volumes and plasma NfL (pNfL) levels in persons with MS. In Study I, we assessed how cerebrospinal fluid (CSF) and pNfL levels were associated with T1- and T2-lesion volumes as well as whole-brain, cortical and subcortical grey matter, white matter and thalamic volume fractions of total intracranial volume based on magnetic resonance imaging (MRI). High baseline CSF and pNfL levels were associated with lower whole-brain, subcortical grey matter, thalamic, white matter and corpus callosal volume fractions over time. A further analysis showed that there was an association between baseline pNfL and baseline cortical grey matter fractions also in absence of radiological signs of inflammatory disease activity. A topographic analysis of cortical thickness showed that loss of cortical volume preferentially involved frontotemporal cortical regions. These findings indicate that NfL levels contribute information about MS severity not provided by traditional MRI lesion metrics. In Study II, we showed that associations between baseline MRI variables, and baseline physical disability and self-reported impact of MS rapidly increased in strength in individuals beyond approximately 40-50 years of age. In separate longitudinal analyses using linear mixed-effects models, we showed that among the recorded brain volume measures, cortical and subcortical grey matter and thalamic volume fractions at baseline were the strongest predictors of future worsening in clinical disability over a median of approximately ten years’ follow-up time. They were also stronger predictors than T1- and T2-lesion volumes. In Study III, we assessed if a weighted risk score comprising 12 known MS risk human leukocyte antigen (HLA) alleles was associated with baseline and longitudinal MRI measures as described in Studies I and II. While this risk score was not significantly associated with baseline MRI measures, we found that a high score was associated with lower cortical grey matter fractions longitudinally. A further analysis showed that this effect was primarily driven by the HLA-DRB1*15:01 allele. These results suggest that MS HLA risk variants not only affect inflammatory, but also neurodegenerative aspects of the disease. In Studies IV and V, we performed genome-wide association studies of pNfL levels and whole-brain volume fractions, respectively, in persons with MS (and controls in Study IV). While no genome-wide significant associations were found in Study IV, gene set analyses highlighted a neural crest and odontogenesis development pathway in the regulation of pNfL levels, and a weighted MS susceptibility polygenic risk score was associated with higher pNfL levels in MS with statistical significance. These findings suggest that there is some degree of genetic regulation of pNfL levels, which partially overlap with MS risk. In Study V, we identified a genome-wide significant locus upstream of the glycerol kinase 2 (GK2) gene, previously implicated in the propensity for tobacco smoking, which is a known MS risk and severity factor. Gene set analyses in Study V also implicated Hypoxia Inducible Factor-1 (HIF1) in the regulation of whole-brain volume fractions, indicating that iron metabolism and response to hypoxia play a role in the neurodegenerative processes in MS

Genetic and environmental prediction of opioid cessation using machine learning, GWAS, and a mouse model

The United States is currently experiencing an epidemic of opioid use, use disorder, and overdose-related deaths. While studies have identified several loci that are associated with opioid use disorder (OUD) risk, the genetic basis for the ability to discontinue opioid use has not been investigated. Furthermore, very few studies have investigated the non-genetic factors that are predictive of opioid cessation or their predictive ability. In this thesis, I studied a novel phenotype–opioid cessation, defined as the time since last use of illicit opioids (1 year ago as cease) among persons meeting lifetime DSM-5 criteria for opioid use disorder (OUD). In chapter two, I identified novel genetic variants and biological pathways that potentially regulate opioid cessation success through a genome wide study, as well as genetic overlap between opioid cessation and other substance cessation traits. In chapter three, I identified multiple non-genetic risk factors specific to each racial group that are predictive of opioid cessation from the same individuals analyzed in chapter two by applying several linear and non-linear machine learning techniques to a set of more than 3,000 variables assessed by a structured psychiatric interview. Factors identified from this atheoretical approach can be grouped into opioid use activities, other drug use, health conditions, and demographics, while the predictive accuracy as high as nearly 80% was achieved. The findings from this research generated more hypotheses for future studies to reference. In chapter four, I performed differential gene expression and network analysis on mice with different oxycodone (an opioid receptor agonist)-induced behaviors and compared the significantly associated genes and network modules with top-ranked genes identified in humans. The pathway cross-talks and gene homologs identified from both species illuminate the potential molecular mechanism of opioid behaviors. In summary, this thesis utilized statistical genetics, machine learning, and a computational biology framework to address factors that are associative with opioid cessation in humans, and cross-referenced the genetic findings in a mouse model. These findings serve as references for future studies and provide a framework for personalizing the treatment of OUD

Genetics of membranous nephropathy

Autoimmune membranous nephropathy (AMN) is a rare kidney disease. The genetics of AMN have been partially elucidated and confirmed the role of phospholipase A2 receptor-1 (PLA2R1) and HLA. The functional effect of the genetic variations is not fully understood. This thesis investigates these unexplored genetic aspects utilising a range of methodologies and unique cohorts. Analysing genomic sequencing data of PLA2R1 in 335 AMN patients identified 109 strongly associated variants; 9 with a very strong association, p-value <10-50. In a larger cohort of 1158 European AMN patients, the findings from previous GWAS were confirmed with a strong association with HLA-DQA1, HLA-DRB1 and PLA2R1. No associations were found on a genome wide scale with clinical correlates of disease such as proteinuria, sex, and age. HLA typing by imputation in 372 anti-PLA2R1 antibody positive and uniquely 32 antithrombospondin type-1 domain-containing 7A (THSD7A) antibody positive AMN confirmed the dominant HLA type in European AMN as HLA-DRB1*03:01 and HLADQA1*05:01; replicating previous studies. No statistically significant HLA type was identified for anti-THSD7A AMN. Anti-PLA2R1 AMN has a different genetic risk than anti-THSD7A and anti-contactin AMN as determined by the genetic risk score (GRS), and this can help differentiate between them. Interestingly, 33% of dual antibody negative AMN is likely to be anti-PLA2R1 AMN. AMN patients with a higher genetic risk have a younger age of onset. In a rare, undescribed cohort of 15 non-familial paediatric cases of AMN the GRS proved that these individuals did not have the same genetic risk factors as anti-PLA2R1 AMN. Finally, the genetic risk of AMN in UK Biobank Europeans is 0.8%. Even though there is a high genetic risk for AMN this does not mean this proportion of individuals will develop AMN. In conclusion, this thesis highlights important differences between antibody status groups, confirms previous GWAS findings and reports unique features about rare AMN cohorts

Prescription Fraud detection via data mining : a methodology proposal

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- -Bilkent University, 2009.Includes bibliographical references leaves 61-69Fraud is the illegitimate act of violating regulations in order to gain personal profit. These kinds of violations are seen in many important areas including, healthcare, computer networks, credit card transactions and communications. Every year health care fraud causes considerable amount of losses to Social Security Agencies and Insurance Companies in many countries including Turkey and USA. This kind of crime is often seem victimless by the committers, nonetheless the fraudulent chain between pharmaceutical companies, health care providers, patients and pharmacies not only damage the health care system with the financial burden but also greatly hinders the health care system to provide legitimate patients with quality health care. One of the biggest issues related with health care fraud is the prescription fraud. This thesis aims to identify a data mining methodology in order to detect fraudulent prescriptions in a large prescription database, which is a task traditionally conducted by human experts. For this purpose, we have developed a customized data-mining model for the prescription fraud detection. We employ data mining methodologies for assigning a risk score to prescriptions regarding Prescribed Medicament- Diagnosis consistency, Prescribed Medicaments’ consistency within a prescription, Prescribed Medicament- Age and Sex consistency and Diagnosis- Cost consistency. Our proposed model has been tested on real world data. The results we obtained from our experimentations reveal that the proposed model works considerably well for the prescription fraud detection problem with a 77.4% true positive rate. We conclude that incorporating such a system in Social Security Agencies would radically decrease human-expert auditing costs and efficiency.Aral, Karca DuruM.S

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer