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Motivation: Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes., Results: We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach., (© The Author(s) 2024. Published by Oxford University Press.)

Background: The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci., Methods: We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci., Results: We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci., Conclusions: Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Motivation: Apart from single marker-based tests classically used in genome-wide association studies (GWAS), network-assisted analysis has become a promising approach to identify a set of genes associated with disease. To date, most network-assisted methods aim at finding genes connected in a background network, whatever the density or strength of their connections. This can hamper the findings as sparse connections are non-robust against noise from either the GWAS results or the network resource., Results: We present SigMod, a novel and efficient method integrating GWAS results and gene network to identify a strongly interconnected gene module enriched in high association signals. Our method is formulated as a binary quadratic optimization problem, which can be solved exactly through graph min-cut algorithms. Compared to existing methods, SigMod has several desirable properties: (i) edge weights quantifying confidence of connections between genes are taken into account, (ii) the selection path can be computed rapidly, (iii) the identified gene module is strongly interconnected, hence includes genes of high functional relevance, and (iv) the method is robust against noise from either the GWAS results or the network resource. We applied SigMod to both simulated and real data. It was found to outperform state-of-the-art network-assisted methods in identifying disease-associated genes. When SigMod was applied to childhood-onset asthma GWAS results, it successfully identified a gene module enriched in consistently high association signals and made of functionally related genes that are biologically relevant for asthma., Availability and Implementation: An R package SigMod is available at: https://github.com/YuanlongLiu/SigMod., Contact: yuanlong.liu@inserm.fr., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com)

Telomere length has been associated with risk of many cancers, but results are inconsistent. Seven single nucleotide polymorphisms (SNPs) previously associated with mean leukocyte telomere length were either genotyped or well-imputed in 11108 case patients and 13933 control patients from Europe, Israel, the United States and Australia, four of the seven SNPs reached a P value under .05 (two-sided). A genetic score that predicts telomere length, derived from these seven SNPs, is strongly associated (P = 8.92x10(-9), two-sided) with melanoma risk. This demonstrates that the previously observed association between longer telomere length and increased melanoma risk is not attributable to confounding via shared environmental effects (such as ultraviolet exposure) or reverse causality. We provide the first proof that multiple germline genetic determinants of telomere length influence cancer risk., (© The Author 2014. Published by Oxford University Press.)

Background: Genital and anorectal mucosal melanomas (GAMMs) are rare compared with cutaneous melanoma (CM). Many epidemiological and genetic studies have been carried out on CM. In contrast, the genetic and environmental risk factors for GAMM have been poorly documented up to now., Objectives: To compare the distribution of pigmentation and naevus phenotypes, sun exposure and family history of melanoma between patients with GAMM and CM., Methods: We compared two series of patients, 81 with GAMM and 293 with CM., Results: Patients with GAMM and CM did not show significant differences for phenotypic risk factors. However, patients with GAMM tended to display red hair (11% vs. 5·5%, P = 0·08) and a poor tanning ability (22% vs. 13·3%, P = 0·06) at a higher frequency than patients with CM. A family history of melanoma was significantly more frequent with GAMM than with CM (18% vs. 7·5%, P = 0·005). Apart from the GAMM index case, affected relatives had CM except in one family. The frequency of multiple primary melanomas (MPMs) was similar in the GAMM and CM series (6% vs. 5·3%, P = 0·43). All patients with GAMM and MPM had only one GAMM primary, while the other primary was cutaneous. No CDKN2A germline mutation was detected in patients with GAMM., Conclusions: This study shows that GAMM and CM may occur in the same patient, and GAMM may develop in a familial setting. The association of both GAMM and CM in patients and families suggests shared genetic factors by these two types of melanoma., (© 2013 British Association of Dermatologists.)

Background: Carrying the cyclin-dependent kinase inhibitor 2A (CDKN2A) germline mutations is associated with a high risk for melanoma. Penetrance of CDKN2A mutations is modified by pigmentation characteristics, nevus phenotypes, and some variants of the melanocortin-1 receptor gene (MC1R), which is known to have a role in the pigmentation process. However, investigation of the associations of both MC1R variants and host phenotypes with melanoma risk has been limited., Methods: We included 815 CDKN2A mutation carriers (473 affected, and 342 unaffected, with melanoma) from 186 families from 15 centers in Europe, North America, and Australia who participated in the Melanoma Genetics Consortium. In this family-based study, we assessed the associations of the four most frequent MC1R variants (V60L, V92M, R151C, and R160W) and the number of variants (1, ≥2 variants), alone or jointly with the host phenotypes (hair color, propensity to sunburn, and number of nevi), with melanoma risk in CDKN2A mutation carriers. These associations were estimated and tested using generalized estimating equations. All statistical tests were two-sided., Results: Carrying any one of the four most frequent MC1R variants (V60L, V92M, R151C, R160W) in CDKN2A mutation carriers was associated with a statistically significantly increased risk for melanoma across all continents (1.24 × 10(-6) ≤ P ≤ .0007). A consistent pattern of increase in melanoma risk was also associated with increase in number of MC1R variants. The risk of melanoma associated with at least two MC1R variants was 2.6-fold higher than the risk associated with only one variant (odds ratio = 5.83 [95% confidence interval = 3.60 to 9.46] vs 2.25 [95% confidence interval = 1.44 to 3.52]; P(trend) = 1.86 × 10(-8)). The joint analysis of MC1R variants and host phenotypes showed statistically significant associations of melanoma risk, together with MC1R variants (.0001 ≤ P ≤ .04), hair color (.006 ≤ P ≤ .06), and number of nevi (6.9 × 10(-6) ≤ P ≤ .02)., Conclusion: Results show that MC1R variants, hair color, and number of nevi were jointly associated with melanoma risk in CDKN2A mutation carriers. This joint association may have important consequences for risk assessments in familial settings.

Background: Few family studies have investigated the effects of genetic, environmental, and host factors on melanoma risk, and most have been restricted to high-risk families. We assessed the role of these factors on melanoma risk in two types of families: families ascertained through melanoma probands but unselected by family history and melanoma-prone families., Methods: Data on pigmentary traits, nevus phenotypes, exposure to sun, and reactions to sunlight were collected from 295 families unselected by family history and 53 melanoma-prone families. We modeled melanoma risk using a logistic regressive model incorporating the effect of a melanoma-predisposing gene, familial dependence, and potential risk factors (e.g., pigmentary traits, nevus phenotypes, history of sun exposure, skin reactions to sunlight). Maximum-likelihood estimates of the parameters of the regressive model were used to compute odds ratios associated with each risk factor and age-specific melanoma risk depending on the genotype at the melanoma-predisposing gene and the effects of risk factors. All statistical tests were two-sided., Results: In the families unselected by family history, there was statistically significant evidence (P<.001) for a dominant gene, with melanoma risk reaching 0.49 and 0.67 by age 80 years in male and female gene carriers, respectively. Melanoma risk was statistically significantly influenced by total nevi (odds ratio of hazard function [OR] = 5.81, 95% confidence interval [CI] = 3.47 to 8.99), sun exposure (OR = 5.37, 95% CI = 4.44 to 6.36), and sunburn interacting with the gene (OR = 26.31, 95% CI = 7.56 to 99.22 in gene carriers and OR = 1.67, 95% CI = 1.36 to 2.03 in noncarriers). Twenty of the 53 melanoma-prone families had cosegregating mutations in CDKN2A, a gene known to be associated with melanoma. In these 53 families, three risk factors in addition to CDKN2A mutations increased melanoma risk: dysplastic nevi (OR = 2.32, 95% CI = 2.08 to 2.58), total nevi (OR = 1.99, 95% CI = 1.61 to 2.20) and sunburn (OR = 5.16, 95% CI = 4.82 to 5.52)., Conclusions: Together, a melanoma-predisposing gene (identified as being CDKN2A in melanoma-prone families), number of nevi and/or dysplastic nevi, and sun-related covariates influence melanoma risk in both families unselected by family history and melanoma-prone families.

Background: Germline mutations in the CDKN2A gene, which encodes two proteins (p16INK4A and p14ARF), are the most common cause of inherited susceptibility to melanoma. We examined the penetrance of such mutations using data from eight groups from Europe, Australia and the United States that are part of The Melanoma Genetics Consortium., Methods: We analyzed 80 families with documented CDKN2A mutations and multiple cases of cutaneous melanoma. We modeled penetrance for melanoma using a logistic regression model incorporating survival analysis. Hypothesis testing was based on likelihood ratio tests. Covariates included gender, alterations in p14ARF protein, and population melanoma incidence rates. All statistical tests were two-sided., Results: The 80 analyzed families contained 402 melanoma patients, 320 of whom were tested for mutations and 291 were mutation carriers. We also tested 713 unaffected family members for mutations and 194 were carriers. Overall, CDKN2A mutation penetrance was estimated to be 0.30 (95% confidence interval (CI) = 0.12 to 0.62) by age 50 years and 0.67 (95% CI = 0.31 to 0.96) by age 80 years. Penetrance was not statistically significantly modified by gender or by whether the CDKN2A mutation altered p14ARF protein. However, there was a statistically significant effect of residing in a location with a high population incidence rate of melanoma (P =.003). By age 50 years CDKN2A mutation penetrance reached 0.13 in Europe, 0.50 in the United States, and 0.32 in Australia; by age 80 years it was 0.58 in Europe, 0.76 in the United States, and 0.91 in Australia., Conclusions: This study, which gives the most informed estimates of CDKN2A mutation penetrance available, indicates that the penetrance varies with melanoma population incidence rates. Thus, the same factors that affect population incidence of melanoma may also mediate CDKN2A penetrance.

Background: Besides melanoma susceptibility genes and shared environmental exposures, part of the familial clustering of cutaneous malignant melanoma (CMM) might be due to familial aggregation of melanoma-associated phenotypes. Our goal was to assess the patterns of familial aggregation of three melanoma risk factors: great number of naevi (GNN), light phototype (LP) and high degree of sun exposure (HDSE)., Methods: Familial aggregation of GNN, LP and HDSE was investigated in 66 French families with at least two CMM cases and was measured by the asssociation of the relatives' traits with the probands' traits, using the generalized estimating equations approach. The probands were the melanoma cases leading to ascertainment of the families, subdivided into cases (with the trait studied) and controls (without the trait)., Results: We found significant evidence for familial aggregation of GNN only among sibs (OR = 3.7, 95% CI : 1.4-10.5, P = 0.01), of LP among blood relatives (OR = 3.8, 95% CI : 1.8-8.0, P = 0.004) and of HDSE among blood relatives (OR = 4.5, 95% CI : 2.1-9.9, P < 0.001) and spouses (OR = 44.3, 95% CI : 5.1-382.2, P < 10(-3)). These results suggest that genetic factors might account for the clustering of GNN and LP and shared environment for the aggregation of HDSE. The GNN clustering was lower in families with increasing numbers of CMM (>/=3 cases) or presence of p16 mutations, the opposite being observed for LP and HDSE. Moreover, the familial aggregation of LP was significantly lower in families with highly sun-exposed members., Conclusion: Melanoma might not only result from specific genetic and environmental factors but also from those underlying melanoma-associated traits involving complex gene-gene and gene-environment interactions.

Summary: We developed a collaborative pedigree environment called CoPE. This environment includes a Java program for drawing pedigrees and a standardized system for pedigree storage. Unlike other existing pedigree programs, this software is particularly intended for epidemiologists in the sense that it allows customized automatic drawing of large numbers of pedigrees and remote and distributed consultation of pedigrees., Availability: At http://www.infobiogen.fr/services/CoPE