Gusakova Mariia, Dzhumaniiazova Irina, Zelenova Elena, Kashtanova Daria, Ivanov Mikhail, Mamchur Aleksandra, Rumyantseva Antonina, Terekhov Mikhail, Mitrofanov Sergey, Golubnikova Liliya, Akinshina Aleksandra, Grammatikati Konstantin, Kalashnikova Irina, Yudin Vladimir, Makarov Valentin, Keskinov Anton, Yudin Sergey
The Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency, Moscow, Russia.
Front Oncol. 2024 Sep 5;14:1420176. doi: 10.3389/fonc.2024.1420176. eCollection 2024.
Population studies are essential for gathering critical disease prevalence data. Automated pathogenicity assessment tools enhance the capacity to interpret and annotate large amounts of genetic data. In this study, we assessed the prevalence of cancer-associated germline variants in Russia using a semiautomated variant interpretation algorithm.
We examined 74,996 Russian adults (Group 1) and 2,872 long-living individuals aged ≥ 90 years (Group 2) for variants in 28 ACMG-recommended cancer-associated genes in three steps: InterVar annotation; ClinVar interpretation; and a manual review of the prioritized variants based on the available data. Using the data on the place of birth and the region of residence, we determined the geographical distribution of the detected variants and tracked the migration dynamics of their carriers.
We report 175 novel del-VUSs. We detected 232 pathogenic variants, 46 likely pathogenic variants, and 216 del-VUSs in Group 1 and 19 pathogenic variants, 2 likely pathogenic variants, and 16 del-VUSs in Group 2. For each detected variant, we provide a description of its functional significance and geographical distribution.
The present study offers extensive genetic data on the Russian population, critical for future genetic research and improved primary cancer prevention and genetic screening strategies. The proposed hybrid assessment algorithm streamlines variant prioritization and pathogenicity assessment and offers a reliable and verifiable way of identifying variants of uncertain significance that need to be manually reviewed.
人群研究对于收集关键疾病患病率数据至关重要。自动化致病性评估工具增强了对大量遗传数据进行解读和注释的能力。在本研究中，我们使用半自动变异解读算法评估了俄罗斯癌症相关种系变异的患病率。
我们分三步检查了74996名俄罗斯成年人（第1组）和2872名年龄≥90岁的长寿个体（第2组），以检测28个美国医学遗传学与基因组学学会（ACMG）推荐的癌症相关基因中的变异：InterVar注释；ClinVar解读；以及根据现有数据对优先变异进行人工审核。利用出生地和居住地区的数据，我们确定了检测到的变异的地理分布，并追踪了其携带者的迁移动态。
我们报告了175个新的缺失意义未明变异（del-VUS）。在第1组中，我们检测到232个致病性变异、46个可能致病性变异和216个del-VUS；在第2组中，检测到19个致病性变异、2个可能致病性变异和16个del-VUS。对于每个检测到的变异，我们提供了其功能意义和地理分布的描述。
本研究提供了关于俄罗斯人群的广泛遗传数据，这对于未来的遗传研究以及改进原发性癌症预防和基因筛查策略至关重要。所提出的混合评估算法简化了变异优先级排序和致病性评估，并提供了一种可靠且可验证的方法来识别需要人工审核的意义未明变异。
