Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes

Aims We evaluated whether incorporating information on ethnic background and polygenic risk enhanced the Leicester Risk Assessment (LRA) score for predicting 10-year risk of type 2 diabetes. Methods The sample included 202,529 UK Biobank participants aged 40–69 years. We computed the LRA score, and developed two new risk scores using training data (80% sample): LRArev, which incorporated additional information on ethnic background, and LRAprs, which incorporated polygenic risk for type 2 diabetes. We assessed discriminative and reclassification performance in a test set (20% sample). Type 2 diabetes was ascertained using primary care, hospital inpatient and death registry records. Results Over 10 years, 7476 participants developed type 2 diabetes. The Harrell's C indexes were 0.796 (95% Confidence Interval [CI] 0.785, 0.806), 0.802 (95% CI 0.792, 0.813), and 0.829 (95% CI 0.820, 0.839) for the LRA, LRArev and LRAprs scores, respectively. The LRAprs score significantly improved the overall reclassification compared to the LRA (net reclassification index [NRI] = 0.033, 95% CI 0.015, 0.049) and LRArev (NRI = 0.040, 95% CI 0.024, 0.055) scores. Conclusions Polygenic risk moderately improved the performance of the existing LRA score for 10-year risk prediction of type 2 diabetes.</p

Summary statistics for kidney stone GWAS in UK Biobank

Genome-wide association studies (GWAS) were performed in the UK Biobank, excluding participants with conditions predisposing to kidney stone disease (Supplementary Table 3). Genotyping was undertaken using UK-BiLEVE and UK-Biobank Axiom Arrays and called using array intensity data and a custom genotype-calling pipeline. PLINKv1.9 and Rv3.6.1 were used for quality control (QC). Sample-, individual-, and SNP-level QC exclusions are shown in Supplementary Methods.UK Biobank phasing on autosomes was performed with SHAPEIT3 using the 1000 Genomes phase 3 dataset as a reference panel. The Haplotype Reference Consortium reference panel and a merged UK10K/1000 Genomes Phase 3 panel were used in imputation. The resultant dataset comprised 92,693,895 autosomal SNPs, short indels, and large structural variants.A total of 547,011 genotyped and 8,397,548 imputed autosomal SNPs and 733,758 genotyped and 2,635,881 X-chromosome SNPs with MAF ≥0.01 and Info Score ≥0.9 were used at GWAS, using a linear mixed noninfinitesimal model implemented in BOLT-LMMv2.3</p

Kidney stone disease GWAS meta-analysis- FinnGen & UK Biobank

A fixed-effects meta-analysis of kidney stone disease was undertaken using UK Biobank and FinnGen kidney stone GWAS summary statistics for autosomes and the X-chromosome. FinnGen r8 GWAS data are publicly available for the phenotype N14 calculus of kidney and ureter comprising 8597 cases and 333,128 controls. Information on sample phenotyping, genotyping, and GWAS in the FinnGen sample has been previously described. SNPs with MAF 75%) were excluded. The resultant summary statistics were used to perform MR analyses.</p