Genome-wide assessment of genetic risk for systemic lupus erythematosus and disease severity.

Using three European and two Chinese genome-wide association studies (GWAS), we investigated the performance of genetic risk scores (GRSs) for predicting the susceptibility and severity of systemic lupus erythematosus (SLE), using renal disease as a proxy for severity. We used four GWASs to test the performance of GRS both cross validating within the European population and between European and Chinese populations. The performance of GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves. We then analyzed the polygenic nature of SLE statistically. We also partitioned patients according to their age-of-onset and evaluated the predictability of GRS in disease severity in each age group. We found consistently that the best GRS in the prediction of SLE used SNPs associated at the level of P < 1e-05 in all GWAS data sets and that SNPs with P-values above 0.2 were inflated for SLE true positive signals. The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European and between the European and Chinese populations. We further showed a significant positive correlation between a GRS and renal disease in two independent European GWAS (Pcohort1 = 2.44e-08; Pcohort2 = 0.00205) and a significant negative correlation with age of SLE onset (Pcohort1 = 1.76e-12; Pcohort2 = 0.00384). We found that the GRS performed better in the prediction of renal disease in the 'later onset' compared with the 'earlier onset' group. The GRS predicts SLE in both European and Chinese populations and correlates with poorer prognostic factors: young age-of-onset and lupus nephritis.China Scholarship Council (CSC) & National Science Foundation of China grant 8180163

Functional filter for whole genome sequencing data identifies HHT and stress-associated non-coding SMAD4 polyadenylation site variants >5kb from coding DN

Acknowledgments: This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The work was cofounded by the National Institute for Health Research Imperial Biomedical Research Centre, the D’Almeida Charitable Trust, and Imperial College Healthcare NHS Trust. AA was supported by Prince Sultan Military Medical City, Saudi Arabia. MAA was supported by the National Institutes of Health (grant R35HL140019). The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. We thank the National Health Service staff of the UK Genomic Medicine Centres and the participants for their willing participation; the Genomics England Clinical Research Interface team, specifically Susan Walker, for separately reviewing bam file variant sequences; Charlotte Bevan, Michael Hubank and Santiago Vernia for helpful discussions and manuscript review; and our academic and public partners within the NIHR Imperial BRC’s Social Genetic and Environmental Determinants of Health (SGE) theme. We specifically thank the presented families for confirmation of their clinical phenotypes and consent to share in this manuscript. The views expressed are those of the authors and not necessarily those of funders, the NHS, the NIHR, or the Department of Health and Social Care.Peer reviewedPublisher PD