Molecular insights into genome-wide association studies of chronic kidney disease-defining traits.
Xiaoguang XuJames M EalesArtur AkbarovHui GuoLorenz K BeckerDavid TalaveraFehzan AshrafJabran NawazSanjeev PramanikJohn David BowesXiao JiangJohn DormerMatthew DenniffAndrzej AntczakMonika SzulińskaIngrid WisePriscilla R PrestesMaciej GlydaPawel BogdanskiEwa Zukowska-SzczechowskaCarlo BerzuiniAdrian S WoolfNilesh J SamaniFadi J CharcharMaciej TomaszewskiPublished in: Nature communications (2018)
Genome-wide association studies (GWAS) have identified >100 loci of chronic kidney disease-defining traits (CKD-dt). Molecular mechanisms underlying these associations remain elusive. Using 280 kidney transcriptomes and 9958 gene expression profiles from 44 non-renal tissues we uncover gene expression partners (eGenes) for 88.9% of CKD-dt GWAS loci. Through epigenomic chromatin segmentation analysis and variant effect prediction we annotate functional consequences to 74% of these loci. Our colocalisation analysis and Mendelian randomisation in >130,000 subjects demonstrate causal effects of three eGenes (NAT8B, CASP9 and MUC1) on estimated glomerular filtration rate. We identify a common alternative splice variant in MUC1 (a gene responsible for rare Mendelian form of kidney disease) and observe increased renal expression of a specific MUC1 mRNA isoform as a plausible molecular mechanism of the GWAS association signal. These data highlight the variants and genes underpinning the associations uncovered in GWAS of CKD-dt.
Keyphrases
- genome wide association
- chronic kidney disease
- genome wide
- end stage renal disease
- gene expression
- dna methylation
- copy number
- genome wide association study
- poor prognosis
- genome wide identification
- deep learning
- electronic health record
- big data
- single cell
- case control
- hepatitis c virus
- convolutional neural network
- long non coding rna