Support teaching, research, and patient care.
I am a board-certified clinical cardiologist with a doctorate degree in Epidemiology & Biostatistics. I have been practicing medicine for nearly 30 years and I have over 20 years of experience conducting research. I was born and raised in Montreal, Canada, where I received my medical degree from McGill University in 1994. I then pursued training in surgery for nearly two years before switching into internal medicine. I completed my residency in internal medicine as well as a Master's degree in Epidemiology and Biostatistics at McGill under the supervision of Dr. Samy Suissa before moving to Stanford University in 2001 to pursue fellowship training in adult cardiology. During my fellowship and Instructorship years at Stanford University, I completed a PhD in Epidemiology and Biostatistics in pharmacoepidemiology once again under Dr. Suissa's supervision.My principal research focus since moving to Stanford has been the identification of the genomic determinants of coronary heart disease (CHD) and risk factors of CHD. This transition in my research focus occurred thanks to the sage advice and unique opportunities provided to me by Dr. Thomas Quertermous, former chief of the Division of Cardiovascular Medicine and my primary mentor for many years after my arrival to Stanford. Since that transition, I have devoted a majority of my time performing advanced population based studies on the genomic causes of heart attacks and the common conditions that predispose people to heart attacks including high cholesterol, smoking, diabetes, obesity, high blood pressure, and insulin resistance. These research efforts go beyond the standard genetic variant association analyses and include analyses, interpretation, and integration of multi-omic data, construction and validation of polygenic scores, as well as Mendelian randomization, epigenetic association, and gene set enrichment analyses to help identify novel pathways of CHD in diverse populations.In this context, I have heavily contributed to and/or led several translational team science endeavors at both the national and international level by representing Stanford in large consortia meta analyzing genomic data. These consortia include CARDIoGRAMplusC4D, GLGC, GIANT, GENESIS, TAICHI, and PAGE. I have also been an active Women's Health Initiative (WHI) investigator since 2010 serving as chair/co-chair of the WHI Genetics, Proteomics, and Biomarkers Scientific Interest Group and a member of the WHI Ancillary Studies Committee, while concurrently launching several genomic studies that have generated blood methylation, circulating miRNA, telomere lengths, and bulk RNA-seq resources within WHI. Through WHI, I have also served as a senior/key co-Investigator in NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program where I have led whole genome sequencing projects related to CHD.Starting in 2016, I became intricately involved in the Million Veteran Program (MVP) and have since served as a senior/key co-Investigator and/or a PI in multiple funded projects focused on the genetics of cardiometabolic traits. I also serve, or have served, as a co-chair of the MVP P&P Committee, the MVP CVD/Lipids Working Group, and the MVP COVID-19 Science Program Genomics and PRS Working Group. As a consequence of my heavy involvement in MVP, I was dually appointed (full-time) at the VA Palo Alto Healthcare System in 2018. In partnership with Dr. Phil Tsao, overall/national co-PI of the MVP, I hold key administrative positions and coordinate the local genomics research program within the newly formed Precision Health Service at the Palo Alto VA. Concurrently, I teach general cardiology as well as echocardiography to medical students, residents, and cardiology fellows-in-training at the Palo Alto VA Hospital echocardiography lab.
Coronary artery disease (CAD) is a leading cause of death among adults in the United States. Its prevalence is highest in individuals of African ancestry. It has been estimated that genetic factors account for 26% to 69% of interindividual variation in CAD risk. Large-scale genome-wide association studies (GWAS) of CAD have mainly been conducted in populations of European ancestry and identified 161 independent loci so far. Few of the loci identified in European-ancestry populations have been replicated in populations of African ancestry. Large-scale GWAS of CAD in African-ancestry populations are lacking. This proposal will efficiently leverage the existing resources of the Population Architecture using Genomics and Epidemiology Consortium, Million Veteran Program and other established cohorts to create the largest-ever sample size for a genetic study of African- ancestry populations comprehensively phenotyped for CAD and related cardiometabolic traits. We propose to address the following Specific Aims.Aim 1 will interrogate the genome using admixture mapping, univariate GWAS, multi-variate GWAS and trans-ethnic GWAS approaches to identify loci associated with CAD in African- ancestry populations.Aim 2 will use phenome-wide association studies, variant-trait hierarchical clustering and integrative genomics methods to characterize CAD loci and gain insights into phenotypic, physiologic, and mechanistic impacts that underlie the pathophysiology of CAD.Aim 3 will explore the public health impact and clinical relevance of CAD risk variants by constructing polygenic CAD risk scores and identifying pathogenic variants in Mendelian syndromes of CAD genes that are relevant to African-ancestry populations. The construction of population-specific polygenic risk scores and identification of rare and low-frequency pathogenic variants of large effect in Mendelian syndromes of CAD genes will facilitate quantification of CAD risk in individuals of African ancestry and potentially narrow the translational gap towards clinical use of genetic information across diverse populations. The comprehensive cross-trait associations of identified CAD risk loci will facilitate the discovery of subtypes of CAD. Both improved genetic CAD risk classifications and refined CAD sub-phenotyping would help with the implementation of precision medicine in CAD. The new biological insights elucidated from novel loci identified in African-ancestry populations may also be generalized to other populations for the diagnosis, prevention, and treatment of CAD.Public Health RelevanceThis study aims to identify and characterize genetic loci underlying coronary artery disease in populations of African ancestry. We will efficiently leverage the existing resources of the Population Architecture using Genomics and Epidemiology Consortium, Million Veteran Program and other established cohorts to create the largest-ever sample size for a study of an African-ancestry population comprehensively phenotyped for CAD and related cardiometabolic traits. The outcome of this study will provide a better understanding of the genetics of CAD and its risk factors in this high-risk population and has a strong likelihood of leading to measures that can help with the control and prevention of CAD in populations of African ancestry.
Cardiovascular disease (CVD) and its risk factors impose major societal burdens, are leading causes of morbidity, mortality, and disability. Precision medicine is uniquely positioned to address CVD and its risk factors, enabled by decades of investigation and billions of dollars of investment that have established their strong underlying genetic basis. Polygenic risk scores (PRS), the aggregation of risk variants into a single score, provides one such example. Research on PRS in CVD has transitioned from estimation to examining the clinical utility; i.e., determining when and how PRS adoption will occur and how similarly conceived environmental/lifestyle risk scores (ERS) can be used clinically in concert with PRS. However, the majority of participants included in large-scale CVD research have been of European ancestry (EA), limiting the global translation of genetic associations into clinical and public health applications relevant for all populations.The PAGE consortium and others have demonstrated that EA-derived PRS are not directly translatable to racially/ethnically diverse populations. Statistical tools for PRS estimation and interpretation are founded on strong assumptions that are violated, and create bias, in the context of population structure that characterizes racially/ethnically diverse populations. These research gaps will exacerbate long-standing racial/ethnic disparities in CVD and its risk factors, underscoring the need for research that enables all groups to reap the benefits of PRS-enabled personalized prevention.In this revised application, we address the limitations previously identified in our original application by leveraging high-quality, harmonized, and centrally available data from a network of cohorts and biobanks with linked electronic health records, capturing CVD and its risk factors. Through this effort, we will include over 1.5M non-European ancestry participants to develop and validate PRS for CVD-associated traits in racially/ethnically diverse populations. We will create the methods, resources, and best practices for the clinical and public health communities. This research will permit adoption and application of PRS for the detection, intervention, and treatment of CVD risk factors. Our ultimate goal is to reduce and prevent the burden of CVD in all populations. Our Specific Aims are (1) Creation of unbiased PRS: Develop and evaluate CVD PRS in combination with ERS in the large and racially/ethnically diverse PAGE study; and (2) Validation, calibration and dissemination: Externally validate and improve upon risk score models in biobanks and translate risk score models for improved access and understanding for the medical community.We will build the next generation of methods, resources, and best-practices to empower appropriate development of PRS and subsequent prediction and clinical interrogation in CVD. Deliberate focus on non-EA populations will ensure that they are not the last to benefit in the new era of genomic medicine.
Fred Hutchison Cancer Center
In the last decade there has been major progress toward identifying the genetic bases of complex diseases and developing polygenic predictors for individuals who are at increased risk. Polygenic prediction models are now approaching the point of clinical relevance for several important diseases. However, since most of the polygenic risk is due to extremely large numbers of small-effect variants it is difficult to construct maximally efficient prediction models even using very large GWAS samples. At present, the largest samples are currently available for European ancestry individuals. Prediction models developed in these samples usually do not port well into other groups, although the precise reasons for the limited portability are not yet fully understood. In this project we will (1) measure the specific importance of different factors that contribute to the limited portability across groups; (2) implement and evaluate new statistical methods for computing polygenic predictors using joint inference across populations, and using functional information as priors; and (3) implement and evaluate new statistical methods for combining genetic information with other types of clinical data for prospective prediction in clinical settings. In summary our project will provide a framework of efficient statistical methods for polygenic prediction within and across populations.
Genome-wide association studies have identified common single nucleotide variants at over 160 genetic loci associated with coronary artery disease (CAD) and subclinical atherosclerosis (coronary artery calcification, carotid intima media thickness, and carotid plaque). These discoveries have led to important insights into the pathways that contribute to subclinical atherosclerosis and CAD, as well as insights into the genetic architecture of atherosclerosis. For example, the heritability explained by common genetic variants for CAD appears to be concentrated in regulatory regions. Nevertheless, neither the genome-wide association studies nor exome sequencing studies performed to date have been able to examine both coding and non-coding variants across the allele frequency spectrum in relation to subclinical atherosclerosis and CAD. Furthermore, these studies have largely focused on European ancestry participants. Approaches that identify pleiotropic loci or quantify genetic correlation among phenotypes exist, but have not yet been applied to subclinical atherosclerosis and CAD. Genetic risk prediction studies based on common variants show promise with regards to improving primary prevention, but the extent to which adding low-frequency and rare variants to polygenic risk scores improves risk prediction is not known, nor have scores been developed and tested in those of non-European ancestry. A wealth of whole-genome sequencing (WGS) data has been generated by initiatives such as the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program and the National Human Genome Research Institute (NHGRI) Centers for Common Disease Genomics (CCDG) program in populations from different ancestries. To expand our knowledge of genetic factors contributing to CAD and subclinical atherosclerosis phenotypes, we propose to use WGS data from TOPMed and CCDG (up to 101,295 individuals from diverse ancestries, of which 58% are non-European ancestry), with extended genomic coverage of low-frequency and rare genetic variants as well as more complex genetic variants such as structural variants. Findings from the WGS analysis will be replicated in several large-scale data sources, including exome sequencing data and genotype data imputed using TOPMed as the reference panel. Thus, we will examine genetic variation that has so far been missed, including structural variants. We will leverage the results of these analyses to explore the genetic architecture of subclinical atherosclerosis and CAD, investigate pleiotropy and genetic correlation between subclinical atherosclerosis and CAD and related cardiovascular traits, as well as assess the contribution of low-frequency and rare variants to risk prediction of CAD. Finally, we will create and test a polygenic risk score designed specifically for African ancestry population. This proposal brings together large-scale WGS datasets, clinical and subclinical atherosclerosis phenotypes, and exploits advances in genomic technologies and computational approaches. In doing so, we will advance the realization of precision medicine for CAD.
Obesity, Type 2 diabetes (T2DM), and dyslipidemia are metabolic disorders that promote the development of coronary artery (CAD) and peripheral arterial disease (PAD). Collectively, these cardiometabolic conditions are leading causes of illness and death among Veterans. A substantial proportion of the variation in risk of clinical complications related to these conditions remains unexplained despite an understanding of the root factors involved. The VA Million Veteran Program (MVP) links information from Veterans’ electronic heath record (EHR) to biomarker data measured from blood and provides an unparalleled opportunity to further explore the genetic basis of cardiometabolic diseases. We propose to use the genome wide genotyping data from the first 200,000 participants in MVP linked to the EHR to uncover novel associations between genetic variation and risk of cardiometabolic disease. To perform this research, we have assembled a team of investigators with extensive experience in VA based clinical research and population genetics. Many members of our team have not only participated in, but also have led, the most productive international collaborations over the last 10 years that have studied the genetic basis of cardiometabolic diseases. Our consortium includes investigators from 5 VISNs based at Palo Alto, Philadelphia, Phoenix, Bedford, and Albany as well as from Stanford University and the University of Pennsylvania. In Aim 1, we will establish optimal definitions of five cardiometabolic traits: body mass index, blood levels of cholesterol, as well as diagnoses of Type 2 diabetes (T2DM), CAD, PAD, using EHR derived information on medical diagnoses and treatments, physical exam and lab measures, and medication usage. Preliminary results of our queries of VA EHR data using the most liberal definitions of the traits have identified approximately 160,000 participants with lipid measurements, 195,000 participants with measurements of body-mass index, 100,000 participants with T2DM or prediabetes, 46,000 participants with CAD, and 9,000 participants with PAD. For quantitative traits, we will derive and study not only single time point measures but also long term averages for each individual. For outcomes, we will optimize our definitions by assessing the relationship between established risk factors including phenotype specific genetic risk scores and case-control status. In Aim 2, we will perform a series of genome wide association studies to confirm known loci and to identify novel genetic variation associated with our traits of interest. We will also use the comprehensive VA EHR to examine for the presence of gene-environment interactions. Finally, in Aim 3, we will apply novel statistical algorithms that will improve our understanding of the genetic variation that contributes to the risk of cardiometabolic diseases in both the African American and the Hispanic American populations by leveraging similarities in the genetic architecture among different race/ethnic groups. Successful completion of this project will help us to more thoroughly comprehend the underlying causes of cardiometabolic disease and to develop novel therapies that are safe, effective, and personalized. These discoveries will also result in the more reliable identification of individuals at risk for these disorders, allowing for the more optimal delivery of primary prevention strategies within the VA population.
3801 Miranda 94304
The Million Veteran Program (MVP) is currently the largest biobank study in the world. The resource provides an unprecedented opportunity to identify the genetic causes of a variety of human diseases that disproportionally affect our veterans including diseases that affect the neurological, cardiovascular, pulmonary, gastrointestinal, endocrine, and musculoskeletal organs. Fast-paced technological progress over the last 10 years now allows us to reliably and densely profile individuals across their entire genome. Such data has already been generated and linked to a wide spectrum of human diseases and physiologic traits. However, many more links remain to be made which will provide the scientific community with additional important clues on the root causes of many life-threatening diseases as well as valuable insights on how to develop new drugs to treat or prevent these same diseases. The current challenge in making these additional discoveries is no longer the generation of high quality genetic data in large numbers but rather the organization and querying of very large and complex electronic health records (EHR) being leveraged by these large biobank studies. Until now, much effort and time has been expended to painstakingly develop and validate rules-based definitions to identify individuals with a specific disease, syndrome, or state across a variety of EHR platforms. However, the recent mapping of the VA corporate data warehouse to the Observational Medical Outcomes Partnership common data model (OMOP-CDM) provides us with unprecedented opportunities to apply new “electronic phenotyping” tools that can identify individuals with a specific disease, syndrome, or state in a much more efficient manner than rules-based methods. The goal of this proposal is to comprehensively test the ability of one of these new tools named APHRODITE (Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation) to identify established genetic links among MVP participants. APHRODITE was developed at Stanford by one of our co-investigators and uses state of the art machine learning algorithms to identify individuals with a condition in a fraction of the time it takes to identify them through rules-based definitions. The algorithm has shown great promise within the Stanford clinical data warehouse but requires validation in other EHR cohorts. In aim 1, we will test the accuracy of an APHRODITE classifier to that of a rules-based classifier for at least 5 diseases using gold-standard sets in the VA. In aim 2, we will test whether APHRODITE classifiers from aim 1 can be applied to MVP participants to replicate established genetic associations. If automated methods in APHRODITE perform equally well or better than rules-based methods for multiple diseases, automated methods may be leveraged for phenotypes where rules based methods may not exist, maximizing the efficiency of genetic discovery in MVP and facilitating rapid replication of findings within MVP in other EHRs mapped to the OMOP-CDM.
Palo Alto Health Care System, Palo Alto
A developing yet still inconclusive literature suggests that exposures early in life may play an important role in health disparities found at older ages. This literature suggests that early life environment may be a key place for intervening to reduce health disparities in the most cost-effective manner. A separate literature has suggested that environmental factors early in life have an important impact on patterns of DNA methylation that extend into old age. However, there remains a critical gap in knowledge of how DNA methylation may be a key underlying mechanism of linking early life exposures to racial/ethnic and socioeconomic disparities in chronic disease. This application seeks funding for innovative exploratory work to test whether linking currentlongitudinal cohorts to historical individual level data from the 1940 U.S. Census at the time of childhood will offer a solution to answering this critical question. Our long-term goal is to understand the extent to which DNA methylation contributes to racial/ethnic and socioeconomic disparities in chronic disease incidence. The overall objective of this application is to test whether linkage to administrative data from the U.S. Census is an efficient and effective way of determining links between the early life environment and disparities in health due to changes in DNA methylation. Our central hypothesis, based on the literature and our prior research, is that early life household conditions will have a substantial impact on both racial/ethnic and socioeconomicdisparities in DNA methylation. To test this hypothesis we propose the following three specific aims: Aim 1: Link Women’s Health Initiative study participants to their childhood household data from the 1940 U.S. full population census, Aim 2: Test the association between early life household environment and racial/ethnic and socioeconomic differences in DNA methylation later in life, Aim 3: Perform quantitative bias analysis to assess the likelihood of bias due to differential linkage, survival, and study participation by race/ethnicity and socioeconomic position. The innovation of our proposed research is in testing a new approach to capturing the early life environment that does not rely on retrospective self-reports. If validated, our approach could beapplied to dozens of other currently available cohort studies with DNA methylation data that have participants who were alive in 1940. Critically, the 1940 U.S. census measures could then become a commonly used environmental metric applied across multiple studies facilitating large-scale meta-analyses of environmental impacts on health. Overall, we believe our innovative, exploratory study has the potential to guide a large number of future studies that will allow robust and useful estimates of how the early life environment contributes to differences in DNA methylation and subsequent chronic disease disparities. We believe ourapproach represents a very cost-effective and efficient means to leverage currently available DNA methylation data to study the impact of the early life environment on health disparities.
Whole genome data will soon be available for tens to hundreds of thousands of individuals. This information is unprecedented in its ability to understand individual risk factors for disease. However, the volume of these data presents several major challenges to its interpretation. One powerful approach for interpreting genomes and identifying functional variants is to combine whole genome data with functional genomics or multi-omics data. Our research project focuses on multi-omics analyses in the TOPMED project to improve our understanding of individual and environmental genetic risk factors in heart, lung, blood and sleep (HLBS) disorders. In Aim 1, we will apply multi-omic outlier analysis to identify rare variants with large effects on multi-omics phenotypes. We will apply approaches we have developed in GTEx and SardiNIA that integrate genome and functional genomics data. Our premise is that rare genetic variants with large effects on -omics phenotypes will be strong candidates to contribute to an individual’s risk of genetic disease. Using these rare variants, we propose to improve understanding of the combined effects of common and rare variants in HLBS disorders. In Aim 2, we will apply and advance software we have developed to improve the mapping of gene-by-environment (GxE) and gene-by-gene (GxG) effects. Specifically, we have demonstrated that allele-specific signals have improved power for identifying both GxE and GxG genes and variants and we will apply our model to both transcriptome and methylome data in TOPMED to identify diverse hits for observed and latent environments. We will further conduct analyses to identify GxE hits for measured metabolites and, overall, with respect to differences in ancestry. Our premise is that GxE variants identified through multi-omics data analysis will define or modify genetic risk factors for HLBS and other disorders. The impact of discovered GxE and GxG variants will be evaluated through association analyses in the entire TOPMED cohort. Our activities will bring new opportunities to study and understand both individual and gene-by-environment effects influencing disease risk. By leveraging multi-omics data, we will integrate rare variant and gene-by-environment analyses within TOPMED; an activity that would typically require enormous investment and hundreds of thousands of samples if conducted with only genetic data. All software, pipelines and research results developed by our group will be rapidly available on standard websites, in the Cloud and available to support collaborative efforts within TOPMED and the larger research community. Further, as our team has extensive experience with large-scale genomics and functional genomics analysis, we will provide assistance and effort in implementing world-class analytical pipelines and further complement TOPMED with data from GTEx, MoTrPAC, DGN, WHI and other project data to enhance the power of analyses. Our effort will provide multiple avenues, from rare variants to environmental genetics, to aid in interpreting whole genomes and the impact of genetic variation in health and disease
The consequences of insulin resistance (IR) include not only type 2 diabetes mellitus but also a cluster of metabolic abnormalities that double the risk of developing life-threatening complications of atherosclerosis including myocardial infarction, ischemic strokes, and peripheral arterial disease. The prevalence IR is increasing at an alarming rate as western populations become heavier and more sedentary. When one further considers the ongoing epidemiological transitions in developing countries in addition to the obesity epidemic in developed countries, the worldwide public health impact of IR is undoubtedly profound. Few pharmacological options exist that improve one’s insulin sensitivity and decrease the risk of complications from IR and recent genomic studies of surrogate measures of IR have yielded a disappointing number of new leads. Furthermore, a critical need exists for the development of more accurate blood-based diagnostic tests for IR. The long-term objective of the proposed research is to discover and validate novel protein markers of IR circulating in the blood of individuals who have undergone either one of the two ‘gold standard’ direct measures of insulin sensitivity: an insulin suppression test (IST) or a euglycemic clamp (EC). This information will be used to identify novel molecular pathways of IR that can be targeted pharmacologically and to develop statistical models that correlate highly with the degree of IR as estimated by direct measures of insulin sensitivity. In aim 1 of this proposal, the blood of 2100 white/European subjects who have undergone an IST at Stanford or an EC in the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) and the Uppsala Longitudinal Study of Adult Men (ULSAM) studies will be measured for the presence of 981 proteins using an emerging platform that leverages novel technology referred to as the proximity extension assay. This technology allows for the accurate and reliable quantification of proteins in plasma down to the femtomolar or attomolar level. We will further validate the top signals identified in these subjects in an additional ~300 non-European subjects and a subset of 300 subjects from Stanford who underwent a second IST after weight loss or use of a thiazolidinedione. In aim 2, we will examine validated signals from Aim 1 for causality using the principal of Mendelian randomization, and we will quantify improvements afforded by validated markers over conventional measures in identifying subjects at risk of complications from IR. In aim 3, validated associations between proteins that appear causal in nature will be further examined through knockdown of the genes producing these proteins in human cell lines relevant to IR. These cell lines will include adipocytes, hepatocytes, and skeletal myocytes. This study is the largest study of the plasma proteome in relation to direct measures of insulin sensitivity ever proposed. Findings are expected to yield important mechanistic insights into the molecular basis of IR and provide the foundation for the development of a blood-based diagnostic test that can very reliably detect subjects at low or high risk of complications from IR.
Stroke is among the understudied disorders despite its high burden to morbidity and mortality in the US. Ischemic stroke, which is due to cerebral vessel occlusion, accounts for 80% of cases. Ischemic stroke is a complex, multi-factorial disease, with heterogeneity by age, sex, and stroke subtype. A substantial proportion of stroke risk remains unexplained. The relatively low yield of stroke genetic studies to date may reflect the heterogeneous causes and clinical presentations of the various subtypes. Many of the studies participating in stroke GWAS have included have had little or no data available on stroke-specific risk factors or other CVD outcomes, which are key to understanding causal mechanisms and potential gene?environment interactions. Next generation sequencing (NGS) and multi-omics integrative biology research offer new opportunities in the way we research and understand stroke. Whole genome sequence (WGS) data, including both coding and functional non-coding variants, are required to identify the full spectrum of contributions of uncommon variants to stroke risk. Deep WGS data are currently being generated in over 11,000 WHI participants through the NHLBI TOPMed project, including over 4,000 ischemic stroke cases. Here we propose to apply innovative statistical approaches to perform a well-powered analysis to discover, replicate, and functionally characterize new loci (particularly rare or low frequency coding and non-coding regulatory variants) for ischemic stroke (and its subtypes) using WGS and imputation. Discovery will be performed in ~4,000 incident ischemic stroke cases and over 5,000 controls from WHI with WGS through TOPMed. Single variant and gene-based tests will be performed, prioritizing ~100 genomic regions based on prior GWAS and current epigenomic and proteomic analyses. Replication will be performed through state-of-the art WGS-based exome and GWAS imputation in up to ~77,000 additional ischemic stroke cases (and controls) obtained through UKBiobank, Million Veteran Program, and the SiGN and METASTROKE stroke genomics consortia. To assess the biologic mechanism of stroke-associated genetic loci, we will further test any newly identified stroke loci for association with: (1) a rich set of CVD risk factors and ~40 plasma biomarkers related to atherosclerosis, thrombosis, inflammation, and hormones available in WHI; (2) a new, commercial panel of 184 emerging biomarkers related to neurovascular disease and CVD in 2000 WHI TOPMed samples selected on the basis of genotype. Using casual inference methodology, we will perform mediation analyses to determine mechanistic relationships between genotype, intermediate biomarker phenotype, and stroke outcome.
To perform extensive phenotypic and integrative omic profiling of up to 10,000 newly enrolled individuals at risk of or with existing CHD or cancer.
Cardiovascular diseases (CVD) comprise the global leading cause of morbidity and mortality, and in the United States, CVD account for more than one-third of all deaths, of which ~150,000 deaths per year occurs in individuals younger than 65 years. Over the past decades, hundreds of circulating biomarkers have been associated with CVD, but their relative importance and potential involvement in the actual disease processes have been less investigated. Using a very large cohort study that recently became available to the scientific community, we will deploy Mendelian randomization methods to study the causal role of biomarkers proposed to be associated with CVD. In 2006-2010, the UK Biobank recruited 502,650 participants aged 37-73 years to undergo physical measurements, detailed assessments about risk factors and future disease events, and sampling of blood, urine and saliva. Genome-wide genotyping on the UK Biobank Axiom Array (820,967 genetic markers) and measurement of 36 circulating biomarkers with relevance for CVD will be finished during 2016. We will study the associations of 36 circulating biomarkers representing coagulation and inflammation (fibrinogen, D-dimer, hsCRP, rheumatoid factor), glucose homeostasis (HbA1c, glucose, IGF-1), lipid metabolism (total cholesterol, LDL-C, HDL-C, triglycerides, ApoAI, ApoB, Lp(a)), liver function (ALT, AST, ALP, direct and total bilirubin, GGT, albumin, total protein), kidney function (creatinine, cystatin C, phosphate, urate, urea, urinary sodium, potassium, microalbumin and creatinine), reproductive system (SHBG, testosterone, oestradiol), and mineral metabolism (calcium, vitamin D) with incidence of coronary heart disease, stroke, heart failure, atrial fibrillation and type 2 diabetes in traditional observational multivariable-adjusted analyses. We will then perform genome-wide association studies (GWAS) of all 36 biomarkers to establish common genetic variation associated with respective biomarker. With a sample size of ~390,000 individuals, we will have excellent statistical power to uncover a substantial fraction of common genetic variants associated with the biomarkers. These associations will be used to develop robust instrumental variables. Finally, using instrumental variable analyses, we will study the causal roles of these circulating biomarkers for development of cardiovascular disease. The large sample size of the present study will allow for unprecedented possibilities of Mendelian randomization studies of CVD biomarkers with adequate statistical power and with low risk of pleiotropy. Knowledge about the causal roles of CVD-related biomarkers for development of coronary heart disease, stroke, heart failure, atrial fibrillation and type 2 diabetes will provide important insights regarding the etiological understanding of these diseases and accelerate new prevention strategies, including druggable targets.
While life expectancy continues to rise, healthspan is not keeping pace because current disease treatments often decrease mortality without preventing the decline in overall health. It is crucial to understand how the underlying processes of aging affect susceptibility to chronic disease and related conditions. Epigenetic mechanisms have arguably become an important frontier in geroscience. We and others have shown that epigenetic biomarkers tend to be more strongly related with chronological age than existing biomarkers of aging. Importantly, we have recently demonstrated that epigenetic biomarkers of aging are prognostic of all-cause mortality in later life and correlate with measures of physical and cognitive fitness in older age. These data suggest that epigenetic mechanisms may play a role in mediating the effect of age on disease susceptibility. In this planning grant we lay out th framework needed to design a large-scale study that tests the overall hypothesis that epigenetic changes during aging collectively underlie aging as a risk factor for chronic diseases and degenerative conditions. We will generate preliminary results by leveraging existing epigenetic and phenotypic data available to our team of co-investigators and collaborators. These resources include data from the ENCODE project, various epigenetic data generated in multiple tissues, and richly phenotyped cohorts, such as the Baltimore Longitudinal Study of Aging (BLSA), InCHIANTI, the Women's Health Initiative, and the Lothian Birth Cohorts. We will evaluate different platforms for measuring epigenetic age, DNA methylation levels, chromatin states, and non-coding RNAs in terms of their relevance to our overall hypothesis, data quality, coverage, and price. While there exists a large body of literature on epigenetics and aging, our proposal is novel in terms of its breadth and depth: we will lay the groundwork for a study that investigates multiple epigenetic processes (DNA methylation, histone modifications, non-coding RNAs), multiple human tissues, multiple chronic conditions, at multiple time points using multiple well characterized human cohort studies and state-of-the-art statistical and bioinformatics techniques. Using pilot data from these and other studies, we will assess the reliability and precision of cutting-edge epigenetic measures and to estimate the resources needed for a future study. We will also assess to what extent epigenetic features in accessible human tissues (e.g., blood, buccal epithelium) can serve as surrogates for affected tissues and cell types. By organizing two workshops at UCLA, we will establish a research network comprised of leading researchers in the fields of aging research, epigenetics, epidemiology, genomics, and systems biology.
The major goal of this project is to conduct large-scale whole exome sequencing in multiple Taiwanese cohorts to identify novel genetic determinants of CAD related traits in Han Chinese. The main sponsor of this study is the Regeneron Genetics Center - Regeneron Pharmaceuticals
Los Angeles, California
This represents the third extension study of the WHI involving 5 additional years follow-up for cardiovascular, cancer and other health outcomes in participants and a continued leveraging of the bioresource to conduct genomic and other biomarker studies. This contract supports the western regional center based at Stanford University.
RNAseq on whole blood from 100 participants of the Women's Health Initiative Long Life Study
The purpose of this award is to provide Dr. Themistocles (Tim) Assimes, Assistant Professor of Medicine at Stanford University, the support necessary to transition him from a junior investigator to an independent physician scientist studying the genetic determinants of various human complex traits related to cardiovascular medicine. Dr. Assimes is an adult cardiologist with an advanced degree in epidemiology and biostatistics and significant experience conducting human genetic studies using existing sample sets. Career development activities focus on consolidating his expertise by 1) designing and implementing his first human subjects clinical research study involving handling of biospecimens, 2) increasing his involvement in several international genetic epidemiology collaborations, and 3) attending didactic courses to expand his knowledge base in contemporary genetics, molecular biology, advanced statistical genetics and the pathophysiology of insulin resistance (IR). An advisory committee, which includes his mentors, Drs. Thomas Quertermous and Gerald Reaven, will carefully monitor his progress towards independence. The research proposal builds on ongoing efforts in the candidate’s division to identify the root causes of IR by studying South Asians, a racial/ethnic group known to be strongly predisposed to IR and its adverse consequences for unclear reasons. In this context, Specific Aim 1 proposes to quantify IR and its primary established determinants of adiposity and physical fitness in ~330 South Asians and ~100 white/Europeans using ‘gold standard’ measuring tools. These include an insulin suppression test to assess insulin mediated glucose uptake, DXA and abdominal MRI scans to determine total and regional body fat, pedometers to estimate current physical activity, and a symptom limited cardiopulmonary stress test to estimate maximum oxygen uptake (max VO2). A new research partnership between the non-profit South Asian Heart Center at El Camino Hospital, Mountain View, CA, and Division of Cardiovascular Medicine at Stanford University will facilitate recruitment. This aim will test the hypothesis that a predisposition to IR in South Asians is evident even after taking into account significant differences between the two racial/ethnic groups in adiposity and physical fitness not captured by more traditional methods of assessment of these variables (e.g., BMI and physical activity questionnaires). In Specific Aim 2, the candidate will perform genome wide genotyping in ~400 South Asians followed by gene/SNP set pathway analyses using innovative analytical techniques developed by collaborators at SAGE Bionetworks. The same analytical techniques will be applied to multiple other sample sets with GWAS and direct measures of IR representing 3 other race/ethnic groups including Europeans, East Asians, and Hispanics. This aim will compare and contrast pathways associated with IR across all groups and test the hypothesis that South Asians are more IR because they have inherited a relatively inefficient cellular mechanism of handling glucose compared to other racial/ethnic groups.
To test the ability of serum levels of 7 novel biomarkers to improve prediction for incident CHD over standard risk scores in the WHI observational study.
To test the hypothesis that giving patients information about their genetic risk of CAD will increase their adherence to therapy, behavior or help them affect lifestyle changes.
To identify genomic signatures and pathways relevant to CHD and its risk factors through large scale methylation and circulating micro-RNA profiling in the WHI
Perform a whole genome association study on the ADVANCE study in search of novel genetic determinants of CAD and CAD risk factors.
Our investigative focus is the design, conduct, analysis, and interpretation of human molecular epidemiology studies of complex cardiovascular disease (CVD) related traits. While we have focused on the study of coronary atherosclerosis, a condition that causes heart attacks, the number one cause of death worldwide, and risk factors for coronary atherosclerosis, we also examine many other traits related to cardiovascular disease. In addition to performing discovery and validation population genomic studies, we use contemporary genetic studies to gain important insight on the causal and mechanistic nature of associations between purported risk factors and adverse cardiovascular related health outcomes through instrumental variable analyses and genetic risk score association studies of intermediate phenotypes. Our group is also actively involved in studies assessing the clinical utility of novel genetic markers in isolation or in combination with other biomarkers. Lastly, we communicate the significance of genomic findings at the population level to molecular biologists who may lack a strong background in human genetics as well as human geneticists who lack a strong background in clinical medicine. Our group's broad translational knowledge base allows us to serve as a key collaborator in multidisciplinary investigative groups involved in the design and the interpretation of important functional experiments that will shed light on the biology behind these new genetic associations, as well as clinical trials the will help further delineate the utility of genomics in clinical practice.If you are interested in working with us as a postdoctoral scholar, please check to see if we have any open positions at https://postdocs.stanford.edu/prospective/opportunities (search Assimes as last name). If you are interested in joining the team as a trainee in any other capacity, please do not hesitate to contact us as well.
Personal Genomics for Preventive Cardiology
The purpose of this study is to see if providing information to a person on their inherited
(genetic) risk of cardiovascular disease (CVD) helps to motivate that person to change their
diet, lifestyle or medication regimen to alter their risk.
Stanford is currently not accepting patients for this trial.
For more information, please contact Josh Knowles, 650-804-2526.
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