Expanding cancer predisposition genes with ultra-rare cancer-exclusive human variations

Rasnic, Roni; Linial, Nathan

doi:10.1038/s41598-020-70494-0

Cited by 12 publications

(7 citation statements)

References 58 publications

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“…It is anticipated that by increasing cohort sizes, more genetic associations will be found. Interestingly, the overlaps of significant loci in independent GWAS works studying the same cancer types tend to be surprisingly small, supporting the claim that GWAS efforts in this domain are far from saturated 20 , 21 . Benefits from GWAS are also constrained by the lack of interpretability of most implicated loci, which do not appear to affect any functional elements in the genome 22 .…”

Section: Introductionmentioning

confidence: 55%

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Brandes

Linial

2021

Sci Rep

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The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.

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Section: Introductionmentioning

confidence: 55%

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Brandes

Linial

2021

Sci Rep

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“…In recent years, the cancer genomics research community has become increasingly interested in studying and understanding the role of extremely rare variants, such as singletons, that is, observed in only one patient. Evidence suggests that rare deleterious variants can have far stronger effect sizes than common variants (Rasnic, Linial, and Linial 2020 ) and can play an important role in the development of cancer. For example, in breast cancer, it is well accepted that the risk of a variant is inversely proportional with respect to its prevalence: the rarer the variant, the higher the risk (Wendt and Margolin 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation

Camerlenghi

Favaro

Masoero

et al. 2022

Journal of the American Statistical Association

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There is a growing interest in the estimation of the number of unseen features, mostly driven by biological applications. A recent work brought out a peculiar property of the popular completely random measures (CRMs) as prior models in Bayesian nonparametric (BNP) inference for the unseen-features problem: for fixed prior’s parameters, they all lead to a Poisson posterior distribution for the number of unseen features, which depends on the sampling information only through the sample size. CRMs are thus not a flexible prior model for the unseen-features problem and, while the Poisson posterior distribution may be appealing for analytical tractability and ease of interpretability, its independence from the sampling information makes the BNP approach a questionable oversimplification, with posterior inferences being completely determined by the estimation of unknown prior’s parameters. In this article, we introduce the stable-Beta scaled process (SB-SP) prior, and we show that it allows to enrich the posterior distribution of the number of unseen features arising under CRM priors, while maintaining its analytical tractability and interpretability. That is, the SB-SP prior leads to a negative Binomial posterior distribution, which depends on the sampling information through the sample size and the number of distinct features, with corresponding estimates being simple, linear in the sampling information and computationally efficient. We apply our BNP approach to synthetic data and to real cancer genomic data, showing that: (i) it outperforms the most popular parametric and nonparametric competitors in terms of estimation accuracy; (ii) it provides improved coverage for the estimation with respect to a BNP approach under CRM priors. Supplementary materials for this article are available online.

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“…observed in only one patient -have gained a lot of interest by the research community. Indeed, evidence suggests that rare deleterious variants can have far stronger effect sizes than common variants [Li et al, 2011, Lamina, 2011, Kang et al, 2012, Rasnic et al, 2020 and can play an important role in the development of cancer. For example, it is well accepted in the breast cancer community that the risk of variants shows an inversely proportional effect with respect to the variant's frequency -the rarer the variant, the higher risk [Wendt and Margolin, 2019].…”

Section: Predicting the Number Of New Rare Genetic Variants In Cancer...mentioning

confidence: 99%

Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation

Camerlenghi¹,

Favaro²,

Masoero³

et al. 2021

Preprint

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There is a growing interest in the estimation of the number of unseen features, mostly driven by applications in biological sciences. A recent work brought out the upside and the downside of the popular stable-Beta process prior, and generalizations thereof, in Bayesian nonparametric inference for the unseen-features problem: i) the downside lies in the limited use of the sampling information in the posterior distributions, which depend on the observable sample only through the sample size; ii) the upside lies in the analytical tractability and interpretability of the posterior distributions, which are simple Poisson distributions whose parameters are simple to compute, and depend on the sample size and the prior's parameter. In this paper, we introduce and investigate an alternative nonparametric prior, referred to as the stable-Beta scaled process prior, which is the first prior that allows to enrich the posterior distribution of the number of unseen features, through the inclusion of the sampling information on the number of distinct features in the observable sample, while maintaining the same analytical tractability and interpretability as the stable-Beta process prior. Our prior leads to a negative Binomial posterior distribution, whose parameters depends on the sample size, the observed number of distinct features and the prior's parameter, providing estimates that are simple, linear in the sampling information and computationally efficient. We apply our approach to synthetic and real genetic data, showing that it outperforms parametric and nonparametric competitors in terms of estimation accuracy.

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Expanding cancer predisposition genes with ultra-rare cancer-exclusive human variations

Cited by 12 publications

References 58 publications

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition

Scaled Process Priors for Bayesian Nonparametric Estimation of the Unseen Genetic Variation

Scaled process priors for Bayesian nonparametric estimation of the unseen genetic variation

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