Background Genome wide association studies (GWAS) have identified numerous SLE risk genes, however, few genes are reported as predisposing to the lupus nephritis (LN) phenotype.1
Objective To explore whether gene-based aggregates of low frequency (MAF<0.05) and/or rare (MAF<0.01) single nucleotide polymorphisms (SNPs) were associated with different subsets of LN or end stage renal disease (ESRD).
Methods We analyzed genotype data from Swedish SLE patients (n=958). Data was generated by targeted sequencing of the coding and regulatory regions of 1900 genes involved in basic immune functions, inflammation and autoimmune diseases. The softwares GenePy2 and optimized sequence Kernel association test (SKAT-O)3 were used to analyze the impact of low frequency and rare variants in LN patients with different sub-phenotypes (n=208) as compared to those (n=621) who had not developed LN at the time of the study. We also performed logistic regression models with different sub phenotypes of LN as the outcome, adjusted for sex and age.
Results Two candidate genes; gelsolin (GSN; 5.97×10 –5 ) and Insulin like growth factor binding protein 7 were identified (IGFBP7; 5.95×10-6) to have significantly higher GenePy scores in cases vs controls (bootstrap test, 1000 permutations). GSN, an actin-binding protein, is involved in non-AL renal amyloidosis and in the development of IgA nephropathy. GSN was associated with proteinuria (OR, [95% CI]; 14.1 [2.9–66.9], p=0.001 and ESRD (5.9[2.4–15.0], p=0.001) after adjusted for gender and age. IGFBP7, an emerging biomarker for acute kidney injury, was also significantly associated with Class3/4and ESRD (p=4.9×10-6). SKAT-O identified one gene was associated with Class V; CTF1 (suggestive p<2.97×10 –5, Bonferroni corrected p = 0.05).
Conclusion An aggregate association testing approach incorporating functional annotation revealed two putative risk loci (GSN and IGFBP7) associated with LN and ESRD. Further investigation needs to test their functional roles in detail.
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Mossotto E, Ashton JJ, O’Gorman L, Pengelly RJ, Beattie RM, MacArthur BD, et al. GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data. BMC Bioinformatics. 2019 May;20(1):254.
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