Author
Aravinda Chakravarti
Other affiliations: University of Texas Southwestern Medical Center, Johns Hopkins University, Los Angeles Biomedical Research Institute ...read more
Bio: Aravinda Chakravarti is an academic researcher from Johns Hopkins University School of Medicine. The author has contributed to research in topic(s): Population & Genome-wide association study. The author has an hindex of 120, co-authored 451 publication(s) receiving 99632 citation(s). Previous affiliations of Aravinda Chakravarti include University of Texas Southwestern Medical Center & Johns Hopkins University.
Papers published on a yearly basis
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TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.
9,821 citations
National Institutes of Health1, University of Chicago2, Duke University3, Harvard University4, University of Oxford5, GlaxoSmithKline6, Johns Hopkins University7, Yale University8, deCODE genetics9, Howard Hughes Medical Institute10, Princeton University11, Washington University in St. Louis12, University of California, Berkeley13, Stanford University14, University of Michigan15, Cornell University16, University of Washington17, University of Queensland18, Vanderbilt University19, North Carolina State University20, QIMR Berghofer Medical Research Institute21
TL;DR: This paper examined potential sources of missing heritability and proposed research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
Abstract: Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
7,195 citations
Baylor College of Medicine1, Chinese Academy of Sciences2, Chinese National Human Genome Center3, University of Hong Kong4, The Chinese University of Hong Kong5, Hong Kong University of Science and Technology6, Illumina7, McGill University8, Washington University in St. Louis9, University of California, San Francisco10, Wellcome Trust Sanger Institute11, Beijing Normal University12, Health Sciences University of Hokkaido13, Shinshu University14, University of Tsukuba15, Howard University16, University of Ibadan17, Case Western Reserve University18, University of Utah19, Cold Spring Harbor Laboratory20, Johns Hopkins University21, University of Oxford22, North Carolina State University23, National Institutes of Health24, Massachusetts Institute of Technology25, Chinese Academy of Social Sciences26, Kyoto University27, Nagasaki University28, Wellcome Trust29, Genome Canada30, Foundation for the National Institutes of Health31, University of Maryland, Baltimore32, Vanderbilt University33, Stanford University34, University of California, Berkeley35, New York University36, University of Oklahoma37, University of New Mexico38, Université de Montréal39, University of California, Los Angeles40, University of Michigan41, University of Wisconsin-Madison42, London School of Economics and Political Science43, Genetic Alliance44, GlaxoSmithKline45, University of Washington46, Harvard University47, University of Chicago48, Fred Hutchinson Cancer Research Center49, University of Tokyo50
TL;DR: The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance the ability to choose targets for therapeutic intervention.
Abstract: The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention.
5,704 citations
27 Oct 2005
TL;DR: A public database of common variation in the human genome: more than one million single nucleotide polymorphisms for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted.
Abstract: Inherited genetic variation has a critical but as yet largely uncharacterized role in human disease. Here we report a public database of common variation in the human genome: more than one million single nucleotide polymorphisms (SNPs) for which accurate and complete genotypes have been obtained in 269 DNA samples from four populations, including ten 500-kilobase regions in which essentially all information about common DNA variation has been extracted. These data document the generality of recombination hotspots, a block-like structure of linkage disequilibrium and low haplotype diversity, leading to substantial correlations of SNPs with many of their neighbours. We show how the HapMap resource can guide the design and analysis of genetic association studies, shed light on structural variation and recombination, and identify loci that may have been subject to natural selection during human evolution.
5,359 citations
TL;DR: The Phase II HapMap is described, which characterizes over 3.1 million human single nucleotide polymorphisms genotyped in 270 individuals from four geographically diverse populations and includes 25–35% of common SNP variation in the populations surveyed, and increased differentiation at non-synonymous, compared to synonymous, SNPs is demonstrated.
Abstract: We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25-35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10-30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.
4,408 citations
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TL;DR: The results of an international collaboration to produce and make freely available a draft sequence of the human genome are reported and an initial analysis is presented, describing some of the insights that can be gleaned from the sequence.
Abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.
21,023 citations
TL;DR: The purpose of this discussion is to offer some unity to various estimation formulae and to point out that correlations of genes in structured populations, with which F-statistics are concerned, are expressed very conveniently with a set of parameters treated by Cockerham (1 969, 1973).
Abstract: This journal frequently contains papers that report values of F-statistics estimated from genetic data collected from several populations. These parameters, FST, FIT, and FIS, were introduced by Wright (1951), and offer a convenient means of summarizing population structure. While there is some disagreement about the interpretation of the quantities, there is considerably more disagreement on the method of evaluating them. Different authors make different assumptions about sample sizes or numbers of populations and handle the difficulties of multiple alleles and unequal sample sizes in different ways. Wright himself, for example, did not consider the effects of finite sample size. The purpose of this discussion is to offer some unity to various estimation formulae and to point out that correlations of genes in structured populations, with which F-statistics are concerned, are expressed very conveniently with a set of parameters treated by Cockerham (1 969, 1973). We start with the parameters and construct appropriate estimators for them, rather than beginning the discussion with various data functions. The extension of Cockerham's work to multiple alleles and loci will be made explicit, and the use of jackknife procedures for estimating variances will be advocated. All of this may be regarded as an extension of a recent treatment of estimating the coancestry coefficient to serve as a mea-
16,821 citations
TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
16,404 citations
TL;DR: In this article, a randomized controlled trial of Aliskiren in the Prevention of Major Cardiovascular Events in Elderly people was presented. But the authors did not discuss the effect of the combination therapy in patients living with systolic hypertension.
Abstract: ABCD
: Appropriate Blood pressure Control in Diabetes
ABI
: ankle–brachial index
ABPM
: ambulatory blood pressure monitoring
ACCESS
: Acute Candesartan Cilexetil Therapy in Stroke Survival
ACCOMPLISH
: Avoiding Cardiovascular Events in Combination Therapy in Patients Living with Systolic Hypertension
ACCORD
: Action to Control Cardiovascular Risk in Diabetes
ACE
: angiotensin-converting enzyme
ACTIVE I
: Atrial Fibrillation Clopidogrel Trial with Irbesartan for Prevention of Vascular Events
ADVANCE
: Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation
AHEAD
: Action for HEAlth in Diabetes
ALLHAT
: Antihypertensive and Lipid-Lowering Treatment to Prevent Heart ATtack
ALTITUDE
: ALiskiren Trial In Type 2 Diabetes Using Cardio-renal Endpoints
ANTIPAF
: ANgioTensin II Antagonist In Paroxysmal Atrial Fibrillation
APOLLO
: A Randomized Controlled Trial of Aliskiren in the Prevention of Major Cardiovascular Events in Elderly People
ARB
: angiotensin receptor blocker
ARIC
: Atherosclerosis Risk In Communities
ARR
: aldosterone renin ratio
ASCOT
: Anglo-Scandinavian Cardiac Outcomes Trial
ASCOT-LLA
: Anglo-Scandinavian Cardiac Outcomes Trial—Lipid Lowering Arm
ASTRAL
: Angioplasty and STenting for Renal Artery Lesions
A-V
: atrioventricular
BB
: beta-blocker
BMI
: body mass index
BP
: blood pressure
BSA
: body surface area
CA
: calcium antagonist
CABG
: coronary artery bypass graft
CAPPP
: CAPtopril Prevention Project
CAPRAF
: CAndesartan in the Prevention of Relapsing Atrial Fibrillation
CHD
: coronary heart disease
CHHIPS
: Controlling Hypertension and Hypertension Immediately Post-Stroke
CKD
: chronic kidney disease
CKD-EPI
: Chronic Kidney Disease—EPIdemiology collaboration
CONVINCE
: Controlled ONset Verapamil INvestigation of CV Endpoints
CT
: computed tomography
CV
: cardiovascular
CVD
: cardiovascular disease
D
: diuretic
DASH
: Dietary Approaches to Stop Hypertension
DBP
: diastolic blood pressure
DCCT
: Diabetes Control and Complications Study
DIRECT
: DIabetic REtinopathy Candesartan Trials
DM
: diabetes mellitus
DPP-4
: dipeptidyl peptidase 4
EAS
: European Atherosclerosis Society
EASD
: European Association for the Study of Diabetes
ECG
: electrocardiogram
EF
: ejection fraction
eGFR
: estimated glomerular filtration rate
ELSA
: European Lacidipine Study on Atherosclerosis
ESC
: European Society of Cardiology
ESH
: European Society of Hypertension
ESRD
: end-stage renal disease
EXPLOR
: Amlodipine–Valsartan Combination Decreases Central Systolic Blood Pressure more Effectively than the Amlodipine–Atenolol Combination
FDA
: U.S. Food and Drug Administration
FEVER
: Felodipine EVent Reduction study
GISSI-AF
: Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto Miocardico-Atrial Fibrillation
HbA1c
: glycated haemoglobin
HBPM
: home blood pressure monitoring
HOPE
: Heart Outcomes Prevention Evaluation
HOT
: Hypertension Optimal Treatment
HRT
: hormone replacement therapy
HT
: hypertension
HYVET
: HYpertension in the Very Elderly Trial
IMT
: intima-media thickness
I-PRESERVE
: Irbesartan in Heart Failure with Preserved Systolic Function
INTERHEART
: Effect of Potentially Modifiable Risk Factors associated with Myocardial Infarction in 52 Countries
INVEST
: INternational VErapamil SR/T Trandolapril
ISH
: Isolated systolic hypertension
JNC
: Joint National Committee
JUPITER
: Justification for the Use of Statins in Primary Prevention: an Intervention Trial Evaluating Rosuvastatin
LAVi
: left atrial volume index
LIFE
: Losartan Intervention For Endpoint Reduction in Hypertensives
LV
: left ventricle/left ventricular
LVH
: left ventricular hypertrophy
LVM
: left ventricular mass
MDRD
: Modification of Diet in Renal Disease
MRFIT
: Multiple Risk Factor Intervention Trial
MRI
: magnetic resonance imaging
NORDIL
: The Nordic Diltiazem Intervention study
OC
: oral contraceptive
OD
: organ damage
ONTARGET
: ONgoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial
PAD
: peripheral artery disease
PATHS
: Prevention And Treatment of Hypertension Study
PCI
: percutaneous coronary intervention
PPAR
: peroxisome proliferator-activated receptor
PREVEND
: Prevention of REnal and Vascular ENdstage Disease
PROFESS
: Prevention Regimen for Effectively Avoiding Secondary Strokes
PROGRESS
: Perindopril Protection Against Recurrent Stroke Study
PWV
: pulse wave velocity
QALY
: Quality adjusted life years
RAA
: renin-angiotensin-aldosterone
RAS
: renin-angiotensin system
RCT
: randomized controlled trials
RF
: risk factor
ROADMAP
: Randomized Olmesartan And Diabetes MicroAlbuminuria Prevention
SBP
: systolic blood pressure
SCAST
: Angiotensin-Receptor Blocker Candesartan for Treatment of Acute STroke
SCOPE
: Study on COgnition and Prognosis in the Elderly
SCORE
: Systematic COronary Risk Evaluation
SHEP
: Systolic Hypertension in the Elderly Program
STOP
: Swedish Trials in Old Patients with Hypertension
STOP-2
: The second Swedish Trial in Old Patients with Hypertension
SYSTCHINA
: SYSTolic Hypertension in the Elderly: Chinese trial
SYSTEUR
: SYSTolic Hypertension in Europe
TIA
: transient ischaemic attack
TOHP
: Trials Of Hypertension Prevention
TRANSCEND
: Telmisartan Randomised AssessmeNt Study in ACE iNtolerant subjects with cardiovascular Disease
UKPDS
: United Kingdom Prospective Diabetes Study
VADT
: Veterans' Affairs Diabetes Trial
VALUE
: Valsartan Antihypertensive Long-term Use Evaluation
WHO
: World Health Organization
### 1.1 Principles
The 2013 guidelines on hypertension of the European Society of Hypertension (ESH) and the European Society of Cardiology …
13,846 citations
TL;DR: Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.
Abstract: Summary: Research over the last few years has revealed significant haplotype structure in the human genome. The characterization of these patterns, particularly in the context of medical genetic association studies, is becoming a routine research activity. Haploview is a software package that provides computation of linkage disequilibrium statistics and population haplotype patterns from primary genotype data in a visually appealing and interactive interface.
Availability: http://www.broad.mit.edu/mpg/haploview/
Contact: jcbarret@broad.mit.edu
13,185 citations