Rare De Novo Germline Copy-Number Variation in Testicular Cancer

Stadler, Zsofia K.; Esposito, Diane; Shah, Sohela; Vijai, Joseph; Yamrom, Boris; Levy, Dan; Lee, Yoon-ha; Kendall, Jude; Leotta, Anthony; Ronemus, Michael; Hansen, Nichole; Sarrel, Kara; Rau-Murthy, Rohini; Schrader, Kasmintan A.; Kauff, Noah D.; Klein, Robert J.; Lipkin, Steven M.; Murali, Rajmohan; Sheinfeld, Joel; Feldman, Darren R.; Bosl, George J.; Norton, Larry; Wigler, Michael; Offit, Kenneth

doi:10.1016/j.ajhg.2012.06.019

Cited by 22 publications

(24 citation statements)

References 15 publications

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“…We then determine the remaining population level observables such as female incidence and sibling recurrence rates. The results for 1 are represented in Figure 1. The other two cases yield similar representations and are not shown.…”

Section: Threshold Modelsmentioning

confidence: 99%

“…The match to 1 is shown in Figure 2. Targeting the higher and lower incidence rates does little to perturb the higher moments and those plots are included in the Supplement.…”

Section: Penetrance Modelsmentioning

confidence: 99%

“…Operationally, we achieve this by applying Newton's method (see Methods). Since there is some uncertainty even in the value of male incidence, we solve for three possible values, 1 = 1: 140, 1 = 1: 100, and 1 ℎ ℎ = 1: 70. For each value of 1 , we evaluate the fit of the model to five remaining observables: the female to male affected ratio, and the sibling recurrence rates for each gender, given one or two previously affected children.…”

Section: General Frameworkmentioning

confidence: 99%

“…The disabilities associated with ASD severely reduce the probability that affected individuals become parents. Therefore, the population genetics of autism has elements in common with other pediatric or juvenile disorders that greatly reduce fecundity, such as pediatric cancer or congenital heart disease [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Simple genetic models for autism spectrum disorder

Mukhopadhyay

Wigler

Levy

2015

Preprint

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To explore the interplay between new mutation, transmission, and gender bias in genetic disease requires formal quantitative modeling. Autism spectrum disorders offer an ideal case: they are genetic in origin, complex, and show a gender bias. The high reproductive costs of autism ensure that most strongly associated genetic mutations are short-lived, and indeed the disease exhibits both transmitted and de novo components. There is a large body of both epidemiologic and genomic data that greatly constrain the genetic mechanisms that may contribute to the disorder. We develop a computational framework that assumes classes of additive variants, each member of a class having equal effect. We restrict our initial exploration to single class models, each having three parameters. Only one model matches epidemiological data. It also independently matches the incidence of de novo mutation in simplex families, the gender bias in unaffected siblings in simplex populations, and rates of mutation in target genes. This model makes strong and as yet not fully tested predictions, namely that females are the primary carriers in cases of genetic transmission, and that the incidence of de novo mutation in target genes for families at high risk for autism are not especially elevated. In its simplicity, this model does not account for MZ twin concordance or the distorted gender bias of high functioning children with ASD, and does not accommodate all the known mechanisms contributing to ASD. We point to the next steps in applying the same computational framework to explore more complex models. Author summaryFor understanding complex genetic diseases one needs both data and molecular/genetic models. In the absence of any model, it is impossible to do more than summarize observations. A good model will be consistent with much or all of the existing data and puts the data in the context of known genetic principles. Ideally the model will make testable predictions. Where the good models fail often shows the directions that require more thought about mechanisms. In this paper we describe a new computational framework that we use to explore a complex genetic disorder with many gene targets, with both de novo and transmitted variants, and with gender bias. The disorder we consider is autism spectrum disorder (ASD), and our framework rules out some previous models that make unsustainable predictions. We identify a formal model that satisfies diverse epidemiologic and genomic observations. This model makes strong and untested predictions and thereby suggests new studies that would resolve outstanding aspects of autism genetics.

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Section: Threshold Modelsmentioning

confidence: 99%

“…The match to 1 is shown in Figure 2. Targeting the higher and lower incidence rates does little to perturb the higher moments and those plots are included in the Supplement.…”

Section: Penetrance Modelsmentioning

confidence: 99%

Section: General Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simple genetic models for autism spectrum disorder

Mukhopadhyay

Wigler

Levy

2015

Preprint

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“…Analysis of CNVs on a genomic scale is useful for assessing cancer progression and identifying congenital genetic abnormalities (Hicks et al 2006;Sebat et al 2007;Marshall et al 2008;Xu et al 2008;Levy et al 2011;Stadler et al 2012;Warburton et al 2014; for review, see Malhotra and Sebat 2012;Weischenfeldt et al 2013). CNVs are typically identified by microarray hybridization (Iafrate et al 2004;Sebat et al 2004) but can also be detected by next-generation sequencing (NGS).…”

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confidence: 99%

SMASH, a fragmentation and sequencing method for genomic copy number analysis

et al. 2016

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Copy number variants (CNVs) underlie a significant amount of genetic diversity and disease. CNVs can be detected by a number of means, including chromosomal microarray analysis (CMA) and whole-genome sequencing (WGS), but these approaches suffer from either limited resolution (CMA) or are highly expensive for routine screening (both CMA and WGS). As an alternative, we have developed a next-generation sequencing-based method for CNV analysis termed SMASH, for short multiply aggregated sequence homologies. SMASH utilizes random fragmentation of input genomic DNA to create chimeric sequence reads, from which multiple mappable tags can be parsed using maximal almost-unique matches (MAMs). The SMASH tags are then binned and segmented, generating a profile of genomic copy number at the desired resolution. Because fewer reads are necessary relative to WGS to give accurate CNV data, SMASH libraries can be highly multiplexed, allowing large numbers of individuals to be analyzed at low cost. Increased genomic resolution can be achieved by sequencing to higher depth.

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Genome‐wide CNV analysis in 221 unrelated patients and targeted high‐throughput sequencing reveal novel causative candidate genes for colorectal adenomatous polyposis

Horpaopan

Spier

Zink

et al. 2014

Intl Journal of Cancer

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To uncover novel causative genes in patients with unexplained adenomatous polyposis, a model disease for colorectal cancer, we performed a genome-wide analysis of germline copy number variants (CNV) in a large, well characterized APC and MUTYH mutation negative patient cohort followed by a targeted next generation sequencing (NGS) approach. Genomic DNA from 221 unrelated German patients was genotyped on high-resolution SNP arrays. Putative CNVs were filtered according to stringent criteria, compared with those of 531 population-based German controls, and validated by qPCR. Candidate genes were prioritized using in silico, expression, and segregation analyses, data mining and enrichment analyses of genes and pathways. In 27% of the 221 unrelated patients, a total of 77 protein coding genes displayed rare, nonrecurrent, germline CNVs. The set included 26 candidates with molecular and cellular functions related to tumorigenesis. Targeted high-throughput sequencing found truncating point mutations in 12% (10/77) of the prioritized genes. No clear evidence was found for autosomal recessive subtypes. Six patients had potentially causative mutations in more than one of the 26 genes. Combined with data from recent studies of early-onset colorectal and breast cancer, recurrent potential loss-of-function alterations were detected in CNTN6, FOCAD (KIAA1797), HSPH1, KIF26B, MCM3AP, YBEY and in three genes from the ARHGAP family. In the canonical Wnt pathway oncogene CTNNB1 (b-catenin), two potential gain-of-function mutations were found. In conclusion, the present study identified a group of rarely affected genes which are likely to predispose to colorectal adenoma formation and confirmed previously published candidates for tumor predisposition as etiologically relevant.Germline copy number variants (CNVs) have been recognized as the most prevalent type of structural genetic variation, which also predispose to human disease as lowpenetrance risk factors or high-penetrance mutations. 1,2 Recent research has identified potentially causative germline deletions and duplications for a number of sporadic and familial types of cancer. [3][4][5][6][7] In hereditary cancer syndromes, heterozygous microdeletions of single exons or whole genes contribute substantially to the mutation spectrum. 8,9 A plausible hypothesis therefore is that in particular deletion CNVs are part of the mutation spectrum of yet unidentified genes responsible for monogenic tumor syndromes for which no causative germline mutation in established genes has yet been identified.

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Rare De Novo Germline Copy-Number Variation in Testicular Cancer

Cited by 22 publications

References 15 publications

Simple genetic models for autism spectrum disorder

Simple genetic models for autism spectrum disorder

SMASH, a fragmentation and sequencing method for genomic copy number analysis

Genome‐wide CNV analysis in 221 unrelated patients and targeted high‐throughput sequencing reveal novel causative candidate genes for colorectal adenomatous polyposis

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