7 results on '"Francks, C"'
Search Results
2. Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries.
- Author
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García-Marín LM, Campos AI, Diaz-Torres S, Rabinowitz JA, Ceja Z, Mitchell BL, Grasby KL, Thorp JG, Agartz I, Alhusaini S, Ames D, Amouyel P, Andreassen OA, Arfanakis K, Arias-Vasquez A, Armstrong NJ, Athanasiu L, Bastin ME, Beiser AS, Bennett DA, Bis JC, Boks MPM, Boomsma DI, Brodaty H, Brouwer RM, Buitelaar JK, Burkhardt R, Cahn W, Calhoun VD, Carmichael OT, Chakravarty M, Chen Q, Ching CRK, Cichon S, Crespo-Facorro B, Crivello F, Dale AM, Smith GD, de Geus EJC, De Jager PL, de Zubicaray GI, Debette S, DeCarli C, Depondt C, Desrivières S, Djurovic S, Ehrlich S, Erk S, Espeseth T, Fernández G, Filippi I, Fisher SE, Fleischman DA, Fletcher E, Fornage M, Forstner AJ, Francks C, Franke B, Ge T, Goldman AL, Grabe HJ, Green RC, Grimm O, Groenewold NA, Gruber O, Gudnason V, Håberg AK, Haukvik UK, Heinz A, Hibar DP, Hilal S, Himali JJ, Ho BC, Hoehn DF, Hoekstra PJ, Hofer E, Hoffmann W, Holmes AJ, Homuth G, Hosten N, Ikram MK, Ipser JC, Jack CR Jr, Jahanshad N, Jönsson EG, Kahn RS, Kanai R, Klein M, Knol MJ, Launer LJ, Lawrie SM, Hellard SL, Lee PH, Lemaître H, Li S, Liewald DCM, Lin H, Longstreth WT Jr, Lopez OL, Luciano M, Maillard P, Marquand AF, Martin NG, Martinot JL, Mather KA, Mattay VS, McMahon KL, Mecocci P, Melle I, Meyer-Lindenberg A, Mirza-Schreiber N, Milaneschi Y, Mosley TH, Mühleisen TW, Müller-Myhsok B, Maniega SM, Nauck M, Nho K, Niessen WJ, Nöthen MM, Nyquist PA, Oosterlaan J, Pandolfo M, Paus T, Pausova Z, Penninx BWJH, Pike GB, Psaty BM, Pütz B, Reppermund S, Rietschel MD, Risacher SL, Romanczuk-Seiferth N, Romero-Garcia R, Roshchupkin GV, Rotter JI, Sachdev PS, Sämann PG, Saremi A, Sargurupremraj M, Saykin AJ, Schmaal L, Schmidt H, Schmidt R, Schofield PR, Scholz M, Schumann G, Schwarz E, Shen L, Shin J, Sisodiya SM, Smith AV, Smoller JW, Soininen HS, Steen VM, Stein DJ, Stein JL, Thomopoulos SI, Toga AW, Tordesillas-Gutiérrez D, Trollor JN, Valdes-Hernandez MC, van T Ent D, van Bokhoven H, van der Meer D, van der Wee NJA, Vázquez-Bourgon J, Veltman DJ, Vernooij MW, Villringer A, Vinke LN, Völzke H, Walter H, Wardlaw JM, Weinberger DR, Weiner MW, Wen W, Westlye LT, Westman E, White T, Witte AV, Wolf C, Yang J, Zwiers MP, Ikram MA, Seshadri S, Thompson PM, Satizabal CL, Medland SE, and Rentería ME
- Subjects
- Humans, Female, Male, Organ Size genetics, Attention Deficit Disorder with Hyperactivity genetics, Attention Deficit Disorder with Hyperactivity pathology, Parkinson Disease genetics, Parkinson Disease pathology, Polymorphism, Single Nucleotide, Genomics methods, Adult, Genetic Predisposition to Disease, Middle Aged, White People genetics, Genome-Wide Association Study, Multifactorial Inheritance genetics, Brain pathology
- Abstract
Subcortical brain structures are involved in developmental, psychiatric and neurological disorders. Here we performed genome-wide association studies meta-analyses of intracranial and nine subcortical brain volumes (brainstem, caudate nucleus, putamen, hippocampus, globus pallidus, thalamus, nucleus accumbens, amygdala and the ventral diencephalon) in 74,898 participants of European ancestry. We identified 254 independent loci associated with these brain volumes, explaining up to 35% of phenotypic variance. We observed gene expression in specific neural cell types across differentiation time points, including genes involved in intracellular signaling and brain aging-related processes. Polygenic scores for brain volumes showed predictive ability when applied to individuals of diverse ancestries. We observed causal genetic effects of brain volumes with Parkinson's disease and attention-deficit/hyperactivity disorder. Findings implicate specific gene expression patterns in brain development and genetic variants in comorbid neuropsychiatric disorders, which could point to a brain substrate and region of action for risk genes implicated in brain diseases., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)
- Published
- 2024
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3. Discovery of 42 genome-wide significant loci associated with dyslexia.
- Author
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Doust C, Fontanillas P, Eising E, Gordon SD, Wang Z, Alagöz G, Molz B, Pourcain BS, Francks C, Marioni RE, Zhao J, Paracchini S, Talcott JB, Monaco AP, Stein JF, Gruen JR, Olson RK, Willcutt EG, DeFries JC, Pennington BF, Smith SD, Wright MJ, Martin NG, Auton A, Bates TC, Fisher SE, and Luciano M
- Subjects
- Child, Adult, Humans, Reading, Language, Asian People, Genome-Wide Association Study, Dyslexia genetics, Dyslexia psychology
- Abstract
Reading and writing are crucial life skills but roughly one in ten children are affected by dyslexia, which can persist into adulthood. Family studies of dyslexia suggest heritability up to 70%, yet few convincing genetic markers have been found. Here we performed a genome-wide association study of 51,800 adults self-reporting a dyslexia diagnosis and 1,087,070 controls and identified 42 independent genome-wide significant loci: 15 in genes linked to cognitive ability/educational attainment, and 27 new and potentially more specific to dyslexia. We validated 23 loci (13 new) in independent cohorts of Chinese and European ancestry. Genetic etiology of dyslexia was similar between sexes, and genetic covariance with many traits was found, including ambidexterity, but not neuroanatomical measures of language-related circuitry. Dyslexia polygenic scores explained up to 6% of variance in reading traits, and might in future contribute to earlier identification and remediation of dyslexia., (© 2022. The Author(s).)
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- 2022
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4. Genetic architecture of subcortical brain structures in 38,851 individuals.
- Author
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Satizabal CL, Adams HHH, Hibar DP, White CC, Knol MJ, Stein JL, Scholz M, Sargurupremraj M, Jahanshad N, Roshchupkin GV, Smith AV, Bis JC, Jian X, Luciano M, Hofer E, Teumer A, van der Lee SJ, Yang J, Yanek LR, Lee TV, Li S, Hu Y, Koh JY, Eicher JD, Desrivières S, Arias-Vasquez A, Chauhan G, Athanasiu L, Rentería ME, Kim S, Hoehn D, Armstrong NJ, Chen Q, Holmes AJ, den Braber A, Kloszewska I, Andersson M, Espeseth T, Grimm O, Abramovic L, Alhusaini S, Milaneschi Y, Papmeyer M, Axelsson T, Ehrlich S, Roiz-Santiañez R, Kraemer B, Håberg AK, Jones HJ, Pike GB, Stein DJ, Stevens A, Bralten J, Vernooij MW, Harris TB, Filippi I, Witte AV, Guadalupe T, Wittfeld K, Mosley TH, Becker JT, Doan NT, Hagenaars SP, Saba Y, Cuellar-Partida G, Amin N, Hilal S, Nho K, Mirza-Schreiber N, Arfanakis K, Becker DM, Ames D, Goldman AL, Lee PH, Boomsma DI, Lovestone S, Giddaluru S, Le Hellard S, Mattheisen M, Bohlken MM, Kasperaviciute D, Schmaal L, Lawrie SM, Agartz I, Walton E, Tordesillas-Gutierrez D, Davies GE, Shin J, Ipser JC, Vinke LN, Hoogman M, Jia T, Burkhardt R, Klein M, Crivello F, Janowitz D, Carmichael O, Haukvik UK, Aribisala BS, Schmidt H, Strike LT, Cheng CY, Risacher SL, Pütz B, Fleischman DA, Assareh AA, Mattay VS, Buckner RL, Mecocci P, Dale AM, Cichon S, Boks MP, Matarin M, Penninx BWJH, Calhoun VD, Chakravarty MM, Marquand AF, Macare C, Kharabian Masouleh S, Oosterlaan J, Amouyel P, Hegenscheid K, Rotter JI, Schork AJ, Liewald DCM, de Zubicaray GI, Wong TY, Shen L, Sämann PG, Brodaty H, Roffman JL, de Geus EJC, Tsolaki M, Erk S, van Eijk KR, Cavalleri GL, van der Wee NJA, McIntosh AM, Gollub RL, Bulayeva KB, Bernard M, Richards JS, Himali JJ, Loeffler M, Rommelse N, Hoffmann W, Westlye LT, Valdés Hernández MC, Hansell NK, van Erp TGM, Wolf C, Kwok JBJ, Vellas B, Heinz A, Olde Loohuis LM, Delanty N, Ho BC, Ching CRK, Shumskaya E, Singh B, Hofman A, van der Meer D, Homuth G, Psaty BM, Bastin ME, Montgomery GW, Foroud TM, Reppermund S, Hottenga JJ, Simmons A, Meyer-Lindenberg A, Cahn W, Whelan CD, van Donkelaar MMJ, Yang Q, Hosten N, Green RC, Thalamuthu A, Mohnke S, Hulshoff Pol HE, Lin H, Jack CR Jr, Schofield PR, Mühleisen TW, Maillard P, Potkin SG, Wen W, Fletcher E, Toga AW, Gruber O, Huentelman M, Davey Smith G, Launer LJ, Nyberg L, Jönsson EG, Crespo-Facorro B, Koen N, Greve DN, Uitterlinden AG, Weinberger DR, Steen VM, Fedko IO, Groenewold NA, Niessen WJ, Toro R, Tzourio C, Longstreth WT Jr, Ikram MK, Smoller JW, van Tol MJ, Sussmann JE, Paus T, Lemaître H, Schroeter ML, Mazoyer B, Andreassen OA, Holsboer F, Depondt C, Veltman DJ, Turner JA, Pausova Z, Schumann G, van Rooij D, Djurovic S, Deary IJ, McMahon KL, Müller-Myhsok B, Brouwer RM, Soininen H, Pandolfo M, Wassink TH, Cheung JW, Wolfers T, Martinot JL, Zwiers MP, Nauck M, Melle I, Martin NG, Kanai R, Westman E, Kahn RS, Sisodiya SM, White T, Saremi A, van Bokhoven H, Brunner HG, Völzke H, Wright MJ, van 't Ent D, Nöthen MM, Ophoff RA, Buitelaar JK, Fernández G, Sachdev PS, Rietschel M, van Haren NEM, Fisher SE, Beiser AS, Francks C, Saykin AJ, Mather KA, Romanczuk-Seiferth N, Hartman CA, DeStefano AL, Heslenfeld DJ, Weiner MW, Walter H, Hoekstra PJ, Nyquist PA, Franke B, Bennett DA, Grabe HJ, Johnson AD, Chen C, van Duijn CM, Lopez OL, Fornage M, Wardlaw JM, Schmidt R, DeCarli C, De Jager PL, Villringer A, Debette S, Gudnason V, Medland SE, Shulman JM, Thompson PM, Seshadri S, and Ikram MA
- Subjects
- Adult, Aged, Animals, Cohort Studies, Drosophila melanogaster genetics, Drosophila melanogaster growth & development, Humans, Magnetic Resonance Imaging, Middle Aged, Organ Size, Brain anatomy & histology, Brain metabolism, Drosophila melanogaster metabolism, Genetic Variation, Genome-Wide Association Study, Neurodevelopmental Disorders genetics, Neurodevelopmental Disorders pathology
- Abstract
Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
- Published
- 2019
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5. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function.
- Author
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Soler Artigas M, Loth DW, Wain LV, Gharib SA, Obeidat M, Tang W, Zhai G, Zhao JH, Smith AV, Huffman JE, Albrecht E, Jackson CM, Evans DM, Cadby G, Fornage M, Manichaikul A, Lopez LM, Johnson T, Aldrich MC, Aspelund T, Barroso I, Campbell H, Cassano PA, Couper DJ, Eiriksdottir G, Franceschini N, Garcia M, Gieger C, Gislason GK, Grkovic I, Hammond CJ, Hancock DB, Harris TB, Ramasamy A, Heckbert SR, Heliövaara M, Homuth G, Hysi PG, James AL, Jankovic S, Joubert BR, Karrasch S, Klopp N, Koch B, Kritchevsky SB, Launer LJ, Liu Y, Loehr LR, Lohman K, Loos RJ, Lumley T, Al Balushi KA, Ang WQ, Barr RG, Beilby J, Blakey JD, Boban M, Boraska V, Brisman J, Britton JR, Brusselle GG, Cooper C, Curjuric I, Dahgam S, Deary IJ, Ebrahim S, Eijgelsheim M, Francks C, Gaysina D, Granell R, Gu X, Hankinson JL, Hardy R, Harris SE, Henderson J, Henry A, Hingorani AD, Hofman A, Holt PG, Hui J, Hunter ML, Imboden M, Jameson KA, Kerr SM, Kolcic I, Kronenberg F, Liu JZ, Marchini J, McKeever T, Morris AD, Olin AC, Porteous DJ, Postma DS, Rich SS, Ring SM, Rivadeneira F, Rochat T, Sayer AA, Sayers I, Sly PD, Smith GD, Sood A, Starr JM, Uitterlinden AG, Vonk JM, Wannamethee SG, Whincup PH, Wijmenga C, Williams OD, Wong A, Mangino M, Marciante KD, McArdle WL, Meibohm B, Morrison AC, North KE, Omenaas E, Palmer LJ, Pietiläinen KH, Pin I, Pola Sbreve Ek O, Pouta A, Psaty BM, Hartikainen AL, Rantanen T, Ripatti S, Rotter JI, Rudan I, Rudnicka AR, Schulz H, Shin SY, Spector TD, Surakka I, Vitart V, Völzke H, Wareham NJ, Warrington NM, Wichmann HE, Wild SH, Wilk JB, Wjst M, Wright AF, Zgaga L, Zemunik T, Pennell CE, Nyberg F, Kuh D, Holloway JW, Boezen HM, Lawlor DA, Morris RW, Probst-Hensch N, Kaprio J, Wilson JF, Hayward C, Kähönen M, Heinrich J, Musk AW, Jarvis DL, Gläser S, Järvelin MR, Ch Stricker BH, Elliott P, O'Connor GT, Strachan DP, London SJ, Hall IP, Gudnason V, and Tobin MD
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- Child, Humans, Pulmonary Disease, Chronic Obstructive genetics, Pulmonary Disease, Chronic Obstructive physiopathology, White People, Genome-Wide Association Study, Respiratory Function Tests
- Abstract
Pulmonary function measures reflect respiratory health and are used in the diagnosis of chronic obstructive pulmonary disease. We tested genome-wide association with forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced vital capacity in 48,201 individuals of European ancestry with follow up of the top associations in up to an additional 46,411 individuals. We identified new regions showing association (combined P < 5 × 10(-8)) with pulmonary function in or near MFAP2, TGFB2, HDAC4, RARB, MECOM (also known as EVI1), SPATA9, ARMC2, NCR3, ZKSCAN3, CDC123, C10orf11, LRP1, CCDC38, MMP15, CFDP1 and KCNE2. Identification of these 16 new loci may provide insight into the molecular mechanisms regulating pulmonary function and into molecular targets for future therapy to alleviate reduced lung function.
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- 2011
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6. Meta-analysis and imputation refines the association of 15q25 with smoking quantity.
- Author
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Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G, Vollenweider P, Preisig M, Wareham NJ, Zhao JH, Loos RJ, Barroso I, Khaw KT, Grundy S, Barter P, Mahley R, Kesaniemi A, McPherson R, Vincent JB, Strauss J, Kennedy JL, Farmer A, McGuffin P, Day R, Matthews K, Bakke P, Gulsvik A, Lucae S, Ising M, Brueckl T, Horstmann S, Wichmann HE, Rawal R, Dahmen N, Lamina C, Polasek O, Zgaga L, Huffman J, Campbell S, Kooner J, Chambers JC, Burnett MS, Devaney JM, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Epstein S, Wilson JF, Wild SH, Campbell H, Vitart V, Reilly MP, Li M, Qu L, Wilensky R, Matthai W, Hakonarson HH, Rader DJ, Franke A, Wittig M, Schäfer A, Uda M, Terracciano A, Xiao X, Busonero F, Scheet P, Schlessinger D, St Clair D, Rujescu D, Abecasis GR, Grabe HJ, Teumer A, Völzke H, Petersmann A, John U, Rudan I, Hayward C, Wright AF, Kolcic I, Wright BJ, Thompson JR, Balmforth AJ, Hall AS, Samani NJ, Anderson CA, Ahmad T, Mathew CG, Parkes M, Satsangi J, Caulfield M, Munroe PB, Farrall M, Dominiczak A, Worthington J, Thomson W, Eyre S, Barton A, Mooser V, Francks C, and Marchini J
- Subjects
- Adult, Aged, Alleles, Chromosome Mapping methods, Cohort Studies, Female, Genetic Markers genetics, Genome, Human, Humans, Male, Middle Aged, Models, Genetic, Neurons metabolism, Polymorphism, Single Nucleotide, Receptors, Nicotinic metabolism, Chromosomes, Human, Pair 15, Smoking
- Abstract
Smoking is a leading global cause of disease and mortality. We established the Oxford-GlaxoSmithKline study (Ox-GSK) to perform a genome-wide meta-analysis of SNP association with smoking-related behavioral traits. Our final data set included 41,150 individuals drawn from 20 disease, population and control cohorts. Our analysis confirmed an effect on smoking quantity at a locus on 15q25 (P = 9.45 x 10(-19)) that includes CHRNA5, CHRNA3 and CHRNB4, three genes encoding neuronal nicotinic acetylcholine receptor subunits. We used data from the 1000 Genomes project to investigate the region using imputation, which allowed for analysis of virtually all common SNPs in the region and offered a fivefold increase in marker density over HapMap2 (ref. 2) as an imputation reference panel. Our fine-mapping approach identified a SNP showing the highest significance, rs55853698, located within the promoter region of CHRNA5. Conditional analysis also identified a secondary locus (rs6495308) in CHRNA3.
- Published
- 2010
- Full Text
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7. Independent genome-wide scans identify a chromosome 18 quantitative-trait locus influencing dyslexia.
- Author
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Fisher SE, Francks C, Marlow AJ, MacPhie IL, Newbury DF, Cardon LR, Ishikawa-Brush Y, Richardson AJ, Talcott JB, Gayán J, Olson RK, Pennington BF, Smith SD, DeFries JC, Stein JF, and Monaco AP
- Subjects
- Child, Chromosomes, Human, Pair 6 genetics, Diseases in Twins genetics, Female, Genetic Heterogeneity, Genetic Linkage, Genetic Markers, Genotype, Humans, Lod Score, Male, Psychological Tests, United Kingdom, United States, Chromosome Mapping methods, Chromosomes, Human, Pair 18 genetics, Dyslexia genetics, Quantitative Trait, Heritable
- Abstract
Developmental dyslexia is defined as a specific and significant impairment in reading ability that cannot be explained by deficits in intelligence, learning opportunity, motivation or sensory acuity. It is one of the most frequently diagnosed disorders in childhood, representing a major educational and social problem. It is well established that dyslexia is a significantly heritable trait with a neurobiological basis. The etiological mechanisms remain elusive, however, despite being the focus of intensive multidisciplinary research. All attempts to map quantitative-trait loci (QTLs) influencing dyslexia susceptibility have targeted specific chromosomal regions, so that inferences regarding genetic etiology have been made on the basis of very limited information. Here we present the first two complete QTL-based genome-wide scans for this trait, in large samples of families from the United Kingdom and United States. Using single-point analysis, linkage to marker D18S53 was independently identified as being one of the most significant results of the genome in each scan (P< or =0.0004 for single word-reading ability in each family sample). Multipoint analysis gave increased evidence of 18p11.2 linkage for single-word reading, yielding top empirical P values of 0.00001 (UK) and 0.0004 (US). Measures related to phonological and orthographic processing also showed linkage at this locus. We replicated linkage to 18p11.2 in a third independent sample of families (from the UK), in which the strongest evidence came from a phoneme-awareness measure (most significant P value=0.00004). A combined analysis of all UK families confirmed that this newly discovered 18p QTL is probably a general risk factor for dyslexia, influencing several reading-related processes. This is the first report of QTL-based genome-wide scanning for a human cognitive trait.
- Published
- 2002
- Full Text
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