Characteristics of familial pancreatic cancer families with additional colorectal carcinoma

Lehman, Bettina; Matthäi, Elvira; Gercke, Norman; Denzer, Ulrike W.; Figiel, Jens; Hess, Timo; Slater, Emily P.; Bartsch, Detlef K.

doi:10.1007/s10689-023-00328-1

Cited by 1 publication

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“…However, the genetic predisposition for many FPC families is still unknown [101]. Hence, in order to focus the gene discovery on those FPC families, for which the predisposing genetic background is unknown, we excluded families having at least one known predisposing genetic mutation in the gene set including BRCA2, PALB2, CDNK2a, SUFU, and CHEK2 (see also [101,102] for more details about the mutation screening panel). Individuals of an FPC family that solely had BC were marked as "unknown" because it has been shown that BC and PC have a common causal pathway, mediated, e.g., by BRCA1/2 or PALB2 mutations [103].…”

Section: Analysis Of Fapaca Familiesmentioning

confidence: 99%

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer

Brugger,

Lutz,

Müller-Nurasyid

et al. 2024

Hum Hered

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Introduction: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. Methods: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modelled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). Results: Based on the simulated data, we demonstrated the validity of our JLA MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. Conclusion: Our newly proposed JLA MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

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