Now showing items 265-284 of 422

    • Mannose and fructose metabolism in red blood cells during cold storage in SAGM 

      Rolfsson Ó; Johannsson F; Magnusdottir M; Paglia G; Sigurjónsson ÓE; Bordbar A; Palsson S; Brynjólfsson S; Guðmundsson S; Palsson B (2017)
      BACKGROUND: Alternate sugar metabolism during red blood cell (RBC) storage is not well understood. Here we report fructose and mannose metabolism in RBCs during cold storage in SAGM and the impact that these monosaccharides ...
    • Maps of open chromatin highlight cell type-restricted patterns of regulatory sequence variation at hematological trait loci 

      Paul DS; Albers CA; Rendon A; Voss K; Stephens J; HaemGen Consortium; van der Harst P; Chambers JC; Soranzo N; Ouwehand WH; Deloukas P (2013)
      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 ...
    • A marker for the end of adolescence 

      Roenneberg T; Kuehnle T; Pramstaller PP; Ricken J; Havel M; Guth A; Merrow M (2004)
      Between childhood and adulthood, we go through puberty and adolescence. While the end of puberty is defined as the point of cessation of bone growth (epiphyseal closure; girls: 16 y; boys: 17.5 y), the end of adolescence ...
    • Mendelian Randomization as an Approach to Assess Causality Using Observational Data 

      Sekula P; Del Greco M F; Pattaro C; Köttgen A (2016)
      Mendelian randomization refers to an analytic approach to assess the causality of an observed association between a modifiable exposure or risk factor and a clinically relevant outcome. It presents a valuable tool, especially ...
    • Mendelian randomization incorporating uncertainty about pleiotropy 

      Thompson JR; Minelli C; Bowden J; Del Greco M F; Gill D; Jones EM; Shapland CY; Sheehan NA (2017)
      Mendelian randomization (MR) requires strong assumptions about the genetic instruments, of which the most difficult to justify relate to pleiotropy. In a two-sample MR, different methods of analysis are available if we are ...
    • Mendelian Randomization using Public Data from Genetic Consortia 

      Thompson JR; Minelli C; Del Greco M F (2016)
      Mendelian randomization (MR) is a technique that seeks to establish causation between an exposure and an outcome using observational data. It is an instrumental variable analysis in which genetic variants are used as the ...
    • Meta consent: Is it new and is it fit for purpose? 

      Budin-Ljøsne I; Teare H; Kaye J; Mascalzoni D (2015)
    • Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution 

      Heid IM1; Jackson AU; Randall JC; Winkler TW; Qi L; Steinthorsdottir V; Thorleifsson G; Zillikens MC; Speliotes EK; Mägi R; Workalemahu T; White CC; Bouatia-Naji N; Harris TB; Berndt SI; Ingelsson E; Willer CJ; Weedon MN; Luan J; Vedantam S; Esko T; Kilpeläinen TO; Kutalik Z; Li S; Monda KL; Dixon AL; Holmes CC; Kaplan LM; Liang L; Min JL; Moffatt MF; Molony C; Nicholson G; Schadt EE; Zondervan KT; Feitosa MF; Ferreira T; Lango Allen H; Weyant RJ; Wheeler E; Wood AR; MAGIC Consortium; Estrada K; Goddard ME; Lettre G; Mangino M; Nyholt DR; Purcell S; Smith AV; Visscher PM; Yang J; McCarroll SA; Nemesh J; Voight BF; Absher D; Amin N; Aspelund T; Coin L; Glazer NL; Hayward C; Heard-Costa NL; Hottenga JJ; Johansson A; Johnson T; Kaakinen M; Kapur K; Ketkar S; Knowles JW; Kraft P; Kraja AT; Lamina C; Leitzmann MF; McKnight B; Morris AP; Ong KK; Perry JR; Peters MJ; Polasek O; Prokopenko I; Rayner NW; Ripatti S; Rivadeneira F; Robertson NR; Sanna S; Sovio U; Surakka I; Teumer A; van Wingerden S; Vitart V; Zhao JH; Cavalcanti-Proença C; Chines PS; Fisher E; Kulzer JR; Lecoeur C; Narisu N; Sandholt C; Scott LJ; Silander K; Stark K; Tammesoo ML; Teslovich TM; Timpson NJ; Watanabe RM; Welch R; Chasman DI; Cooper MN; Jansson JO; Kettunen J; Lawrence RW; Pellikka N; Perola M; Vandenput L; Alavere H; Almgren P; Atwood LD; Bennett AJ; Biffar R; Bonnycastle LL; Bornstein SR; Buchanan TA; Campbell H; Day IN; Dei M; Dörr M; Elliott P; Erdos MR; Eriksson JG; Freimer NB; Fu M; Gaget S; Geus EJ; Gjesing AP; Grallert H; Grässler J; Groves CJ; Guiducci C; Hartikainen AL; Hassanali N; Havulinna AS; Herzig KH; Hicks AA; Hui J; Igl W; Jousilahti P; Jula A; Kajantie E; Kinnunen L; Kolcic I; Koskinen S; Kovacs P; Kroemer HK; Krzelj V; Kuusisto J; Kvaloy K; Laitinen J; Lantieri O; Lathrop GM; Lokki ML; Luben RN; Ludwig B; McArdle WL; McCarthy A; Morken MA; Nelis M; Neville MJ; Paré G; Parker AN; Peden JF; Pichler I; Pietiläinen KH; Platou CG; Pouta A; Ridderstråle M; Samani NJ; Saramies J; Sinisalo J; Smit JH; Strawbridge RJ; Stringham HM; Swift AJ; Teder-Laving M; Thomson B; Usala G; van Meurs JB; van Ommen GJ; Vatin V; Volpato CB; Wallaschofski H; Walters GB; Widen E; Wild SH; Willemsen G; Witte DR; Zgaga L; Zitting P; Beilby JP; James AL; Kähönen M; Lehtimäki T; Nieminen MS; Ohlsson C; Palmer LJ; Raitakari O; Ridker PM; Stumvoll M; Tönjes A; Viikari J; Balkau B; Ben-Shlomo Y; Bergman RN; Boeing H; Smith GD; Ebrahim S; Froguel P; Hansen T; Hengstenberg C; Hveem K; Isomaa B; Jørgensen T; Karpe F; Khaw KT; Laakso M; Lawlor DA; Marre M; Meitinger T; Metspalu A; Midthjell K; Pedersen O; Salomaa V; Schwarz PE; Tuomi T; Tuomilehto J; Valle TT; Wareham NJ; Arnold AM; Beckmann JS; Bergmann S; Boerwinkle E; Boomsma DI; Caulfield MJ; Collins FS; Eiriksdottir G; Gudnason V; Gyllensten U; Hamsten A; Hattersley AT; Hofman A; Hu FB; Illig T; Iribarren C; Jarvelin MR; Kao WH; Kaprio J; Launer LJ; Munroe PB; Oostra B; Penninx BW; Pramstaller PP; Psaty BM; Quertermous T; Rissanen A; Rudan I; Shuldiner AR; Soranzo N; Spector TD; Syvänen AC; Uda M; Uitterlinden A; Völzke H; Vollenweider P; Wilson JF; Witteman JC; Wright AF; Abecasis GR; Boehnke M; Borecki IB; Deloukas P; Frayling TM; Groop LC; Haritunians T; Hunter DJ; Kaplan RC; North KE; O'Connell JR; Peltonen L; Schlessinger D; Strachan DP; Hirschhorn JN; Assimes TL; Wichmann HE; Thorsteinsdottir U; van Duijn CM; Stefansson K; Cupples LA; Loos RJ; Barroso I; McCarthy MI; Fox CS; Mohlke KL; Lindgren CM (2010)
      Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. ...
    • Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci 

      Liu C; Kraja AT; Smith JA; Brody JA; Franceschini N; Bis JC; Rice K; Morrison AC; Lu Y; Weiss S; Guo X; Palmas W; Martin LW; Chen YI; Surendran P; Drenos F; Cook JP; Auer PL; Chu AY; Giri A; Zhao W; Jakobsdottir J; Lin LA; Stafford JM; Amin N; Mei H; Yao J; Voorman A; CHD Exome+ Consortium; ExomeBP Consortium; GoT2DGenes Consortium; T2D-GENES Consortium; Larson MG; Grove ML; Smith AV; Hwang SJ; Chen H; Huan T; Kosova G; Stitziel NO; Kathiresan S; Samani N; Schunkert H; Deloukas P; Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia; Li M; Fuchsberger C; Pattaro C; Gorski M; CKDGen Consortium; Kooperberg C; Papanicolaou GJ; Rossouw JE; Faul JD; Kardia SL; Bouchard C; Raffel LJ; Uitterlinden AG; Franco OH; Vasan RS; O'Donnell CJ; Taylor KD; Liu K; Bottinger EP; Gottesman O; Daw EW; Giulianini F; Ganesh S; Salfati E; Harris TB; Launer LJ; Dörr M; Felix SB; Rettig R; Völzke H; Kim E; Lee WJ; Lee IT; Sheu WH; Tsosie KS; Edwards DR; Liu Y; Correa A; Weir DR; Völker U; Ridker PM; Boerwinkle E; Gudnason V; Reiner AP; van Duijn CM; Borecki IB; Edwards TL; Chakravarti A; Rotter JI; Psaty BM; Loos RJ; Fornage M; Ehret GB; Newton-Cheh C; Levy D; Chasman DI (2016)
      Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in ...
    • Meta-analysis identifies multiple loci associated with kidney function-related traits in east Asian populations 

      Okada Y; Sim X; Go MJ; Wu JY; Gu D; Takeuchi F; Takahashi A; Maeda S; Tsunoda T; Chen P; Lim SC; Wong TY; Liu J; Young TL; Aung T; Seielstad M; Teo YY; Kim YJ; Lee JY; Han BG; Kang D; Chen CH; Tsai FJ; Chang LC; Fann SJ; Mei H; Rao DC; Hixson JE; Chen S; Katsuya T; Isono M; Ogihara T; Chambers JC; Zhang W; Kooner JS; KidneyGen Consortium; CKDGen Consortium; Albrecht E; GUGC consortium; Yamamoto K; Kubo M; Nakamura Y; Kamatani N; Kato N; He J; Chen YT; Cho YS; Tai ES; Tanaka T (2012)
      Chronic kidney disease (CKD), impairment of kidney function, is a serious public health problem, and the assessment of genetic factors influencing kidney function has substantial clinical relevance. Here, we report a ...
    • Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations 

      Kolz M; Johnson T; Sanna S; Teumer A; Vitart V; Perola M; Mangino M; Albrecht E; Wallace C; Farrall M; Johansson A; Nyholt DR; Aulchenko Y; Beckmann JS; Bergmann S; Bochud M; Brown M; Campbell H; EUROSPAN Consortium; Connell J; Dominiczak A; Homuth G; Lamina C; McCarthy MI; ENGAGE Consortium; Meitinger T; Mooser V; Munroe P; Nauck M; Peden J; Prokisch H; Salo P; Salomaa V; Samani NJ; Schlessinger D; Uda M; Völker U; Waeber G; Waterworth D; Wang-Sattler R; Wright AF; Adamski J; Whitfield JB; Gyllensten U; Wilson JF; Rudan I; Pramstaller P; Watkins H; PROCARDIS Consortium; Doering A; Wichmann HE; KORA Study; Spector TD; Peltonen L; Völzke H; Nagaraja R; Vollenweider P; Caulfield M; WTCCC; Illig T; Gieger C (2009)
      Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association ...
    • Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics 

      Hu YJ; Berndt SI; Gustafsson S; Ganna A; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; Hirschhorn J; North KE; Ingelsson E; Lin DY (2013)
      Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants ...
    • Meta-analysis of genetic association studies: magic tool or dangerous black box? 

      Minelli C; Thompson J (2010)
      In the field of genetic epidemiology, where primary studies are often underpowered to detect small genetic effects and where conflicting results and non-replication of initial findings are often encountered [1], the ...
    • The meta-analysis of genome-wide association studies 

      Thompson JR; Attia J; Minelli C (2011)
      The pressure to publish novel genetic associations has meant that meta-analysis has been applied to genome-wide association studies without the time for a careful consideration of the methods that are used. This review ...
    • Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians 

      Cho YS; Chen CH; Hu C; Long J; Ong RT; Sim X; Takeuchi F; Wu Y; Go MJ; Yamauchi T; Chang YC; Kwak SH; Ma RC; Yamamoto K; Adair LS; Aung T; Cai Q; Chang LC; Chen YT; Gao Y; Hu FB; Kim HL; Kim S; Kim YJ; Lee JJ; Lee NR; Li Y; Liu JJ; Lu W; Nakamura J; Nakashima E; Ng DP; Tay WT; Tsai FJ; Wong TY; Yokota M; Zheng W; Zhang R; Wang C; So WY; Ohnaka K; Ikegami H; Hara K; Cho YM; Cho NH; Chang TJ; Bao Y; Hedman ÅK; Morris AP; McCarthy MI; DIAGRAM Consortium; MuTHER Consortium; Takayanagi R; Park KS; Jia W; Chuang LM; Chan JC; Maeda S; Kadowaki T; Lee JY; Wu JY; Teo YY; Tai ES; Shu XO; Mohlke KL; Kato N; Han BG; Seielstad M (2011)
      We conducted a three-stage genetic study to identify susceptibility loci for type 2 diabetes (T2D) in east Asian populations. We followed our stage 1 meta-analysis of eight T2D genome-wide association studies (6,952 cases ...
    • Meta-analysis of genome-wide association studies identifies two loci associated with circulating osteoprotegerin levels 

      Kwan JS; Hsu YH; Cheung CL; Dupuis J; Saint-Pierre A; Eriksson J; Handelman SK; Aragaki A; Karasik D; Pramstaller PP; Kooperberg C; Lacroix AZ; Larson MG; Lau KS; Lorentzon M; Pichler I; Sham PC; Taliun D; Vandenput L; Kiel DP; Hicks AA; Jackson RD; Ohlsson C; Benjamin EJ; Kung AW (2014)
      Osteoprotegerin (OPG) is involved in bone homeostasis and tumor cell survival. Circulating OPG levels are also important biomarkers of various clinical traits, such as cancers and atherosclerosis. OPG levels were measured ...
    • Meta-analysis of genome-wide association studies in >80 000 subjects identifies multiple loci for C-reactive protein levels 

      Dehghan A; Dupuis J; Barbalic M; Bis JC; Eiriksdottir G; Lu C; Pellikka N; Wallaschofski H; Kettunen J; Henneman P; Baumert J; Strachan DP; Fuchsberger C; Vitart V; Wilson JF; Paré G; Naitza S; Rudock ME; Surakka I; de Geus EJ; Alizadeh BZ; Guralnik J; Shuldiner A; Tanaka T; Zee RY; Schnabel RB; Nambi V; Kavousi M; Ripatti S; Nauck M; Smith NL; Smith AV; Sundvall J; Scheet P; Liu Y; Ruokonen A; Rose LM; Larson MG; Hoogeveen RC; Freimer NB; Teumer A; Tracy RP; Launer LJ; Buring JE; Yamamoto JF; Folsom AR; Sijbrands EJ; Pankow J; Elliott P; Keaney JF; Sun W; Sarin AP; Fontes JD; Badola S; Astor BC; Hofman A; Pouta A; Werdan K; Greiser KH; Kuss O; Meyer Zu Schwabedissen H; Thiery J; Jamshidi Y; Nolte IM; Soranzo N; Spector TD; Völzke H; Parker AN; Aspelund T; Bates D; Young L; Tsui K; Siscovick DS; Guo X; Rotter JI; Uda M; Schlessinger D; Rudan I; Hicks AA; Penninx BW; Thorand B; Gieger C; Coresh J; Willemsen G; Harris TB; Uitterlinden AG; Järvelin MR; Rice K; Radke D; Salomaa V; Willems van Dijk K; Boerwinkle E; Vasan RS; Ferrucci L; Gibson QD; Bandinelli S; Snieder H; Boomsma DI; Xiao X; Campbell H; Hayward C; Pramstaller PP; van Duijn CM; Peltonen L; Psaty BM; Gudnason V; Ridker PM; Homuth G; Koenig W; Ballantyne CM; Witteman JC; Benjamin EJ; Perola M; Chasman DI (2011)
      BACKGROUND: C-reactive protein (CRP) is a heritable marker of chronic inflammation that is strongly associated with cardiovascular disease. We sought to identify genetic variants that are associated with CRP levels. METHODS ...
    • Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes 

      Ng MC; Shriner D; Chen BH; Li J; Chen WM; Guo X; Liu J; Bielinski SJ; Yanek LR; Nalls MA; Comeau ME; Rasmussen-Torvik LJ; Jensen RA; Evans DS; Sun YV; An P; Patel SR; Lu Y; Long J; Armstrong LL; Wagenknecht L; Yang L; Snively BM; Palmer ND; Mudgal P; Langefeld CD; Keene KL; Freedman BI; Mychaleckyj JC; Nayak U; Raffel LJ; Goodarzi MO; Chen YD; Taylor HA; Correa A; Sims M; Couper D (2014)
      Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by ...
    • A meta-analysis of genome-wide data from five European isolates reveals an association of COL22A1, SYT1, and GABRR2 with serum creatinine level 

      Pattaro C; De Grandi A; Vitart V; Hayward C; Franke A; Aulchenko YS; Johansson A; Wild SH; Melville SA; Isaacs A; Polasek O; Ellinghaus D; Kolcic I; Nöthlings U; Zgaga L; Zemunik T; Gnewuch C; Schreiber S; Campbell S; Hastie N; Boban M; Meitinger T; Oostra BA; Riegler P; Minelli C; Wright AF; Campbell H; van Duijn CM; Gyllensten U; Wilson JF; Krawczak M; Rudan I; Pramstaller PP; EUROSPAN Consortium (2010)
      BACKGROUND: Serum creatinine (S CR) is the most important biomarker for a quick and non-invasive assessment of kidney function in population-based surveys. A substantial proportion of the inter-individual variability in ...