Renal cell carcinoma (RCC) has an undisputed genetic component and a stable 2:1 male to female sex ratio in its incidence across populations, suggesting possible sexual dimorphism in its genetic susceptibility. We conducted the first sex-specific genome-wide association analysis of RCC for men (3227 cases, 4916 controls) and women (1992 cases, 3095 controls) of European ancestry from two RCC genome-wide scans and replicated the top findings using an additional series of men (2261 cases, 5852 controls) and women (1399 cases, 1575 controls) from two independent cohorts of European origin. Our study confirmed sex-specific associations for two known RCC risk loci at 14q24.2 (DPF3) and 2p21(EPAS1). We also identified two additional suggestive male-specific loci at 6q24.3 (SAMD5, male odds ratio (ORmale) = 0.83 [95% CI = 0.78-0.89], Pmale = 1.71 × 10-8 compared with female odds ratio (ORfemale) = 0.98 [95% CI = 0.90-1.07], Pfemale = 0.68) and 12q23.3 (intergenic, ORmale = 0.75 [95% CI = 0.68-0.83], Pmale = 1.59 × 10-8 compared with ORfemale = 0.93 [95% CI = 0.82-1.06], Pfemale = 0.21) that attained genome-wide significance in the joint meta-analysis. Herein, we provide evidence of sex-specific associations in RCC genetic susceptibility and advocate the necessity of larger genetic and genomic studies to unravel the endogenous causes of sex bias in sexually dimorphic traits and diseases like RCC.

• MeSH
• Genome-Wide Association Study * MeSH
• Genetic Predisposition to Disease * MeSH
• Polymorphism, Single Nucleotide MeSH
• Carcinoma, Renal Cell epidemiology   genetics   MeSH
• Humans MeSH
• Quantitative Trait Loci MeSH
• Kidney Neoplasms epidemiology   genetics   MeSH
• Odds Ratio MeSH
• Sex Factors MeSH
• Computational Biology MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Meta-Analysis MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: Several obesity-related factors have been associated with renal cell carcinoma (RCC), but it is unclear which individual factors directly influence risk. We addressed this question using genetic markers as proxies for putative risk factors and evaluated their relation to RCC risk in a mendelian randomization (MR) framework. This methodology limits bias due to confounding and is not affected by reverse causation. METHODS AND FINDINGS: Genetic markers associated with obesity measures, blood pressure, lipids, type 2 diabetes, insulin, and glucose were initially identified as instrumental variables, and their association with RCC risk was subsequently evaluated in a genome-wide association study (GWAS) of 10,784 RCC patients and 20,406 control participants in a 2-sample MR framework. The effect on RCC risk was estimated by calculating odds ratios (ORSD) for a standard deviation (SD) increment in each risk factor. The MR analysis indicated that higher body mass index increases the risk of RCC (ORSD: 1.56, 95% confidence interval [CI] 1.44-1.70), with comparable results for waist-to-hip ratio (ORSD: 1.63, 95% CI 1.40-1.90) and body fat percentage (ORSD: 1.66, 95% CI 1.44-1.90). This analysis further indicated that higher fasting insulin (ORSD: 1.82, 95% CI 1.30-2.55) and diastolic blood pressure (DBP; ORSD: 1.28, 95% CI 1.11-1.47), but not systolic blood pressure (ORSD: 0.98, 95% CI 0.84-1.14), increase the risk for RCC. No association with RCC risk was seen for lipids, overall type 2 diabetes, or fasting glucose. CONCLUSIONS: This study provides novel evidence for an etiological role of insulin in RCC, as well as confirmatory evidence that obesity and DBP influence RCC risk.

• MeSH
• Genome-Wide Association Study MeSH
• Diabetes Mellitus, Type 2 complications   MeSH
• Genetic Markers MeSH
• Body Mass Index MeSH
• Insulin blood   MeSH
• Carcinoma, Renal Cell etiology   genetics   MeSH
• Blood Glucose analysis   MeSH
• Blood Pressure MeSH
• Humans MeSH
• Lipids blood   MeSH
• Mendelian Randomization Analysis MeSH
• Kidney Neoplasms etiology   genetics   MeSH
• Obesity complications   genetics   MeSH
• Risk Factors MeSH
• Check Tag
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, N.I.H., Intramural MeSH

BACKGROUND: Relative telomere length in peripheral blood leukocytes has been evaluated as a potential biomarker for renal cell carcinoma (RCC) risk in several studies, with conflicting findings. OBJECTIVE: We performed an analysis of genetic variants associated with leukocyte telomere length to assess the relationship between telomere length and RCC risk using Mendelian randomization, an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations. DESIGN, SETTING, AND PARTICIPANTS: Genotypes from nine telomere length-associated variants for 10 784 cases and 20 406 cancer-free controls from six genome-wide association studies (GWAS) of RCC were aggregated into a weighted genetic risk score (GRS) predictive of leukocyte telomere length. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Odds ratios (ORs) relating the GRS and RCC risk were computed in individual GWAS datasets and combined by meta-analysis. RESULTS AND LIMITATIONS: Longer genetically inferred telomere length was associated with an increased risk of RCC (OR=2.07 per predicted kilobase increase, 95% confidence interval [CI]:=1.70-2.53, p<0.0001). As a sensitivity analysis, we excluded two telomere length variants in linkage disequilibrium (R2>0.5) with GWAS-identified RCC risk variants (rs10936599 and rs9420907) from the telomere length GRS; despite this exclusion, a statistically significant association between the GRS and RCC risk persisted (OR=1.73, 95% CI=1.36-2.21, p<0.0001). Exploratory analyses for individual histologic subtypes suggested comparable associations with the telomere length GRS for clear cell (N=5573, OR=1.93, 95% CI=1.50-2.49, p<0.0001), papillary (N=573, OR=1.96, 95% CI=1.01-3.81, p=0.046), and chromophobe RCC (N=203, OR=2.37, 95% CI=0.78-7.17, p=0.13). CONCLUSIONS: Our investigation adds to the growing body of evidence indicating some aspect of longer telomere length is important for RCC risk. PATIENT SUMMARY: Telomeres are segments of DNA at chromosome ends that maintain chromosomal stability. Our study investigated the relationship between genetic variants associated with telomere length and renal cell carcinoma risk. We found evidence suggesting individuals with inherited predisposition to longer telomere length are at increased risk of developing renal cell carcinoma.

• MeSH
• Genome-Wide Association Study MeSH
• Phenotype MeSH
• Genetic Predisposition to Disease MeSH
• Risk Assessment MeSH
• Telomere Homeostasis * MeSH
• Polymorphism, Single Nucleotide * MeSH
• Carcinoma, Renal Cell blood   genetics   pathology   MeSH
• Leukocytes chemistry   MeSH
• Humans MeSH
• Mendelian Randomization Analysis MeSH
• Kidney Neoplasms blood   genetics   pathology   MeSH
• Odds Ratio MeSH
• Risk Factors MeSH
• Case-Control Studies MeSH
• Telomere genetics   pathology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Meta-Analysis MeSH
• Multicenter Study MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, N.I.H., Intramural MeSH

Previous genome-wide association studies (GWAS) have identified six risk loci for renal cell carcinoma (RCC). We conducted a meta-analysis of two new scans of 5,198 cases and 7,331 controls together with four existing scans, totalling 10,784 cases and 20,406 controls of European ancestry. Twenty-four loci were tested in an additional 3,182 cases and 6,301 controls. We confirm the six known RCC risk loci and identify seven new loci at 1p32.3 (rs4381241, P=3.1 × 10-10), 3p22.1 (rs67311347, P=2.5 × 10-8), 3q26.2 (rs10936602, P=8.8 × 10-9), 8p21.3 (rs2241261, P=5.8 × 10-9), 10q24.33-q25.1 (rs11813268, P=3.9 × 10-8), 11q22.3 (rs74911261, P=2.1 × 10-10) and 14q24.2 (rs4903064, P=2.2 × 10-24). Expression quantitative trait analyses suggest plausible candidate genes at these regions that may contribute to RCC susceptibility.

• MeSH
• White People genetics   MeSH
• Genome-Wide Association Study * MeSH
• Adult MeSH
• Phenotype MeSH
• Genetic Predisposition to Disease * MeSH
• Genetic Loci MeSH
• Polymorphism, Single Nucleotide MeSH
• Carcinoma, Renal Cell genetics   MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Kidney Neoplasms genetics   MeSH
• Aged MeSH
• Germ-Line Mutation MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Adolescent MeSH
• Young Adult MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Meta-Analysis MeSH
• Research Support, Non-U.S. Gov't MeSH
• Research Support, N.I.H., Extramural MeSH

The term 'ancient DNA' (aDNA) is coming of age, with over 1,200 hits in the PubMed database, beginning in the early 1980s with the studies of 'molecular paleontology'. Rooted in cloning and limited sequencing of DNA from ancient remains during the pre-PCR era, the field has made incredible progress since the introduction of PCR and next-generation sequencing. Over the last decade, aDNA analysis ushered in a new era in genomics and became the method of choice for reconstructing the history of organisms, their biogeography, and migration routes, with applications in evolutionary biology, population genetics, archaeogenetics, paleo-epidemiology, and many other areas. This change was brought by development of new strategies for coping with the challenges in studying aDNA due to damage and fragmentation, scarce samples, significant historical gaps, and limited applicability of population genetics methods. In this review, we describe the state-of-the-art achievements in aDNA studies, with particular focus on human evolution and demographic history. We present the current experimental and theoretical procedures for handling and analysing highly degraded aDNA. We also review the challenges in the rapidly growing field of ancient epigenomics. Advancement of aDNA tools and methods signifies a new era in population genetics and evolutionary medicine research.

• MeSH
• Genome, Human * MeSH
• Genomics methods   MeSH
• Humans MeSH
• Evolution, Molecular * MeSH
• Genetics, Population methods   MeSH
• Sequence Analysis, DNA methods   MeSH
• DNA, Ancient * MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

The incidence of renal cell carcinoma (RCC) is increasing worldwide, and its prevalence is particularly high in some parts of Central Europe. Here we undertake whole-genome and transcriptome sequencing of clear cell RCC (ccRCC), the most common form of the disease, in patients from four different European countries with contrasting disease incidence to explore the underlying genomic architecture of RCC. Our findings support previous reports on frequent aberrations in the epigenetic machinery and PI3K/mTOR signalling, and uncover novel pathways and genes affected by recurrent mutations and abnormal transcriptome patterns including focal adhesion, components of extracellular matrix (ECM) and genes encoding FAT cadherins. Furthermore, a large majority of patients from Romania have an unexpected high frequency of A:T>T:A transversions, consistent with exposure to aristolochic acid (AA). These results show that the processes underlying ccRCC tumorigenesis may vary in different populations and suggest that AA may be an important ccRCC carcinogen in Romania, a finding with major public health implications.

• MeSH
• Adult MeSH
• Focal Adhesions metabolism   MeSH
• Phosphatidylinositol 3-Kinases genetics   MeSH
• Oncogene Proteins, Fusion genetics   MeSH
• Genetic Variation * MeSH
• Genome, Human genetics   MeSH
• Genomics * MeSH
• Carcinoma, Renal Cell genetics   MeSH
• Cohort Studies MeSH
• Middle Aged MeSH
• Humans MeSH
• Mutation MeSH
• Mutation Rate MeSH
• Gene Expression Regulation, Neoplastic MeSH
• Sequence Analysis, DNA MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• RNA Splicing genetics   MeSH
• Signal Transduction genetics   MeSH
• Gene Expression Profiling MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Europe MeSH