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Kardia Lab - Genetic Epidemiology
Our laboratory focuses on the genetic analysis of complex chronic diseases, such as cardiovascular disease, heart failure, hypertension, kidney disease, brain injury, dementia, and cancer.
Research Areas
Genetic Epidemiology of Cardiovascular Disease
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>> Coronary Artery Calcification
Coronary Artery Calcification (CAC) is a subclinical measure of coronary artery atherosclerosis. Our studies of CAC in the Genetic Epidemiology Network of Arteriopathy (GENOA) indicate that it is heritable and associated with variation in multiple genes.
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>> Heart Failure
Cardiac hypertrophy is heritable and a clearly recognized risk factor for heart failure. Our studies and others have identified potential hypertrophy-associated SNPs in various heterogeneous populations and we are also trying to identify SNPs associated with heart failure mortality.
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>> Rochester Family Heart Study
The overall objective of the Rochester Family Heart Study was to identify and characterize genetic variations that influence the risk of cardiovascular disease and hypertension in the general population of Rochester, MN.
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Genetics of Hypertension and its Target Organ Damage
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>> Exomic Sequencing in Sibships to Identify Target Organ Damage Genes
As part of the GENOA study, quantitative measures of subclinical target organ disease have already been collected. Measurement of 900,000 single nucleotide polymorphisms distributed across the genome, enabled pursuit of candidate genes under significant linkage peaks and unbiased genome-wide association analyses to identify common variants predisposing to subclinical disease. Yet, most of the heritable variation in these measures of target organ damage still remains unexplained and unexplored. In this project, we are gearing up to identify low-frequency and rare DNA sequence variants accounting for this "missing" heritability by whole exome sequencing.
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>> Exomic Sequencing of Low-Renin Hypertensives
High blood pressure (BP), or hypertension, affects one billion people worldwide and is the most prevalent modifiable risk factor for vascular diseases of the brain, heart, and kidneys. The underlying causes remain enigmatic in most cases (90%), referred to as primary hypertension. Biometrical analyses have consistently demonstrated the heritability of BP levels and hypertension; however, most DNA sequence variants contributing to BP elevation have eluded discovery through candidate gene or whole-genome analyses. Consequently, the potential to improve public health through more individualized, genotype-based tailoring of detection, evaluation, treatment, and prevention of hypertension is yet to be realized.
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>> Genetic Architecture of Leukoaraiosis
Ischemic damage to the subcortical white matter of the brain, referred to as leukoaraiosis, is a frequent complication of hypertension-related microvascular disease and contributes to the risk of stroke and vascular dementia. As part of a NINDS grant entitled Genetics of Microangiopathic Brain Injury (RO1 NS41558), GENOA participants have been assessed for leukoaraiosis as well as brain atrophy and ventricular volume using magnetic resonance imaging (MRI).
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>> Genetic Epidemiology of Arteriopathy (GENOA)
From its inception in 1995, the Genetic Epidemiology Network of Arteriopathy's (GENOA) long-term objective was to elucidate the genetics of arteriosclerotic target organ complications of hypertension, including both atherosclerotic or "macrovascular" and arteriolosclerotic or "microvascular" complications involving the heart, brain, kidneys, and peripheral arteries.
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>> Genetics of Kidney Phenotypes
In 2002, the Kidney Disease Outcome Quality Initiative of the National Kidney Foundation defined chronic kidney disease (CKD) as the presence of a marker of kidney damage, such as proteinuria (e.g., albumin/creatinine ratio 30 mg×g-1 on spot urine testing), or a decreased glomerular filtration rate (GFR) for 3 months. In GENOA, serum creatinine and urinary albumin were measured during both Phase I and II providing the ability to estimate GFR and the urinary albumin-creatinine ratio.
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>> Genetics of Kidney Stones
We are currently using genome-wide association data to identify key genes and gene-diet interactions in the development of kidney stones.
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>> Peripheral Vascular Disease
Peripheral arterial disease is a common condition in the elderly. It has two important clinical consequences. First, affected individuals generally have widespread atherosclerosis and consequently are at increased risk of stroke, myocardial infarction, and cardiovascular death. Second, PAD can markedly affect quality-of-life by causing exertional leg pain (i.e., intermittent claudication) and functional impairment of the lower extremities. In the GENOA cohort, the ankle-brachial index was measured to assess PAD.
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>> Predictors of Blood Pressure Control
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Multigenic Modeling
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>> Graphical Methods (Kgraph)
The KGraph is a data visualization system that we developed to display the complex relationships between the univariate and bivariate associations among an outcome of interest, a set of covariates, and a set of genetic variations such as single nucleotide polymorphisms (SNPs).
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>> Random Forests
As part of the GENOA study, a large amount of genetic and phenotypic data has been collected on individuals from multiple ethnic groups. As a first step toward understanding the capability of novel machine learning algorithms to capture high dimensional structure for prediction of who is at risk in the population we applied two machine learning algorithms, Random Forests and RuleFit, to identify the best predictors of having high coronary artery calcification burden (CAC) burden.
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>> Risk Index Methods
Integrating clinical and genetic information to improve clinicians' ability to estimate an individual's disease risk is an important biomedical research challenge. Beginning from the concept of genetic risk scores, which estimate an individual's risk of developing a disease by summing risk information from single nucleotide polymorphisms (SNPs), we have developed a "risk index" procedure that combines clinical data and genome-wide genotypes to make a prediction about an individual's risk of disease.
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>> Scan Statistics
We have developed a scan statistic methodology that is suitably general to be used in a variety of genome-wide studies.
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Gene Expression Profiles of Common Disease
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>> Gene Expression Predictors of Ischemic Brain Injury
In this project we are using a functional genomic strategy based on gene expression measurements to identify genetic variants influencing MRI measures of structural brain injury (leukoaraiosis, cerebral atrophy, and ventricular volume).
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>> Toward a New Molecular Classification of Cancer
For most types of cancer, histopathology is insufficient to predict disease progression and clinical outcome. Working with researchers at the UM Cancer Center, we helped develop and apply methods for identifying genes expression profiles based on microarray analysis that could be used for identifying histological subtypes of cancers, distinguishing between cancer stages, and predicting patient survival.
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Epigenetics
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>> Epigenetic Predictors of Common Chronic Diseases
Epigenetic mechanisms play a key role in multiple cellular processes and have been hypothesized as a link between environmental factors, lifestyle, and alterations in chronic disease susceptibility. We are studying inter-individual differences in DNA methylation profiles to identify individuals at risk for the development of disease outcomes at a presymptomatic stage when preventive efforts will be most beneficial.
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