\u3ci\u3eIam hiQ\u3c/i\u3e—A Novel Pair of Accuracy Indices for Imputed Genotypes

Background: Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper, we introduce Iam hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. Iam (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. Results: Applying both measures to a large case–control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for Iam and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of Iam and hiQ can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure info (implemented in IMPUTE2). Conclusion: We recommend using Iam hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data

Circulating vitamin D concentration and risk of seven cancers: Mendelian randomisation study

Objective To determine if circulating concentrations of vitamin D are causally associated with risk of cancer.Design Mendelian randomisation study.Setting Large genetic epidemiology networks (the Genetic Associations and Mechanisms in Oncology (GAME-ON), the Genetic and Epidemiology of Colorectal Cancer Consortium (GECCO), and the Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortiums, and the MR-Base platform).Participants 70 563 cases of cancer (22 898 prostate cancer, 15 748 breast cancer, 12 537 lung cancer, 11 488 colorectal cancer, 4369 ovarian cancer, 1896 pancreatic cancer, and 1627 neuroblastoma) and 84 418 controls.Exposures Four single nucleotide polymorphisms (rs2282679, rs10741657, rs12785878 and rs6013897) associated with vitamin D were used to define a multi-polymorphism score for circulating 25-hydroxyvitamin D (25(OH)D) concentrations.Main outcomes measures The primary outcomes were the risk of incident colorectal, breast, prostate, ovarian, lung, and pancreatic cancer and neuroblastoma, which was evaluated with an inverse variance weighted average of the associations with specific polymorphisms and a likelihood based approach. Secondary outcomes based on cancer subtypes by sex, anatomic location, stage, and histology were also examined.Results There was little evidence that the multi-polymorphism score of 25(OH)D was associated with risk of any of the seven cancers or their subtypes. Specifically, the odds ratios per 25 nmol/L increase in genetically determined 25(OH)D concentrations were 0.92 (95% confidence interval 0.76 to 1.10) for colorectal cancer, 1.05 (0.89 to 1.24) for breast cancer, 0.89 (0.77 to 1.02) for prostate cancer, and 1.03 (0.87 to 1.23) for lung cancer. The results were consistent with the two different analytical approaches, and the study was powered to detect relative effect sizes of moderate magnitude (for example, 1.20-1.50 per 25 nmol/L decrease in 25(OH)D for most primary cancer outcomes. The Mendelian randomisation assumptions did not seem to be violated.Conclusions There is little evidence for a linear causal association between circulating vitamin D concentration and risk of various types of cancer, though the existence of causal clinically relevant effects of low magnitude cannot be ruled out. These results, in combination with previous literature, provide evidence that population-wide screening for vitamin D deficiency and subsequent widespread vitamin D supplementation should not currently be recommended as a strategy for primary cancer prevention

Quantifying the Genetic Correlation between Multiple Cancer Types.

Background: Many cancers share specific genetic risk factors, including both rare high-penetrance mutations and common SNPs identified through genome-wide association studies (GWAS). However, little is known about the overall shared heritability across cancers. Quantifying the extent to which two distinct cancers share genetic origin will give insights to shared biological mechanisms underlying cancer and inform design for future genetic association studies.Methods: In this study, we estimated the pair-wise genetic correlation between six cancer types (breast, colorectal, lung, ovarian, pancreatic, and prostate) using cancer-specific GWAS summary statistics data based on 66,958 case and 70,665 control subjects of European ancestry. We also estimated genetic correlations between cancers and 14 noncancer diseases and traits.Results: After adjusting for 15 pair-wise genetic correlation tests between cancers, we found significant (P < 0.003) genetic correlations between pancreatic and colorectal cancer (rg = 0.55, P = 0.003), lung and colorectal cancer (rg = 0.31, P = 0.001). We also found suggestive genetic correlations between lung and breast cancer (rg = 0.27, P = 0.009), and colorectal and breast cancer (rg = 0.22, P = 0.01). In contrast, we found no evidence that prostate cancer shared an appreciable proportion of heritability with other cancers. After adjusting for 84 tests studying genetic correlations between cancer types and other traits (Bonferroni-corrected P value: 0.0006), only the genetic correlation between lung cancer and smoking remained significant (rg = 0.41, P = 1.03 × 10-6). We also observed nominally significant genetic correlations between body mass index and all cancers except ovarian cancer.Conclusions: Our results highlight novel genetic correlations and lend support to previous observational studies that have observed links between cancers and risk factors.Impact: This study demonstrates modest genetic correlations between cancers; in particular, breast, colorectal, and lung cancer share some degree of genetic basis. Cancer Epidemiol Biomarkers Prev; 26(9); 1427-35. ©2017 AACR

Gene–gene interaction of AhRwith and within the Wntcascade affects susceptibility to lung cancer

Background Aberrant Wnt signalling, regulating cell development and stemness, influences the development of many cancer types. The Aryl hydrocarbon receptor (AhR) mediates tumorigenesis of environmental pollutants. Complex interaction patterns of genes assigned to AhR/Wnt-signalling were recently associated with lung cancer susceptibility. Aim To assess the association and predictive ability of AhR/Wnt-genes with lung cancer in cases and controls of European descent. Methods Odds ratios (OR) were estimated for genomic variants assigned to the Wnt agonist and the antagonistic genes DKK2, DKK3, DKK4, FRZB, SFRP4 and Axin2. Logistic regression models with variable selection were trained, validated and tested to predict lung cancer, at which other previously identified SNPs that have been robustly associated with lung cancer risk could also enter the model. Furthermore, decision trees were created to investigate variant x variant interaction. All analyses were performed for overall lung cancer and for subgroups. Results No genome-wide significant association of AhR/Wnt-genes with overall lung cancer was observed, but within the subgroups of ever smokers (e.g., maker rs2722278 SFRP4; OR = 1.20; 95% CI 1.13-1.27; p = 5.6 x 10(-10)) and never smokers (e.g., maker rs1133683 Axin2; OR = 1.27; 95% CI 1.19-1.35; p = 1.0 x 10(-12)). Although predictability is poor, AhR/Wnt-variants are unexpectedly overrepresented in optimized prediction scores for overall lung cancer and for small cell lung cancer. Remarkably, the score for never-smokers contained solely two AhR/Wnt-variants. The optimal decision tree for never smokers consists of 7 AhR/Wnt-variants and only two lung cancer variants. Conclusions The role of variants belonging to Wnt/AhR-pathways in lung cancer susceptibility may be underrated in main-effects association analysis. Complex interaction patterns in individuals of European descent have moderate predictive capacity for lung cancer or subgroups thereof, especially in never smokers

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis

Quantifying the Genetic Correlation between Multiple Cancer Types.

Background: Many cancers share specific genetic risk factors, including both rare high-penetrance mutations and common SNPs identified through genome-wide association studies (GWAS). However, little is known about the overall shared heritability across cancers. Quantifying the extent to which two distinct cancers share genetic origin will give insights to shared biological mechanisms underlying cancer and inform design for future genetic association studies.Methods: In this study, we estimated the pair-wise genetic correlation between six cancer types (breast, colorectal, lung, ovarian, pancreatic, and prostate) using cancer-specific GWAS summary statistics data based on 66,958 case and 70,665 control subjects of European ancestry. We also estimated genetic correlations between cancers and 14 noncancer diseases and traits.Results: After adjusting for 15 pair-wise genetic correlation tests between cancers, we found significant (P P = 0.003), lung and colorectal cancer (rg = 0.31, P = 0.001). We also found suggestive genetic correlations between lung and breast cancer (rg = 0.27, P = 0.009), and colorectal and breast cancer (rg = 0.22, P = 0.01). In contrast, we found no evidence that prostate cancer shared an appreciable proportion of heritability with other cancers. After adjusting for 84 tests studying genetic correlations between cancer types and other traits (Bonferroni-corrected P value: 0.0006), only the genetic correlation between lung cancer and smoking remained significant (rg = 0.41, P = 1.03 × 10-6). We also observed nominally significant genetic correlations between body mass index and all cancers except ovarian cancer.Conclusions: Our results highlight novel genetic correlations and lend support to previous observational studies that have observed links between cancers and risk factors.Impact: This study demonstrates modest genetic correlations between cancers; in particular, breast, colorectal, and lung cancer share some degree of genetic basis. Cancer Epidemiol Biomarkers Prev; 26(9); 1427-35. ©2017 AACR

The association between genetically elevated polyunsaturated fatty acids and risk of cancer

BACKGROUND: The causal relevance of polyunsaturated fatty acids (PUFAs) for risk of site-specific cancers remains uncertain. METHODS: Using a Mendelian randomization (MR) framework, we assessed the causal relevance of PUFAs for risk of cancer in European and East Asian ancestry individuals. We defined the primary exposure as PUFA desaturase activity, proxied by rs174546 at the FADS locus. Secondary exposures were defined as omega 3 and omega 6 PUFAs that could be proxied by genetic polymorphisms outside the FADS region. Our study used summary genetic data on 10 PUFAs and 67 cancers, corresponding to 562,871 cases and 1,619,465 controls, collected by the Fatty Acids in Cancer Mendelian Randomization Collaboration. We estimated odds ratios (ORs) for cancer per standard deviation increase in genetically proxied PUFA exposures. FINDINGS: Genetically elevated PUFA desaturase activity was associated (P < 0.0007) with higher risk (OR [95% confidence interval]) of colorectal cancer (1.09 [1.07-1.11]), esophageal squamous cell carcinoma (1.16 [1.06-1.26]), lung cancer (1.06 [1.03-1.08]) and basal cell carcinoma (1.05 [1.02-1.07]). There was little evidence for associations with reproductive cancers (OR = 1.00 [95% CI: 0.99-1.01]; Pheterogeneity = 0.25), urinary system cancers (1.03 [0.99-1.06], Pheterogeneity = 0.51), nervous system cancers (0.99 [0.95-1.03], Pheterogeneity = 0.92) or blood cancers (1.01 [0.98-1.04], Pheterogeneity = 0.09). Findings for colorectal cancer and esophageal squamous cell carcinoma remained compatible with causality in sensitivity analyses for violations of assumptions. Secondary MR analyses highlighted higher omega 6 PUFAs (arachidonic acid, gamma-linolenic acid and dihomo-gamma-linolenic acid) as potential mediators. PUFA biosynthesis is known to interact with aspirin, which increases risk of bleeding and inflammatory bowel disease. In a phenome-wide MR study of non-neoplastic diseases, we found that genetic lowering of PUFA desaturase activity, mimicking a hypothetical intervention to reduce cancer risk, was associated (P < 0.0006) with increased risk of inflammatory bowel disease but not bleeding. INTERPRETATION: The PUFA biosynthesis pathway may be an intervention target for prevention of colorectal cancer and esophageal squamous cell carcinoma but with potential for increased risk of inflammatory bowel disease. FUNDING: Cancer Resesrch UK (C52724/A20138, C18281/A19169). UK Medical Research Council (MR/P014054/1). National Institute for Health Research (NIHR202411). UK Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4). National Cancer Institute (R00 CA215360). National Institutes of Health (U01 CA164973, R01 CA60987, R01 CA72520, U01 CA74806, R01 CA55874, U01 CA164973 and U01 CA164973)

Assessment of polygenic architecture and risk prediction based on common variants across fourteen cancers

Abstract: Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence

Genome-wide interaction analysis identified low-frequency variants with sex disparity in lung cancer risk

Differences by sex in lung cancer incidence and mortality have been reported which cannot be fully explained by sex differences in smoking behavior, implying existence of genetic and molecular basis for sex disparity in lung cancer development. However, the information about sex dimorphism in lung cancer risk is quite limited despite the great success in lung cancer association studies. By adopting a stringent two-stage analysis strategy, we performed a genome-wide gene-sex interaction analysis using genotypes from a lung cancer cohort including ~ 47 000 individuals with European ancestry. Three low-frequency variants (minor allele frequenc

Iam hiQ-a novel pair of accuracy indices for imputed genotypes

BACKGROUND: Imputation of untyped markers is a standard tool in genome-wide association studies to close the gap between directly genotyped and other known DNA variants. However, high accuracy with which genotypes are imputed is fundamental. Several accuracy measures have been proposed and some are implemented in imputation software, unfortunately diversely across platforms. In the present paper, we introduce Iam hiQ, an independent pair of accuracy measures that can be applied to dosage files, the output of all imputation software. Iam (imputation accuracy measure) quantifies the average amount of individual-specific versus population-specific genotype information in a linear manner. hiQ (heterogeneity in quantities of dosages) addresses the inter-individual heterogeneity between dosages of a marker across the sample at hand. RESULTS: Applying both measures to a large case-control sample of the International Lung Cancer Consortium (ILCCO), comprising 27,065 individuals, we found meaningful thresholds for Iam and hiQ suitable to classify markers of poor accuracy. We demonstrate how Manhattan-like plots and moving averages of Iam and hiQ can be useful to identify regions enriched with less accurate imputed markers, whereas these regions would by missed when applying the accuracy measure info (implemented in IMPUTE2). CONCLUSION: We recommend using Iam hiQ additional to other accuracy scores for variant filtering before stepping into the analysis of imputed GWAS data