Cancer Predisposition Genes in Cancer-Free Families

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Zheng, G.; Catalano, C.; Bandapalli, O.R.; Paramasivam, N.; Chattopadhyay, S.; Schlesner, M.; Sijmons, R.; Hemminki, A.; Dymerska, D.; Lubinski, J.; Hemminki, K.; Försti, A. Cancer Predisposition Genes in Cancer-Free Families. Cancers 2020, 12, 2770.

Title: Cancer Predisposition Genes in Cancer-Free Families
Author: Zheng, Guoqiao; Catalano, Calogerina; Bandapalli, Obul Reddy; Paramasivam, Nagarajan; Chattopadhyay, Subhayan; Schlesner, Matthias; Sijmons, Rolf; Hemminki, Akseli; Dymerska, Dagmara; Lubinski, Jan; Hemminki, Kari; Försti, Asta
Publisher: Multidisciplinary Digital Publishing Institute
Date: 2020-09-27
Abstract: Familial clustering, twin concordance, and identification of high- and low-penetrance cancer predisposition variants support the idea that there are families that are at a high to moderate excess risk of cancer. To what extent there may be families that are protected from cancer is unknown. We wanted to test genetically whether cancer-free families share fewer breast, colorectal, and prostate cancer risk alleles than the population at large. We addressed this question by whole-genome sequencing (WGS) of 51 elderly cancer-free individuals whose numerous (ca. 1000) family members were found to be cancer-free (‘cancer-free families’, CFFs) based on face-to-face interviews. The average coverage of the 51 samples in the WGS was 42x. We compared cancer risk allele frequencies in cancer-free individuals with those in the general population available in public databases. The CFF members had fewer loss-of-function variants in suggested cancer predisposition genes compared to the ExAC data, and for high-risk cancer predisposition genes, no pathogenic variants were found in CFFs. For common low-penetrance breast, colorectal, and prostate cancer risk alleles, the results were not conclusive. The results suggest that, in line with twin and family studies, random environmental causes are so dominant that a clear demarcation of cancer-free populations using genetic data may not be feasible.

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