Control procedures and estimators of the false discovery rate and their application in low-dimensional settings: an empirical investigation

Background: When many (up to millions) of statistical tests are conducted in discovery set analyses such as genome-wide association studies (GWAS), approaches controlling family-wise error rate (FWER) or false discovery rate (FDR) are required to reduce the number of false positive decisions. Some methods were specifically developed in the context of high-dimensional settings and partially rely on the estimation of the proportion of true null hypotheses. However, these approaches are also applied in low-dimensional settings such as replication set analyses that might be restricted to a small number of specific hypotheses. The aim of this study was to compare different approaches in low-dimensional settings using (a) real data from the CKDGen Consortium and (b) a simulation study. Results: In both application and simulation FWER approaches were less powerful compared to FDR control methods, whether a larger number of hypotheses were tested or not. Most powerful was the q-value method. However, the specificity of this method to maintain true null hypotheses was especially decreased when the number of tested hypotheses was small. In this low-dimensional situation, estimation of the proportion of true null hypotheses was biased. Conclusions: The results highlight the importance of a sizeable data set for a reliable estimation of the proportion of true null hypotheses. Consequently, methods relying on this estimation should only be applied in high-dimensional settings. Furthermore, if the focus lies on testing of a small number of hypotheses such as in replication settings, FWER methods rather than FDR methods should be preferred to maintain high specificity

Role of BMI in the Association of the TCF7L2 rs7903146 Variant with Coronary Heart Disease: The Atherosclerosis Risk in Communities (ARIC) Study

We examined the association of variation in the type 2 diabetes risk-conferring TCF7L2 gene with the risk of incident coronary heart disease (CHD) among the lean, overweight, and obese members of the Atherosclerosis Risk in Communities (ARIC) Study cohort. Cox proportional hazard regression analyses were performed using a general model, with the major homozygote as the reference category. For 9,865 whites, a significant increase in the risk of CHD was seen only among lean ( BMI < 25 kg/m2) individuals homozygous for the T allele of the TCF7L2 rs7903146 gene risk variant (hazard ratio 1.42; 95% CI 1.03,1.97; P = .01). No association was found among 3,631 blacks, regardless of BMI status. An attenuated hazard ratio was observed among the nondiabetic ARIC cohort members. This study suggests that body mass modifies the association of the TCF7L2 rs7903146 T allele with CHD risk

Validated SNPs for eGFR and their associations with albuminuria

Albuminuria and reduced glomerular filtration rate are manifestations of chronic kidney disease (CKD) that predict end-stage renal disease, acute kidney injury, cardiovascular disease and death. We hypothesized that SNPs identified in association with the estimated glomerular filtration rate (eGFR) would also be associated with albuminuria. Within the CKDGen Consortium cohort (n= 31 580, European ancestry), we tested 16 eGFR-associated SNPs for association with the urinary albumin-to-creatinine ratio (UACR) and albuminuria [UACR >25 mg/g (women); 17 mg/g (men)]. In parallel, within the CARe Renal Consortium (n= 5569, African ancestry), we tested seven eGFR-associated SNPs for association with the UACR. We used a Bonferroni-corrected P-value of 0.003 (0.05/16) in CKDGen and 0.007 (0.05/7) in CARe. We also assessed whether the 16 eGFR SNPs were associated with the UACR in aggregate using a beta-weighted genotype score. In the CKDGen Consortium, the minor A allele of rs17319721 in the SHROOM3 gene, known to be associated with a lower eGFR, was associated with lower ln(UACR) levels (beta = −0.034, P-value = 0.0002). No additional eGFR-associated SNPs met the Bonferroni-corrected P-value threshold of 0.003 for either UACR or albuminuria. In the CARe Renal Consortium, there were no associations between SNPs and UACR with a P< 0.007. Although we found the genotype score to be associated with albuminuria (P= 0.0006), this result was driven almost entirely by the known SHROOM3 variant, rs17319721. Removal of rs17319721 resulted in a P-value 0.03, indicating a weak residual aggregate signal. No alleles, previously demonstrated to be associated with a lower eGFR, were associated with the UACR or albuminuria, suggesting that there may be distinct genetic components for these trait

Genetics of serum urate concentrations and gout in a high risk population, patients with chronic kidney disease

We evaluated genetics of hyperuricemia and gout, their interaction with kidney function and medication intake in chronic kidney disease (CKD) patients. Genome-wide association studies (GWAS) of urate and gout were performed in 4941 CKD patients in the German Chronic Kidney Disease (GCKD) study. Effect estimates of 26 known urate-associated population-based single nucleotide polymorphisms (SNPs) were examined. Interactions of urate-associated variants with urate-altering medications and clinical characteristics of gout were evaluated. Genome-wide significant associations with serum urate and gout were identified for known loci at SLC2A9 and ABCG2, but not for novel loci. Effects of the 26 known SNPs were of similar magnitude in CKD patients compared to population based individuals, except for SNPs at ABCG2 that showed greater effects in CKD. Gene-medication interactions were not significant when accounting for multiple testing. Associations with gout in specific joints were significant for SLC2A9 rs12498742 in wrists and midfoot joints. Known genetic variants in SLC2A9 and ABCG2 were associated with urate and gout in a CKD cohort, with effect sizes for ABCG2 significantly greater in CKD compared to the general population. CKD patients are at high risk of gout due to reduced kidney function, diuretics intake and genetic predisposition, making treatment to target challenging

Role of BMI in the Association of the TCF7L2 rs7903146 Variant with Coronary Heart Disease: The Atherosclerosis Risk in Communities (ARIC) Study

We examined the association of variation in the type 2 diabetes risk-conferring TCF7L2 gene with the risk of incident coronary heart disease (CHD) among the lean, overweight, and obese members of the Atherosclerosis Risk in Communities (ARIC) Study cohort. Cox proportional hazard regression analyses were performed using a general model, with the major homozygote as the reference category. For 9,865 whites, a significant increase in the risk of CHD was seen only among lean (BMI &lt; 25 kg/m 2 ) individuals homozygous for the T allele of the TCF7L2 rs7903146 gene risk variant (hazard ratio 1.42; 95% CI 1.03,1.97; P = .01). No association was found among 3,631 blacks, regardless of BMI status. An attenuated hazard ratio was observed among the nondiabetic ARIC cohort members. This study suggests that body mass modifies the association of the TCF7L2 rs7903146 T allele with CHD risk

Building a network of ADPKD reference centres across Europe: the EuroCYST initiative

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the most common monogenic inherited kidney disease, affecting an estimated 600 000 individuals in Europe. The disease is characterized by age-dependent development of a multiple cysts in the kidneys, ultimately leading to end-stage renal failure and the need of renal replacement therapy in the majority of patients, typically by the fifth or sixth decade of life. The variable disease course, even within the same family, remains largely unexplained. Similarly, assessing disease severity and prognosis in an individual with ADPKD remains difficult. Epidemiological studies are limited due to the fragmentation of ADPKD research in Europe. METHODS: The EuroCYST initiative aims: (i) to harmonize and develop common standards for ADPKD research by starting a collaborative effort to build a network of ADPKD reference centres across Europe and (ii) to establish a multicentric observational cohort of ADPKD patients. This cohort will be used to study factors influencing the rate of disease progression, disease modifiers, disease stage-specific morbidity and mortality, health economic issues and to identify predictive disease progression markers. Overall, 1100 patients will be enrolled in 14 study sites across Europe. Patients will be prospectively followed for at least 3 years. Eligible patients will not have participated in a pharmaceutical clinical trial 1 year before enrollment, have clinically proven ADPKD, an estimated glomerular filtration rate (eGFR) of 30 mL/min/1.73 m(2) and above, and be able to provide written informed consent. The baseline visit will include a physical examination and collection of blood, urine and DNA for biomarker and genetic studies. In addition, all participants will be asked to complete questionnaires detailing self-reported health status, quality of life, socioeconomic status, health-care use and reproductive planning. All subjects will undergo annual follow-up. A magnetic resonance imaging (MRI) scan will be carried out at baseline, and patients are encouraged to undergo a second MRI at 3-year follow-up for qualitative and quantitative kidney and liver assessments. CONCLUSIONS: The ADPKD reference centre network across Europe and the observational cohort study will enable European ADPKD researchers to gain insights into the natural history, heterogeneity and associated complications of the disease as well as how it affects the lives of patients across Europ

Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine.

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments

Glycaemic control and antidiabetic therapy in patients with diabetes mellitus and chronic kidney disease - cross-sectional data from the German Chronic Kidney Disease (GCKD) cohort

Background: Diabetes mellitus (DM) is the leading cause of end-stage renal disease. Little is known about practice patterns of anti-diabetic therapy in the presence of chronic kidney disease (CKD) and correlates with glycaemic control. We therefore aimed to analyze current antidiabetic treatment and correlates of metabolic control in a large contemporary prospective cohort of patients with diabetes and CKD. Methods: The German Chronic Kidney Disease (GCKD) study enrolled 5217 patients aged 18-74 years with an estimated glomerular filtration rate (eGFR) between 30-60 mL/min/1.73 m(2) or proteinuria >0.5 g/d. The use of diet prescription, oral anti-diabetic medication, and insulin was assessed at baseline. HbA1c, measured centrally, was the main outcome measure. Results: At baseline, DM was present in 1842 patients (35 %) and the median HbA1C was 7.0 % (25th-75th percentile: 6.8-7.9 %), equalling 53 mmol/mol (51, 63);24.2 % of patients received dietary treatment only, 25.5 % oral antidiabetic drugs but not insulin, 8.4 % oral antidiabetic drugs with insulin, and 41.8 % insulin alone. Metformin was used by 18.8 %. Factors associated with an HbA1C level >7.0 % (53 mmol/mol) were higher BMI (OR = 1.04 per increase of 1 kg/m(2), 95 % CI 1.02-1.06), hemoglobin (OR = 1.11 per increase of 1 g/dL, 95 % CI 1.04-1.18), treatment with insulin alone (OR = 5.63, 95 % CI 4.26-7.45) or in combination with oral antidiabetic agents (OR = 4.23, 95 % CI 2.77-6.46) but not monotherapy with metformin, DPP-4 inhibitors, or glinides. Conclusions: Within the GCKD cohort of patients with CKD stage 3 or overt proteinuria, antidiabetic treatment patterns were highly variable with a remarkably high proportion of more than 50 % receiving insulin-based therapies. Metabolic control was overall satisfactory, but insulin use was associated with higher HbA1C levels

Impact of repeated measures and sample selection on genome-wide association studies of fasting glucose

Although GWAS have been performed in longitudinal studies, most used only a single trait measure. GWAS of fasting glucose have generally included only normoglycemic individuals. We examined the impact of both repeated measures and sample selection on GWAS in ARIC, a study which obtained four longitudinal measures of fasting glucose and included both individuals with and without prevalent diabetes. The sample included Caucasians and the Affymetrix 6.0 chip was used for genotyping. Sample sizes for GWAS analyses ranged from 8372 (first study visit) to 5782 (average fasting glucose). Candidate SNP analyses with SNPs identified through fasting glucose or diabetes GWAS were conducted in 9133 individuals, including 761 with prevalent diabetes. For a constant sample size, smaller p-values were obtained for the average measure of fasting glucose compared to values at any single visit, and two additional significant GWAS signals were detected. For four candidate SNPs (rs780094, rs10830963, rs7903146, and rs4607517), the strength of association between genotype and glucose was significantly (p-interaction < .05) different in those with and without prevalent diabetes and for all five fasting glucose candidate SNPs (rs780094, rs10830963, rs560887, rs4607517, rs13266634) the association with measured fasting glucose was more significant in the smaller sample without prevalent diabetes than in the larger combined sample of those with and without diabetes. This analysis demonstrates the potential utility of averaging trait values in GWAS studies and explores the advantage of using only individuals without prevalent diabetes in GWAS of fasting glucose