Search

Your search keyword '"QUANTITATIVE-TRAIT LOCI"' showing total 5 results
Start Over Descriptor "QUANTITATIVE-TRAIT LOCI" Topic 0303 health sciences
5 results on '"QUANTITATIVE-TRAIT LOCI"'

1. Meta-analysis of genome-wide linkage studies in BMI and obesity

Author

Mariza de Andrade, Philippe Froguel, Steven Stone, Adebowale Adeyemo, Michael P. Stern, David A. Collier, Cathryn M. Lewis, Rector Arya, Catherine L. Saunders, Susan Redline, Miina Öhman, Anthony G. Comuzzie, Robert L. Hanson, Gerald S. Berenson, John Blangero, Benedetta D. Chiodini, Wei Chen, Lisa J. Martin, Lyle J. Palmer, Leena Peltonen, Amy Luke, Ingrid B. Borecki, Hong-Wen Deng, Pak C. Sham, Yvon C. Chagnon, M W Nash, R. Arlen Price, Heather M. Stringham, Mary F. Feitosa, Stephen T. Turner, Cisca Wijmenga, Sathanur R. Srinivasan, Michael Boehnke, Markus Perola, Ravindranath Duggirala, Patricia Huezo-Dias, Victor Abkevich, Ahmed H. Kissebah, Weidong Li, Johannes Hebebrand, and Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI)

Subjects
SCAN METAANALYSIS, hypertension, Genetic Linkage, Endocrinology, Diabetes and Metabolism, Medizin, Medicine (miscellaneous), Locus (genetics), Quantitative trait locus, FTO gene, Childhood obesity, Body Mass Index, 03 medical and health sciences, Diabetes mellitus genetics, Endocrinology, Genetic linkage, OBSTRUCTIVE SLEEP-APNEA, medicine, Diabetes Mellitus, Humans, genetics, Obesity, SUSCEPTIBILITY LOCUS, 030304 developmental biology, METABOLIC SYNDROME, Genetics, 0303 health sciences, adiposity, Nutrition and Dietetics, diabetes, business.industry, Genetic heterogeneity, Genome, Human, 030305 genetics & heredity, DIABETES-MELLITUS, BIPOLAR DISORDER, SEARCH METAANALYSIS, medicine.disease, meta-analysis, BODY-MASS INDEX, Meta-analysis, CHILDHOOD OBESITY, business, QUANTITATIVE-TRAIT LOCI
Abstract

Objective: The objective was to provide an overall assessment of genetic linkage data of BMI and BMI-defined obesity using a nonparametric genome scan meta-analysis. Research Methods and Procedures: We identified 37 published studies containing data on over 31,000 individuals from more than >10,000 families and obtained genome-wide logarithm of the odds (LOD) scores, non-parametric linkage (NPL) scores, or maximum likelihood scores (MLS). BMI was analyzed in a pooled set of all studies, as a subgroup of 10 studies that used BMI-defined obesity, and for subgroups ascertained through type 2 diabetes, hypertension, or subjects of European ancestry. Results: Bins at chromosome 13q13.2- q33.1, 12q23-q24.3 achieved suggestive evidence of linkage to BMI in the pooled analysis and samples ascertained for hypertension. Nominal evidence of linkage to these regions and suggestive evidence for 11q13.3-22.3 were also observed for BMI-defined obesity. The FTO obesity gene locus at 16q12.2 also showed nominal evidence for linkage. However, overall distribution of summed rank p values

Published
2007

2. Multiple Phenotype Modeling in Gene-Mapping Studies of Quantitative Traits: Power Advantages

Author

Robert C. Elston, David B. Allison, Nicholas J. Schork, Pamela St. Jean, Ming C. Infante, and Bonnie Thiel

Subjects
Multifactorial Inheritance, Multivariate analysis, Statistical methods, Genetic Linkage, Locus (genetics), Computational biology, Biology, Quantitative trait locus, 03 medical and health sciences, Quantitative Trait, Heritable, Gene mapping, Genetic linkage, Genetics, Humans, Genetics(clinical), Genetics (clinical), Quantitative-trait loci, 030304 developmental biology, 0303 health sciences, Models, Statistical, Models, Genetic, Siblings, 030305 genetics & heredity, Chromosome Mapping, Reproducibility of Results, Statistical model, Phenotype, Sample size determination, Linkage analysis, Research Article
Abstract

SummaryGenomewide searches for loci influencing complex human traits and diseases such as diabetes, hypertension, and obesity are often plagued by low power and interpretive difficulties. Attempts to remedy these difficulties have typically relied on, and have promoted the use of, novel subject-ascertainment schemes, larger sample sizes, a greater density of DNA markers, and more-sophisticated statistical modeling and analysis strategies. Many of these remedies can be costly to implement. We investigate the utility of a simple statistical model for the mapping of quantitative-trait loci that incorporates multiple phenotypic or diagnostic endpoints into a gene-mapping analysis. The approach considers finding a linear combination of multiple phenotypic values that maximizes the evidence for linkage to a locus. Our results suggest that substantial increases in the power to map loci can be obtained with the proposed technique, although the increase in power obtained is a function of the size and direction of the residual correlation among the phenotypes used in the analysis. Extensive simulation studies are described that justify these claims, for cases in which two phenotypic measures are analyzed. This approach can be easily extended to cover more-complex situations and may provide a basis for more insightful genetic-analysis paradigms.

Published
1998
Full Text
View/download PDF

3. Integration of mouse and human genome-wide association data identifies KCNIP4 as an asthma gene

Author

Scott T. Weiss, Steve D. Shapiro, Annerose Berndt, W. James Gauderman, Jody Senter-Sylvia, Rachel A. Myers, Mayumi Tamari, Dan L. Nicolae, Robert S. Zeiger, Stephen P. Peters, John Ziniti, Benjamin A. Raby, Lindsey A. Roth, James J. Yang, Charles G. Irvin, Barbara J. Klanderman, Isabelle Romieu, John J. Lima, Maartje A.E. Nieuwenhuis, Kelan G. Tantisira, Michiaki Kubo, Judith M. Vonk, Fernando D. Martinez, Dara G. Torgerson, Christopher R. Gignoux, Beverly Paigen, Homer A. Boushey, Carole Ober, Stephanie J. London, Elliot Israel, Keith Sheppard, Eugene R. Bleecker, Blanca E. Himes, Edwin K. Silverman, Esteban G. Burchard, Kathleen C. Barnes, Frank D. Gilliland, Albert M. Levin, Gary A. Churchill, David T. Mauger, Dawn L. DeMeo, Scott Huntsman, Stanley J. Szefler, Augusto A. Litonjua, Vernon M. Chinchilli, Robert C. Strunk, Celeste Eng, L. Keoki Williams, Deborah A. Meyers, Yusuke Nakamura, Robert F. Lemanske, Gerard H. Koppelman, Adriana S. Leme, Dirkje S. Postma, Rasika A. Mathias, Groningen Research Institute for Asthma and COPD (GRIAC), and Groningen Research Institute of Pharmacy

Subjects
Male, Mouse, Pulmonology, lcsh:Medicine, Genome-wide association study, Mice, 0302 clinical medicine, Genetics of the Immune System, lcsh:Science, POPULATION, Animal Management, Genetics, 0303 health sciences, education.field_of_study, Multidisciplinary, Kv Channel-Interacting Proteins, Genomics, Animal Models, 3. Good health, INBRED MICE, Phenotype, Medicine, INDUCED AIRWAY HYPERRESPONSIVENESS, Female, SUBSPECIFIC ORIGIN, Research Article, QUANTITATIVE-TRAIT LOCI, Genotype, Clinical Research Design, Population, Immunology, Single-nucleotide polymorphism, Biology, Quantitative trait locus, UNDERLYING ASTHMA, Polymorphism, Single Nucleotide, 03 medical and health sciences, Model Organisms, Genome Analysis Tools, SEARCH, Genome-Wide Association Studies, SNP, Animals, Humans, education, 030304 developmental biology, Base Sequence, lcsh:R, Laboratory mouse, Computational Biology, CHILDHOOD ASTHMA, Human Genetics, Human genetics, Asthma, LABORATORY MOUSE, respiratory tract diseases, MODEL, 030228 respiratory system, Genetics of Disease, lcsh:Q, Human genome, Clinical Immunology, Veterinary Science, Meta-Analyses, Animal Genetics, Genome-Wide Association Study
Abstract

Asthma is a common chronic respiratory disease characterized by airway hyperresponsiveness (AHR). The genetics of asthma have been widely studied in mouse and human, and homologous genomic regions have been associated with mouse AHR and human asthma-related phenotypes. Our goal was to identify asthma-related genes by integrating AHR associations in mouse with human genome-wide association study (GWAS) data. We used Efficient Mixed Model Association (EMMA) analysis to conduct a GWAS of baseline AHR measures from males and females of 31 mouse strains. Genes near or containing SNPs with EMMA p-values

Published
2013
Full Text
View/download PDF

4. QTL meta-analysis provides a comprehensive view of loci controlling partial resistance to Aphanomyces euteiches in four sources of resistance in pea

Author

Céline Hamon, Kevin E. McPhee, Angélique Lesné, Rebecca J. McGee, Clarice J. Coyne, Marie Hervé, Martine Roux-Duparque, Alain Baranger, Pierre Mangin, R. Esnault, Isabelle Le Goff, Marie-Laure Pilet-Nayel, Régine Delourme, G. Deniot, G. Morin, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Institut National de la Recherche Agronomique (INRA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), USDA, ARS, Western Reg Plant Intro Stn, Washington State University (WSU), Grain Legume Genet & Physiol Res Unit, United States Department of Agriculture, Domaine expérimental d'Époisses - UE0115 U2E (DIJ EPOISSES), Institut National de la Recherche Agronomique (INRA), GSP, Domaine Brunehaut, Department 7670 [Fargo], North Dakota State University (NDSU), INRA, Departement de Genetique et Amelioration des Plantes (France), MAP (Ministere de l'Agriculture et de la peche, Paris, France), UNIP (Union Nationale Interprofessionnelle des Plantes riches en proteines, Paris, France), AGROCAMPUS OUEST-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Recherche Agronomique (INRA), Domaine expérimental d'Époisses (DIJ EPOISSES), and Institut National de la Recherche Agronomique (INRA)-Université de Rennes (UR)-AGROCAMPUS OUEST

Subjects
0106 biological sciences, Germplasm, Candidate gene, Genetic Linkage, QTL diversity, [SDV]Life Sciences [q-bio], Plant Science, 01 natural sciences, Plant Roots, CANDIDATE GENES, DISEASE RESISTANCE, 2. Zero hunger, Genetics, 0303 health sciences, education.field_of_study, biology, Chromosome Mapping, food and beverages, MYCOSPHAERELLA-PINODES, Aphanomyces root rot, Pisum sativum, Meta-QTL, Consistent genomic regions, Resistance alleles, Undesirable alleles, QUANTITATIVE-TRAIT LOCI, PISUM-SATIVUM-L, ROOT-ROT, GENETIC ARCHITECTURE, MEDICAGO-TRUNCATULA, CONFERRING RESISTANCE, PLANT-RESISTANCE, Aphanomyces euteiches, Research Article, Population, Quantitative Trait Loci, Quantitative trait locus, Plant disease resistance, Aphanomyces, Chromosomes, Plant, 03 medical and health sciences, Genetic linkage, education, 030304 developmental biology, Plant Diseases, Peas, biology.organism_classification, Genetic architecture, Agronomy, 010606 plant biology & botany
Abstract

Background Development of durable plant genetic resistance to pathogens through strategies of QTL pyramiding and diversification requires in depth knowledge of polygenic resistance within the available germplasm. Polygenic partial resistance to Aphanomyces root rot, caused by Aphanomyces euteiches, one of the most damaging pathogens of pea worldwide, was previously dissected in individual mapping populations. However, there are no data available regarding the diversity of the resistance QTL across a broader collection of pea germplasm. In this study, we performed a meta-analysis of Aphanomyces root rot resistance QTL in the four main sources of resistance in pea and compared their genomic localization with genes/QTL controlling morphological or phenological traits and with putative candidate genes. Results Meta-analysis, conducted using 244 individual QTL reported previously in three mapping populations (Puget x 90–2079, Baccara x PI180693 and Baccara x 552) and in a fourth mapping population in this study (DSP x 90–2131), resulted in the identification of 27 meta-QTL for resistance to A. euteiches. Confidence intervals of meta-QTL were, on average, reduced four-fold compared to mean confidence intervals of individual QTL. Eleven consistent meta-QTL, which highlight seven highly consistent genomic regions, were identified. Few meta-QTL specificities were observed among mapping populations, suggesting that sources of resistance are not independent. Seven resistance meta-QTL, including six of the highly consistent genomic regions, co-localized with six of the meta-QTL identified in this study for earliness and plant height and with three morphological genes (Af, A, R). Alleles contributing to the resistance were often associated with undesirable alleles for dry pea breeding. Candidate genes underlying six main meta-QTL regions were identified using colinearity between the pea and Medicago truncatula genomes. Conclusions QTL meta-analysis provided an overview of the moderately low diversity of loci controlling partial resistance to A. euteiches in four main sources of resistance in pea. Seven highly consistent genomic regions with potential use in marker-assisted-selection were identified. Confidence intervals at several main QTL regions were reduced and co-segregation among resistance and morphological/phenological alleles was identified. Further work will be required to identify the best combinations of QTL for durably increasing partial resistance to A. euteiches.

Published
2013
Full Text
View/download PDF

5. Blood Pressure Loci Identified with a Gene-Centric Array

Author

Meena Kumari, Claire E. Hastie, Jonathan Stephens, Patricia B. Munroe, Christopher P. Nelson, John M. C. Connell, Martin D. Tobin, Hani Neuvrith, Jean Tichet, Paul Burton, Gudrun Veldre, Daniele Cusi, Anna Levinsson, Paul Elliott, Elin Org, Erika Salvi, Tina Shah, Nabila Devos, Harm-Jan Westra, Juan-Pablo Casas, Olle Melander, Jan A. Staessen, Nilesh J. Samani, Jackie A. Cooper, Peter S. Braund, Neil R Poulter, Alive V. Stanton, Abiodun Onipinla, Marcel G. M. Wolfs, Steve E. Humphries, Philippa J. Talmud, Rebecca Hardy, Fotios Drenos, Maris Laan, Mark J. Caulfield, Richard Dobson, Willem H. Ouwehand, Yun Zhang, Li Chen, SG Wannamethee, Sue Shaw-Hawkins, Christopher Newton-Cheh, Eoin O'Brien, Simon A. McG. Thom, Maria Grazia Franzosi, Charles A. Mein, Mika Kivimäki, Tom R. Gaunt, Jennifer G. Sambrook, Andrea Stucchi, Annika Rosengren, Sonia Shah, Thomas Hedner, George A. Wells, Dag S. Thelle, Debbie A Lawlor, Pierre-François Plouin, Fredrik Nyberg, Richard W Morris, Ian N M Day, Vesela Gateva, Lude Franke, Peter S. Sever, Morris J. Brown, Margus Putku, Sandosh Padmanabhan, John F. Peden, Pim van der Harst, Jutta Palmen, Ioanna Tzoulaki, Alexandre F.R. Stewart, Andrew Wong, Peeter Juhanson, Marciej Tomaszewski, Alison H. Goodall, Martin Farrall, Wai K. Lee, Nicola Glorioso, Hugh Watkins, Xavier Jeunemaitre, Anders Hamsten, Robert Clarke, Anna F. Dominiczak, Mark Lathrop, Stephen Newhouse, Margus Viigimaa, George Davey Smith, Robert Roberts, John C. Whittaker, Michael V. Holmes, Toby Johnson, F. Gerald R. Fowkes, Aroon D. Hingorani, Udo Seedorf, Siim Sõber, Denis C. Shields, Diana Kuh, Chris Wallace, Philip Howard, Peter H. Whincup, Gonçalo R. Abecasis, Anuj Goel, Christian Delles, Björn Wahlstrand, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Cardiovascular Centre (CVC), Stem Cell Aging Leukemia and Lymphoma (SALL), Cardiogenics Consortium, Global BPgen Consortium, Newton-Cheh, C., Johnson, T., Gateva, V., Tobin, M.D., Bochud, M., Coin, L., Najjar, S.S., Hua, J., Heath, S.C., Eyheramendy, S., Papadakis, K., Voight, B.F., Scott, L.J., Zhang, F., Farrall, M., Tanaka, T., Wallace, C., Chambers, J.C., Khaw, K.T., Nilsson, P., van der Harst, P., Polidoro, S., Grobbee, D.E., Onland-Moret, N.C., Bots, M.L., Wain, L.V., Elliott, K.S., Teumer, A., Luan, J., Lucas, G., Kuusisto, J., Burton, P.R., Hadley, D., McArdle, W.L., Wellcome Trust Case Control Consortium, X, Brown, M., Dominiczak, A., Newhouse, S.J., Samani, N.J., Webster, J., Zeggini, E., Beckmann, J.S., Bergmann, S., Lim, N., Song, K., Vollenweider, P., Waeber, G., Waterworth, D.M., Yuan, X., Groop, L., Orho, M., Allione, A., Di Gregorio, A., Guarrera, S., Panico, S., Ricceri, F., Romanazzi, V., Sacerdote, C., Vineis, P., Barroso, I., Sandhu, M.S., Luben, R.N., Crawford, G.J., Jousilahti, P., Perola, M., Boehnke, M., Bonnycastle, L.L., Collins, F.S., Jackson, A.U., Mohlke, K.L., Stringham, H.M., Valle, T.T., Willer, C.J., Bergman, R.N., Morken, M.A., Döring, A., Gieger, C., Illig, T., Meitinger, T., Org, E., Pfeufer, A., Wichmann, H.E., Kathiresan, S., Marrugat, J., O'Donnell, C.J., Schwartz, S.M., Siscovick, D.S., Subirana, I., Freimer, N.B., Hartikainen, A.L., McCarthy, M.I., OReilly, P.F., Peltonen, L., Pouta, A., de Jong, P.E., Snieder, H., van Gilst, W.H., Clarke, R., Goel, A., Hamsten, A., Peden, J.F., Seedorf, U., Syv, C., Tognoni, G., Lakatta, E.G., Sanna, S., Scheet, P., Schlessinger, D., Scuteri, A., Dörr, M., Ernst, F., Felix, S.B., Homuth, G., Lorbeer, R., Reffelmann, T., Rettig, R., Völker, U., Galan, P., Gut, I.G., Hercberg, S., Lathrop, G.M., Zeleneka, D., Deloukas, P., Soranzo, N., Williams, F.M., Zhai, G., Salomaa, V., Laakso, M., Elosua, R., Forouhi, N.G., Völzke, H., Uiterwaal, C.S., van der Schouw, Y.T., Numans, M.E., Matullo, G., Navis, G., Berglund, G., Bingham, S.A., Kooner, J.S., Paterson, A.D., Connell, J.M., Bandinelli, S., Ferrucci, L., Watkins, H., Spector, T.D., Tuomilehto, J., Altshuler, D., Strachan, D.P., Laan, M., Meneton, P., Wareham, N.J., Uda, M., Jarvelin, M.R., Mooser, V., Melander, O., Loos, R.J., Elliott, P., Abecasis, G.R., Caulfield, M., Munroe, P.B., Attwood, T., Belz, S., Braund, P., Brocheton, J., Cambien, F., Cooper, J., Crisp-Hihn, A., Diemert, P., Eardman, J., Foad, N., Godefroy, T., Goodall, A.H., Gracey, J., Gray, E., Gwilliams, R., Heimer, S., Hengstenberg, C., Jolley, J., Krishnan, U., Lloyd-Jones, H., Liljedahl, U., Lugauer, I., Lundmark, P., Maouche, S., Moore, J.S., Montalescot, G., Muir, D., Murray, E., Nelson, C.P., Neudert, J., Niblett, D., O'Leary, K., Ouwehand, W.H., Pollard, H., Proust, C., Rankin, A., Rendon, A., Rice, C.M., Sager, H.B., Sambrook, J., Schmitz, G., Scholz, M., Schroeder, L., Schunkert, H., Stephens, J., Syvannen, A.C., and Tennstedt, S.

Subjects
Male, Linkage disequilibrium, Methylenetetrahydrofolate Reductase (NADPH2)/genetics, cardiovascular-disease, population, Genome-wide association study, Genetic Loci, Blood Pressure, ANGIOTENSINOGEN, 030204 cardiovascular system & hematology, Linkage Disequilibrium, 0302 clinical medicine, Gene Frequency, Receptors, common variants, Genetics(clinical), ESSENTIAL-HYPERTENSION, Genetics (clinical), POPULATION, Oligonucleotide Array Sequence Analysis, Genetics, 0303 health sciences, education.field_of_study, Adult, Aged, Blood Pressure/genetics, Case-Control Studies, Female, Gene Expression Profiling, Genome-Wide Association Study, Haplotypes, Humans, Hypertension/genetics, Middle Aged, Plasma Membrane Calcium-Transporting ATPases/genetics, Polymorphism, Single Nucleotide, Receptors, Atrial Natriuretic Factor/genetics, Sequence Analysis, DNA, COMMON VARIANTS, Single Nucleotide, 3. Good health, SNP genotyping, CARDIOVASCULAR-DISEASE, Hypertension, Sequence Analysis, Atrial Natriuretic Factor, QUANTITATIVE-TRAIT LOCI, metaanalysis, essential-hypertension, SUSCEPTIBILITY LOCI, Population, Single-nucleotide polymorphism, Biology, Article, 03 medical and health sciences, Plasma Membrane Calcium-Transporting ATPases, Polymorphism, GENOME-WIDE ASSOCIATION, education, Genotyping, Allele frequency, METAANALYSIS, Methylenetetrahydrofolate Reductase (NADPH2), 030304 developmental biology, quantitative-trait loci, Haplotype, Receptors, Atrial Natriuretic Factor, DNA, susceptibility loci, angiotensinogen, Hypertension/*genetics, genome-wide association, linkage disequilibrium
Abstract

Raised blood pressure (BP) is a major risk factor for cardiovascular disease. Previous studies have identified 47 distinct genetic variants robustly associated with BP, but collectively these explain only a few percent of the heritability for BP phenotypes. To find additional BP loci, we used a bespoke gene-centric array to genotype an independent discovery sample of 25,118 individuals that combined hypertensive case-control and general population samples. We followed up four SNPs associated with BP at our p < 8.56 x 10(-7) study-specific significance threshold and six suggestively associated SNPs in a further 59,349 individuals. We identified and replicated a SNP at LSP1/TNNT3, a SNP at MTHFR-NPPB independent (r(2) = 0.33) of previous reports, and replicated SNPs at AGT and ATP2B1 reported previously. An analysis of combined discovery, and follow-up data identified SNPs significantly associated with BP at p < 8.56 x 10(-7) at four further loci (NPR3, FIFE, NOS3, and SOX6). The high number of discoveries made with modest genotyping effort can be attributed to using a large-scale yet targeted genotyping array and to the development of a weighting scheme that maximized power when meta-analyzing results from samples ascertained with extreme phenotypes, in combination with results from nonascertained or population samples. Chromatin immunoprecipitation and transcript expression data highlight potential gene regulatory mechanisms at the MTHFR and NOS3 loci. These results provide candidates for further study to help dissect mechanisms affecting BP and highlight the utility of studying SNPs and samples that are independent of those studied previously even when the sample size is smaller than that in previous studies. ispartof: American Journal of Human Genetics vol:89 issue:6 pages:688-700 ispartof: location:United States status: published

Published
2011
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results