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Abraham A. Palmer

Bio: Abraham A. Palmer is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Genome-wide association study & Quantitative trait locus. The author has an hindex of 54, co-authored 254 publications receiving 10674 citations. Previous affiliations of Abraham A. Palmer include Oregon Health & Science University & University of Copenhagen.


Papers
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Journal ArticleDOI
Gary A. Churchill, David C. Airey1, Hooman Allayee2, Joe M. Angel3, Alan D. Attie4, Jackson Beatty5, Willam D. Beavis6, John K. Belknap7, Beth Bennett8, Wade H. Berrettini9, André Bleich10, Molly A. Bogue, Karl W. Broman11, Kari J. Buck12, Edward S. Buckler13, Margit Burmeister14, Elissa J. Chesler15, James M. Cheverud16, Steven J. Clapcote17, Melloni N. Cook18, Roger D. Cox19, John C. Crabbe12, Wim E. Crusio20, Ariel Darvasi21, Christian F. Deschepper22, Rebecca W. Doerge23, Charles R. Farber24, Jiri Forejt25, Daniel Gaile26, Steven J. Garlow27, Hartmut Geiger28, Howard K. Gershenfeld29, Terry Gordon30, Jing Gu15, Weikuan Gu15, Gerald de Haan31, Nancy L. Hayes32, Craig Heller33, Heinz Himmelbauer34, Robert Hitzemann12, Kent W. Hunter35, Hui-Chen Hsu36, Fuad A. Iraqi37, Boris Ivandic38, Howard J. Jacob39, Ritsert C. Jansen31, Karl J. Jepsen40, Dabney K. Johnson41, Thomas E. Johnson8, Gerd Kempermann42, Christina Kendziorski4, Malak Kotb15, R. Frank Kooy43, Bastien Llamas22, Frank Lammert44, J. M. Lassalle45, Pedro R. Lowenstein5, Lu Lu15, Aldons J. Lusis5, Kenneth F. Manly15, Ralph S. Marcucio46, Doug Matthews18, Juan F. Medrano24, Darla R. Miller41, Guy Mittleman18, Beverly A. Mock35, Jeffrey S. Mogil47, Xavier Montagutelli48, Grant Morahan49, David G. Morris50, Richard Mott51, Joseph H. Nadeau52, Hiroki Nagase53, Richard S. Nowakowski32, Bruce F. O'Hara54, Alexander V. Osadchuk, Grier P. Page36, Beverly Paigen, Kenneth Paigen, Abraham A. Palmer, Huei Ju Pan, Leena Peltonen-Palotie55, Leena Peltonen-Palotie5, Jeremy L. Peirce15, Daniel Pomp56, Michal Pravenec25, Daniel R. Prows28, Zonghua Qi1, Roger H. Reeves11, John C. Roder17, Glenn D. Rosen57, Eric E. Schadt58, Leonard C. Schalkwyk59, Ze'ev Seltzer17, Kazuhiro Shimomura60, Siming Shou61, Mikko J. Sillanpää55, Linda D. Siracusa62, Hans-Willem Snoeck40, Jimmy L. Spearow24, Karen L. Svenson, Lisa M. Tarantino63, David W. Threadgill64, Linda A. Toth65, William Valdar51, Fernando Pardo-Manuel de Villena64, Craig H Warden24, Steve Whatley59, Robert W. Williams15, Tom Wiltshire63, Nengjun Yi36, Dabao Zhang66, Min Zhang13, Fei Zou64 
Vanderbilt University1, University of Southern California2, University of Texas MD Anderson Cancer Center3, University of Wisconsin-Madison4, University of California, Los Angeles5, National Center for Genome Resources6, Portland VA Medical Center7, University of Colorado Boulder8, University of Pennsylvania9, Hannover Medical School10, Johns Hopkins University11, Oregon Health & Science University12, Cornell University13, University of Michigan14, University of Tennessee Health Science Center15, Washington University in St. Louis16, University of Toronto17, University of Memphis18, Medical Research Council19, University of Massachusetts Medical School20, Hebrew University of Jerusalem21, Université de Montréal22, Purdue University23, University of California, Davis24, Academy of Sciences of the Czech Republic25, University at Buffalo26, Emory University27, University of Cincinnati28, University of Texas Southwestern Medical Center29, New York University30, University of Groningen31, Rutgers University32, Stanford University33, Max Planck Society34, National Institutes of Health35, University of Alabama at Birmingham36, International Livestock Research Institute37, Heidelberg University38, Medical College of Wisconsin39, Icahn School of Medicine at Mount Sinai40, Oak Ridge National Laboratory41, Charité42, University of Antwerp43, RWTH Aachen University44, Paul Sabatier University45, University of California, San Francisco46, McGill University47, Pasteur Institute48, University of Western Australia49, Yale University50, University of Oxford51, Case Western Reserve University52, Roswell Park Cancer Institute53, University of Kentucky54, University of Helsinki55, University of Nebraska–Lincoln56, Harvard University57, Merck & Co.58, King's College London59, Northwestern University60, Shriners Hospitals for Children61, Thomas Jefferson University62, Novartis63, University of North Carolina at Chapel Hill64, Southern Illinois University Carbondale65, University of Rochester66
TL;DR: The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way the authors approach human health and disease.
Abstract: The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.

1,040 citations

Journal ArticleDOI
TL;DR: The largest genome-wide association study to date of DSM-IV-diagnosed AD found loci associated with AD and characterized the relationship between AD and other psychiatric and behavioral outcomes, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.
Abstract: Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case-control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 × 10-13) and African ancestries (rs2066702; P = 2.2 × 10-9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit-hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.

434 citations

Journal ArticleDOI
01 Feb 2012-Genetics
TL;DR: Analytical methods for genetic mapping using the JAX Diversity Outbred population are described and the power and high mapping resolution achieved with this population are demonstrated by mapping a serum cholesterol trait to a 2-Mb region on chromosome 3 containing only 11 genes.
Abstract: The JAX Diversity Outbred population is a new mouse resource derived from partially inbred Collaborative Cross strains and maintained by randomized outcrossing. As such, it segregates the same allelic variants as the Collaborative Cross but embeds these in a distinct population architecture in which each animal has a high degree of heterozygosity and carries a unique combination of alleles. Phenotypic diversity is striking and often divergent from phenotypes seen in the founder strains of the Collaborative Cross. Allele frequencies and recombination density in early generations of Diversity Outbred mice are consistent with expectations based on simulations of the mating design. We describe analytical methods for genetic mapping using this resource and demonstrate the power and high mapping resolution achieved with this population by mapping a serum cholesterol trait to a 2-Mb region on chromosome 3 containing only 11 genes. Analysis of the estimated allele effects in conjunction with complete genome sequence data of the founder strains reduced the pool of candidate polymorphisms to seven SNPs, five of which are located in an intergenic region upstream of the Foxo1 gene.

428 citations

Journal ArticleDOI
Oduola Abiola1, Joe M. Angel2, Philip Avner3, Alexander A. Bachmanov4, John K. Belknap5, Beth Bennett6, Elizabeth P. Blankenhorn7, David A. Blizard8, Valerie J. Bolivar9, Gudrun A. Brockmann10, Kari J. Buck5, Jean Francois Bureau3, William L. Casley11, Elissa J. Chesler12, James M. Cheverud13, Gary A. Churchill, Melloni N. Cook14, John C. Crabbe5, Wim E. Crusio15, Ariel Darvasi16, Gerald de Haan17, Peter Demant18, Rebecca W. Doerge19, Rosemary W. Elliott18, Charles R. Farber20, Lorraine Flaherty9, Jonathan Flint21, Howard K. Gershenfeld22, John P. Gibson23, Jing Gu12, Weikuan Gu12, Heinz Himmelbauer24, Robert Hitzemann5, Hui-Chen Hsu25, Kent W. Hunter26, Fuad A. Iraqi23, Ritsert C. Jansen17, Thomas E. Johnson6, Byron C. Jones8, Gerd Kempermann27, Frank Lammert28, Lu Lu12, Kenneth F. Manly18, Douglas B. Matthews14, Juan F. Medrano20, Margarete Mehrabian29, Guy Mittleman14, Beverly A. Mock26, Jeffrey S. Mogil30, Xavier Montagutelli3, Grant Morahan31, John D. Mountz25, Hiroki Nagase18, Richard S. Nowakowski32, Bruce F. O'Hara33, Alexander V. Osadchuk, Beverly Paigen, Abraham A. Palmer34, Jeremy L. Peirce35, Daniel Pomp36, Michael Rosemann, Glenn D. Rosen37, Leonard C. Schalkwyk1, Ze'ev Seltzer38, Stephen H. Settle39, Kazuhiro Shimomura40, Siming Shou41, James M. Sikela42, Linda D. Siracusa43, Jimmy L. Spearow20, Cory Teuscher44, David W. Threadgill45, Linda A. Toth46, A. A. Toye47, Csaba Vadasz48, Gary Van Zant49, Edward K. Wakeland22, Robert W. Williams12, Huang-Ge Zhang25, Fei Zou45 
TL;DR: This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits.
Abstract: This white paper by eighty members of the Complex Trait Consortium presents a community's view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

404 citations

Journal ArticleDOI
Richard Karlsson Linnér1, Richard Karlsson Linnér2, Pietro Biroli3, Edward Kong4, S. Fleur W. Meddens1, S. Fleur W. Meddens2, Robbee Wedow, Mark Alan Fontana5, Mark Alan Fontana6, Maël Lebreton7, Stephen P. Tino8, Abdel Abdellaoui2, Anke R. Hammerschlag2, Michel G. Nivard2, Aysu Okbay2, Cornelius A. Rietveld1, Pascal Timshel9, Pascal Timshel10, Maciej Trzaskowski11, Ronald de Vlaming2, Ronald de Vlaming1, Christian L. Zund3, Yanchun Bao12, Laura Buzdugan3, Laura Buzdugan13, Ann H. Caplin, Chia-Yen Chen4, Chia-Yen Chen14, Peter Eibich15, Peter Eibich16, Peter Eibich17, Pierre Fontanillas, Juan R. González18, Peter K. Joshi19, Ville Karhunen20, Aaron Kleinman, Remy Z. Levin21, Christina M. Lill22, Gerardus A. Meddens, Gerard Muntané23, Gerard Muntané18, Sandra Sanchez-Roige21, Frank J. A. van Rooij1, Erdogan Taskesen2, Yang Wu11, Futao Zhang11, Adam Auton, Jason D. Boardman24, David W. Clark19, Andrew Conlin20, Conor C. Dolan2, Urs Fischbacher25, Patrick J. F. Groenen1, Kathleen Mullan Harris26, Gregor Hasler27, Albert Hofman1, Albert Hofman4, Mohammad Arfan Ikram1, Sonia Jain21, Robert Karlsson28, Ronald C. Kessler4, Maarten Kooyman, James MacKillop29, James MacKillop30, Minna Männikkö20, Carlos Morcillo-Suarez18, Matthew B. McQueen24, Klaus M. Schmidt31, Melissa C. Smart12, Matthias Sutter32, Matthias Sutter33, Matthias Sutter17, Roy Thurik1, André G. Uitterlinden1, Jon White34, Harriet de Wit35, Jian Yang11, Lars Bertram36, Lars Bertram22, Dorret I. Boomsma2, Tõnu Esko37, Ernst Fehr3, David A. Hinds, Magnus Johannesson38, Meena Kumari12, David Laibson4, Patrik K. E. Magnusson28, Michelle N. Meyer39, Arcadi Navarro18, Arcadi Navarro40, Abraham A. Palmer21, Tune H. Pers10, Tune H. Pers9, Danielle Posthuma2, Daniel Schunk41, Murray B. Stein21, Rauli Svento20, Henning Tiemeier1, Paul R. H. J. Timmers19, Patrick Turley42, Patrick Turley14, Patrick Turley4, Robert J. Ursano43, Gert G. Wagner16, Gert G. Wagner17, James F. Wilson19, James F. Wilson44, Jacob Gratten11, Jacob Gratten45, James J. Lee46, David Cesarini47, Daniel J. Benjamin42, Daniel J. Benjamin48, Philipp Koellinger2, Philipp Koellinger16, Jonathan P. Beauchamp8 
TL;DR: This paper found evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of their other GWAS, and general risk-tolerance is genetically correlated with a range of risky behaviors.
Abstract: Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

395 citations


Cited by
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TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

Book
01 Jan 2009

8,216 citations

Journal ArticleDOI
TL;DR: TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure and allows for linkage disequilibrium statistics to be calculated and visualized graphically.
Abstract: Summary: Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable, user manual, example data sets and tutorial document are freely available at http://www. maizegenetics.net/tassel. The source code for TASSEL can be found at http://sourceforge.net/projects/tassel.

5,460 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations