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1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.

ABSTRACT: HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value?

SUBMITTER: Gorski M

PROVIDER: S-EPMC5408227 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.

Gorski Mathias M van der Most Peter J PJ Teumer Alexander A Chu Audrey Y AY Li Man M Mijatovic Vladan V Nolte Ilja M IM Cocca Massimiliano M Taliun Daniel D Gomez Felicia F Li Yong Y Tayo Bamidele B Tin Adrienne A Feitosa Mary F MF Aspelund Thor T Attia John J Biffar Reiner R Bochud Murielle M Boerwinkle Eric E Borecki Ingrid I Bottinger Erwin P EP Chen Ming-Huei MH Chouraki Vincent V Ciullo Marina M Coresh Josef J Cornelis Marilyn C MC Curhan Gary C GC d'Adamo Adamo Pio AP Dehghan Abbas A Dengler Laura L Ding Jingzhong J Eiriksdottir Gudny G Endlich Karlhans K Enroth Stefan S Esko Tõnu T Franco Oscar H OH Gasparini Paolo P Gieger Christian C Girotto Giorgia G Gottesman Omri O Gudnason Vilmundur V Gyllensten Ulf U Hancock Stephen J SJ Harris Tamara B TB Helmer Catherine C Höllerer Simon S Hofer Edith E Hofman Albert A Holliday Elizabeth G EG Homuth Georg G Hu Frank B FB Huth Cornelia C Hutri-Kähönen Nina N Hwang Shih-Jen SJ Imboden Medea M Johansson Åsa Å Kähönen Mika M König Wolfgang W Kramer Holly H Krämer Bernhard K BK Kumar Ashish A Kutalik Zoltan Z Lambert Jean-Charles JC Launer Lenore J LJ Lehtimäki Terho T de Borst Martin M Navis Gerjan G Swertz Morris M Liu Yongmei Y Lohman Kurt K Loos Ruth J F RJF Lu Yingchang Y Lyytikäinen Leo-Pekka LP McEvoy Mark A MA Meisinger Christa C Meitinger Thomas T Metspalu Andres A Metzger Marie M Mihailov Evelin E Mitchell Paul P Nauck Matthias M Oldehinkel Albertine J AJ Olden Matthias M Wjh Penninx Brenda B Pistis Giorgio G Pramstaller Peter P PP Probst-Hensch Nicole N Raitakari Olli T OT Rettig Rainer R Ridker Paul M PM Rivadeneira Fernando F Robino Antonietta A Rosas Sylvia E SE Ruderfer Douglas D Ruggiero Daniela D Saba Yasaman Y Sala Cinzia C Schmidt Helena H Schmidt Reinhold R Scott Rodney J RJ Sedaghat Sanaz S Smith Albert V AV Sorice Rossella R Stengel Benedicte B Stracke Sylvia S Strauch Konstantin K Toniolo Daniela D Uitterlinden Andre G AG Ulivi Sheila S Viikari Jorma S JS Völker Uwe U Vollenweider Peter P Völzke Henry H Vuckovic Dragana D Waldenberger Melanie M Jin Wang Jie J Yang Qiong Q Chasman Daniel I DI Tromp Gerard G Snieder Harold H Heid Iris M IM Fox Caroline S CS Köttgen Anna A Pattaro Cristian C Böger Carsten A CA Fuchsberger Christian C

Scientific reports 20170428

HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular f ...[more]

PMID: 28452372

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Project description:Abstract Background Schizophrenia is a highly heritable psychiatric disorder. In the past 30 years, thousands of case-control and family-designed association studies have examined candidate genes for schizophrenia. To assist the field in interpreting this large volume of gene-association studies, the online SzGene database was created and included meta analyses for 287 polymorphisms at the time of its final update in 2010. However, since then more than one-thousand new gene-association studies in schizophrenia have been conducted. As such, we have conducted an updated systematic review and meta-analysis of all gene-association studies in schizophrenia published before March 2017. Methods Studies published between January 2010 and March 2017 were identified using a customized querying strategy. Identified gene-association studies were included in the review and meta-analysis if they: 1) were case-control or family-designed studies with polymorphisms detected in schizophrenia or schizoaffective patients, 2) provide sufficient data to perform meta-analysis, and 3) were not genome-wide association studies (GWAS). Random-effects meta-analysis was performed on polymorphisms with four or more independent studies. Results Raw data of 1711 studies included in the SzGene database were integrated with 1368 studies identified from our systematic literature search. Random-effects meta-analysis were applied to 540 polymorphisms with at least four studies and 89 of these polymorphisms were nominally associated with SZ (unadjusted p < 0.05). After bonferroni correction, 12 polymorphisms remained statistically significant, including five associations not reported in the most recent Psychiatric Genomics Consortium schizophrenia GWAS: 1) rs11098403, p = 1.45e-10, odds ratio = 0.65; 2) rs12807809, p = 7.04e-06, odds ratio = 0.91; 3) rs910694 in PDE4B: p = 1.05e-05, odds ratio = 0.77; 4) rs1801133 in MTHFR: p = 2.99e-05, odds ratio = 1.13 and 5) rs1602565: p = 7.73e-05, odds ratio = 1.11. Discussion Our results provide a comprehensive and up-to-date review of candidate gene association studies in schizophrenia. Findings complement results from GWASs in schizophrenia but also expand the current list of candidate loci for schizophrenia.

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1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.

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1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.

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