Search

Your search keyword '"QUANTITATIVE-TRAIT LOCI"' showing total 4 results
Start Over Descriptor "QUANTITATIVE-TRAIT LOCI" Topic dna
4 results on '"QUANTITATIVE-TRAIT LOCI"'

1. Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits.

Author

Hannon E, Gorrie-Stone TJ, Smart MC, Burrage J, Hughes A, Bao Y, Kumari M, Schalkwyk LC, and Mill J

Subjects
Genome-Wide Association Study methods, Humans, Longitudinal Studies, Phenotype, Quantitative Trait, Heritable, Transcription, Genetic genetics, DNA genetics, DNA Methylation genetics, Epigenesis, Genetic genetics, Gene Expression genetics, Genetic Variation genetics, Quantitative Trait Loci genetics, Transcriptome genetics
Abstract

Characterizing the complex relationship between genetic, epigenetic, and transcriptomic variation has the potential to increase understanding about the mechanisms underpinning health and disease phenotypes. We undertook a comprehensive analysis of common genetic variation on DNA methylation (DNAm) by using the Illumina EPIC array to profile samples from the UK Household Longitudinal study. We identified 12,689,548 significant DNA methylation quantitative trait loci (mQTL) associations (p < 6.52 × 10 -14 ) occurring between 2,907,234 genetic variants and 93,268 DNAm sites, including a large number not identified by previous DNAm-profiling methods. We demonstrate the utility of these data for interpreting the functional consequences of common genetic variation associated with > 60 human traits by using summary-data-based Mendelian randomization (SMR) to identify 1,662 pleiotropic associations between 36 complex traits and 1,246 DNAm sites. We also use SMR to characterize the relationship between DNAm and gene expression and thereby identify 6,798 pleiotropic associations between 5,420 DNAm sites and the transcription of 1,702 genes. Our mQTL database and SMR results are available via a searchable online database as a resource to the research community., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Published
2018
Full Text
View/download PDF

2. Molecular biology to infer phenotypes of forensic and ancient remains in bioarchaeology -- Review.

Author

Dorado, Gabriel, Luque, Fernando, Pascual, Plácido, Jiménez, Inmaculada, Sánchez-Cañete, Francisco Javier S., Raya, Patricia, Sáiz, Jesús, Sánchez, Adela, Rosales, Teresa E., Vásquez, Víctor F., and Hernández, Pilar

Subjects
*NEANDERTHALS, *PHENOTYPES, *MOLECULAR biology, *ARTIFICIAL intelligence, *FORENSIC anthropology, *ARCHAEOLOGICAL human remains, *GENOTYPES
Abstract

The phenotype is the result of the interaction of the genotype with epigenetic factors and the environment. It has been considered that genetics is responsible for >70% of facial phenotype. Interestingly, besides sequencing nucleic acids, it is possible to generate DNA methylation maps of ancient remains, to determine phenotypes. Other application areas are medicine, forensics and law-enforcement. Thus, breakthroughs in nucleic-acid analyses are allowing to determine the phenotype from genotypic data. That is also being facilitated by developments in software (including parallel processing) and hardware, including neural engines (neural-network hardware). New strategies involving artificial intelligence and machine learning have been also deployed to reach such a goal. Phenotyping is a challenging task, on the edge of current technology. Promising results have already been obtained, including prediction of Neanderthal (Homo sapiens neanderthalensis) and Denisovan (Homo sapiens denisova) faces. The future is promising in this research area, in which ethical and legal implications should also be considered. [ABSTRACT FROM AUTHOR]

Published
2021

3. Biología molecular para inferir fenotipos de restos forenses y antiguos en bioarqueología -- Revisión.

Author

Dorado, Gabriel, Luque, Fernando, Pascual, Plácido, Jiménez, Inmaculada, Sánchez-Cañete, Francisco Javier S., Raya, Patricia, Sáiz, Jesús, Sánchez, Adela, Rosales, Teresa E., Vásquez, Víctor F., and Hernández, Pilar

Subjects
*NEANDERTHALS, *ARTIFICIAL intelligence, *DNA methylation, *NUCLEIC acids, *GENOTYPES
Abstract

The phenotype is the result of the interaction of the genotype with epigenetic factors and the environment. It has been considered that genetics is responsible for >70% of facial phenotype. Interestingly, besides sequencing nucleic acids, it is possible to generate DNA methylation maps of ancient remains, to determine phenotypes. Other application areas are medicine, forensics and law-enforcement. Thus, breakthroughs in nucleic-acid analyses are allowing to determine the phenotype from genotypic data. That is also being facilitated by developments in software (including parallel processing) and hardware, including neural engines (neural-network hardware). New strategies involving artificial intelligence and machine learning have been also deployed to reach such a goal. Phenotyping is a challenging task, on the edge of current technology. Promising results have already been obtained, including prediction of Neanderthal (Homo sapiens neanderthalensis) and Denisovan (Homo sapiens denisova) faces. The future is promising in this research area, in which ethical and legal implications should also be considered. [ABSTRACT FROM AUTHOR]

Published
2021

4. 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