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Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

ABSTRACT: In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.

SUBMITTER: Chasman DI

PROVIDER: S-EPMC3607468 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

Chasman Daniel I DI Fuchsberger Christian C Pattaro Cristian C Teumer Alexander A Böger Carsten A CA Endlich Karlhans K Olden Matthias M Chen Ming-Huei MH Tin Adrienne A Taliun Daniel D Li Man M Gao Xiaoyi X Gorski Mathias M Yang Qiong Q Hundertmark Claudia C Foster Meredith C MC O'Seaghdha Conall M CM Glazer Nicole N Isaacs Aaron A Liu Ching-Ti CT Smith Albert V AV O'Connell Jeffrey R JR Struchalin Maksim M Tanaka Toshiko T Li Guo G Johnson Andrew D AD Gierman Hinco J HJ Feitosa Mary F MF Hwang Shih-Jen SJ Atkinson Elizabeth J EJ Lohman Kurt K Cornelis Marilyn C MC Johansson Asa A Tönjes Anke A Dehghan Abbas A Lambert Jean-Charles JC Holliday Elizabeth G EG Sorice Rossella R Kutalik Zoltan Z Lehtimäki Terho T Esko Tõnu T Deshmukh Harshal H Ulivi Sheila S Chu Audrey Y AY Murgia Federico F Trompet Stella S Imboden Medea M Coassin Stefan S Pistis Giorgio G Harris Tamara B TB Launer Lenore J LJ Aspelund Thor T Eiriksdottir Gudny G Mitchell Braxton D BD Boerwinkle Eric E Schmidt Helena H Cavalieri Margherita M Rao Madhumathi M Hu Frank F Demirkan Ayse A Oostra Ben A BA de Andrade Mariza M Turner Stephen T ST Ding Jingzhong J Andrews Jeanette S JS Freedman Barry I BI Giulianini Franco F Koenig Wolfgang W Illig Thomas T Meisinger Christa C Gieger Christian C Zgaga Lina L Zemunik Tatijana T Boban Mladen M Minelli Cosetta C Wheeler Heather E HE Igl Wilmar W Zaboli Ghazal G Wild Sarah H SH Wright Alan F AF Campbell Harry H Ellinghaus David D Nöthlings Ute U Jacobs Gunnar G Biffar Reiner R Ernst Florian F Homuth Georg G Kroemer Heyo K HK Nauck Matthias M Stracke Sylvia S Völker Uwe U Völzke Henry H Kovacs Peter P Stumvoll Michael M Mägi Reedik R Hofman Albert A Uitterlinden Andre G AG Rivadeneira Fernando F Aulchenko Yurii S YS Polasek Ozren O Hastie Nick N Vitart Veronique V Helmer Catherine C Wang Jie Jin JJ Stengel Bénédicte B Ruggiero Daniela D Bergmann Sven S Kähönen Mika M Viikari Jorma J Nikopensius Tiit T Province Michael M Ketkar Shamika S Colhoun Helen H Doney Alex A Robino Antonietta A Krämer Bernhard K BK Portas Laura L Ford Ian I Buckley Brendan M BM Adam Martin M Thun Gian-Andri GA Paulweber Bernhard B Haun Margot M Sala Cinzia C Mitchell Paul P Ciullo Marina M Kim Stuart K SK Vollenweider Peter P Raitakari Olli O Metspalu Andres A Palmer Colin C Gasparini Paolo P Pirastu Mario M Jukema J Wouter JW Probst-Hensch Nicole M NM Kronenberg Florian F Toniolo Daniela D Gudnason Vilmundur V Shuldiner Alan R AR Coresh Josef J Schmidt Reinhold R Ferrucci Luigi L Siscovick David S DS van Duijn Cornelia M CM Borecki Ingrid B IB Kardia Sharon L R SL Liu Yongmei Y Curhan Gary C GC Rudan Igor I Gyllensten Ulf U Wilson James F JF Franke Andre A Pramstaller Peter P PP Rettig Rainer R Prokopenko Inga I Witteman Jacqueline J Hayward Caroline C Ridker Paul M PM Parsa Afshin A Bochud Murielle M Heid Iris M IM Kao W H Linda WH Fox Caroline S CS Köttgen Anna A

Human molecular genetics 20120908 24

In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that ar ...[more]

PMID: 22962313

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Project description:Aims/hypothesisDiabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.MethodsWe performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.ResultsThe meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10-9; although not withstanding correction for multiple testing, p>9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p<2.7×10-6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10-6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10-11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10-8] and negatively with tubulointerstitial fibrosis [p=2.0×10-9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10-16], and SNX30 expression correlated positively with eGFR [p=5.8×10-14] and negatively with fibrosis [p<2.0×10-16]).Conclusions/interpretationAltogether, the results point to novel genes contributing to the pathogenesis of DKD.Data availabilityThe GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( https://t1d.hugeamp.org/downloads.html ; https://t2d.hugeamp.org/downloads.html ; https://hugeamp.org/downloads.html ).

Project description:BackgroundThe automation of many common molecular biology techniques has resulted in the accumulation of vast quantities of experimental data. One of the major challenges now facing researchers is how to process this data to yield useful information about a biological system (e.g. knowledge of genes and their products, and the biological roles of proteins, their molecular functions, localizations and interaction networks). We present a technique called Global Mapping of Unknown Proteins (GMUP) which uses the Gene Ontology Index to relate diverse sources of experimental data by creation of an abstraction layer of evidence data. This abstraction layer is used as input to a neural network which, once trained, can be used to predict function from the evidence data of unannotated proteins. The method allows us to include almost any experimental data set related to protein function, which incorporates the Gene Ontology, to our evidence data in order to seek relationships between the different sets.ResultsWe have demonstrated the capabilities of this method in two ways. We first collected various experimental datasets associated with yeast (Saccharomyces cerevisiae) and applied the technique to a set of previously annotated open reading frames (ORFs). These ORFs were divided into training and test sets and were used to examine the accuracy of the predictions made by our method. Then we applied GMUP to previously un-annotated ORFs and made 1980, 836 and 1969 predictions corresponding to the GO Biological Process, Molecular Function and Cellular Component sub-categories respectively. We found that GMUP was particularly successful at predicting ORFs with functions associated with the ribonucleoprotein complex, protein metabolism and transportation.ConclusionThis study presents a global and generic gene knowledge discovery approach based on evidence integration of various genome-scale data. It can be used to provide insight as to how certain biological processes are implemented by interaction and coordination of proteins, which may serve as a guide for future analysis. New data can be readily incorporated as it becomes available to provide more reliable predictions or further insights into processes and interactions.

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Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

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Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

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