Department of Internal Medicine and Medical Specialties, University of Genoa, Genova, Italy.
IRCCS Ospedale Policlinico San Martino, Genetics of Rare Cancers, Genoa, Italy.
Expert Rev Mol Diagn. 2020 May;20(5):523-531. doi: 10.1080/14737159.2020.1738221. Epub 2020 Mar 14.
: Around 10% of melanoma patients have a positive family history of melanoma and/or related cancers. Although a germline pathogenic variant in a high-risk gene can be identified in up to 40% of these patients, the remaining part of melanoma heritability remains largely unexplained.: The aim of this review is to provide an overview of the impact that new technologies and new research approaches had and are having on finding more efficient ways to unravel the missing heritability in melanoma.: High-throughput sequencing technologies have been crucial in increasing the number of genes/loci that might be implicated in melanoma predisposition. However, results from these approaches may have been inferior to the expectations, due to an increase in quantitative information which hasn't been followed at the same speed by an improvement of the methods to correctly interpret these data. Optimal approaches for improving our knowledge on melanoma heritability are currently based on segregation analysis coupled with functional assessment of candidate genes. An improvement of computational methods to infer genotype-phenotype correlations could help address the issue of missing heritability.
: 约 10%的黑色素瘤患者有黑色素瘤和/或相关癌症的阳性家族史。尽管在多达 40%的这些患者中可以识别出高风险基因中的种系致病性变体,但黑色素瘤遗传率的其余部分仍在很大程度上未得到解释。: 本综述的目的是概述新技术和新研究方法对寻找更有效的方法来解开黑色素瘤中缺失遗传率的影响。: 高通量测序技术对于增加可能与黑色素瘤易感性相关的基因/基因座数量至关重要。然而,由于定量信息的增加,而这些方法的改进速度没有跟上,这些方法的结果可能不如预期,正确解释这些数据。目前,提高我们对黑色素瘤遗传率的认识的最佳方法是基于分离分析,同时对候选基因进行功能评估。改进推断基因型-表型相关性的计算方法可能有助于解决遗传缺失问题。