Chronic kidney disease (CKD) is a significant public health problem, and recent genetic studies have identified common CKD susceptibility variants. The CKDGen consortium performed a meta-analysis of genome-wide association data in 67,093 Caucasian individuals from 20 population-based studies to identify new susceptibility loci for reduced renal function, estimated by serum creatinine (eGFRcrea), cystatin C (eGFRcys), and CKD (eGFRcrea <60 ml/min/1.73m2; n = 5,807 CKD cases). Follow-up of the 23 genome-wide significant loci (p<5×10−8) in 22,982 replication samples identified 13 novel loci for renal function and CKD (in or near LASS2, GCKR, ALMS1, TFDP2, DAB2, SLC34A1, VEGFA, PRKAG2, PIP5K1B, ATXN2, DACH1, UBE2Q2, and SLC7A9) and 7 creatinine production and secretion loci (CPS1, SLC22A2, TMEM60, WDR37, SLC6A13, WDR72, BCAS3). These results further our understanding of biologic mechanisms of kidney function by identifying loci potentially influencing nephrogenesis, podocyte function, angiogenesis, solute transport, and metabolic functions of the kidney.

Background C-reactive protein (CRP) is a heritable marker of chronic inflammation that is strongly associated with cardiovascular disease. We aimed to identify genetic variants that are associated with CRP levels. Methods and Results We performed a genome wide association (GWA) analysis of CRP in 66,185 participants from 15 population-based studies. We sought replication for the genome wide significant and suggestive loci in a replication panel comprising 16,540 individuals from ten independent studies. We found 18 genome-wide significant loci and we provided evidence of replication for eight of them. Our results confirm seven previously known loci and introduce 11 novel loci that are implicated in pathways related to the metabolic syndrome (APOC1, HNF1A, LEPR, GCKR, HNF4A, and PTPN2), immune system (CRP, IL6R, NLRP3, IL1F10, and IRF1), or that reside in regions previously not known to play a role in chronic inflammation (PPP1R3B, SALL1, PABPC4, ASCL1, RORA, and BCL7B). We found significant interaction of body mass index (BMI) with LEPR (p<2.9×10−6). A weighted genetic risk score that was developed to summarize the effect of risk alleles was strongly associated with CRP levels and explained approximately 5% of the trait variance; however, there was no evidence for these genetic variants explaining the association of CRP with coronary heart disease. Conclusion We identified 18 loci that were associated with CRP levels. Our study highlights immune response and metabolic regulatory pathways involved in the regulation of chronic inflammation.

To identify susceptibility alleles associated with rheumatoid arthritis, we genotyped 397 individuals with rheumatoid arthritis for 116,204 SNPs and carried out an association analysis in comparison to publicly available genotype data for 1,211 related individuals from the Framingham Heart Study 1 . After evaluating and adjusting for technical and population biases, we identified a SNP at 6q23 (rs10499194, ∼150 kb from TNFAIP3 and OLIG3) that was reproducibly associated with rheumatoid arthritis both in the genome-wide association (GWA) scan and in 5,541 additional case-control samples (P = 10 −3 , GWA scan; P < 10 −6 , replication; P = 10 −9 , combined). In a concurrent study, the Wellcome Trust Case Control Consortium (WTCCC) has reported strong association of rheumatoid arthritis susceptibility to a different SNP located 3.8 kb from rs10499194 (rs6920220; P = 5 × 10 −6 in WTCCC) 2 . We show that these two SNP associations are statistically independent, are each reproducible in the comparison of our data and WTCCC data, and define risk and protective haplotypes for rheumatoid arthritis at 6q23.Rheumatoid arthritis is the most common inflammatory arthritis, affecting up to 1% of the adult population 3 . Two loci (HLA-DRB14 and PTPN22 5 ) have previously been associated with rheumatoid arthritis susceptibility in individuals with circulating antibodies to cyclic citrullinated peptides (CCP). Most of the inheritance of rheumatoid arthritis remains unexplained.To identify additional common variants associated with risk of CCP antibody-associated (CCP + ) rheumatoid arthritis, we conducted a GWA study using the Affymetrix 100K GeneChip microarray in a longitudinal case series of individuals with CCP + rheumatoid arthritis (the Brigham Rheumatoid Arthritis Sequential Study (BRASS) cohort). As we lacked epidemiologically matched controls, we compared case data to publicly available genotype data collected using the same platform from 1,211 related Framingham Heart Study (FHS) participants 1 , drawn from the same geographical region as the individuals in our study (near Boston, Massachusetts, USA).Before comparing allele frequencies between cases and controls, we considered biases that may be introduced by the use of shared controls. Such biases, whether due to nonrandom distribution of technical artifacts 6 or to population differences between case and control data 7,8 , would result in a non-null distribution of test statistics with excess false-positive associations. In an initial analysis of unrelated case-control samples, we assessed the median distribution of test statistics with the genomic-control parameter λ GC 9 (where 1.0 indicates no inflation) and examined the tail of the distribution of association statistics in a comparison of observed and expected P values (Q-Q plot; Fig. 1).Using published data quality control parameters from early studies on this genotyping platform (genotype call rates > 90%, minor allele frequency (MAF) >5%) 1 , we observed λ GC = 1.19 and an excess of associations in the e...

Chronic kidney disease (CKD), impairment of kidney function, is a serious public health problem, and the assessment of genetic factors influencing kidney function has substantial clinical relevance. Here, we report a meta-analysis of genome-wide association studies for kidney function–related traits, including 71,149 east Asian individuals from 18 studies in 11 population-, hospital- or family-based cohorts, conducted as part of the Asian Genetic Epidemiology Network (AGEN). Our meta-analysis identified 17 loci newly associated with kidney function–related traits, including the concentrations of blood urea nitrogen, uric acid and serum creatinine and estimated glomerular filtration rate based on serum creatinine levels (eGFRcrea) (P < 5.0 × 10−8). We further examined these loci with in silico replication in individuals of European ancestry from the KidneyGen, CKDGen and GUGC consortia, including a combined total of ~110,347 individuals. We identify pleiotropic associations among these loci with kidney function–related traits and risk of CKD. These findings provide new insights into the genetics of kidney function.

Type 2 diabetes is a leading cause of morbidity and mortality. While genetic variants have been found to influence the risk of type 2 diabetes mellitus, relatively few studies have focused on genes associated with glycated hemoglobin, an index of the mean blood glucose concentration of the preceding 8–12 weeks. Epidemiologic studies and randomized clinical trials have documented the relationship between glycated hemoglobin levels and the development of long-term complications in diabetes; moreover, higher glycated hemoglobin levels in the subdiabetic range have been shown to predict type 2 diabetes risk and cardiovascular disease. To examine the common genetic determinants of glycated hemoglobin levels, we performed a genome-wide association study that evaluated 337,343 SNPs in 14,618 apparently healthy Caucasian women. The results show that glycated hemoglobin levels are associated with genetic variation at the GCK (rs730497; P = 2.8×10−12), SLC30A8 (rs13266634; P = 9.8×10−8), G6PC2 (rs1402837; P = 6.8×10−10), and HK1 (rs7072268; P = 6.4×10−9) loci. While associations at the GCK, SLC30A8, and G6PC2 loci are confirmatory, the findings at HK1 are novel. We were able to replicate this novel association in an independent validation sample of 455 additional non-diabetic men and women. HK1 encodes the enzyme hexokinase, the first step in glycolysis and a likely candidate for the control of glucose metabolism. This observed genetic association between glycated hemoglobin levels and HK1 polymorphisms paves the way for further studies of the role of HK1 in hemoglobin glycation, glucose metabolism, and diabetes.