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Posts Tagged ‘Washington University School of Medicine’


Gene Study of Blood Pressure Response to Dietary Potassium Intervention: Genetic Epidemiology of Salt Sensitivity

Reporter: Aviva Lev-Ari, PhD, RN

Genome-Wide Linkage and Positional Candidate Gene Study of Blood Pressure Response to Dietary Potassium Intervention

The Genetic Epidemiology Network of Salt Sensitivity Study

Tanika N. Kelly, PhD, James E. Hixson, PhD, Dabeeru C. Rao, PhD, Hao Mei, MD, PhD,Treva K. Rice, PhD, Cashell E. Jaquish, PhD, Lawrence C. Shimmin, PhD, Karen Schwander, MS, Chung-Shuian Chen, MS, Depei Liu, PhD, Jichun Chen, MD,Concetta Bormans, PhD, Pramila Shukla, MS, Naveed Farhana, MS, Colin Stuart, BS,Paul K. Whelton, MD, MSc, Jiang He, MD, PhD and Dongfeng Gu, MD, PhD

Author Affiliations

From the Department of Epidemiology (T.N.K., H.M., C.-S.C., J.H.), Tulane University School of Public Health and Tropical Medicine, and Department of Medicine (J.H.), Tulane University School of Medicine, New Orleans, La; Department of Epidemiology (J.E.H., L.C.S., C.B., P.S., N.F., C.S.), University of Texas School of Public Health, Houston, Tex; Division of Biostatistics (D.C.R., T.K.R., K.S.), Washington University School of Medicine, St Louis, Mo; Division of Prevention and Population Sciences (C.E.J.), National Heart, Lung, Blood Institute, Bethesda, Md; National Laboratory of Medical Molecular Biology (D.L.), Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Cardiovascular Institute and Fuwai Hospital (J.C., D.G.), Chinese Academy of Medical Sciences and Peking Union Medical College and Chinese National Center for Cardiovascular Disease Control and Research, Beijing, China; and Office of the President (P.K.W.), Loyola University Health System and Medical Center, Maywood, Ill.

Correspondence to Dongfeng Gu, MD, PhD, Division of Population Genetics and Prevention, Cardiovascular Institute and Fuwai Hospital, 167 Beilishi Rd, Beijing 100037, China. E-mail gudongfeng@vip.sina.com

Abstract

Background— Genetic determinants of blood pressure (BP) response to potassium, or potassium sensitivity, are largely unknown. We conducted a genome-wide linkage scan and positional candidate gene analysis to identify genetic determinants of potassium sensitivity.

Conclusions— Genetic regions on chromosomes 3 and 11 may harbor important susceptibility loci for potassium sensitivity. Furthermore, the AGTR1 gene was a significant predictor of BP responses to potassium intake.

SOURCE:

Circulation: Cardiovascular Genetics. 2010; 3: 539-547

Published online before print September 22, 2010,

doi: 10.1161/ CIRCGENETICS.110.940635

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Common Heart Failure: Clinical Considerations of Heritable Factors

Reporter: Aviva Lev-Ari, PhD, RN

 

Clinical Considerations of Heritable Factors in Common Heart Failure

Thomas P. Cappola, MD, ScM and Gerald W. Dorn II, MD

Author Affiliations

From the Department of Medicine, University of Pennsylvania, Philadelphia, PA (T.P.C.), and Center for Pharmacogenomics, Washington University School of Medicine, St Louis, MO (G.W.D.II.).

Correspondence to Gerald W. Dorn II, MD, Center for Pharmacogenomics, Washington University, 660 S Euclid Ave, Campus Box 8220, St Louis, MO 63110. E-mail gdorn@dom.wustl.edu

Introduction

Heart failure is a common condition responsible for at least 290 000 deaths each year in the United States alone.1 A small minority of heart failure cases are attributed to Mendelian or familial cardiomyopathies. The majority of systolic heart failure cases are not familial but represent the end result of 1 or many conditions that primarily injure the myocardium sufficiently to diminish cardiac output in the absence of compensatory mechanisms. Paradoxically, because they also injure the myocardium, it is the chronic actions of the compensatory mechanisms that in many instances contribute to the progression from simple cardiac injury to dilated cardiomyopathy and overt heart failure. Thus, the epidemiology of common heart failure appears to be just as sporadic as its major antecedent conditions (atherosclerosis, diabetes, hypertension, and viral myocarditis).

Familial trends in preclinical cardiac remodeling2 and risk of developing heart failure3reveal an important role for genetic modifiers in addition to clinical and environmental factors. Candidate gene studies performed over the past 10 years have identified a few polymorphic gene variants that modify risk or progression of common heart failure.4 Whole-genome sequencing will lead to the discovery of other genetic modifiers that were not candidates.5 The imminent availability of individual whole-genome sequences at a cost competitive with available genetic tests for familial cardiomyopathy will no doubt further expand the list of putative genetic heart failure modifiers. Heart failure risk alleles along with traditional clinical factors will need to be considered by clinical cardiologists in their design of optimal disease surveillance and prevention programs and in individually tailoring heart failure management.

The use of individual genetic make-up is likely to have the earliest and greatest impact on managing patients with heart failure by tailoring available pharmacotherapeutics to optimize patient response and minimize adverse effects (ie, the area of pharmacogenetics). Modern heart failure management has been derived and directed by the results of large, randomized, multicenter clinical trials. When standard therapies are applied according to the selection criteria used in these trials, they prolong average survival across affected populations or decrease the incidence of heart failure in populations at risk.6 For this reason, standardized treatment guidelines prescribe heart failure therapies according to trial designs, aiming for the same target doses and general treatment approaches,7 and largely ignore individual characteristics. In this article, we review established and emerging knowledge of genetic influence on common heart failure and try to anticipate how these genetic factors may be best used to eschew the cookie-cutter approach to heart failure management and move toward implementing a personalized medicine approach for the treatment and prevention of this important and prevalent disease.

The Concept of Genotype-Directed Personal Medical Management in Heart Failure

Variation in clinical heart failure progression and therapeutic response (either benefits or side effects) supports the need for a more individualized approach to disease management. On the basis of clinical stratification (eg, by etiology of heart failure as ischemic versus nonischemic, functional status, comorbid disease), physicians try to match each patient’s specific heart failure syndrome with a therapeutic regime devised to provide the most benefit. Standard heart failure pharmacotherapy currently comprises a minimum of 3 medications (angiotensin-converting enzyme [ACE] inhibitors, β-blockers, and aldosterone antagonists), with consideration of additional medications (hydralazine/isosorbide, angiotensin receptor blockers) and diuretics. The recommended target dosages for these agents, derived from their respective clinical trials, is rarely achieved,8 partly because of untoward clinical side effects such as low blood pressure or renal dysfunction. Accordingly, the published guidelines most often are applied in each individual patient using ad hoc approaches derived from personal experience and the “art of medicine.”

Technological advances in human genomics promise a different approach and are bringing cardiology into an era of clinically applied pharmacogenetics9 (whether we want to or not). As sequencing costs decline, it is not hard to envision that patients will present having had their entire genome already sequenced. The imperative to apply genome information in clinical settings will increase, as demonstrated by recent proof-of-concept studies.10 Our field seems poorly prepared for this type of evolution in care; Roden et al9 identified 3 major barriers: First is the absence of rapidly available genotype information in the clinical workflow. This barrier is being overcome with whole-genome sequencing, which (with proper analysis) promises a permanent and largely immutable genetic roadmap for individual disease risk and drug response at a cost comparable to many other clinical tests.11 Second, we must have the knowledge to properly apply information on genetic variants for the diseases we are managing and the drugs we are using. As we describe, this knowledge is accumulating for heart failure and for other cardiac conditions, and the rate at which we are gaining additional information and developing further expertise appears to be accelerating.

The third and perhaps most formidable barrier is the lack of clinical evidence showing how real-time application of genetic information can best benefit patients. As has been broadly communicated to the medical community and lay public, common functional gene variants in CYP2C19 can impair the transformation of clopidogrel into its active metabolite, leading to increased risk of stent thrombosis after percutaneous coronary intervention.12 The relevant question thus becomes the following: If physicians have this information at the time of clinical care and reacted by adjusting clopidogrel dose or substituting prasugrel, which is unaffected by CYP2C19genotype,13 would there be any improvement in clinical outcome? It is also important to consider whether any observed benefits justify the additional costs of genetic testing and for the alternate drug. Studies are currently examining these questions, and similar clinical trials will prospectively examine whether a genotype-guided strategy of warfarin dosing will be superior to the standard genotype-blinded approach in reaching target anticoagulation goals. At this time, there are no similar prospective, randomized, blinded trials of genotype-guided care for common heart failure.

Emerging Variants

The variants described here are established, but new ones are emerging. Although findings in heart failure genome-wide association studies have been limited, we can expect additional common heart failure variants to emerge as sample sizes increase.65 The CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium published a genome-wide association study of incident heart failure that tested for associations between >2.4 million HapMap-imputed polymorphisms in >20 000 subjects.7 They identified 2 loci associated with heart failure, rs10519210 (15q22, containing USP3 encoding a ubiquitin-specific protease) in subjects of European ancestry and rs11172782 (12q14, containing LRIG3encoding a leucine-rich, immunoglobulin-like domain-containing protein of uncertain function) in subjects of African ancestry.66 In a companion study using the same population and genotyping results, mortality analysis of the subgroup of individuals who developed heart failure implicated an intronic SNP in CMTM7 (CKLF-like MARVEL transmembrane domain-containing 7).67 These genetic associations require independent replication and further study to identify the underlying biological mechanisms.

A recently published genome-wide association study by a European consortium on dilated cardiomyopathy identified common variants in BAG3 (BCL2-associated athanogene 3) associated with heart failure57 and identified rare BAG3 missense and truncation mutations that segregate with familial cardiomyopathy. These findings were consistent with an earlier exome-sequencing study that identifiedBAG3 as a familial dilated cardiomyopathy gene and showed recapitulation of cardiomyopathy with BAG3 morpholino knockdown in zebra fish.68 Together, these studies convincingly support variation in BAG3 as a genetic risk factor of cardiomyopathy and heart failure. It is noteworthy that both common and rare functional variations were identified at this locus. A unifying hypothesis for these findings, which needs to be formally tested, is that common variants in BAG3 serve as proxies for rare functional BAG3 mutations with large effects. In this situation, the underlying genetic lesion is a rare variant with a large functional effect. This has recently been described for common variants in MYH6 that correlated with rare functional MYH6 variants to cause sick sinus syndrome.69 It is premature to speculate on the clinical applications of these newer findings.

Moving Knowledge to Practice

A small number of genomic variants have been identified that modify heart failure by affecting well-understood physiological systems. The principal barrier preventing their adoption in practice may be lack of evidence showing how application of this information can best be used for clinical benefit. Trials testing genotype targeting of antiplatelet therapy and anticoagulation will be completed in the coming years. The findings from these studies will likely determine the level of enthusiasm for conducting genotype-guided trials of β-blockers and RAAS antagonists in heart failure. Given that the lifetime risk of heart failure in the United States is estimated at 1 in 5, even a small favorable effect on heart failure prevention or outcome through use of genome-guided therapy has the potential for a large public health impact. We therefore believe that a near-term goal should be to conduct pharmacogenomic trials in heart failure based on our current understanding of heart failure variants.

Looking ahead, unbiased approaches will continue to reveal a large number heart failure-modifying variants (both common and rare). Based on experience in other complex phenotypes, such has height70 and plasma lipid levels,71 the underlying genetic mechanisms for many new heart failure variants will be completely unknown, and their sheer number will preclude detailed experimentation using murine models to figure them out. Leveraging these variants for clinical application is a challenge that we will be forced to confront.

As our ability to identify rare, disease-causing variants improves through personal genome sequencing, we will be faced with the additional problem of how best to estimate the disease risk conferred by a sequence variant for which there has been no biological validation. In probabilistic terms, because there are 3 billion nucleotides in the human genome and over twice that many humans on the planet, it is likely that a nucleotide substitution for every position is represented in someone. Obviously, it will be impossible to recombinantly express and functionally characterize every DNA variant that is going to be implicated in heart failure. Bioinformatics filters have been used to try and separate functionally significant from insignificant variants based on the likelihood of changing transcript expression or protein function. These tools are limited but will improve if we tailor their results to the known characteristics of each gene product. For example, current approaches to categorize amino acid substitutions as conservative or nonconservative based only on charge or side chains can be improved by molecular modeling that incorporates protein-specific structure-function information. This approach has been used to estimate the pathogenicity of myosin heavy chain (MHC) mutations in an effort to determine which mutations are likely to cause familial cardiomyopathy when linkage analysis is not feasible.72 In concept, this approach can be applied to any protein for which structure-function activities have been finely mapped to distinct domains.

A promising extension of this approach may be to use evolutionary genetics to infer disease causality. Again, using the MHC genes as examples, human genome data show a greater prevalence of nonsynonymous gene variants in MYH6, which encodes the minor cardiac α-MHC isoform, compared with the adjacent MYH7, which encodes the major β-MHC isoform. This disparity suggests a greater tolerance for protein changes in the α-MHC isoform and negative selection against these in β-MHC. We can infer, therefore, that amino acid changes are more likely to have adverse impacts in MYH7-encoded β-MHC. If this paradigm survives prospective testing, then the forthcoming explosion of individual genetic data not only will present a massive problem in interpretation, but also will provide the genetic information by which analyses of rare sequence variants across large unaffected populations can help to differentiate the tolerable variants from those that are more likely to alter disease risk.

Each Reference above is found in:

http://circgenetics.ahajournals.org/content/4/6/701.full

SOURCE: 

Circulation: Cardiovascular Genetics.2011; 4: 701-709

doi: 10.1161/ CIRCGENETICS.110.959379

 

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Reporter: Aviva Lev-Ari, PhD, RN

 

Nature Genetics (2013) doi:10.1038/ng.2705

Independent specialization of the human and mouse X chromosomes for the male germ line

  1. Whitehead Institute, Cambridge, Massachusetts, USA.

    • Jacob L Mueller,
    • Helen Skaletsky,
    • Laura G Brown,
    • Sara Zaghlul &
    • David C Page
  2. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • Helen Skaletsky,
    • Laura G Brown &
    • David C Page
  3. The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA.

    • Susan Rock,
    • Tina Graves,
    • Wesley C Warren &
    • Richard K Wilson
  4. The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.

    • Katherine Auger
  5. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

    • David C Page

Contributions

J.L.M., H.S., W.C.W., R.K.W. and D.C.P. planned the project. J.L.M. and L.G.B. performed BAC mapping. J.L.M. performed RNA deep sequencing. T.G., S.R., K.A. and S.Z. were responsible for finished BAC sequencing. J.L.M. and H.S. performed sequence analyses. J.L.M. and D.C.P. wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Nature Genetics (2013) doi:10.1038/ng.2705

Received

 11 February 2013 Accepted

20 June 2013 Published online

21 July 2013

We compared the human and mouse X chromosomes to systematically test Ohno’s law, which states that the gene content of X chromosomes is conserved across placental mammals1. First, we improved the accuracy of the human X-chromosome reference sequence through single-haplotype sequencing of ampliconic regions. The new sequence closed gaps in the reference sequence, corrected previously misassembled regions and identified new palindromic amplicons. Our subsequent analysis led us to conclude that the evolution of human and mouse X chromosomes was bimodal. In accord with Ohno’s law, 94–95% of X-linked single-copy genes are shared by humans and mice; most are expressed in both sexes. Notably, most X-ampliconic genes are exceptions to Ohno’s law: only 31% of human and 22% of mouse X-ampliconic genes had orthologs in the other species. X-ampliconic genes are expressed predominantly in testicular germ cells, and many were independently acquired since divergence from the common ancestor of humans and mice, specializing portions of their X chromosomes for sperm production.

Refined X Chromosome Assembly Hints at Possible Role in Sperm Production

July 22, 2013

NEW YORK (GenomeWeb News) – A US and UK team that delved into previously untapped stretches of sequence on the mammalian X chromosome has uncovered clues that sequences on the female sex chromosome may play a previously unappreciated role in sperm production.

The work, published online yesterday in Nature Genetics, also indicated such portions of the X chromosome may be prone to genetic changes that are more rapid than those described over other, better-characterized X chromosome sequences.

“We view this as the double life of the X chromosome,” senior author David Page, director of the Whitehead Institute, said in a statement.

“[T]he story of the X has been the story of X-linked recessive diseases, such as color blindness, hemophilia, and Duchenne’s muscular dystrophy,” he said. “But there’s another side to the X, a side that is rapidly evolving and seems to be attuned to the reproductive needs of males.”

As part of a mouse and human X chromosome comparison intended to assess the sex chromosome’s similarities across placental mammals, Page and his colleagues used a technique called single-haplotype iterative mapping and sequencing, or SHIMS, to scrutinize human X chromosome sequence and structure in more detail than was available previously.

With the refined human X chromosome assembly and existing mouse data, the team did see cross-mammal conservation for many X-linked genes, particularly those present in single copies. But that was not the case for a few hundred species-specific genes, many of which fell in segmentally duplicated, or “ampliconic,” parts of the X chromosome. Moreover, those genes were prone to expression by germ cells in male testes tissue, pointing to a potential role in sperm production-related processes.

“X-ampliconic genes are expressed predominantly in testicular germ cells,” the study authors noted, “and many were independently acquired since divergence from the common ancestor of humans and mice, specializing portions of their X chromosomes for sperm production.”

The work was part of a larger effort to look at a theory known as Ohno’s law, which predicts extensive X-linked gene similarities from one placental mammal to the next, Page and company turned to the same SHIMS method they used to get a more comprehensive view of the Y chromosome for previous studies.

Using that sequencing method, the group resequenced portions of the human X chromosome, originally assembled from a mishmash of sequence from the 16 or more individuals whose DNA was used to sequence the human X chromosome reference.

Their goal: to track down sections of segmental duplication, called ampliconic regions, that may have been missed or assembled incorrectly in the mosaic human X chromosome sequence.

“Ampliconic regions assembled from multiple haplotypes may have expansions, contractions, or inversions that do not accurately reflect the structure of any extant haplotype,” the study’s authors explained.

“To thoroughly test Ohno’s law,” they wrote, “we constructed a more accurate assembly of the human X chromosome’s ampliconic regions to compare the gene contents of the human and mouse X chromosomes.”

The team focused their attention on 29 predicted ampliconic regions of the human X chromosome, using SHIMS to generate millions of bases of non-overlapping X chromosome sequence.

With that sequence in hand, they went on to refine the human X chromosome assembly before comparing it with the reference sequence for the mouse X chromosome, which already represented just one mouse haplotype.

The analysis indicated that 144 of the genes on the human X chromosome don’t have orthologs in mice, while 197 X-linked mouse genes lack human orthologs.

A minority of those species-specific genes arose as the result of gene duplication or gene loss events since the human and mouse lineages split from one around 80 million years ago, researchers determined. But most appear to have resulted from retrotransposition or transposition events involving sequences from autosomal chromosomes.

And when the team used RNA sequencing and existing gene expression data to look at which mouse and human tissues flip on particular genes, it found that many of the species-specific genes on the X chromosome showed preferential expression in testicular cells known for their role in sperm production.

Based on such findings, the study’s authors concluded that “the gene repertoires of the human and mouse X chromosomes are products of two complementary evolutionary processes: conservation of single-copy genes that serve in functions shared by the sexes and ongoing gene acquisition, usually involving the formation of amplicons, which leads to the differentiation and specialization of X chromosomes for functions in male gametogenesis.”

The group plans to incorporate results of its SHIMS-based assembly into the X chromosome portion of the human reference genome.

“This is a collection of genes that has largely eluded medical geneticists,” the study’s first author Jacob Mueller, a post-doctoral researcher in Page’s Whitehead lab, said in a statement. “Now that we’re confident of the assembly and gene content of these highly repetitive regions on the X chromosome, we can start to dissect their biological significance.”

Related Stories

SOURCE

http://www.genomeweb.com//node/1256251?utm_source=SilverpopMailing&utm_medium=email&utm_campaign=X%20Chromosome’s%20Possible%20New%20Role;%20NanoString%20Coverage%20Initiated;%20SynapDx%20Raises%20Funds;%20More%20-%2007/22/2013%2010:50:00%20AM

 

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Reporter: Aviva Lev-Ari, PhD, RN

 

SLAC Study Reveals Active Site of Enzyme Linked to Stuttering

By Glennda Chui
May 22, 2013

Scientists from the Joint Center for Structural Genomics (JCSG) at SLAC National Accelerator Laboratory have determined the 3-D structure of the chemically active part of an enzyme involved in stuttering.

While the discovery is not likely to lead to a cure for stuttering any time soon, it is welcome news to scientists who have been studying this enzyme, known as “uncovering enzyme” or UCE, for decades. Not only does UCE play a role in the type of persistent stuttering that is passed down in families, but it’s also an important part of the system that breaks down and recycles unwanted molecules in our cells. Knowing its 3-D structure will aid studies of all these systems, and of the health problems that result when they malfunction.

A team led by SLAC’s Debanu Das reported the finding April 9 in the Journal of Biological Chemistry.Das is a structural biologist and protein crystallographer at SSRL, the Stanford Synchrotron Radiation Lightsource, and a member of the JCSG, a multi-institute consortium that rapidly screens proteins coming out of gene mapping projects to determine their structure and function. The JCSG is part of theProtein Structure Initiative funded by the National Institute of General Medical Sciences, and involves 10 scientists at SSRL.

“We go after interesting proteins for which nothing much is known, try to solve their structure and, based on that structure, try to understand what they’re doing in the cell and what they’re related to,” Das said.

At SSRL, researchers aim powerful X-ray beams at crystallized samples of protein, creating patterns that reveal the protein’s 3-D structure. They analyze the structure to determine the protein’s function, and then scour the scientific literature to find scientists who might benefit from this information.

In this case, Das and his colleagues were working on the structure of DUF2233, a protein taken from one of the microbes that inhabit the human gut. Scanning protein databases and scientific reports, they learned that members of this new protein family were found in thousands of bacteria and in some viruses, but had only one representative in humans – UCE. “The microbe and human forms were not identical, but they were obviously related,” Das said.

They also learned that scientists had been studying UCE for decades. It plays a key role in the functioning of lysosomes, cellular sacs full of digestive enzymes that break down bacteria, viruses and worn-out cell parts for recycling. When this recycling process goes awry, the result can be rare metabolic diseases such as Tay-Sachs and Gaucher, which often kill affected children by their early teens. And three years ago, researchers discovered that three mutations in UCE itself were linked to persistent stuttering that is passed down in families. It is thought, but not yet proven, that these mutations may impair the functioning of critical neurons involved in speech.

Das contacted Stuart Kornfeld, a hematologist at Washington University School of Medicine in St. Louis who has been working on UCE and its role in the workings of lysosomes for three decades, and they agreed to collaborate on further studies.

Working from the structure of microbial DUF2233, Das created a computer model that predicted the structure of the same region in human UCE. It showed a cavity on the surface of UCE that appears to be the “active site” where the enzyme brings other chemicals together and induces them to react with each other, a process known as catalysis.

With that model in hand, Kornfeld and other collaborators created various mutations in UCE to see what effect they had on the enzyme’s function. These experiments verified that Das had indeed identified the enzyme’s active site.

“This study by Debanu was the most important advance we’ve had in all these years,” said Kornfeld, who is a co-author of the resulting paper. “We had no idea at all about what part of the enzyme was involved in its catalytic function.”

Dennis Drayna, a human geneticist at the National Institute on Deafness and Other Communication Disorders whose team discovered the stuttering-linked mutations, said lack of knowledge about the structure and function of UCE had hampered studies of its effects.

“The reason this is so interesting to us is because many of the biochemical details of the nature of the UCE have been really quite obscure,” he said. “It has been something of a black box. It’s a singleton in all of the human genome, as far as we can tell.”

While the three UCE mutations account for only 10 percent of persistent stuttering that runs in families, which in turn make up half of the total cases, that translates to about 3 million people worldwide, Drayna added. And while none of the stuttering mutations discovered so far occur within the cavity of the enzyme’s active site, this does not mean they would not have an impact on its chemical function, since pretty much every part of the protein is involved, in some fashion, in its work.

Paper co-author Ashley Deacon, a structural biologist and head of the Structural Genomics Division at SSRL, said scientists there are continuing to probe the structure of other parts of UCE, outside the active site.

“The whole molecule probably would not crystallize – often human proteins are rather big, with a lot of flexible regions – but we can do a single domain at a time,” he said. “We’ll see how far we can get.”

Other study co-authors include SSRL’s Hsiu-Ju Chiu and Mitchell D. Miller and researchers from the Genomics Institute of the Novartis Research Foundation, The Scripps Research Institute, Sanford-Burnham Medical Research Institute and the Center for Research in Biological Systems at University of California-San Diego, all in San Diego, Calif., which collaborate with SSRL as part of the JCSG.

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Uncovering enzyme (UCE) is an important part of a system that breaks down and recycles unwanted molecules in our cells. It carries out its work in the Golgi apparatus – the folded structure shown here in blue – where it helps process digestive proteins that go on to work in the lysosome, the stomach of the cell. Mutations in UCE are linked with metabolic disease in mice and persistent stuttering in people. Scientists have now uncovered the 3-D structure of the enzyme’s chemically active site, which belongs to a novel protein family. The discovery was made in a version of the protein family that occurs in microbes, and then used to find the active site in human UCE. The clumpy structure in the foreground is the microbial version of the protein. (Illustration by Greg Stewart/SLAC.)

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Reporter: Aviva Lev-Ari, PhD, RN

 

Harnessing New Players in Atherosclerosis to Treat Heart Disease

Tuesday, September 24, 2013 | 8:30 AM – 4:30 PM

The New York Academy of Sciences

Presented by the Biochemical Pharmacology Discussion Group

Atherosclerosis is defined as a chronic inflammatory disease affecting arterial blood vessels involving dysregulation of the endothelial-leukocyte adhesive interactions, increased leukocyte apoptosis within the plaque, and defective phagocytosis of apoptotic cells. Despite the key role of monocytes/macrophages in atherosclerosis, mounting evidence suggests that dysregulation of other cell types may be independent risk factors for atherosclerosis. Leukocytes are produced daily and are derived from hematopoietic stem and progenitor cells within the bone marrow in a process call hematopoiesis. A better understanding of this process will open an avenue to identify new targets to fight atherosclerosis.

*Reception to follow.

Organizers

Mercedes Beyna, MS

Pfizer Global Research and Development

Nadeem Sarwar, PhD

Pfizer Global Research and Development

Laurent Yvan-Charvet, PhD

INSERM U1065/UNS, C3M

Jennifer Henry, PhD

The New York Academy of Sciences

Speakers

Elena V. Galkina, MD, PhD

Eastern Virginia Medical School

Emmanuel L. Gautier, PhD

Washington University School of Medicine, St. Louis

Klaus Ley, MD

La Jolla Institute for Allergy and Immunology

Andrew H. Lichtman, MD, PhD

Brigham and Women’s Hospital, Harvard Medical School

Kathryn J. Moore, PhD

New York University Medical Center

Matthias Nahrendorf, MD, PhD

Harvard Medical School

Alan R. Tall, MD, PhD

Columbia University Medical Center

http://www.nyas.org/Events/Detail.aspx?cid=1103f191-2d94-4f37-b91f-64293dc88019

 

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Reporter: Aviva Lev-Ari, PhD, RN

UPDATED 3/27/2013

The Exome is Not Enough

March 27, 2013

Dan Koboldt at MassGenomics explains why exome sequencing often fails to identify causal variants, even in Mendelian disorders — “the very plausible possibility that a noncoding functional variant is responsible.”

Koboldt, the analysis manager in the human genetics group at the Genome Institute at Washington University, says that researchers shouldn’t overlook the importance of noncoding functional variants, which require a suite of technologies to detect, including RNA-seq, ChiP-seq, DNAse sequencing and footprinting, bisulfite sequencing, and chromosome conformation capture.

“These types of experiments generate a wealth of data about regulatory activity in genomes,” he says. “While studying each of these independently is certainly informative, integrative analysis will be required to elucidate how all of these different regulatory mechanisms work together.”

While this effort will require “robust statistical models, substantial computing resources, and productive collaboration among research groups, the end result “will be a far more complete understanding of how the genome works,” he says.

 
SOURCE:

Dan Koboldt works as a staff scientist in the Human Genetics group of the Genome Institute at Washington University in St. Louis. There, he works with scientists, physicians, programmers, and data analysts to understand the genetic basis of complex human diseases such as cancer, vision disorders, and metabolic syndromes through next-gen sequencing analysis. He received bachelor’s degrees in Computer Science and French from the University of Missouri-Columbia, and a master’s degree in Biology fromWashington University.

Dan has worked in the field of human genetics since 2003, when he joined the lab of Raymond E. Miller, which played a role in the International HapMap Project and later the genetic map of C. briggsae, a model organism related to C. elegans.

Disclaimer: The views expressed on this site, including blog posts and static pages, do not necessarily reflect the opinions of the Genome Institute at Washington University, the Washington University School of Medicine, or Washington University in St. Louis.

Before diving in with both feet, next-generation sequencing neophytes might want to take a gander at a post by Dan Koboldt at MassGenomics where he describes his 10 commandments for good next-gen sequencing.

In his post, Koboldt breaks up his instructions into four categories: analysis, publications, data sharing and submissions, and research ethics and cost.

His list includes some oft repeated warnings. For example, he cautions against reinventing the wheel when it comes to developing analysis software, and, for pity’s sake, don’t invent any more words that end in “ome” or “omics.”

Some other no-no’s, according to Koboldt, include publishing results before they’ve been vetted properly, testing new methods on simulated data only, and taking “unfair advantage of submitted data.”

He also admonishes newcomers to think a little bit about the cost of analysis without which “your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot,” and to have a care for the privacy of their study participants’ samples and data.

Ten Commandments for Next-Gen Sequencing

10 ngs commandmentsJust as the reach of next-generation sequencing has continued to grow — in both research and clinical realms — so too has the community of NGS users.  Some have been around since the early days. The days of 454 and Solexa sequencing. Since then, the field has matured at an astonishing pace. Many standards were established to help everyone make sense of this flood of data. The recent democratization of sequencing has made next-gen sequencing available to just about anyone.

And yet, there have been growing pains. With great power comes great responsibility. To help some of the newcomers into the field, I’ve drafted these ten commandments for next-gen sequencing.

NGS Analysis

1. Thou shalt not reinvent the wheel. In spite of rapid technological advances, NGS is not a new field. Most of the current “workhorse” technologies have been on the market for a couple of years or more. As such, we have a plethora of short read aligners, de novo assemblers, variant callers, and other tools already. Even so, there is a great temptation for bioinformaticians to write their own “custom scripts” to perform these tasks. There’s a new “Applications Note” every day with some tool that claims to do something new or better.

Can you really write an aligner that’s better than BWA? More importantly, do we need one? Unless you have some compelling reason to develop something new (as we did when we developed SomaticSniper and VarScan), take advantage of what’s already out there.

2. Thou shalt not coin any new term ending with “ome” or “omics”. We have enough of these already, to the point where it’s getting ridiculous. Genome, transcriptome, and proteome are obvious applications of this nomenclature. Epigenome, sure. But the metabolome, interactome, and various other “ome” words are starting to detract from the naming system. The ones we need have already been coined. Don’t give in to the temptation.

3. Thou shall follow thy field’s conventions for jargon. Technical terms, acronyms, and abbreviations are inherent to research. We need them both for precision and brevity. When we get into trouble is when people feel the need to create their own acronyms when a suitable one already exists. Is there a significant difference between next-generation sequencing (NGS), high-throughput sequencing (HTS), and massively parallel sequencing (MPS)?

Widely accepted terms provide something of a standard, and they should be used whenever possible. Insertion/deletion variants are indels, not InDels or INDELs DIPs. Structural variants are SVs, not SVars or GVs. We don’t need any more acronyms!

NGS Publications

These commandments address behaviors that get on my nerves, both as a blogger and a peer reviewer.

4. Thou shalt not publish by press release. This is a disturbing trend that seems to happen more and more frequently in our field: the announcement of “discoveries” before they have been accepted for publication. Peer review is the required vetting process for scientific research. Yes, it takes time and yes, your competitors are probably on the verge of the same discovery. That doesn’t mean you get to skip ahead and claim credit by putting out a press release.

There are already examples of how this can come back to bite you. When the reviewers trash your manuscript, or (gasp) you learn that a mistake was made, it looks bad. It reflects poorly on the researchers and the institution, both in the field and in the eyes of the public.

5. Thou shalt not rely only on simulated data. Often when I read a paper on a new method or algorithm, they showcase it using simulated data. This often serves a noble purpose, such as knowing the “correct” answer and demonstrating that your approach can find it. Even so, you’d better apply it to some real data too. Simulations simply can’t replicate the true randomness of nature and the crap-that-can-go-wrong reality of next-gen sequencing. There’s plenty of freely available data out there; go get some of it.

6. Thou shalt obtain enough samples. One consequence of the rapid growth of our field (and accompanying drop in sequencing costs) is that small sample numbers no longer impress anyone. They don’t impress me, and they certainly don’t impress the statisticians upstairs. The novelty of exome or even whole-genome sequencing has long worn off. Now, high-profile studies must back their findings with statistically significant results, and that usually means finding a cohort of hundreds (or thousands) of patients with which to extend your findings.

This new reality may not be entirely bad news, because it surely will foster collaboration between groups that might otherwise not be able to publish individually.

Data Sharing and Submissions

7. Thou shalt withhold no data. With some exceptions, sequencing datasets are meant to be shared. Certain institutions, such as large-scale sequencing centers in the U.S., are mandated by their funding agencies to deposit data generated using public funds on a timely basis following its generation. Since the usual deposition site is dbGaP, this means that IRB approvals and dbGaP certification letters must be in hand before sequencing can begin.

Any researchers who plan to publish their findings based on sequencing datasets will have to submit them to public datasets before publication.This is not optional. It is not “something we should do when we get around to it after the paper goes out.” It is required to reproduce the work, so it should really be done before a manuscript is submitted. Consider this excerpt from Nature‘s publication guidelines:

Data sets must be made freely available to readers from the date of publication, and must be provided to editors and peer-reviewers at submission, for the purposes of evaluating the manuscript.

For the following types of data set, submission to a community-endorsed, public repository is mandatory. Accession numbers must be provided in the paper.

The policies go on to list various types of sequencing data:

  • DNA and RNA sequences
  • DNA sequencing data (traces for capillary electrophoresis and short reads for next-generation sequencing)
  • Deep sequencing data
  • Epitopes, functional domains, genetic markers, or haplotypes.

Every journal should have a similar policy; most top-tier journals already do. Editors and referees need to enforce this submission requirement by rejecting any manuscripts that do not include the submission accession numbers.

8. Thou shalt not take unfair advantage of submitted data. Many investigators are concerned about data sharing (especially when mandated upon generation, not publication) from fear of being scooped. This is a valid concern. When you submit your data to a public repository, others can find it and (if they meet the requirements) use it. Personally, I think most of these fears are not justified — I mean, have you ever tried to get data out of dbGaP? The time it takes for someone to find, request, obtain, and use submitted data should allow the producers of the data to write it up.

Large-scale efforts to which substantial resources have been devoted — such as the Cancer Genome Atlas — have additional safeguards in place. Their data use policy states that, for a given cancer type, submitted data can’t be used until the “marker paper” has been published. This is a good rule of thumb for the NGS community, and something that journal editors (and referees) haven’t always enforced.

Just because you can scoop someone doesn’t mean that you should. It’s not only bad karma, but bad for your reputation. Scientists have long memories. They will likely review your manuscript or grant proposal sometime in the future. When that happens, you want to be the person who took the high road.

Research Ethics and Cost

9. Thou shalt not discount the cost of analysis. It’s true that since the advent of NGS technology, the cost of sequencing has plummeted. The cost of analysis, however, has not. And making sense of genomic data — alignment, quality control, variant calling, annotation, interpretation — is a daunting task indeed. It takes computational resources as well as expertise. This infrastructure is not free; in fact, it can be more expensive than the sequencing itself. 

Without analysis, your sequencing data, your $1,000 genome, is about as useful as a chocolate teapot.

10. Thou shalt honor thy patients and their samples. Earlier this month, I wrote about how supposedly anonymous individuals from the CEPH collection were identified using a combination of genetic markers and online databases. It is a simple fact that we can no longer guarantee a sequenced sample’s anonymity. That simple fact, combined with our growing ability to interpret the possible consequences of an individual genome, means a great deal of risk for study volunteers.

We must safeguard the privacy of study participants — and find ways to protect them from privacy violations and/or discrimination — if we want their continued cooperation.

This means obtaining good consent documents and ensuring that they’re all correct before sequencing begins. It also means adhering to the data use policies those consents specify. As I’ve written before, samples are the new commodity in our field. Anyone can rent time on a sequencer. If you don’t make an effort to treat your samples right, someone else will.

Related Posts:

SOURCE:

Dan Koboldt’s Publications

Bose R, Kavuri SM, Searleman AC, Shen W, Shen D, Koboldt DC, Monsey J, Goel N, Aronson AB, Li S, Ma CX, Ding L, Mardis ER, & Ellis MJ (2013).Activating HER2 mtations in HER2 gene amplification negative breast cancer. Cancer discovery PMID: 23220880

The 1000 Genomes Project Consortium (2012). An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65. DOI: 10.1038/nature11632

Cancer Genome Atlas Network (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490 (7418), 61-70 PMID:23000897

Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, Van Tine BA, Hoog J, Goiffon RJ, Goldstein TC, Ng S, Lin L, Crowder R, Snider J, Ballman K, Weber J, Chen K, Koboldt DC, Kandoth C, Schierding WS, McMichael JF, Miller CA, Lu C, Harris CC, McLellan MD, Wendl MC, DeSchryver K, Allred DC, Esserman L, Unzeitig G, Margenthaler J, Babiera GV, Marcom PK, Guenther JM, Leitch M, Hunt K, Olson J, Tao Y, Maher CA, Fulton LL, Fulton RS, Harrison M, Oberkfell B, Du F, Demeter R, Vickery TL, Elhammali A, Piwnica-Worms H, McDonald S, Watson M, Dooling DJ, Ota D, Chang LW, Bose R, Ley TJ, Piwnica-Worms D, Stuart JM, Wilson RK, & Mardis ER (2012). Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature, 486 (7403), 353-60 PMID: 22722193

Welch JS, Ley TJ, Link DC, Miller CA, Larson DE, Koboldt DC, Wartman LD, Lamprecht TL, Liu F, Xia J, Kandoth C, Fulton RS, McLellan MD, Dooling DJ, Wallis JW, Chen K, Harris CC, Schmidt HK, Kalicki-Veizer JM, Lu C, Zhang Q, Lin L, O’Laughlin MD, McMichael JF, Delehaunty KD, Fulton LA, Magrini VJ, McGrath SD, Demeter RT, Vickery TL, Hundal J, Cook LL, Swift GW, Reed JP, Alldredge PA, Wylie TN, Walker JR, Watson MA, Heath SE, Shannon WD, Varghese N, Nagarajan R, Payton JE, Baty JD, Kulkarni S, Klco JM, Tomasson MH, Westervelt P, Walter MJ, Graubert TA, DiPersio JF, Ding L, Mardis ER, & Wilson RK (2012). The origin and evolution of mutations in acute myeloid leukemia. Cell, 150 (2), 264-78 PMID: 22817890

Cancer Genome Atlas Network (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487(7407), 330-7 PMID: 22810696

Dees ND, Zhang Q, Kandoth C, Wendl MC, Schierding W, Koboldt DC, Mooney TB, Callaway MB, Dooling D, Mardis ER, Wilson RK, & Ding L (2012). MuSiC: identifying mutational significance in cancer genomes.Genome research, 22 (8), 1589-98 PMID: 22759861

Walter MJ, Shen D, Ding L, Shao J, Koboldt DC, Chen K, Larson DE, McLellan MD, Dooling D, Abbott R, Fulton R, Magrini V, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Fan X, Grillot M, Witowski S, Heath S, Frater JL, Eades W, Tomasson M, Westervelt P, DiPersio JF, Link DC, Mardis ER, Ley TJ, Wilson RK, & Graubert TA (2012). Clonal architecture of secondary acute myeloid leukemia. The New England journal of medicine, 366(12), 1090-8 PMID: 22417201

Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, Arthur CD, White JM, Chen YS, Shea LK, Hundal J, Wendl MC, Demeter R, Wylie T, Allison JP, Smyth MJ, Old LJ, Mardis ER, & Schreiber RD (2012).Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature, 482 (7385), 400-4 PMID: 22318521

Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, Miller CA, Mardis ER, Ding L, & Wilson RK (2012). VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Research PMID: 22300766

Koboldt DC, Larson DE, Chen K, Ding L, & Wilson RK (2012). Massively parallel sequencing approaches for characterization of structural variation. Methods in molecular biology (Clifton, N.J.), 838, 369-84 PMID:22228022

Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, Krysiak K, Harris CC, Koboldt DC, Larson DE, McLellan MD, Dooling DJ, Abbott RM, Fulton RS, Schmidt H, Kalicki-Veizer J, O’Laughlin M, Grillot M, Baty J, Heath S, Frater JL, Nasim T, Link DC, Tomasson MH, Westervelt P, DiPersio JF, Mardis ER, Ley TJ, Wilson RK, & Walter MJ (2011). Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature genetics, 44 (1), 53-7 PMID: 22158538

Larson DE, Harris CC, Chen K, Koboldt DC, Abbott TE, Dooling DJ, Ley TJ, Mardis ER, Wilson RK, & Ding L. (2011). SomaticSniper: Identification of Somatic Point Mutations in Whole Genome Sequencing Data.Bioinformatics, Online : doi: 10.1093/bioinformatics/btr665

Cancer Genome Atlas Research Network (2011). Integrated genomic analyses of ovarian carcinoma. Nature, 474 (7353), 609-15 PMID:21720365

Marth GT, Yu F, Indap AR, Garimella K, et al & the 1000 Genomes Project (2011). The functional spectrum of low-frequency coding variation.Genome biology, 12 (9) PMID: 21917140

Ross JA, Koboldt DC, Staisch JE, Chamberlin HM, Gupta BP, Miller RD, Baird SE, & Haag ES (2011). Caenorhabditis briggsae recombinant inbred line genotypes reveal inter-strain incompatibility and the evolution of recombination. PLoS genetics, 7 (7) PMID: 21779179

Bowne SJ, Humphries MM, Sullivan LS, Kenna PF, Tam LC, Kiang AS, Campbell M, Weinstock GM, Koboldt DC, Ding L, Fulton RS, Sodergren EJ, et al (2011). A dominant mutation in RPE65 identified by whole-exome sequencing causes retinitis pigmentosa with choroidal involvement. European journal of human genetics : EJHG, 19 (10) PMID:21938004

Link DC, Schuettpelz LG, Shen D, Wang J, Walter MJ, Kulkarni S, Payton JE, Ivanovich J, Goodfellow PJ, Le Beau M, Koboldt DC, Dooling DJ, Fulton RS, et al (2011). Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA : the journal of the American Medical Association, 305 (15), 1568-76 PMID: 21505135

Ley T, Ding L, Walter M, McLellan M, Lamprecht T, Larson D, Kandoth C, Payton J, Baty J, Welch J, Harris C, Lichti C, Townsend R, Fulton R, Dooling D, Koboldt D, et al. (2010). DNMT3A Mutations in Acute Myeloid Leukemia
New England Journal of Medicine DOI: 10.1056/NEJMoa1005143

Ding L, Wendl MC, Koboldt DC, & Mardis ER (2010). Analysis of next-generation genomic data in cancer: accomplishments and challenges. Human Molecular Genetics, 19 (R2):R188-96. PMID:20843826

Sudmant PH, Kitzman JO, Antonacci F, Alkan C, Malig M, Tsalenko A, Sampas N, Bruhn L, Shendure J, 1000 Genomes Project, & Eichler EE (2010). Diversity of human copy number variation and multicopy genes. Science (New York, N.Y.), 330 (6004), 641-6 PMID: 21030649

The 1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing. Nature, 467(7319), 1061-1073 DOI: 10.1038/nature09534

Bowne SJ, Sullivan LS, Koboldt DC, Ding L, Fulton R, Abbott RM, Sodergren EJ, Birch DG, Wheaton DH, Heckenlively JR, Liu Q, Pierce EA, Weinstock GM, & Daiger SP (2010). Identification of Disease-Causing Mutations in Autosomal Dominant Retinitis Pigmentosa (adRP) Using Next-Generation DNA Sequencing. Investigative ophthalmology & visual science PMID: 20861475

Fehniger, T., Wylie, T., Germino, E., Leong, J., Magrini, V., Koul, S., Keppel, C., Schneider, S., Koboldt, D., Sullivan, R., Heinz, M., Crosby, S., Nagarajan, R., Ramsingh, G., Link, D., Ley, T., & Mardis, E. (2010). Next-generation sequencing identifies the natural killer cell microRNA transcriptome Genome Research DOI: 10.1101/gr.107995.110

Ramsingh G, Koboldt DC, Trissal M, Chiappinelli KB, Wylie T, Koul S, Chang LW, Nagarajan R, Fehniger TA, Goodfellow P, Magrini V, Wilson RK, Ding L, Ley TJ, Mardis ER, & Link DC (2010). Complete characterization of the microRNAome in a patient with acute myeloid leukemia. BloodPMID: 20876853

Koboldt DC, Ding L, Mardis ER & Wilson RK. (2010). Challenges of sequencing human genomes. Briefings in Bioinformatics DOI:10.1093/bib/bbq016

Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, Harris CC, McLellan MD, Fulton RS, Fulton LL, Abbott RM, Hoog J, Dooling DJ, Koboldt DC, et al. (2010). Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature, 464 (7291), 999-1005 PMID:20393555

Koboldt DC and Miller RD (2010). Identification of polymorphic markers for genetic mapping. Genomics: Essential Methods, In Press.

Koboldt DC, Staisch J, Thillainathan B, Haines K, Baird SE, Chamberlin HM, Haag ES, Miller RD, & Gupta BP (2010). A toolkit for rapid gene mapping in the nematode Caenorhabditis briggsae. BMC genomics, 11 (1) PMID: 20385026

Voora D, Koboldt DC, King CR, Lenzini PA, Eby CS, Porche-Sorbet R, Deych E, Crankshaw M, Milligan PE, McLeod HL, Patel SR, Cavallari LH, Ridker PM, Grice GR, Miller RD, & Gage BF (2010). A polymorphism in the VKORC1 regulator calumenin predicts higher warfarin dose requirements in African Americans. Clinical pharmacology and therapeutics, 87 (4), 445-51 PMID: 20200517

Zhang Q, Ding L, Larson DE, Koboldt DC, McLellan MD, Chen K, Shi X, Kraja A, et al (2009). CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data. Bioinformatics (Oxford, England) PMID: 20031968

Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD, Chen K, Koboldt DC, et al (2009). Recurring mutations found by sequencing an acute myeloid leukemia genome. The New England journal of medicine, 361(11), 1058-66 PMID: 19657110

Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, Mardis ER, Weinstock GM, Wilson RK, & Ding L (2009). VarScan: variant detection in massively parallel sequencing of individual and pooled samples.Bioinformatics (Oxford, England), 25 (17), 2283-5 PMID: 19542151

Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M, Cook L, Abbott R, Larson DE, Koboldt DC, et al (2008). DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature, 456 (7218), 66-72 PMID: 18987736

Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, et al (2008). Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 455 (7216), 1069-75 PMID: 18948947

Cancer Genome Atlas Research Network (2008). Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature, 455 (7216), 1061-8 PMID: 18772890

International HapMap Consortium (2007). A second generation human haplotype map of over 3.1 million SNPs. Nature, 449 (7164), 851-61 PMID: 17943122

Sabeti PC, Varilly P, Fry B, et al (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449 (7164), 913-8 PMID: 17943131

Hillier LW, Miller RD, Baird SE, Chinwalla A, Fulton LA, Koboldt DC, & Waterston RH (2007). Comparison of C. elegans and C. briggsaegenome sequences reveals extensive conservation of chromosome organization and synteny. PLoS biology, 5 (7) PMID: 17608563

Stanley SL Jr, Frey SE, Taillon-Miller P, Guo J, Miller RD, Koboldt DC, Elashoff M, Christensen R, Saccone NL, & Belshe RB (2007). The immunogenetics of smallpox vaccination. The Journal of infectious diseases, 196 (2), 212-9 PMID: 17570108

Koboldt DC, Miller RD, & Kwok PY (2006). Distribution of human SNPs and its effect on high-throughput genotyping. Human mutation, 27(3), 249-54 PMID: 16425292

The International HapMap Consortium (2005). A haplotype map of the human genome. Nature, 437 (7063), 1299-1320 PMID: 16255080

Miller RD, Phillips MS, et al (2005). High-density single-nucleotide polymorphism maps of the human genome. Genomics, 86 (2), 117-26 PMID: 15961272

Other Writing by Dan Koboldt

Dan Koboldt is also the author of Get Your Baby to Sleep, a resource to help new parents whose baby won’t sleep with advice on establishing healthy baby sleep habits and handling baby sleep problems. He contributes to The Best of Twins and In Search of Whitetails blogs as well.

How would you like to start your own blog? See this guide to building a blog or website in 20 minutes. It walks you through setting up a site with open-source WordPress software, which happens to be what runs Massgenomics.

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