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

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

Comparison of the associations of biochemically measured and genetically instrumented 25-nmol/l higher plasma 25(OH)D concentrations with risk of diabetes.

<p>*The full details of the adjustments in the observational analyses are provided in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002566#pmed.1002566.s011" target="_blank">S3 Table</a>. Other symbols and conventions as in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002566#pmed.1002566.g001" target="_blank">Fig 1</a>. 25(OH)D, 25-hydroxyvitamin D; CKB, China Kadoorie Biobank.</p

Association of genetic score using synthesis SNPs for 25(OH)D concentration with risk of diabetes in a meta-analysis of all studies per 25-nmol/l higher genetically instrumented 25(OH)D concentration.

<p>Values shown are the odds ratios (95% CIs) per 25-nmol/l higher 25(OH)D concentration among studies stratified by latitude into northern (>50°) or southern latitude (≤50°). The area of the squares is proportional to the inverse variance of each effect size. *The effects of all SNPs on risk of diabetes in Chinese and European populations were weighted by their effects on 25(OH)D concentration. 25(OH)D, 25-hydroxyvitamin D; CCCS, Cambridgeshire case—control study; CKB, China Kadoorie Biobank; DIAGRAM, Diabetes Genetics Replication and Meta-analysis; UKB, UK Biobank.</p

Association of 25(OH)D SNPs with plasma 25(OH)D concentrations and with cardiometabolic risk factors.

<p>Association of 25(OH)D SNPs with plasma 25(OH)D concentrations and with cardiometabolic risk factors.</p

Selected characteristics for all participants with 25-hydroxyvitamin D (25[OH]D) measured and genetic data in the China Kadoorie Biobank (CKB).

<p>Selected characteristics for all participants with 25-hydroxyvitamin D (25[OH]D) measured and genetic data in the China Kadoorie Biobank (CKB).</p

Data_Sheet_1.docx

<p>Blood levels of growth differentiation factor-15 (GDF-15), also known as macrophage inhibitory cytokine-1 (MIC-1), have been associated with various pathological processes and diseases, including cardiovascular disease and cancer. Prior studies suggest genetic factors play a role in regulating blood MIC-1/GDF-15 concentration. In the current study, we conducted the largest genome-wide association study (GWAS) to date using a sample of ∼5,400 community-based Caucasian participants, to determine the genetic variants associated with MIC-1/GDF-15 blood concentration. Conditional and joint (COJO), gene-based association, and gene-set enrichment analyses were also carried out to identify novel loci, genes, and pathways. Consistent with prior results, a locus on chromosome 19, which includes nine single nucleotide polymorphisms (SNPs) (top SNP, rs888663, p = 1.690 × 10^-35), was significantly associated with blood MIC-1/GDF-15 concentration, and explained 21.47% of its variance. COJO analysis showed evidence for two independent signals within this locus. Gene-based analysis confirmed the chromosome 19 locus association and in addition, a putative locus on chromosome 1. Gene-set enrichment analyses showed that the“COPI-mediated anterograde transport” gene-set was associated with MIC-1/GDF15 blood concentration with marginal significance after FDR correction (p = 0.067). In conclusion, a locus on chromosome 19 was associated with MIC-1/GDF-15 blood concentration with genome-wide significance, with evidence for a new locus (chromosome 1). Future studies using independent cohorts are needed to confirm the observed associations especially for the chromosomes 1 locus, and to further investigate and identify the causal SNPs that contribute to MIC-1/GDF-15 levels.</p

Clinical characteristics of the study groups.

<p>Clinical characteristics of the study groups.</p

Results from network analysis of GLP-1 stimulated insulin secretion GWAS.

<p>A) The beta-cell specific GLP-1 response consensus network, annotated with the top enriched KEGG pathways: Focal adhesion (green), ECM-receptor interaction (blue) and Rap1 signaling (purple). Arrows indicate genes that were identified as upstream regulators of differentially expressed genes in the transcriptome analyses of the liraglutide treated mice versus baseline controls. B) The KEGG pathways enriched (BH adjusted <i>P</i>-value < 1 × 10⁻³) within the GLP-1 response consensus network, compared to the whole beta-cell PPI network. C) The red line denotes the combined z-score in the Tübingen validation cohort for 28 consensus network SNPs with discovery GWAS <i>P</i> < 5 × 10⁻⁴ compared to 100,000 z-scores obtained from randomly selected sets of SNPs from the beta-cell network (histogram), empirical <i>P</i>-value = 0.012. D) Top panel: Top regulators for networks of differentially expressed genes in the liraglutide treated mice transcriptome experiment. Bottom panel: Prioritized network modules from human and mouse experiments map to connective tissue and focal adhesion related pathways.</p

Body mass index stratified meta-analysis of genome-wide association studies of polycystic ovary syndrome in women of European ancestry

Background Polycystic ovary syndrome (PCOS) is a complex multifactorial disorder with a substantial genetic component. However, the clinical manifestations of PCOS are heterogeneous with notable differences between lean and obese women, implying a different pathophysiology manifesting in differential body mass index (BMI). We performed a meta-analysis of genome-wide association study (GWAS) data from six well-characterised cohorts, using a case–control study design stratified by BMI, aiming to identify genetic variants associated with lean and overweight/obese PCOS subtypes. Results The study comprised 254,588 women (5,937 cases and 248,651 controls) from individual studies performed in Australia, Estonia, Finland, the Netherlands and United States of America, and separated according to three BMI stratifications (lean, overweight and obese). Genome-wide association analyses were performed for each stratification within each cohort, with the data for each BMI group meta-analysed using METAL software. Almost half of the total study population (47%, n = 119,584) were of lean BMI (≤ 25 kg/m2). Two genome-wide significant loci were identified for lean PCOS, led by rs12000707 within DENND1A (P = 1.55 × 10–12) and rs2228260 within XBP1 (P = 3.68 × 10–8). One additional locus, LINC02905, was highlighted as significantly associated with lean PCOS through gene-based analyses (P = 1.76 × 10–6). There were no significant loci observed for the overweight or obese sub-strata when analysed separately, however, when these strata were combined, an association signal led by rs569675099 within DENND1A reached genome-wide significance (P = 3.22 × 10–9) and a gene-based association was identified with ERBB4 (P = 1.59 × 10–6). Nineteen of 28 signals identified in previous GWAS, were replicated with consistent allelic effect in the lean stratum. There were less replicated signals in the overweight and obese groups, and only 4 SNPs were replicated in each of the three BMI strata. Conclusions Genetic variation at the XBP1, LINC02905 and ERBB4 loci were associated with PCOS within unique BMI strata, while DENND1A demonstrated associations across multiple strata, providing evidence of both distinct and shared genetic features between lean and overweight/obese PCOS-affected women. This study demonstrated that PCOS-affected women with contrasting body weight are not only phenotypically distinct but also show variation in genetic architecture; lean PCOS women typically display elevated gonadotrophin ratios, lower insulin resistance, higher androgen levels, including adrenal androgens, and more favourable lipid profiles. Overall, these findings add to the growing body of evidence supporting a genetic basis for PCOS as well as differences in genetic patterns relevant to PCOS BMI-subtype.</p