Author: "Peter M, Visscher" - Searchworks@Jio Institute Digital Library Search Results

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634 results on '"Peter M, Visscher"'

601. Bodyweight QTL on mouse chromosomes 4 and 11 by selective genotyping: regression v. maximum likelihood

Author: Michael F. Buckley, Beben Benyamin, Chris Moran, I. C. A. Martin, Peter C. Thomson, Peter M. Visscher, and Carol C. Cheung
Subjects: Genetics, Positional cloning, business.industry, Maximum likelihood, food and beverages, Biology, Quantitative trait locus, Confidence interval, Regression, Biotechnology, Selective genotyping, Genotype, Microsatellite, General Agricultural and Biological Sciences, business
Abstract: Two quantitative trait loci (QTL) for mouse bodyweight have previously been reported on mouse chromosomes (MMU) 4 and 11 from crosses of a highly fecund and large mouse strain, Inbred Quackenbush-Swiss 5 (QSi5) and C57Bl/6J. QSi5 has now been crossed with CBA/CaH to produce 1128 F2 mice to confirm the existence and effect of these QTL. In total, 226 mice from the upper and lower deciles for bodyweight were genotyped using 12 microsatellite markers covering MMU4 and MMU11. Regression and maximum likelihood based interval mapping by either including all mice (ungenotyped mice were treated as having missing genotypes) or including only selectively genotyped mice in the analyses were used to estimate the positions and effects of the QTL. The results confirmed the existence and effects of both QTL. Although all methods estimated the same QTL positions, the QTL effects were overestimated compared with the estimates using a suggested method (maximum likelihood by including all mice in the analysis). However, the overestimated QTL effects could be mathematically corrected. Since the confidence intervals of both QTL are still too large for positional cloning, an advanced intercross line is being bred for finely mapping these bodyweight QTL.
Published: 2007
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602. Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates

Author: Grant W. Montgomery, Peter M. Visscher, and Stuart MacGregor
Subjects: Structured analysis, Scale (ratio), Genotype, Endometriosis, Genomics, Biology, Polymorphism, Single Nucleotide, Gene Frequency, Replication (statistics), Genetics, Test statistic, Humans, Genotyping, Oligonucleotide Array Sequence Analysis, General linear model, business.industry, Linear model, Pattern recognition, DNA, Case-Control Studies, Linear Models, Methods Online, Female, Artificial intelligence, business
Abstract: Array based DNA pooling techniques facilitate genome-wide scale genotyping of large samples. We describe a structured analysis method for pooled data using internal replication information in large scale genotyping sets. The method takes advantage of information from single nucleotide polymorphisms (SNPs) typed in parallel on a high density array to construct a test statistic with desirable statistical properties. We utilize a general linear model to appropriately account for the structured multiple measurements available with array data. The method does not require the use of additional arrays for the estimation of unequal hybridization rates and hence scales readily to accommodate arrays with several hundred thousand SNPs. Tests for differences between cases and controls can be conducted with very few arrays. We demonstrate the method on 384 endometriosis cases and controls, typed using Affymetrix Genechip(c) HindIII 50 K arrays. For a subset of this data there were accurate measures of hybridization rates available. Assuming equal hybridization rates is shown to have a negligible effect upon the results. With a total of only six arrays, the method extracted one-third of the information (in terms of equivalent sample size) available with individual genotyping (requiring 768 arrays). With 20 arrays (10 for cases, 10 for controls), over half of the information could be extracted from this sample.
Published: 2006

603. Correction for Thomas et al., 2004, Genet. Epidemiol. 26:116-124

Author: Stuart C. Thomas, David J. Porteous, and Peter M. Visscher
Subjects: Epidemiology, Genetics (clinical)
Published: 2005
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604. Assumption-free Estimation of Heritability from Genome-wide Identity-by-descent Sharing between Fullsibs

Author: Peter M. Visscher, Sarah E. Medland, Manuel A.R. Ferreira, Katherine I. Morley, Gu Zhu, Belinda K. Cornes, Grant W. Montgomery, and Nicholas G. Martin
Subjects: Cancer Research, Genetics, Molecular Biology, Genetics (clinical), Ecology, Evolution, Behavior and Systematics
Published: 2005
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605. Combined genome scans for body stature in 6602 European twins: evidence for common Caucasian loci

Author: Markus Perola, Sampo Sammalisto, Tero Hiekkalinna, Nick Martin, Peter M. Visscher, Grant W. Montgomery, Beben Benyamin, Jennifer R Harris, Dorret Boomsma, Gonneke Willemsen, Jouke-Jan Hottenga, Kaare Christensen, Kirsten Ohm Kyvik, Thorkild I. A. Sørensen, Nancy L. Pedersen, Patrik KE Magnusson, Tim D Spector, Elisabeth Widen, Karri Silventoinen, Jaakko Kaprio, Aarno Palotie, and Leena Peltonen
Subjects: Cancer Research, Genetics, Molecular Biology, Genetics (clinical), Ecology, Evolution, Behavior and Systematics
Published: 2005
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606. Is It in Your Genes? The Influence of Genes on Common Disorders and Diseases that Affect You and Your Family. P. R. R<scp>EILLY</scp>. Cold Spring Harbor Laboratory Press. 2004. 304 pages. ISBN 0 87969 721 0. Price $19.95 (paperback). ISBN 0 87969 719 9. Price $29.95 (hardback)

Author: Peter M. Visscher
Subjects: geography, geography.geographical_feature_category, Spring (hydrology), Genetics, Zoology, General Medicine, Biology, Affect (psychology)
Published: 2004
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607. Mapping genes for growth rate and fatness in a Large White x Meishan F2 pig population

Author: Chris Haley, Peter M. Visscher, Alan Archibald, and G. A. Walling
Subjects: F2 population, Zoology, Large white, Meishan pig, General Medicine, Growth rate, Biology, biology.organism_classification, Gene
Abstract: DNA-based markers and genetic maps of major livestock species have been developed in the last few years. These, together with experimental populations and appropriate statistical methods, provide the tools to dissect causes of economically important genetic variation in livestock. A cross between genetically diverse lines is a powerful experimental design for such a study. In an F2 or backcross population from such a cross, markers are likely to be highly informative and the genes that control the differences between the lines will be segregating. We have been developing such a resource population based on a cross between the British Large White and Chinese Meishan pigs. These breeds differ for many traits, with the Meishan being inferior for growth rate and fatness, but superior for reproduction traits. Here we report the mapping of major genetic effects on growth rate and fatness in our cross.
Published: 1998
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608. Bodyweight QTL on mouse chromosomes 4 and 11 by selective genotyping: regression v. maximum likelihood.

Author: Beben Benyamin, Ian C. A. Martin, Carol C. Cheung, Michael F. Buckley, Peter C. Thomson, Peter M. Visscher, and Chris Moran
Abstract: Two quantitative trait loci (QTL) for mouse bodyweight have previously been reported on mouse chromosomes (MMU) 4 and 11 from crosses of a highly fecund and large mouse strain, Inbred Quackenbush-Swiss 5 (QSi5) and C57Bl/6J. QSi5 has now been crossed with CBA/CaH to produce 1128 F2 mice to confirm the existence and effect of these QTL. In total, 226 mice from the upper and lower deciles for bodyweight were genotyped using 12 microsatellite markers covering MMU4 and MMU11. Regression and maximum likelihood based interval mapping by either including all mice (ungenotyped mice were treated as having missing genotypes) or including only selectively genotyped mice in the analyses were used to estimate the positions and effects of the QTL. The results confirmed the existence and effects of both QTL. Although all methods estimated the same QTL positions, the QTL effects were overestimated compared with the estimates using a suggested method (maximum likelihood by including all mice in the analysis). However, the overestimated QTL effects could be mathematically corrected. Since the confidence intervals of both QTL are still too large for positional cloning, an advanced intercross line is being bred for finely mapping these bodyweight QTL. [ABSTRACT FROM AUTHOR]
Published: 2007
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609. Power of direct vs. indirect haplotyping in association studies.

Author: Stuart Thomas, David Porteous, and Peter M. Visscher
Published: 2004
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610. Simple method to analyze SNP-based association studies using DNA pools.

Author: Peter M. Visscher and Stéphanie Le Hellard
Published: 2003
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611. Molecular genetic contributions to socioeconomic status and intelligence

Author: Gail Davies, David J. Porteous, Peter M. Visscher, Blair H. Smith, Michelle Luciano, Archie Campbell, Nicholas D. Hastie, Lynne J. Hocking, Sandosh Padmanabhan, Caroline Hayward, Alan F. Wright, Riccardo E. Marioni, Ian J. Deary, David C. Liewald, and Shona M. Kerr
Subjects: 0303 health sciences, Intelligence, Social environment, Cognition, Single-nucleotide polymorphism, Experimental and Cognitive Psychology, Heritability, Health outcomes, Article, Education, 03 medical and health sciences, 0302 clinical medicine, Arts and Humanities (miscellaneous), Socioeconomic status, Developmental and Educational Psychology, Trait, Genetics, SNP, Psychology, Generation Scotland, 030217 neurology & neurosurgery, 030304 developmental biology, Demography
Abstract: Education, socioeconomic status, and intelligence are commonly used as predictors of health outcomes, social environment, and mortality. Education and socioeconomic status are typically viewed as environmental variables although both correlate with intelligence, which has a substantial genetic basis. Using data from 6815 unrelated subjects from the Generation Scotland study, we examined the genetic contributions to these variables and their genetic correlations. Subjects underwent genome-wide testing for common single nucleotide polymorphisms (SNPs). DNA-derived heritability estimates and genetic correlations were calculated using the ‘Genome-wide Complex Trait Analyses’ (GCTA) procedures. 21% of the variation in education, 18% of the variation in socioeconomic status, and 29% of the variation in general cognitive ability was explained by variation in common SNPs (SEs ~ 5%). The SNP-based genetic correlations of education and socioeconomic status with general intelligence were 0.95 (SE 0.13) and 0.26 (0.16), respectively. There are genetic contributions to intelligence and education with near-complete overlap between common additive SNP effects on these traits (genetic correlation ~ 1). Genetic influences on socioeconomic status are also associated with the genetic foundations of intelligence. The results are also compatible with substantial environmental contributions to socioeconomic status., Highlights • Generation Scotland is a large family-based cohort of ~ 24,000 people. • We investigate the genetic influences on education, SES, and intelligence. • Both DNA-based (subset of ~ 6500) and pedigree-based analyses are used. • Genetic effects on SES and education are linked to the genetic basis of intelligence. • There are also substantial environmental effects on all three traits.
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612. Dominance Genetic Variation Contributes Little to the Missing Heritability for Human Complex Traits

Author: Jian Yang, Lili Milani, Harold Snieder, Michael E. Goddard, Reedik Mägi, Peter M. Visscher, Gibran Hemani, William G. Hill, Andrew Bakshi, Bruce S. Weir, Ilja M. Nolte, Zhihong Zhu, Sang Hong Lee, Anna A. E. Vinkhuyzen, Andres Metspalu, Tõnu Esko, Jana V. van Vliet-Ostaptchouk, Zhu, Zhihong, Bakshi, Andrew, Vinkhuyzen, Anna AE, Hemani, Gibran, Lee, Sang Hong, Nolte, Ilja M, van Vliet-Ostaptchouk, Jana V, Snieder, Harold, Esko, Tonu, Milani, Lili, Maegi, Reedik, Metspalu, Andres, Hill, William G, Weir, Bruce S, Goddard, Michael E, Visscher, Peter M, Yang, Jian, LifeLines Cohort Study, Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), and Center for Liver, Digestive and Metabolic Diseases (CLDM)
Subjects: Male, SNP, Genome-wide association study, ‘‘missing heritability’’, Quantitative trait locus, Biology, heritability, von Willebrand factor, δSNP2, Genetic correlation, Polymorphism, Single Nucleotide, White People, Article, Cohort Studies, FOUNDER POPULATION, 03 medical and health sciences, 0302 clinical medicine, Quantitative Trait, Heritable, Missing heritability problem, Genetic variation, Genetics, Humans, Genetics(clinical), 10. No inequality, COMMON, Genetics (clinical), 030304 developmental biology, Genetic association, Genetics & Heredity, 0303 health sciences, Models, Genetic, QUANTITATIVE TRAITS, phenotypic variation, ASSOCIATION, Heritability, Genetic architecture, Phenotype, Evolutionary biology, Evaluation Studies as Topic, DISEASES, Linear Models, Female, non-additive genetic variation, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: For human complex traits, non-additive genetic variation has been invoked to explain "missing heritability,'' but its discovery is often neglected in genome-wide association studies. Here we propose a method of using SNP data to partition and estimate the proportion of phenotypic variance attributed to additive and dominance genetic variation at all SNPs (h(SNP)(2) and delta(2)(SNP)) in unrelated individuals based on an orthogonal model where the estimate of h(SNP)(2) is independent of that of delta(2)(SNP). With this method, we analyzed 79 quantitative traits in 6,715 unrelated European Americans. The estimate of delta(2)(SNP) averaged across all the 79 quantitative traits was 0.03, approximately a fifth of that for additive variation (average h(SNP)(2) = 0.15). There were a few traits that showed substantial estimates of delta(2)(SNP), none of which were replicated in a larger sample of 11,965 individuals. We further performed genome-wide association analyses of the 79 quantitative traits and detected SNPs with genome-wide significant dominance effects only at the ABO locus for factor VIII and von Willebrand factor. All these results suggest that dominance variation at common SNPs explains only a small fraction of phenotypic variation for human complex traits and contributes little to the missing narrow-sense heritability problem.
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613. Common Variants of Large Effect in F12, KNG1, and HRG Are Associated with Activated Partial Thromboplastin Time

Author: John M. Starr, David J. Porteous, Albert Tenesa, Alan J. Gow, Gordon D.O. Lowe, Peter M. Visscher, Kevin A. McGhee, Gail Davies, Ian J. Deary, Sarah E. Harris, David C. Liewald, Michelle Luciano, and Lorna M. Houlihan
Subjects: Male, medicine.medical_specialty, Population, Single-nucleotide polymorphism, Genome-wide association study, 030204 cardiovascular system & hematology, Biology, Polymorphism, Single Nucleotide, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Blood Coagulation Disorders, Inherited, Internal medicine, Report, Genetics, medicine, Thromboplastin, Humans, Genetics(clinical), education, Factor XI, Genetics (clinical), Coagulation Disorder, 030304 developmental biology, Aged, 0303 health sciences, Factor XII, education.field_of_study, medicine.diagnostic_test, Kininogens, Proteins, Thrombosis, 3. Good health, Endocrinology, Phenotype, Case-Control Studies, Immunology, Female, Partial Thromboplastin Time, Partial thromboplastin time, Genome-Wide Association Study, circulatory and respiratory physiology
Abstract: Activated partial thromboplastin time (aPTT) is associated with risk of thrombosis and coagulation disorders. We conducted a genome-wide association study for aPTT and identified significant associations with SNPs in three coagulation cascade genes, F12 (rs2731672, combined p = 2.16 x 10(-30)), KNG1 (rs710446, combined p = 9.52 x 10(-22)), and HRG (rs9898, combined p = 1.34 x 10(-11)). These three SNPs explain approximately 18% of phenotypic variance in aPTT in the Lothian Birth Cohorts.
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614. On the mapping of QTL by regression of phenotype on marker-type

Author: Peter M. Visscher, Robin Thompson, and John C. Whittaker
Subjects: Genetics, Genetics & Heredity, Evolutionary Biology, Ecology, food and beverages, Computational biology, Quantitative trait locus, Biology, Phenotype, Regression, Family-based QTL mapping, Inclusive composite interval mapping, Association mapping, Genetics (clinical)
Abstract: We consider the properties of the regression of phenotype on marker-type in Fz and backcross populations. We show that this regression provides exactly the same information about the location and effect of QTL as conventional regression mapping. For certain QTL configurations this information is insufficient to map the QTL. Where the QTL can be mapped, the position and effect of QTL can be estimated directly from the coefficients of the regression of phenotype on marker-type. This requires much less computational effort than conventional regression mapping. Examples are given to illustrate the development of the theory.
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615. Is schizophrenia linked to chromosome 1q?

Author: Walter J. Muir, Peter M. Visscher, Kirsty Millar, David J. Porteous, Stuart MacGregor, Douglas Blackwood, and Sara Knott
Subjects: Male, Canada, Genotype, Genetic Linkage, Locus (genetics), Genes, Recessive, Biology, Article, Genetic linkage, Humans, Genetic Predisposition to Disease, Alleles, Lod score, Genetics, Multidisciplinary, Affected sibling, Genetic heterogeneity, Australia, Chromosome, United States, Pedigree, Europe, Multicenter study, Chromosomes, Human, Pair 1, Africa, Schizophrenia, Female, Lod Score, Microsatellite Repeats
Abstract: Reports of substantial evidence for genetic linkage of schizophrenia to chromosome 1q were evaluated by genotyping 16 DNA markers across 107 centimorgans of this chromosome in a multicenter sample of 779 informative schizophrenia pedigrees. No significant evidence was observed for such linkage, nor for heterogeneity in allele sharing among the eight individual samples. Separate analyses of European-origin families, recessive models of inheritance, and families with larger numbers of affected cases also failed to produce significant evidence for linkage. If schizophrenia susceptibility genes are present on chromosome 1q, their population-wide genetic effects are likely to be small.

616. A genome scan for quantitative trait loci in a wild population of red deer (Cervus elaphus)

Author: Jon Slate, Peter M. Visscher, Josephine M. Pemberton, M L Tate, Stuart MacGregor, and D Stevens
Subjects: Genetic Markers, Male, Genetic Linkage, Quantitative Trait Loci, Population, Animals, Wild, Quantitative trait locus, Biology, Family-based QTL mapping, Genetic variation, Genetics, Animals, education, education.field_of_study, Models, Genetic, Deer, Genetic Variation, Heritability, Genetic architecture, Pedigree, Genetics, Population, Genetic marker, Evolutionary biology, Linear Models, Trait, Female, Research Article
Abstract: Recent empirical evidence indicates that although fitness and fitness components tend to have low heritability in natural populations, they may nonetheless have relatively large components of additive genetic variance. The molecular basis of additive genetic variation has been investigated in model organisms but never in the wild. In this article we describe an attempt to map quantitative trait loci (QTL) for birth weight (a trait positively associated with overall fitness) in an unmanipulated, wild population of red deer (Cervus elaphus). Two approaches were used: interval mapping by linear regression within half-sib families and a variance components analysis of a six-generation pedigree of >350 animals. Evidence for segregating QTL was found on three linkage groups, one of which was significant at the genome-wide suggestive linkage threshold. To our knowledge this is the first time that a QTL for any trait has been mapped in a wild mammal population. It is hoped that this study will stimulate further investigations of the genetic architecture of fitness traits in the wild.

617. The genetic architecture of economic and political preferences

Author: David Laibson, Christopher F. Chabris, Daniel J. Benjamin, Philipp Koellinger, Patrik K. E. Magnusson, David Cesarini, Dalton Conley, Magnus Johannesson, Peter M. Visscher, Christopher T. Dawes, Matthijs J. H. M. van der Loos, Applied Economics, Erasmus School of Health Policy & Management, Economics, and Amsterdam Neuroscience - Complex Trait Genetics
Subjects: Male, Genopolitics, Poison control, Social Sciences, Genome-wide association study, Single-nucleotide polymorphism, Genetics, Behavioral, Biology, Genetic correlation, Choice Behavior, Polymorphism, Single Nucleotide, 03 medical and health sciences, 0302 clinical medicine, Twins, Dizygotic, Humans, Registries, Genetic Association Studies, 030304 developmental biology, Genetic association, Genetics, Sweden, 0303 health sciences, GCTA, Multidisciplinary, Genoeconomics, Economics, Behavioral, Politics, Twins, Monozygotic, Heritability, Genetic architecture, Evolutionary biology, Female, 030217 neurology & neurosurgery, Genome-Wide Association Study, Personality
Abstract: Preferences are fundamental building blocks in all models of economic and political behavior. We study a new sample of comprehensively genotyped subjects with data on economic and political preferences and educational attainment. We use dense single nucleotide polymorphism (SNP) data to estimate the proportion of variation in these traits explained by common SNPs and to conduct genome-wide association study (GWAS) and prediction analyses. The pattern of results is consistent with findings for other complex traits. First, the estimated fraction of phenotypic variation that could, in principle, be explained by dense SNP arrays is around one-half of the narrow heritability estimated using twin and family samples. The molecular-genetic–based heritability estimates, therefore, partially corroborate evidence of significant heritability from behavior genetic studies. Second, our analyses suggest that these traits have a polygenic architecture, with the heritable variation explained by many genes with small effects. Our results suggest that most published genetic association studies with economic and political traits are dramatically underpowered, which implies a high false discovery rate. These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPs.
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618. A parsimonious model for mass-univariate vertex-wise analysis

Author: Peter M. Visscher, Futao Zhang, Jian Yang, Kathryn E. Kemper, Julia Sidorenko, Olivier Colliot, Naomi R. Wray, Baptiste Couvy-Duchesne, Couvy-Duchesne, Baptiste, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Experimental and theoretical investigation of the multi-scale emergence of parametric working memory in prefrontal cortex recurrent networks - - HIPLAY72016 - ANR-16-NEUC-0001 - CRCNS - VALID, Data-driven models for Progression Of Neurological Disease - EuroPOND - - H2020 Pilier Societal Challenges2016-01-01 - 2019-12-31 - 666992 - VALID, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Queensland [Brisbane], Westlake University [Zhejiang], This research was supported by the Australian National Health and Medical Research Council (1078037, 1078901, 1113400, 1161356 and 1107258), the Australian Research Council (FT180100186 and FL180100072), the Sylvia & Charles Viertel Charitable Foundation, the program 'Investissements d’avenir' ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6) and reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute), the European Union H2020 program (project EuroPOND, grant number 666992, the joint NSF/NIH/ANR program 'Collaborative Research in Computational Neuroscience' (project HIPLAY7, grant number ANR-16-NEUC-0001-01),the ICM Big Brain Theory Program (project DYNAMO, project PredictICD), and the Abeona Foundation (project Brain@Scale). The OASIS3 data were provided by Principal Investigators: T. Benzinger, D. Marcus, J. Morris supported by NIH grants: P50AG00561, P30NS09857781, P01AG026276, P01AG003991, R01AG043434, UL1TR000448, R01EB009352., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), ANR-16-NEUC-0001,HIPLAY7,Experimental and theoretical investigation of the multi-scale emergence of parametric working memory in prefrontal cortex recurrent networks(2016), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)
Subjects: General linear model, False discovery rate, 0303 health sciences, [SCCO.NEUR]Cognitive science/Neuroscience, Confounding, [SCCO.NEUR] Cognitive science/Neuroscience, Univariate, Generalized linear mixed model, 03 medical and health sciences, symbols.namesake, 0302 clinical medicine, Bonferroni correction, Statistics, symbols, False positive paradox, False positive rate, 030217 neurology & neurosurgery, 030304 developmental biology, Mathematics
Abstract: 2.AbstractCovariance between grey-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g. age, head size, scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertex-wise analyses. We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise grey matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. We showed that when performed on a large sample (N=8,662, UK Biobank), GLMs yielded large spatial clusters of significant vertices and greatly inflated false positive rate (Family Wise Error Rate: FWER=1, cluster false discovery rate: FDR>0.6). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate (yet FWER>5% after Bonferroni correction) but at a cost of increased computation. In practice, the parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence and smoking status) and LMM yielded fewer and more localised associations. We identified 19 significant clusters displaying small associations with age, sex and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes.

619. Inter-individual cortical and subcortical similarity is associated with differences in psychiatric, cognition and socio-economic traits

Author: Baptiste Couvy-Duchesne, Lachlan T. Strike, Jian Yang, Peter M. Visscher, Margaret J. Wright, Futao Zhang, and Naomi R. Wray
Subjects: Pharmacology, medicine.medical_specialty, Human Connectome Project, Cognition, Best linear unbiased prediction, Psychiatry and Mental health, Neurology, Neuroimaging, medicine, Trait, Anxiety, Pharmacology (medical), Neurology (clinical), medicine.symptom, Psychiatry, Association (psychology), Psychology, Biological Psychiatry, Genetic association
Abstract: Background Many neuroimaging studies have attempted to identify brain features associated with psychiatric traits, cognition or socio-economic status, however, few were large scale studies and even fewer replicated their results. If small samples might explain the lack of success of association studies an important question is how much variation in neuropsychiatric phenotypes is associated with information held in brain image data, and whether this limits our ability to detect brain markers. Methods Here, we extend previous work by Sabuncu et al. (2016) and calculate Brain Relatedness Matrices (BRM) from vertex-wise cortical and subcortical measurement extracted from T1 MRI images (FreeSurfer processing). We used a new versatile mixed linear model software that accommodates multiple types of data to construct relatedness matrices, allowing to estimate the variance associated with information held in brain images in a wide range of traits collected as part of the Human Connectome Project (N=1,110, mean age 28, 54% females). Results We showed that brain cortical and subcortical differences could account for 66% of the variance in IQ (p=5.1E-6), 28% of depression score (p=0.016), 32% of anxiety (p=3.1E-6), 36% of ADHD (p=9.8E-9), 45% of Childhood conduct disorder score (p=1.6E-10) but also 27% of the variance in number of illicit drug used (p=6.1E-6) and 33% in the frequency of Marijuana use (p=1.4E-7). Furthermore, brain T1 individual similarities are associated with 94% of variation in age (p Discussion We anticipate that the availability of larger imaging samples (HCP or UKB) will allow for more exploratory analyses of brain markers and we introduce Brain Wide Association Study (BWAS), as a strategy to test the association of each vertex with a trait of interest in a mixed model framework. Such an approach does not require using anatomical atlases and allows detecting localised markers of the trait or disease. Finally, we detail the consequences of these results: prioritisation of brain modality or structure in association analysis as well as the calculation of brain scores for psychiatric or cognitive traits using Best Linear Unbiased Predictor (BLUP) estimates of the BWAS, which may be used to test new hypotheses.

620. A nonparametric bootstrap method for testing close linkage vs. pleiotropy of coincident quantitative trait loci

Author: Andrei Semikhodskii, Chris Haley, Steve Quarrie, Peter M. Visscher, and C. Lebreton
Subjects: Linkage (software), Genetics, Genetic Markers, education.field_of_study, Models, Genetic, Genetic Linkage, Sulfates, Population, Nonparametric statistics, Biology, Quantitative trait locus, Sodium Chloride, Statistics, Nonparametric, Quantitative Trait, Heritable, Pleiotropy, Computer Simulation, Null hypothesis, education, Selection (genetic algorithm), Triticum, Type I and type II errors, Research Article
Abstract: A novel method using the nonparametric bootstrap is proposed for testing whether a quantitative trait locus (QTL) at one chromosomal position could explain effects on two separate traits. If the single-QTL hypothesis is accepted, pleiotropy could explain the effect on two traits. If it is rejected, then the effects on two traits are due to linked QTLs. The method can be used in conjunction with several QTL mapping methods as long as they provide a straightforward estimate of the number of QTLs detectable from the data set. A selection step was introduced in the bootstrap procedure to reduce the conservativeness of the test of close linkage vs. pleiotropy, so that the erroneous rejection of the null hypothesis of pleiotropy only happens at a frequency equal to the nominal type I error risk specified by the user. The approach was assessed using computer simulations and proved to be relatively unbiased and robust over the range of genetic situations tested. An example of its application on a real data set from a saline stress experiment performed on a recombinant population of wheat (Triticum aestivum L.) doubled haploid lines is also provided.

621. Combined analyses of data from quantitative trait loci mapping studies: Chromosome 4 effects on porcine growth and fatness

Author: L. Wang, Alan Archibald, Grant A. Walling, Chris Haley, Denis Milan, Leif Andersson, Dirk J. de Koning, Gerhard Moser, Martien A. M. Groenen, Peter M. Visscher, Max F. Rothschild, Stanislav Čepica, Hermann Geldermann, Jean Pierre Bidanel, Station de Génétique Quantitative et Appliquée (SGQA), Institut National de la Recherche Agronomique (INRA), Laboratoire de Génétique Cellulaire (LGC), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées
Subjects: Swine, QTL, Birth weight, [SDV]Life Sciences [q-bio], Population, Biology, Quantitative trait locus, Chromosomes, 03 medical and health sciences, ANALYSE CONJOINTE, Quantitative Trait, Heritable, Species Specificity, Wild boar, Gene mapping, Genetic linkage, biology.animal, Confidence Intervals, Genetics, Animals, Birth Weight, PIGS, education, Crosses, Genetic, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, 2. Zero hunger, CARCASSES, 0303 health sciences, education.field_of_study, Models, Genetic, 0402 animal and dairy science, Chromosome Mapping, food and beverages, 04 agricultural and veterinary sciences, JOINT ANALYSIS, 040201 dairy & animal science, Breed, Genetics, Population, Chromosome 4, Adipose Tissue, GROWTH, Research Article, Statistical Distributions
Abstract: For many species several similar QTL mapping populations have been produced and analyzed independently. Joint analysis of such data could be used to increase power to detect QTL and evaluate population differences. In this study, data were collated on almost 3000 pigs from seven different F2 crosses between Western commercial breeds and either the European wild boar or the Chinese Meishan breed. Genotypes were available for 31 markers on chromosome 4 (on average 8.3 markers per population). Data from three traits common to all populations (birth weight, mean backfat depth at slaughter or end of test, and growth rate from birth to slaughter or end of test) were analyzed for individual populations and jointly. A QTL influencing birth weight was detected in one individual population and in the combined data, with no significant interaction of the QTL effect with population. A QTL affecting backfat that had a significantly greater effect in wild boar than in Meishan crosses was detected. Some evidence for a QTL affecting growth rate was detected in all populations, with no significant differences between populations. This study is the largest F2 QTL analysis achieved in a livestock species and demonstrates the potential of joint analysis.

622. Contrasting models for lactation curve analysis

Author: Peter M. Visscher, Florence Jaffrézic, I. M. S. White, Robin Thompson, ProdInra, Migration, Station de Génétique Quantitative et Appliquée (SGQA), and Institut National de la Recherche Agronomique (INRA)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Process modeling, COMPARAISON ENTRE MODELES, Residual, Models, Biological, 03 medical and health sciences, random regression, Quadratic equation, MODELE ALEATOIRE, character process models, Quartic function, Statistics, Genetics, Econometrics, Animals, Lactation, Equivalence (measure theory), orthogonal polynomials, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Mathematics, 0303 health sciences, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, Models, Statistical, Models, Genetic, Model selection, 0402 animal and dairy science, Statistical model, 04 agricultural and veterinary sciences, GENETIQUE, 040201 dairy & animal science, Regression, Regression Analysis, Animal Science and Zoology, Cattle, Female, Food Science, lactation curve
Abstract: Several statistical models have been proposed for the genetic evaluation of production traits in dairy cattle based on test-day records. Three main approaches have been put forward in the literature: random regression, orthogonal polynomials, and, more recently, character process models. The aim of this paper is to show how these different approaches are related, to compare their performance for the genetic analysis of lactation curves, and to assess equivalence between sire and animal models for repeated measures analyses. It was found that, with an animal model, a character process model with 11 parameters performed better, regarding the likelihood criterion, than a quartic random regression model (with 31 parameters). However, although the likelihood was higher, the genetic variance was very different with the character process model from the unstructured model, which raises important issues concerning model selection criteria. There are advantages in combining methodologies. A quadratic random regression model for the environmental part, combined with a character process model for the residual, performed better than the quartic random regression model and had fewer parameters. A character process structure allowing for a correlation pattern modeled the residual better than a simple quadratic variance, and had only one extra parameter.

623. Signatures of negative selection in the genetic architecture of human complex traits

Author: Julia Sidorenko, Luke R. Lloyd-Jones, Ronald de Vlaming, Greg Gibson, Jian Zeng, Tõnu Esko, Naomi R. Wray, Grant W. Montgomery, Andres Metspalu, Jonathan J. Powell, Peter M. Visscher, Chloe X. Yap, Loic Yengo, Allan F. McRae, Jian Yang, Matthew R. Robinson, Yeda Wu, Angli Xue, Economics, and Amsterdam Neuroscience - Complex Trait Genetics
Subjects: 0301 basic medicine, Genetics, Natural selection, Population genetics, Genome-wide association study, Quantitative trait locus, Heritability, Biology, Genetic architecture, Minor allele frequency, 03 medical and health sciences, Negative selection, 030104 developmental biology
Abstract: We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.
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624. Shared genetic control of expression and methylation in peripheral blood

Author: Nicholas G. Martin, Gibran Hemani, Anjali K. Henders, Peter M. Visscher, Grant W. Montgomery, Konstantin Shakhbazov, Joseph E. Powell, and Allan F. McRae
Subjects: 0301 basic medicine, medicine.medical_specialty, Genetic correlation, Quantitative Trait Loci, Genome-wide association study, Biology, Quantitative trait locus, Epigenesis, Genetic, 03 medical and health sciences, Molecular genetics, medicine, Genetics, Humans, Epigenetics, Oligonucleotide Array Sequence Analysis, Regulation of gene expression, DNA methylation, Methylation, 030104 developmental biology, Gene Expression Regulation, Gene expression, DNA microarray, Research Article, Biotechnology
Abstract: Background Expression QTLs and epigenetic marks are often employed to provide an insight into the possible biological mechanisms behind GWAS hits. A substantial proportion of the variation in gene expression and DNA methylation is known to be under genetic control. We address the proportion of genetic control that is shared between these two genomic features. Results An exhaustive search for pairwise phenotypic correlations between gene expression and DNA methylation in samples from human blood (n = 610) was performed. Of the 5 × 109 possible pairwise tests, 0.36 % passed Bonferroni corrected p-value cutoff of 9.9 × 10-12. We determined that the correlation structure between probe pairs was largely due to blood cell type specificity of the expression and methylation probes. Upon adjustment of the expression and methylation values for observed blood cellular composition (n = 422), the number of probe pairs which survived Bonferroni correction reduced by more than 5400 fold. Of the 614 correlated probe pairs located on the same chromosome, 75 % share at least one methylation and expression QTL at nominal 10-5p-value cutoff. Those probe pairs are located within 1Mbp window from each other and have a mean of absolute value of genetic correlation equal to 0.69, further demonstrating the high degree of shared genetic control. Conclusions Overall, this study demonstrates notable genetic covariance between DNA methylation and gene expression and reaffirms the importance of correcting for cell-counts in studies on non-homogeneous tissues. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2498-4) contains supplementary material, which is available to authorized users.
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625. A genome-wide linkage scan for age-at-menarche in three populations of European descent

Author: Mario Falchi, Gu Zhu, Nicholas G. Martin, Susan A. Treloar, Dorret I. Boomsma, Tim D. Spector, Peter M. Visscher, Stéphanie Martine van den Berg, Grant W. Montgomery, Carl A. Anderson, and Biological Psychology
Subjects: Netherlands Twin Register (NTR), METIS-254001, Adolescent, Genetic Linkage, Endocrinology, Diabetes and Metabolism, Clinical Biochemistry, Population, Twins, Locus (genetics), Biology, Biochemistry, White People, Odds, 03 medical and health sciences, 0302 clinical medicine, Endocrinology, Age Distribution, Genetic linkage, parasitic diseases, Humans, education, Child, Chromosome 12, 030304 developmental biology, Netherlands, Genetics, Menarche, 0303 health sciences, education.field_of_study, 030219 obstetrics & reproductive medicine, Genome, Human, Siblings, Biochemistry (medical), Australia, Chromosome Mapping, Heritability, United Kingdom, Genetics, Population, Trait, Original Article, Female, Lod Score
Abstract: Context: Age at menarche (AAM) is an important trait both biologically and socially, a clearly defined event in female pubertal development, and has been associated with many clinically significant phenotypes. Objective: The objective of the study was to identify genetic loci influencing variation in AAM in large population-based samples from three countries. Design/Participants: Recalled AAM data were collected from 13,697 individuals and 4,899 pseudoindependent sister-pairs from three different populations (Australia, The Netherlands, and the United Kingdom) by mailed questionnaire or interview. Genome-wide variance components linkage analysis was implemented on each sample individually and in combination. Results: The mean, sd, and heritability of AAM across the three samples was 13.1 yr, 1.5 yr, and 0.69, respectively. No loci were detected that reached genome-wide significance in the combined analysis, but a suggestive locus was detected on chromosome 12 (logarithm of the odds = 2.0). Three loci of suggestive significance were seen in the U.K. sample on chromosomes 1, 4, and 18 (logarithm of the odds = 2.4, 2.2 and 3.2, respectively). Conclusions: There was no evidence for common highly penetrant variants influencing AAM. Linkage and association suggest that one trait locus for AAM is located on chromosome 12, but further studies are required to replicate these results.
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626. Autosomal genetic control of human gene expression does not differ across the sexes

Author: Greg Gibson, Joseph E. Powell, Andres Metspalu, Alexander Holloway, Biao Zeng, Kerrin S. Small, Peter M. Visscher, Tim D. Spector, Irfahan Kassam, Grant W. Montgomery, Jian Yang, Luke R. Lloyd-Jones, Tõnu Esko, Allan F. McRae, and Andrew Bakshi
Subjects: 0301 basic medicine, Male, medicine.medical_specialty, Genetic correlation, Genotype, Biology, Polymorphism, Single Nucleotide, White People, 03 medical and health sciences, 0302 clinical medicine, Sex-specific genetic architecture, Molecular genetics, Gene expression, medicine, Additive genetic effects, Humans, 10. No inequality, Genetics, Sex Characteristics, Autosome, Research, Human genetics, Genetic architecture, Pedigree, Sexual dimorphism, 030104 developmental biology, Phenotype, Gene Expression Regulation, Evolutionary biology, Female, 030217 neurology & neurosurgery
Abstract: Background Despite their nearly identical genomes, males and females differ in risk, incidence, prevalence, severity and age-at-onset of many diseases. Sexual dimorphism is also seen in human autosomal gene expression, and has largely been explored by examining the contribution of genotype-by-sex interactions to variation in gene expression. Results In this study, we use data from a mixture of pedigree and unrelated individuals with verified European ancestry to investigate the sex-specific genetic architecture of gene expression measured in whole blood across n=1048 males and n=1005 females by treating gene expression intensities in the sexes as two distinct traits and estimating the genetic correlation (r G) between them. These correlations measure the similarity of the combined additive genetic effects of all single-nucleotide polymorphisms across the autosomal chromosomes, and thus the level of common genetic control of gene expression across the sexes. Genetic correlations are estimated across the sexes for the expression levels of 12,528 autosomal gene expression probes using bivariate GREML, and tested for differences in autosomal genetic control of gene expression across the sexes. Overall, no deviation of the distribution of test statistics is observed from that expected under the null hypothesis of a common autosomal genetic architecture for gene expression across the sexes. Conclusions These results suggest that males and females share the same common genetic control of gene expression. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1111-0) contains supplementary material, which is available to authorized users.
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627. Detection of multiple quantitative trait loci and their pleiotropic effects in outbred pig populations

Author: Peter M. Visscher, Yoshitaka Nagamine, Ricardo Pong-Wong, and Chris Haley
Subjects: Male, lcsh:QH426-470, Swine, Population, Quantitative Trait Loci, Quantitative trait locus, Biology, Pleiotropy, Animals, Outbred Strains, Genetics, Animals, Genetics(clinical), Allele, education, Ecology, Evolution, Behavior and Systematics, lcsh:SF1-1100, Chromosome 7 (human), education.field_of_study, Models, Genetic, Research, food and beverages, General Medicine, Heritability, Chromosomes, Mammalian, lcsh:Genetics, Chromosome 4, Trait, Hybridization, Genetic, Female, Animal Science and Zoology, lcsh:Animal culture
Abstract: Background Simultaneous detection of multiple QTLs (quantitative trait loci) may allow more accurate estimation of genetic effects. We have analyzed outbred commercial pig populations with different single and multiple models to clarify their genetic properties and in addition, we have investigated pleiotropy among growth and obesity traits based on allelic correlation within a gamete. Methods Three closed populations, (A) 427 individuals from a Yorkshire and Large White synthetic breed, (B) 547 Large White individuals and (C) 531 Large White individuals, were analyzed using a variance component method with one-QTL and two-QTL models. Six markers on chromosome 4 and five to seven markers on chromosome 7 were used. Results Population A displayed a high test statistic for the fat trait when applying the two-QTL model with two positions on two chromosomes. The estimated heritabilities for polygenic effects and for the first and second QTL were 19%, 17% and 21%, respectively. The high correlation of the estimated allelic effect on the same gamete and QTL test statistics suggested that the two separate QTL which were detected on different chromosomes both have pleiotropic effects on the two fat traits. Analysis of population B using the one-QTL model for three fat traits found a similar peak position on chromosome 7. Allelic effects of three fat traits from the same gamete were highly correlated suggesting the presence of a pleiotropic QTL. In population C, three growth traits also displayed similar peak positions on chromosome 7 and allelic effects from the same gamete were correlated. Conclusion Detection of the second QTL in a model reduced the polygenic heritability and should improve accuracy of estimated heritabilities for both QTLs.
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628. Genomic contributions in livestock gene introgression programmes

Author: John Woolliams, Peter M. Visscher, Eileen Wall, and Revues Inra, Import
Subjects: linkage drag, lcsh:QH426-470, Population, introgression, Population genetics, Introgression, Genomics, Locus (genetics), [SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics, introgression genomic contributions linkage drag backcross genetic lag marker-assisted introgression chromosome segments breeding programs linkage drag isogenic lines locus pigs efficiency phenotype density, Biology, Genome, genomic contributions, backcross, 03 medical and health sciences, genetic lag, Genetics, Animals, Genetics(clinical), education, Ecology, Evolution, Behavior and Systematics, lcsh:SF1-1100, 030304 developmental biology, 0303 health sciences, education.field_of_study, Obligate, Research, 0402 animal and dairy science, 04 agricultural and veterinary sciences, General Medicine, Marker-assisted selection, 040201 dairy & animal science, lcsh:Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, Evolutionary biology, Animals, Domestic, Animal Science and Zoology, lcsh:Animal culture
Abstract: International audience; The composition of the genome after introgression of a marker gene from a donor to a recipient breed was studied using analytical and simulation methods. Theoretical predictions of proportional genomic contributions, including donor linkage drag, from ancestors used at each generation of crossing after an introgression programme agreed closely with simulated results. The obligate drag, the donor genome surrounding the target locus that cannot be removed by subsequent selection, was also studied. It was shown that the number of backcross generations and the length of the chromosome affected proportional genomic contributions to the carrier chromosomes. Population structure had no significant effect on ancestral contributions and linkage drag but it did have an effect on the obligate drag whereby larger offspring groups resulted in smaller obligate drag. The implications for an introgression programme of the number of backcross generations, the population structure and the carrier chromosome length are discussed. The equations derived describing contributions to the genome from individuals from a given generation provide a framework to predict the genomic composition of a population after the introgression of a favourable donor allele. These ancestral contributions can be assigned a value and therefore allow the prediction of genetic lag.
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629. Univariate and multivariate parameter estimates for milk production traits using an animal model. II. Efficiency of selection when using simplified covariance structures

Author: Peter M. Visscher, William G. Hill, Robin Thompson, and Revues Inra, Import
Subjects: Multivariate statistics, Multivariate analysis, lcsh:QH426-470, [SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics, Biology, Animal model, Statistics, Genetics, Genetics(clinical), Selection (genetic algorithm), ComputingMilieux_MISCELLANEOUS, Ecology, Evolution, Behavior and Systematics, lcsh:SF1-1100, Research, 0402 animal and dairy science, Univariate, 04 agricultural and veterinary sciences, General Medicine, Covariance, Heritability, Milk production, 040201 dairy & animal science, lcsh:Genetics, [SDV.GEN.GA]Life Sciences [q-bio]/Genetics/Animal genetics, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, lcsh:Animal culture
Abstract: International audience
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630. The Genetic Architecture of Gene Expression in Peripheral Blood

Author: Jian Yang, Peter M. Visscher, Jing Zhao, Alexander Holloway, Grant W. Montgomery, Andres Metspalu, Tim D. Spector, Andrew Bakshi, Greg Gibson, Joseph E. Powell, Manolis Dermitzakis, Luke R. Lloyd-Jones, Tõnu Esko, Allan F. McRae, Biao Zeng, and Kerrin S. Small
Subjects: 0301 basic medicine, Linkage disequilibrium, Genotype, Quantitative Trait Loci, Inheritance Patterns, Gene Expression, Single-nucleotide polymorphism, HapMap Project, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, Article, Linkage Disequilibrium, 03 medical and health sciences, 0302 clinical medicine, Missing heritability problem, Gene expression, Genetic variation, Genetics, Humans, RNA, Messenger, Genetics (clinical), Genetic Association Studies, Models, Genetic, Genome, Human, Correction, Heritability, Human genetics, Peripheral blood, Genetic architecture, 030104 developmental biology, Phenotype, Expression quantitative trait loci, Linear Models, 030217 neurology & neurosurgery
Abstract: We analyzed the mRNA levels for 36,778 transcript expression traits (probes) from 2,765 individuals to comprehensively investigate the genetic architecture and degree of missing heritability for gene expression in peripheral blood. We identified 11,204 cis and 3,791 trans independent expression quantitative trait loci (eQTL) by using linear mixed models to perform genome-wide association analyses. Furthermore, using information on both closely and distantly related individuals, heritability was estimated for all expression traits. Of the set of expressed probes (15,966), 10,580 (66%) had an estimated narrow-sense heritability (h2) greater than zero with a mean (median) value of 0.192 (0.142). Across these probes, on average the proportion of genetic variance explained by all eQTL (hCOJO2) was 31% (0.060/0.192), meaning that 69% is missing, with the sentinel SNP of the largest eQTL explaining 87% (0.052/0.060) of the variance attributed to all identified cis- and trans-eQTL. For the same set of probes, the genetic variance attributed to genome-wide common (MAF > 0.01) HapMap 3 SNPs (hg2) accounted for on average 48% (0.093/0.192) of h2. Taken together, the evidence suggests that approximately half the genetic variance for gene expression is not tagged by common SNPs, and of the variance that is tagged by common SNPs, a large proportion can be attributed to identifiable eQTL of large effect, typically in cis. Finally, we present evidence that, compared with a meta-analysis, using individual-level data results in an increase of approximately 50% in power to detect eQTL.
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631. The value of relatives with phenotypes but missing genotypes in association studies for quantitative traits.

Author: Peter M. Visscher and David L. Duffy
Published: 2006
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632. Association of polygenic score for major depression with response to lithium in patients with bipolar disorder

Author: Amare, Azmeraw T., Schubert, Klaus Oliver, Hou, Liping, Clark, Scott R., Papiol, Sergi, Cearns, Micah, Heilbronner, Urs, Degenhardt, Franziska, Tekola-Ayele, Fasil, Hsu, Yi Hsiang, Shekhtman, Tatyana, Adli, Mazda, Akula, Nirmala, Akiyama, Kazufumi, Ardau, Raffaella, Arias, Bárbara, Aubry, Jean Michel, Backlund, Lena, Bhattacharjee, Abesh Kumar, Bellivier, Frank, Benabarre, Antonio, Bengesser, Susanne, Biernacka, Joanna M., Birner, Armin, Brichant-Petitjean, Clara, Cervantes, Pablo, Chen, Hsi Chung, Chillotti, Caterina, Cichon, Sven, Cruceanu, Cristiana, Czerski, Piotr M., Dalkner, Nina, Dayer, Alexandre, Del Zompo, Maria, DePaulo, J. Raymond, Étain, Bruno, Jamain, Stephane, Falkai, Peter, Forstner, Andreas J., Frisen, Louise, Frye, Mark A., Fullerton, Janice M., Gard, Sébastien, Garnham, Julie S., Goes, Fernando S., Grigoroiu-Serbanescu, Maria, Grof, Paul, Hashimoto, Ryota, Hauser, Joanna, Herms, Stefan, Hoffmann, Per, Hofmann, Andrea, Jiménez, Esther, Kahn, Jean Pierre, Kassem, Layla, Kuo, Po Hsiu, Kato, Tadafumi, Kelsoe, John R., Kittel-Schneider, Sarah, Kliwicki, Sebastian, König, Barbara, Kusumi, Ichiro, Laje, Gonzalo, Landén, Mikael, Lavebratt, Catharina, Leboyer, Marion, Leckband, Susan G., Tortorella, Alfonso, Manchia, Mirko, Martinsson, Lina, McCarthy, Michael J., McElroy, Susan L., Colom, Francesc, Mitjans, Marina, Mondimore, Francis M., Monteleone, Palmiero, Nievergelt, Caroline M., Nöthen, Markus M., Novák, Tomas, O’Donovan, Claire, Ozaki, Norio, Ösby, Urban, Pfennig, Andrea, Potash, James B., Reif, Andreas, Wray, Naomi R., Ripke, Stephan, Mattheisen, Manuel, Trzaskowski, Maciej, Byrne, Enda M., Abdellaoui, Abdel, Adams, Mark J., Agerbo, Esben, Air, Tracy M., Andlauer, Till F.M., Bacanu, Silviu Alin, Bækvad-Hansen, Marie, Beekman, Aartjan T.F., Bigdeli, Tim B., Binder, Elisabeth B., Blackwood, Douglas H.R., Bryois, Julien, Buttenschøn, Henriette N., Bybjerg-Grauholm, Jonas, Cai, Na, Castelao, Enrique, Christensen, Jane varregaard, Clarke, Toni Kim, Coleman, Jonathan R.I., Colodro-Conde, Lucía, Couvy-Duchesne, Baptiste, Craddock, Nick, Crawford, Gregory E., Davies, Gail, Deary, Ian J., Derks, Eske M., Direk, Nese, Dolan, Conor V., Dunn, Erin C., Eley, Thalia C., Escott-Price, Valentina, Kiadeh, Farnush Farhadi Hassan, Finucane, Hilary K., Frank, Josef, Gaspar, Héléna A., Gill, Michael, Gordon, Scott D., Grove, Jakob, Hall, Lynsey S., Hansen, Christine Søholm, Hansen, Thomas F., Hickie, Ian B., Homuth, Georg, Horn, Carsten, Hottenga, Jouke Jan, Hougaard, David M., Ising, Marcus, Jansen, Rick, Jorgenson, Eric, Knowles, James A., Kohane, Isaac S., Kraft, Julia, Kretzschmar, Warren W., Krogh, Jesper, Kutalik, Zoltán, Li, Yihan, Lind, Penelope A., MacIntyre, Donald J., MacKinnon, Dean F., Maier, Robert M., Maier, Wolfgang, Marchini, Jonathan, Mbarek, Hamdi, McGrath, Patrick, McGuffin, Peter, Medland, Sarah E., Mehta, Divya, Middeldorp, Christel M., Mihailov, Evelin, Milaneschi, Yuri, Milani, Lili, Montgomery, Grant W., Mostafavi, Sara, Mullins, Niamh, Nauck, Matthias, Ng, Bernard, Nivard, Michel G., Nyholt, Dale R., O’Reilly, Paul F., Oskarsson, Hogni, Owen, Michael J., Painter, Jodie N., Pedersen, Carsten Bøcker, Pedersen, Marianne Giørtz, Peterson, Roseann E., Pettersson, Erik, Peyrot, Wouter J., Pistis, Giorgio, Posthuma, Danielle, Quiroz, Jorge A., Qvist, Per, Rice, John P., Riley, Brien P., Rivera, Margarita, Mirza, Saira Saeed, Schoevers, Robert, Schulte, Eva C., Shen, Ling, Shi, Jianxin, Shyn, Stanley I., Sigurdsson, Engilbert, Sinnamon, Grant C.B., Smit, Johannes H., Smith, Daniel J., Stefansson, Hreinn, Steinberg, Stacy, Streit, Fabian, Strohmaier, Jana, Tansey, Katherine E., Teismann, Henning, Teumer, Alexander, Thompson, Wesley, Thomson, Pippa A., Thorgeirsson, Thorgeir E., Traylor, Matthew, Treutlein, Jens, Trubetskoy, Vassily, Uitterlinden, André G., Umbricht, Daniel, Van der Auwera, Sandra, van Hemert, Albert M., Viktorin, Alexander, Visscher, Peter M., Wang, Yunpeng, Webb, Bradley T., Weinsheimer, Shantel Marie, Wellmann, Jürgen, Willemsen, Gonneke, Witt, Stephanie H., Wu, Yang, Xi, Hualin S., Yang, Jian, Zhang, Futao, Arolt, Volker, Baune, Bernhard T., Berger, Klaus, Boomsma, Dorret I., Dannlowski, Udo, de Geus, E. J.C., Domenici, Enrico, Domschke, Katharina, Esko, Tõnu, Grabe, Hans J., Hamilton, Steven P., Hayward, Caroline, Heath, Andrew C., Kendler, Kenneth S., Kloiber, Stefan, Lewis, Glyn, Li, Qingqin S., Lucae, Susanne, Madden, Pamela A.F., Magnusson, Patrik K., Martin, Nicholas G., McIntosh, Andrew M., Metspalu, Andres, Mors, Ole, Mortensen, Preben Bo, Müller-Myhsok, Bertram, Nordentoft, Merete, O’Donovan, Michael C., Paciga, Sara A., Pedersen, Nancy L., Penninx, Brenda W.J.H., Perlis, Roy H., Porteous, David J., Preisig, Martin, Rietschel, Marcella, Schaefer, Catherine, Schulze, Thomas G., Smoller, Jordan W., Stefansson, Kari, Tiemeier, Henning, Uher, Rudolf, Völzke, Henry, Weissman, Myrna M., Werge, Thomas, Lewis, Cathryn M., Levinson, Douglas F., Breen, Gerome, Børglum, Anders D., Sullivan, Patrick F., Reininghaus, Eva, Rouleau, Guy A., Rybakowski, Janusz K., Schalling, Martin, Schofield, Peter R., Schweizer, Barbara W., Severino, Giovanni, Shilling, Paul D., Shimoda, Katzutaka, Simhandl, Christian, Slaney, Claire M., Squassina, Alessio, Stamm, Thomas, Stopkova, Pavla, Maj, Mario, Turecki, Gustavo, Vieta, Eduard, Veeh, Julia, Wright, Adam, Zandi, Peter P., Mitchell, Philip B., Bauer, Michael, Alda, Martin, McMahon, Francis J., APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Reproduction & Development (AR&D), Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Human genetics, APH - Digital Health, APH - Methodology, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Complex Trait Genetics, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Jamain, Stéphane, University of Adelaide, South Australian Health and Medical Research Institute [ Adelaide] (SAHMRI), Mental Health Services [Adelaide, SA, Australia], National Institute of Mental Health (NIMH), Ludwig Maximilian University [Munich] (LMU), Georg-August-University = Georg-August-Universität Göttingen, Institut für Genetik - Universität Bonn / Institute of Genetics - University of Bonn, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), Harvard Medical School [Boston] (HMS), Harvard School of Public Health, University of California [San Diego] (UC San Diego), University of California (UC), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Dokkyo Medical University, Università degli Studi di Cagliari = University of Cagliari (UniCa), Universitat Autònoma de Barcelona (UAB), Centro de Investigación Biomédica en Red de Salud Mental [Barcelona, Spain] (CIBERSAM), Hospital Sant Joan de Déu [Barcelona], Geneva University Hospital (HUG), Karolinska Institutet [Stockholm], Karolinska University Hospital [Stockholm], Optimisation thérapeutique en Neuropsychopharmacologie (OPTeN (UMR_S_1144 / U1144)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona (UB), Karl-Franzens-Universität Graz, Mayo Clinic [Rochester], McGill University Health Center [Montreal] (MUHC), National Taiwan University [Taiwan] (NTU), University Hospital Basel [Basel], Poznan University of Medical Sciences [Poland] (PUMS), Johns Hopkins University (JHU), Institut Mondor de Recherche Biomédicale (IMRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Fondation FondaMental [Créteil], IMRB - 'Neuropsychiatrie translationnelle' [Créteil] (U955 Inserm - UPEC), Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR10-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), University of Basel (Unibas), Neuroscience Research Australia [Sydney, NSW, Australia] (NRA), University of New South Wales [Sydney] (UNSW), Psychiatrie de l'enfant et de l'adolescent [CH C. Perrens, Bordeaux], SECOP - centre hospitalier Charles Perrens, Dalhousie University [Halifax], 'Prof. Dr. Alexandru Obregia' Clinical Hospital of Psychiatry [Bucharest, Romania], Mood Disorders Center of Ottawa (MDCO), University of Ottawa [Ottawa], Osaka University [Osaka], Graduate School of Medicine [Osaka], Centro de Investigación Biomédica en Red Salud Mental [Madrid] (CIBER-SAM), Psychiatrie et Psychologie Clinique de Liaison [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Centre Psychothérapique de Nancy (CPN), National Institutes of Health [Bethesda] (NIH), Environmental Molecular Biology Laboratory (RIKEN), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Goethe-University Frankfurt am Main, Landesklinikum Neunkirchen (LK Neunkirchen), Hokkaido University Graduate School of Medicine [Sapporo, Japan], Sahlgrenska Academy at University of Gothenburg [Göteborg], Research Service VA San Diego Healthcare System, Università degli Studi di Perugia = University of Perugia (UNIPG), University of Cincinnati (UC), IMIM-Hospital del Mar, Generalitat de Catalunya, Max Planck Institute of Experimental Medicine [Göttingen] (MPI), Max-Planck-Gesellschaft, University of Salerno (UNISA), University of the Study of Campania Luigi Vanvitelli, National Institute of Mental Health [Klecany, Czech Republic] (NIMH), Nagoya University Graduate School of Medicine [Japon], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Medical University Graz, Montreal Neurological Institute and Hospital, McGill University = Université McGill [Montréal, Canada], Sigmund Freud University (SFU), Douglas Mental Health University Institute [Montréal], University of Heidelberg, Medical Faculty, Black Dog Institute [Sydney, Australia], Johns Hopkins Bloomberg School of Public Health [Baltimore], Westfälische Wilhelms-Universität Münster = University of Münster (WWU), Melbourne Medical School [Melbourne], Faculty of Medicine, Dentistry and Health Sciences [Melbourne], University of Melbourne-University of Melbourne, The Florey Institute of Neuroscience and Mental Health [Parkville, VIC, Australie], University of Melbourne, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium: Naomi R Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M Byrne, Abdel Abdellaoui, Mark J Adams, Esben Agerbo, Tracy M Air, Till F M Andlauer, Silviu-Alin Bacanu, Marie Bækvad-Hansen, Aartjan T F Beekman, Tim B Bigdeli, Elisabeth B Binder, Douglas H R Blackwood, Julien Bryois, Henriette N Buttenschøn, Jonas Bybjerg-Grauholm, Na Cai, Enrique Castelao, Jane Varregaard Christensen, Toni-Kim Clarke, Jonathan R I Coleman, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, Nick Craddock, Gregory E Crawford, Gail Davies, Ian J Deary, Franziska Degenhardt, Eske M Derks, Nese Direk, Conor V Dolan, Erin C Dunn, Thalia C Eley, Valentina Escott-Price, Farnush Farhadi Hassan Kiadeh, Hilary K Finucane, Andreas J Forstner, Josef Frank, Héléna A Gaspar, Michael Gill, Fernando S Goes, Scott D Gordon, Jakob Grove, Lynsey S Hall, Christine Søholm Hansen, Thomas F Hansen, Stefan Herms, Ian B Hickie, Per Hoffmann, Georg Homuth, Carsten Horn, Jouke-Jan Hottenga, David M Hougaard, Marcus Ising, Rick Jansen, Eric Jorgenson, James A Knowles, Isaac S Kohane, Julia Kraft, Warren W Kretzschmar, Jesper Krogh, Zoltán Kutalik, Yihan Li, Penelope A Lind, Donald J MacIntyre, Dean F MacKinnon, Robert M Maier, Wolfgang Maier, Jonathan Marchini, Hamdi Mbarek, Patrick McGrath, Peter McGuffin, Sarah E Medland, Divya Mehta, Christel M Middeldorp, Evelin Mihailov, Yuri Milaneschi, Lili Milani, Francis M Mondimore, Grant W Montgomery, Sara Mostafavi, Niamh Mullins, Matthias Nauck, Bernard Ng, Michel G Nivard, Dale R Nyholt, Paul F O'Reilly, Hogni Oskarsson, Michael J Owen, Jodie N Painter, Carsten Bøcker Pedersen, Marianne Giørtz Pedersen, Roseann E Peterson, Erik Pettersson, Wouter J Peyrot, Giorgio Pistis, Danielle Posthuma, Jorge A Quiroz, Per Qvist, John P Rice, Brien P Riley, Margarita Rivera, Saira Saeed Mirza, Robert Schoevers, Eva C Schulte, Ling Shen, Jianxin Shi, Stanley I Shyn, Engilbert Sigurdsson, Grant C B Sinnamon, Johannes H Smit, Daniel J Smith, Hreinn Stefansson, Stacy Steinberg, Fabian Streit, Jana Strohmaier, Katherine E Tansey, Henning Teismann, Alexander Teumer, Wesley Thompson, Pippa A Thomson, Thorgeir E Thorgeirsson, Matthew Traylor, Jens Treutlein, Vassily Trubetskoy, André G Uitterlinden, Daniel Umbricht, Sandra Van der Auwera, Albert M van Hemert, Alexander Viktorin, Peter M Visscher, Yunpeng Wang, Bradley T Webb, Shantel Marie Weinsheimer, Jürgen Wellmann, Gonneke Willemsen, Stephanie H Witt, Yang Wu, Hualin S Xi, Jian Yang, Futao Zhang, Volker Arolt, Bernhard T Baune, Klaus Berger, Dorret I Boomsma, Sven Cichon, Udo Dannlowski, E J C de Geus, J Raymond DePaulo, Enrico Domenici, Katharina Domschke, Tõnu Esko, Hans J Grabe, Steven P Hamilton, Caroline Hayward, Andrew C Heath, Kenneth S Kendler, Stefan Kloiber, Glyn Lewis, Qingqin S Li, Susanne Lucae, Pamela A F Madden, Patrik K Magnusson, Nicholas G Martin, Andrew M McIntosh, Andres Metspalu, Ole Mors, Preben Bo Mortensen, Bertram Müller-Myhsok, Merete Nordentoft, Markus M Nöthen, Michael C O'Donovan, Sara A Paciga, Nancy L Pedersen, Brenda W J H Penninx, Roy H Perlis, David J Porteous, James B Potash, Martin Preisig, Marcella Rietschel, Catherine Schaefer, Thomas G Schulze, Jordan W Smoller, Kari Stefansson, Henning Tiemeier, Rudolf Uher, Henry Völzke, Myrna M Weissman, Thomas Werge, Cathryn M Lewis, Douglas F Levinson, Gerome Breen, Anders D Børglum, Patrick F Sullivan., Epidemiology, Internal Medicine, Child and Adolescent Psychiatry / Psychology, Georg-August-University [Göttingen], University of California, Universita degli Studi di Cagliari [Cagliari], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), University of Graz, Università degli Studi di Perugia (UNIPG), University of Münster, Karl-Franzens-Universität [Graz, Autriche], Amare, A. T., Schubert, K. O., Hou, L., Clark, S. R., Papiol, S., Cearns, M., Heilbronner, U., Degenhardt, F., Tekola-Ayele, F., Hsu, Y. -H., Shekhtman, T., Adli, M., Akula, N., Akiyama, K., Ardau, R., Arias, B., Aubry, J. -M., Backlund, L., Bhattacharjee, A. K., Bellivier, F., Benabarre, A., Bengesser, S., Biernacka, J. M., Birner, A., Brichant-Petitjean, C., Cervantes, P., Chen, H. -C., Chillotti, C., Cichon, S., Cruceanu, C., Czerski, P. M., Dalkner, N., Dayer, A., Del Zompo, M., Depaulo, J. R., Etain, B., Jamain, S., Falkai, P., Forstner, A. J., Frisen, L., Frye, M. A., Fullerton, J. M., Gard, S., Garnham, J. S., Goes, F. S., Grigoroiu-Serbanescu, M., Grof, P., Hashimoto, R., Hauser, J., Herms, S., Hoffmann, P., Hofmann, A., Jimenez, E., Kahn, J. -P., Kassem, L., Kuo, P. -H., Kato, T., Kelsoe, J. R., Kittel-Schneider, S., Kliwicki, S., Konig, B., Kusumi, I., Laje, G., Landen, M., Lavebratt, C., Leboyer, M., Leckband, S. G., Tortorella, A., Manchia, M., Martinsson, L., Mccarthy, M. J., Mcelroy, S. L., Colom, F., Mitjans, M., Mondimore, F. M., Monteleone, P., Nievergelt, C. M., Nothen, M. M., Novak, T., O'Donovan, C., Ozaki, N., Osby, U., Pfennig, A., Potash, J. B., Reif, A., Wray, N. R., Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., Agerbo, E., Air, T. M., Andlauer, T. F. M., Bacanu, S. -A., Baekvad-Hansen, M., Beekman, A. T. F., Bigdeli, T. B., Binder, E. B., Blackwood, D. H. R., Bryois, J., Buttenschon, H. N., Bybjerg-Grauholm, J., Cai, N., Castelao, E., Christensen, J., Clarke, T. -K., Coleman, J. R. I., Colodro-Conde, L., Couvy-Duchesne, B., Craddock, N., Crawford, G. E., Davies, G., Deary, I. J., Derks, E. M., Direk, N., Dolan, C. V., Dunn, E. C., Eley, T. C., Escott-Price, V., Kiadeh, F. F. H., Finucane, H. K., Frank, J., Gaspar, H. A., Gill, M., Gordon, S. D., Grove, J., Hall, L. S., Hansen, C. S., Hansen, T. F., Hickie, I. B., Homuth, G., Horn, C., Hottenga, J. -J., Hougaard, D. M., Ising, M., Jansen, R., Jorgenson, E., Knowles, J. A., Kohane, I. S., Kraft, J., Kretzschmar, W. W., Krogh, J., Kutalik, Z., Li, Y., Lind, P. A., Macintyre, D. J., Mackinnon, D. F., Maier, R. M., Maier, W., Marchini, J., Mbarek, H., Mcgrath, P., Mcguffin, P., Medland, S. E., Mehta, D., Middeldorp, C. M., Mihailov, E., Milaneschi, Y., Milani, L., Montgomery, G. W., Mostafavi, S., Mullins, N., Nauck, M., Ng, B., Nivard, M. G., Nyholt, D. R., O'Reilly, P. F., Oskarsson, H., Owen, M. J., Painter, J. N., Pedersen, C. B., Pedersen, M. G., Peterson, R. E., Pettersson, E., Peyrot, W. J., Pistis, G., Posthuma, D., Quiroz, J. A., Qvist, P., Rice, J. P., Riley, B. P., Rivera, M., Mirza, S. S., Schoevers, R., Schulte, E. C., Shen, L., Shi, J., Shyn, S. I., Sigurdsson, E., Sinnamon, G. C. B., Smit, J. H., Smith, D. J., Stefansson, H., Steinberg, S., Streit, F., Strohmaier, J., Tansey, K. E., Teismann, H., Teumer, A., Thompson, W., Thomson, P. A., Thorgeirsson, T. E., Traylor, M., Treutlein, J., Trubetskoy, V., Uitterlinden, A. G., Umbricht, D., Van der Auwera, S., van Hemert, A. M., Viktorin, A., Visscher, P. M., Wang, Y., Webb, B. T., Weinsheimer, S. M., Wellmann, J., Willemsen, G., Witt, S. H., Wu, Y., Xi, H. S., Yang, J., Zhang, F., Arolt, V., Baune, B. T., Berger, K., Boomsma, D. I., Dannlowski, U., de Geus, E. J. C., Domenici, E., Domschke, K., Esko, T., Grabe, H. J., Hamilton, S. P., Hayward, C., Heath, A. C., Kendler, K. S., Kloiber, S., Lewis, G., Li, Q. S., Lucae, S., Madden, P. A. F., Magnusson, P. K., Martin, N. G., Mcintosh, A. M., Metspalu, A., Mors, O., Mortensen, P. B., Muller-Myhsok, B., Nordentoft, M., O'Donovan, M. C., Paciga, S. A., Pedersen, N. L., Penninx, B. W. J. H., Perlis, R. H., Porteous, D. J., Preisig, M., Rietschel, M., Schaefer, C., Schulze, T. G., Smoller, J. W., Stefansson, K., Tiemeier, H., Uher, R., Volzke, H., Weissman, M. M., Werge, T., Lewis, C. M., Levinson, D. F., Breen, G., Borglum, A. D., Sullivan, P. F., Reininghaus, E., Rouleau, G. A., Rybakowski, J. K., Schalling, M., Schofield, P. R., Schweizer, B. W., Severino, G., Shilling, P. D., Shimoda, K., Simhandl, C., Slaney, C. M., Squassina, A., Stamm, T., Stopkova, P., Maj, M., Turecki, G., Vieta, E., Veeh, J., Wright, A., Zandi, P. P., Mitchell, P. B., Bauer, M., Alda, M., Mcmahon, F. J., and Adult Psychiatry
Subjects: 0301 basic medicine, Netherlands Twin Register (NTR), Lithium (medication), [SDV.MHEP.PSM] Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, Genome-wide association study, Logistic regression, THERAPY, ddc:616.89, 0302 clinical medicine, Medicine, Major depression, PREDICTORS, Depression (differential diagnoses), RISK, Depression, Psychiatry and Mental health, Quartile, Cohort, AUGMENTATION, medicine.drug, POLARITY, medicine.medical_specialty, GENETICS, Bipolar disorder, [SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics, Lithium, PROPHYLACTIC LITHIUM, 03 medical and health sciences, Cellular and Molecular Neuroscience, SDG 3 - Good Health and Well-being, Internal medicine, Humans, ddc:610, AGENTS, Molecular Biology, Genetic association, Depressive Disorder, Major, business.industry, medicine.disease, EFFICACY, 030104 developmental biology, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, [SDV.MHEP.PSM]Life Sciences [q-bio]/Human health and pathology/Psychiatrics and mental health, PHARMACOLOGICAL-TREATMENTS, business, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract: © 2020, The Author(s), under exclusive licence to Springer Nature Limited.Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18–2.01) and European sample: OR = 1.75 (95% CI: 1.30–2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61–4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.
Published: 2021
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633. Maps of open chromatin highlight cell type-restricted patterns of regulatory sequence variation at hematological trait loci

Author: Paul, Ds, Albers, Ca, Rendon, A, Voss, K, Stephens, J, Akkerman, Jan Willem N., Albers, Cornelis A., Ale, Algra, Abtehale Al Hussani, Hooman, Allayee, Franco, Anni, Asselbergs, Folkert W., Antony, Attwood, Beverley, Balkau, Stefania, Bandinelli, François, Bastardot, Saonli, Basu, Baumeister, Sebastian E., Jacques, Beckmann, Beben, Benyamin, Ginevra, Biino, Bis, Joshua C., Lorenzo, Bomba, Amélie, Bonnefond, Boomsma, Dorret I., Bradley, John R., François, Cambien, Chambers, John C., Marina, Ciullo, Cookson, William O., Francesco, Cucca, Ana, Cvejic, D'Adamo, ADAMO PIO, John, Danesh, Fabrice, Danjou, Debashish, Das, Gail, Davies, Paul IW de Bakker, de Boer, Rudolf A., Eco JC de Geus, Deary, Ian J., Dedoussis, George V., Panos, Deloukas, Maria, Dimitriou, Christian, Dina, Angela, Döring, Ulrich, Elling, David, Ellinghaus, Paul, Elliott, Gunnar, Engström, Jeanette, Erdmann, Tõnu, Esko, Evans, David M., Eyjolfsson, Gudmundur I., Mario, Falchi, Wei, Feng, Ferreira, Manuel A., Luigi, Ferrucci, Krista, Fischer, Folsom, Aaron R., Paolo, Fortina, Andre, Franke, Lude, Franke, Frazer, Ian H., Philippe, Froguel, Renzo, Galanello, Ganesh, Santhi K., Garner, Stephen F., Gasparini, Paolo, Bernd, Genser, Gibson, Quince D., Christian, Gieger, Girotto, Giorgia, Glazer, Nicole L., Martin, Gögele, Goodall, Alison H., Andreas, Greinacher, Gudbjartsson, Daniel F., Chris, Hammond, Harris, Sarah E., Jaana, Hartiala, Anna Liisa Hartikainen, Hazen, Stanley L., Heckbert, Susan R., Hedblad, Bo, Christian, Hengstenberg, Micha, Hersch, Hicks, Andrew A., Hilma, Holm, Jouke Jan Hottenga, Thomas, Illig, Marjo Riitta Jarvelin, Jennifer, Jolley, Steve, Jupe, Mika, Kähönen, Naoyuki, Kamatani, Stavroula, Kanoni, Kema, Ido P., Kemp, John P., Jyoti, Khadake, Kay Tee Khaw, Kleber, Marcus E., Kooner, Jaspal S., Peter, Kovacs, Brigitte, Kühnel, Marie Christine Kyrtsonis, Yann, Labrune, Vasiliki, Lagou, Claudia, Langenberg, Terho, Lehtimäki, Xinzhong, Li, Liming, Liang, Lifelines Cohort Study, Heather Lloyd Jones, Ruth JF Loos, Lopez, Lorna M., Thomas, Lumley, Leo Pekka Lyytikäinen, Winfried, Maerz, Reedik, Mägi, Massimo, Mangino, Martin, Nicholas G., Andrea, Maschio, Irene Mateo Leach, Barbara, Mcknight, Stuart, Meacham, Medland, Sarah E., Christa, Meisinger, Olle, Melander, Yasin, Memari, Andres, Metspalu, Kathy, Miller, Mitchell, Braxton D., Moffatt, Miriam F., Montgomery, Grant W., Carmel, Moore, Federico, Murgia, Yusuke, Nakamura, Matthias, Nauck, Gerjan, Navis, Nolte, Ilja M., Ute, Nöthlings, Teresa, Nutile, Yukinori, Okada, Isleifur, Olafsson, Onundarson, Pall T., O’Reilly, Paul F., Ouwehand, Willem H., Debora, Parracciani, Afshin, Parsa, Paul, Dirk S., Penninger, Josef M., Penninx, Brenda W., Mario, Pirastu, Pirastu, Nicola, Giorgio, Pistis, Eleonora, Porcu, Laura, Portas, David, Porteous, Anneli, Pouta, Pramstaller, Peter P., Inga, Prokopenko, Psaty, Bruce M., Janne, Pullat, Aparna, Radhakrishnan, Olli, Raitakari, Ramiro Ramirez Solis, Augusto, Rendon, Ried, Janina S., Ring, Susan M., Robino, Antonietta, Rotter, Jerome I., Daniela, Ruggiero, Aimo, Ruokonen, Cinzia, Sala, Andres, Saluments, Samani, Nilesh J., Jennifer, Sambrook, Serena, Sanna, David, Schlessinger, Schmidt, Carsten O., Stefan, Schreiber, Heribert, Schunkert, James, Scott, Joban, Sehmi, Jovana Serbanovic Canic, So Youn Shin, Shuldiner, Alan R., Rob, Sladek, Smit, Johannes H., George Davey Smith, Gustav Smith, J., Smith, Nicholas L., Harold, Snieder, Nicole, Soranzo, Rossella, Sorice, Spector, Timothy D., Starr, John M., Kari, Stefansson, Derek, Stemple, Jonathan, Stephens, Michael, Stumvoll, Patrick, Sulem, Atsushi, Takahashi, Sian Tsung Tan, Toshiko, Tanaka, Clara, Tang, Weihong, Tang, WH Wilson Tang, Kent, Taylor, Albert, Tenesa, Alexander, Teumer, Swee Lay Thein, Unnur, Thorsteinsdottir, Daniela, Toniolo, Anke, Tönjes, Traglia, Michela, Manuela, Uda, Sheila, Ulivi, Pim van der Harst, Ellen van der Schoot, C., van Gilst, Wiek H., Joost van Pelt, L., van Veldhuisen, Dirk J., Niek, Verweij, Visscher, Peter M., Uwe, Völker, Peter, Vollenweider, Katrin, Voss, Wareham, Nicholas J., Lorenz, Wernisch, Harm Jan Westra, Whitfield, John B., Herich, Wichmann, Wiggins, Kerri L., Gonneke, Willemsen, Winkelmann, Bernhard R., Gerald, Wirnsberger, Bruce HR Wolffenbuttel, Jian, Yang, Tsun Po Yang, Weihua, Zhang, Jing Hua Zhao, Paavo, Zitting, Jaap Jan Zwaginga, van der Harst, P, Chambers, Jc, Soranzo, N, Ouwehand, Wh, Deloukas, P., Paul, D, Albers, Ca, Rendon, A, Voss, K, Stephens, J, Jan Willem N., Akkerman, Cornelis A., Alber, Ale, Algra, Abtehale Al, Hussani, Hooman, Allayee, Franco, Anni, Folkert W., Asselberg, Antony, Attwood, Beverley, Balkau, Stefania, Bandinelli, François, Bastardot, Saonli, Basu, Sebastian E., Baumeister, Jacques, Beckmann, Beben, Benyamin, Ginevra, Biino, Joshua C., Bi, Lorenzo, Bomba, Amélie, Bonnefond, Dorret I., Boomsma, John R., Bradley, François, Cambien, John C., Chamber, Marina, Ciullo, William O., Cookson, Francesco, Cucca, Ana, Cvejic, D'Adamo, ADAMO PIO, John, Danesh, Fabrice, Danjou, Debashish, Da, Gail, Davie, Paul IW de, Bakker, Rudolf A., de Boer, Eco JC de, Geu, Ian J., Deary, George V., Dedoussi, Panos, Delouka, Maria, Dimitriou, Christian, Dina, Angela, Döring, Ulrich, Elling, David, Ellinghau, Paul, Elliott, Gunnar, Engström, Jeanette, Erdmann, Tõnu, Esko, David M., Evan, Gudmundur I., Eyjolfsson, Mario, Falchi, Wei, Feng, Manuel A., Ferreira, Luigi, Ferrucci, Krista, Fischer, Aaron R., Folsom, Paolo, Fortina, Andre, Franke, Lude, Franke, Ian H., Frazer, Philippe, Froguel, Renzo, Galanello, Santhi K., Ganesh, Stephen F., Garner, Gasparini, Paolo, Bernd, Genser, Quince D., Gibson, Christian, Gieger, Girotto, Giorgia, Nicole L., Glazer, Martin, Gögele, Alison H., Goodall, Andreas, Greinacher, Daniel F., Gudbjartsson, Chris, Hammond, Sarah E., Harri, Jaana, Hartiala, Anna Liisa, Hartikainen, Stanley L., Hazen, Susan R., Heckbert, Bo, Hedblad, Christian, Hengstenberg, Micha, Hersch, Andrew A., Hick, Hilma, Holm, Jouke Jan, Hottenga, Thomas, Illig, Marjo Riitta, Jarvelin, Jennifer, Jolley, Steve, Jupe, Mika, Kähönen, Naoyuki, Kamatani, Stavroula, Kanoni, Ido P., Kema, John P., Kemp, Jyoti, Khadake, Kay Tee, Khaw, Marcus E., Kleber, Jaspal S., Kooner, Peter, Kovac, Brigitte, Kühnel, Marie Christine, Kyrtsoni, Yann, Labrune, Vasiliki, Lagou, Claudia, Langenberg, Terho, Lehtimäki, Xinzhong, Li, Liming, Liang, Lifelines Cohort, Study, Heather Lloyd, Jone, Ruth JF, Loo, Lorna M., Lopez, Thomas, Lumley, Leo Pekka, Lyytikäinen, Winfried, Maerz, Reedik, Mägi, Massimo, Mangino, Nicholas G., Martin, Andrea, Maschio, Irene Mateo, Leach, Barbara, Mcknight, Stuart, Meacham, Sarah E., Medland, Christa, Meisinger, Olle, Melander, Yasin, Memari, Andres, Metspalu, Kathy, Miller, Braxton D., Mitchell, Miriam F., Moffatt, Grant W., Montgomery, Carmel, Moore, Federico, Murgia, Yusuke, Nakamura, Matthias, Nauck, Gerjan, Navi, Ilja M., Nolte, Ute, Nöthling, Teresa, Nutile, Yukinori, Okada, Isleifur, Olafsson, Pall T., Onundarson, Paul F., O’Reilly, Willem H., Ouwehand, Debora, Parracciani, Afshin, Parsa, Dirk S., Paul, Josef M., Penninger, Brenda W., Penninx, Mario, Pirastu, Pirastu, Nicola, Giorgio, Pisti, Eleonora, Porcu, Laura, Porta, David, Porteou, Anneli, Pouta, Peter P., Pramstaller, Inga, Prokopenko, Bruce M., Psaty, Janne, Pullat, Aparna, Radhakrishnan, Olli, Raitakari, Ramiro Ramirez, Soli, Augusto, Rendon, Janina S., Ried, Susan M., Ring, Robino, Antonietta, Jerome I., Rotter, Daniela, Ruggiero, Aimo, Ruokonen, Cinzia, Sala, Andres, Salument, Nilesh J., Samani, Jennifer, Sambrook, Serena, Sanna, David, Schlessinger, Carsten O., Schmidt, Stefan, Schreiber, Heribert, Schunkert, James, Scott, Joban, Sehmi, Jovana Serbanovic, Canic, So Youn, Shin, Alan R., Shuldiner, Rob, Sladek, Johannes H., Smit, George Davey, Smith, J., Gustav Smith, Nicholas L., Smith, Harold, Snieder, Nicole, Soranzo, Rossella, Sorice, Timothy D., Spector, John M., Starr, Kari, Stefansson, Derek, Stemple, Jonathan, Stephen, Michael, Stumvoll, Patrick, Sulem, Atsushi, Takahashi, Sian Tsung, Tan, Toshiko, Tanaka, Clara, Tang, Weihong, Tang, WH Wilson, Tang, Kent, Taylor, Albert, Tenesa, Alexander, Teumer, Swee Lay, Thein, Unnur, Thorsteinsdottir, Daniela, Toniolo, Anke, Tönje, Traglia, Michela, Manuela, Uda, Sheila, Ulivi, Pim van der, Harst, C., Ellen van der Schoot, Wiek H., van Gilst, L., Joost van Pelt, Dirk J., van Veldhuisen, Niek, Verweij, Peter M., Visscher, Uwe, Völker, Peter, Vollenweider, Katrin, Vo, Nicholas J., Wareham, Lorenz, Wernisch, Harm Jan, Westra, John B., Whitfield, Herich, Wichmann, Kerri L., Wiggin, Gonneke, Willemsen, Bernhard R., Winkelmann, Gerald, Wirnsberger, Bruce HR, Wolffenbuttel, Jian, Yang, Tsun Po, Yang, Weihua, Zhang, Jing Hua, Zhao, Paavo, Zitting, Jaap Jan, Zwaginga, van der Harst, P, Chambers, Jc, Soranzo, N, Ouwehand, Wh, Deloukas, P., Biological Psychology, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, EMGO+ - Mental Health, Neuroscience Campus Amsterdam - Brain Imaging Technology, Neuroscience Campus Amsterdam - Brain Mechanisms in Health & Disease, Wellcome Trust Sanger Inst, Cambridge CB10 1SA, England, UCL, UCL Canc Inst, London WC1E 6BT, England, Univ Cambridge, Dept Haematol, Cambridge CB2 0PT, England, Natl Hlth Serv NHS Blood & Transplant, Cambridge CB2 0PT, England, Radboud Univ Nijmegen, Med Ctr, Dept Human Genet, NL-6500 HB Nijmegen, Netherlands, Inst Publ Hlth, MRC Biostat Unit, Cambridge CB2 0SR, England, NIHR Biomed Res Ctr, Cambridge CB2 0PT, England, Univ Groningen, Univ Med Ctr Groningen, Dept Cardiol, NL-9713 GZ Groningen, Netherlands, Univ Groningen, Univ Med Ctr Groningen, Dept Genet, NL-9713 GZ Groningen, Netherlands, Univ London Imperial Coll Sci Technol & Med, Dept Epidemiol & Biostat, London W2 1NY, England, Hammersmith Hosp, Imperial Coll Healthcare NHS Trust, London W12 0HS, England, Royal Brompton & Harefield Hosp NHS Trust, London SW3 6NP, England, Ealing Hosp NHS Trust, Southall UB1 3HW, Middx, England, Psychiatry, Epidemiology and Data Science, NCA - Brain mechanisms in health and disease, NCA - Neurobiology of mental health, EMGO - Mental health, NCA - Brain imaging technology, and Cardiovascular Centre (CVC)
Subjects: Netherlands Twin Register (NTR), Erythrocytes, Genome-wide association study, UNCERTAINTY, Regulatory Sequences, Nucleic Acid, VARIANTS, ANNOTATION, COLORECTAL-CANCER, Histones, 0302 clinical medicine, BINDING, Cluster Analysis, Myeloid Cells, Genetics (clinical), Genetics, hematological trait, 0303 health sciences, Chromosome Mapping, MRP4 ABCC4, Chromatin, Nucleosomes, Phenotype, Organ Specificity, 030220 oncology & carcinogenesis, Blood Platelets, Quantitative Trait Loci, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, DYNAMIN 3, 03 medical and health sciences, Quantitative Trait, Heritable, MEGAKARYOCYTES, Humans, Cell Lineage, ddc:610, GENOME-WIDE ASSOCIATION, Enhancer, Transcription factor, Gene, ChIA-PET, 030304 developmental biology, Research, Genetic Variation, DNA, Chromatin Assembly and Disassembly, Genetic architecture, Gene Expression Regulation, Genome-Wide Association Study
Abstract: To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access. Nearly three-quarters of the 143 genetic signals associated with platelet and erythrocyte phenotypes identified by meta-analyses of genome-wide association (GWA) studies are located at non-protein-coding regions. Here, we assessed the role of candidate regulatory variants associated with cell type-restricted, closely related hematological quantitative traits in biologically relevant hematopoietic cell types. We used formaldehyde-assisted isolation of regulatory elements followed by next-generation sequencing (FAIRE-seq) to map regions of open chromatin in three primary human blood cells of the myeloid lineage. In the precursors of platelets and erythrocytes, as well as in monocytes, we found that open chromatin signatures reflect the corresponding hematopoietic lineages of the studied cell types and associate with the cell type-specific gene expression patterns. Dependent on their signal strength, open chromatin regions showed correlation with promoter and enhancer histone marks, distance to the transcription start site, and ontology classes of nearby genes. Cell type-restricted regions of open chromatin were enriched in sequence variants associated with hematological indices. The majority (63.6%) of such candidate functional variants at platelet quantitative trait loci (QTLs) coincided with binding sites of five transcription factors key in regulating megakaryopoiesis. We experimentally tested 13 candidate regulatory variants at 10 platelet QTLs and found that 10 (76.9%) affected protein binding, suggesting that this is a frequent mechanism by which regulatory variants influence quantitative trait levels. Our findings demonstrate that combining large-scale GWA data with open chromatin profiles of relevant cell types can be a powerful means of dissecting the genetic architecture of closely related quantitative traits. Marie-Curie Initial Training Network NETSIM British Heart Foundation RG/09/12/28096 National Institutes for Health RP-PG-0310-1002 Wellcome Trust 098051 info:eu-repo/grantAgreement/EC/FP7/282510
Published: 2013
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634. New gene functions in megakaryopoiesis and platelet formation

Author: Christian Gieger1, Aparna Radhakrishnan2, 3, Ana Cvejic2, Weihong Tang4, Eleonora Porcu5, Giorgio Pistis6, Jovana Serbanovic-Canic2, Ulrich Elling7, Alison H. Goodall8, 9, Yann Labrune10, Lorna M. Lopez11, Reedik Ma¨gi13, Stuart Meacham2, Yukinori Okada14, Nicola Pirastu16, 102, Rossella Sorice17, Alexander Teumer18, Katrin Voss2, Weihua Zhang19, Ramiro Ramirez-Solis3, Joshua C. Bis20, David Ellinghaus21, Martin Go¨ gele22, Jouke-Jan Hottenga23, Claudia Langenberg24, Peter Kovacs25, Paul F. O'Reilly26, So-Youn Shin3, To~nu Esko28, Jaana Hartiala31, Stavroula Kanoni3, Federico Murgia34, Afshin Parsa35, Jonathan Stephens2, Pim van der Harst36, C. Ellen van der Schoot37, Hooman Allayee31, Antony Attwood2, Beverley Balkau38, François Bastardot40, Saonli Basu41, Sebastian E. Baumeister42, Ginevra Biino34, Lorenzo Bomba45, Ame´lie Bonnefond10, François Cambien46, John C. Chambers19, Francesco Cucca5, Pio D'Adamo16, Gail Davies12, Rudolf A. de Boer36, Eco J. C. de Geus23, Angela Do¨ ring47, Paul Elliott19, Jeanette Erdmann50, 101, David M. Evans51, Mario Falchi52, Wei Feng41, Aaron R. Folsom4, Ian H. Frazer53, Quince D. Gibson35, Nicole L. Glazer54, Chris Hammond27, Anna-Liisa Hartikainen55, Susan R. Heckbert56, Christian Hengstenberg58, Micha Hersch59, Thomas Illig60, Ruth J. F. Loos24, Jennifer Jolley2, Kay Tee Khaw61, Brigitte Ku¨hnel1, Marie-Christine Kyrtsonis63, Vasiliki Lagou64, Heather Lloyd-Jones2, Thomas Lumley65, Massimo Mangino27, Andrea Maschio5, Irene Mateo Leach36, Barbara McKnight65, Yasin Memari3, Braxton D. Mitchell35, Grant W. Montgomery66, Yusuke Nakamura67, Matthias Nauck68, Gerjan Navis69, Ute No¨ thlings70, Ilja M. Nolte72, David J. Porteous11, Anneli Pouta55, Peter P. Pramstaller22, Janne Pullat28, Susan M. Ring51, Jerome I. Rotter75, Daniela Ruggiero17, Aimo Ruokonen76, Cinzia Sala6, Nilesh J. Samani8, Jennifer Sambrook2, David Schlessinger78, Stefan Schreiber21, Heribert Schunkert50, James Scott79, Nicholas L. Smith56, Harold Snieder72, John M. Starr11, Michael Stumvoll82, Atsushi Takahashi14, W. H. Wilson Tang83, Kent Taylor75, Albert Tenesa85, Swee Lay Thein87, Anke To¨njes82, Manuela Uda5, Sheila Ulivi16, Dirk J. van Veldhuisen36, Peter M. Visscher11, Uwe Vo¨lker18, H.-Erich Wichmann47, Kerri L. Wiggins20, Gonneke Willemsen23, Tsun-Po Yang3, Jing Hua Zhao24, Paavo Zitting89, John R. Bradley77, George V. Dedoussis33, Paolo Gasparini16, Stanley L. Hazen83, Andres Metspalu28, Mario Pirastu34, Alan R. Shuldiner35, L. Joost van Pelt91, Jaap-Jan Zwaginga92, Dorret I.Boomsma23, Ian J. Deary11, Andre Franke21, Philippe Froguel10, Santhi K. Ganesh93, Marjo-Riitta Jarvelin26, Nicholas G. Martin66, Christa Meisinger48, Bruce M. Psaty57, Timothy D. Spector27, Nicholas J. Wareham24, Jan-Willem N. Akkerman97, Marina Ciullo17, Panos Deloukas3, Andreas Greinacher98, Steve Jupe99, Naoyuki Kamatani14, Jyoti Khadake99, Jaspal S. Kooner79, Josef Penninger7, Inga Prokopenko64, Derek Stemple3, Daniela Toniolo6, Lorenz Wernisch100, Serena Sanna5, Andrew A. Hicks22, Augusto Rendon2, 100, Manuel A. Ferreira 66, Willem H. Ouwehand 2, Nicole Soranzo3, Christian, Gieger, Aparna, Radhakrishnan, Ana, Cvejic, Weihong, Tang, Eleonora, Porcu, Giorgio, Pisti, Jovana Serbanovic, Canic, Ulrich, Elling, Alison H., Goodall, Yann, Labrune, Lorna M., Lopez, Reedik, Mägi, Stuart, Meacham, Yukinori, Okada, Pirastu, Nicola, Rossella, Sorice, Alexander, Teumer, Katrin, Vo, Weihua, Zhang, Ramiro Ramirez, Soli, Joshua C., Bi, David, Ellinghau, Martin, Gögele, Jouke Jan, Hottenga, Claudia, Langenberg, Peter, Kovac, Paul F., O’Reilly, So Youn, Shin, Tõnu, Esko, Jaana, Hartiala, Stavroula, Kanoni, Federico, Murgia, Afshin, Parsa, Jonathan, Stephen, Pim van der, Harst, C., Ellen van der Schoot, Hooman, Allayee, Antony, Attwood, Beverley, Balkau, François, Bastardot, Saonli, Basu, Sebastian E., Baumeister, Ginevra, Biino, Lorenzo, Bomba, Amélie, Bonnefond, François, Cambien, John C., Chamber, Francesco, Cucca, D'Adamo, ADAMO PIO, Gail, Davie, Rudolf A., de Boer, Eco J. C., de Geu, Angela, Döring, Paul, Elliott, Jeanette, Erdmann, David M., Evan, Mario, Falchi, Wei, Feng, Aaron R., Folsom, Ian H., Frazer, Quince D., Gibson, Nicole L., Glazer, Chris, Hammond, Anna Liisa, Hartikainen, Susan R., Heckbert, Christian, Hengstenberg, Micha, Hersch, Thomas, Illig, Ruth J. F., Loo, Jennifer, Jolley, Kay Tee, Khaw, Brigitte, Kühnel, Marie Christine, Kyrtsoni, Vasiliki, Lagou, Heather Lloyd, Jone, Thomas, Lumley, Massimo, Mangino, Andrea, Maschio, Irene Mateo, Leach, Barbara, Mcknight, Yasin, Memari, Braxton D., Mitchell, Grant W., Montgomery, Yusuke, Nakamura, Matthias, Nauck, Gerjan, Navi, Ute, Nöthling, Ilja M., Nolte, David J., Porteou, Anneli, Pouta, Peter P., Pramstaller, Janne, Pullat, Susan M., Ring, Jerome I., Rotter, Daniela, Ruggiero, Aimo, Ruokonen, Cinzia, Sala, Nilesh J., Samani, Jennifer, Sambrook, David, Schlessinger, Stefan, Schreiber, Heribert, Schunkert, James, Scott, Nicholas L., Smith, Harold, Snieder, John M., Starr, Michael, Stumvoll, Atsushi, Takahashi, W. H., Wilson Tang, Kent, Taylor, Albert, Tenesa, Swee Lay, Thein, Anke, Tönje, Manuela, Uda, Sheila, Ulivi, Dirk J., van Veldhuisen, Peter M., Visscher, Uwe, Völker, H., Erich Wichmann, Kerri L., Wiggin, Gonneke, Willemsen, Tsun Po, Yang, Jing Hua, Zhao, Paavo, Zitting, John R., Bradley, George V., Dedoussi, Gasparini, Paolo, Stanley L., Hazen, Andres, Metspalu, Mario, Pirastu, Alan R., Shuldiner, L., Joost van Pelt, Jaap Jan, Zwaginga, Dorret I., Boomsma, Ian J., Deary, Andre, Franke, Philippe, Froguel, Santhi K., Ganesh, Marjo Riitta, Jarvelin, Nicholas G., Martin, Christa, Meisinger, Bruce M., Psaty, Timothy D., Spector, Nicholas J., Wareham, Jan Willem N., Akkerman, Marina, Ciullo, Panos, Delouka, Andreas, Greinacher, Steve, Jupe, Naoyuki, Kamatani, Jyoti, Khadake, Jaspal S., Kooner, Josef, Penninger, Inga, Prokopenko, Derek, Stemple, Daniela, Toniolo, Lorenz, Wernisch, Serena, Sanna, Andrew A., Hick, Augusto, Rendon, Manuel A., Ferreira, Willem H., Ouwehand, Nicole, Soranzo, Faculteit Medische Wetenschappen/UMCG, Cardiovascular Centre (CVC), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Vascular Ageing Programme (VAP), Life Course Epidemiology (LCE), Biological Psychology, EMGO+ - Mental Health, Landsteiner Laboratory, and Clinical Haematology
Subjects: Blood Platelets, Netherlands Twin Register (NTR), BLOOD, Transcription, Genetic, Population, LOCI, Genome-wide association study, Genomics, 030204 cardiovascular system & hematology, Biology, HEMATOPOIESIS, Article, 03 medical and health sciences, 0302 clinical medicine, Animals, Drosophila Proteins, Humans, Gene silencing, Gene Silencing, Protein Interaction Maps, GENOME-WIDE ASSOCIATION, education, Gene, Zebrafish, POPULATION, Cell Size, 030304 developmental biology, Megakaryopoiesis, Genetics, platelet, 0303 health sciences, education.field_of_study, Multidisciplinary, Genome, Human, Platelet Count, Gene Expression Profiling, Zebrafish Proteins, Europe, Gene expression profiling, DROSOPHILA, Drosophila melanogaster, Human genome, Megakaryocytes, Genome-Wide Association Study
Abstract: Platelets are the second most abundant cell type in blood and are essential for maintaining haemostasis. Their count and volume are tightly controlled within narrow physiological ranges, but there is only limited understanding of the molecular processes controlling both traits. Here we carried out a high-powered meta-analysis of genome-wide association studies (GWAS) in up to 66,867 individuals of European ancestry, followed by extensive biological and functional assessment. We identified 68 genomic loci reliably associated with platelet count and volume mapping to established and putative novel regulators of megakaryopoiesis and platelet formation. These genes show megakaryocyte-specific gene expression patterns and extensive network connectivity. Using gene silencing in Danio rerio and Drosophila melanogaster, we identified 11 of the genes as novel regulators of blood cell formation. Taken together, our findings advance understanding of novel gene functions controlling fate-determining events during megakaryopoiesis and platelet formation, providing a new example of successful translation of GWAS to function. © 2011 Macmillan Publishers Limited. All rights reserved.
Published: 2011
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634 results on '"Peter M, Visscher"'

601. Bodyweight QTL on mouse chromosomes 4 and 11 by selective genotyping: regression v. maximum likelihood

602. Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates

603. Correction for Thomas et al., 2004, Genet. Epidemiol. 26:116-124

604. Assumption-free Estimation of Heritability from Genome-wide Identity-by-descent Sharing between Fullsibs

605. Combined genome scans for body stature in 6602 European twins: evidence for common Caucasian loci

606. Is It in Your Genes? The Influence of Genes on Common Disorders and Diseases that Affect You and Your Family. P. R. R<scp>EILLY</scp>. Cold Spring Harbor Laboratory Press. 2004. 304 pages. ISBN 0 87969 721 0. Price $19.95 (paperback). ISBN 0 87969 719 9. Price $29.95 (hardback)

607. Mapping genes for growth rate and fatness in a Large White x Meishan F2 pig population

608. Bodyweight QTL on mouse chromosomes 4 and 11 by selective genotyping: regression v. maximum likelihood.

609. Power of direct vs. indirect haplotyping in association studies.

610. Simple method to analyze SNP-based association studies using DNA pools.

611. Molecular genetic contributions to socioeconomic status and intelligence

612. Dominance Genetic Variation Contributes Little to the Missing Heritability for Human Complex Traits

613. Common Variants of Large Effect in F12, KNG1, and HRG Are Associated with Activated Partial Thromboplastin Time

614. On the mapping of QTL by regression of phenotype on marker-type

615. Is schizophrenia linked to chromosome 1q?

616. A genome scan for quantitative trait loci in a wild population of red deer (Cervus elaphus)

617. The genetic architecture of economic and political preferences

618. A parsimonious model for mass-univariate vertex-wise analysis

619. Inter-individual cortical and subcortical similarity is associated with differences in psychiatric, cognition and socio-economic traits

620. A nonparametric bootstrap method for testing close linkage vs. pleiotropy of coincident quantitative trait loci

621. Combined analyses of data from quantitative trait loci mapping studies: Chromosome 4 effects on porcine growth and fatness

622. Contrasting models for lactation curve analysis

623. Signatures of negative selection in the genetic architecture of human complex traits

624. Shared genetic control of expression and methylation in peripheral blood

625. A genome-wide linkage scan for age-at-menarche in three populations of European descent

626. Autosomal genetic control of human gene expression does not differ across the sexes

627. Detection of multiple quantitative trait loci and their pleiotropic effects in outbred pig populations

628. Genomic contributions in livestock gene introgression programmes

629. Univariate and multivariate parameter estimates for milk production traits using an animal model. II. Efficiency of selection when using simplified covariance structures

630. The Genetic Architecture of Gene Expression in Peripheral Blood

631. The value of relatives with phenotypes but missing genotypes in association studies for quantitative traits.

632. Association of polygenic score for major depression with response to lithium in patients with bipolar disorder

633. Maps of open chromatin highlight cell type-restricted patterns of regulatory sequence variation at hematological trait loci

634. New gene functions in megakaryopoiesis and platelet formation

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634 results on '"Peter M, Visscher"'

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