Dawood Moez, Fayer Shawn, Pendyala Sriram, Post Mason, Kalra Divya, Patterson Karynne, Venner Eric, Muffley Lara A, Fowler Douglas M, Rubin Alan F, Posey Jennifer E, Plon Sharon E, Lupski James R, Gibbs Richard A, Starita Lea M, Robles-Espinoza Carla Daniela, Coyote-Maestas Willow, Gallego Romero Irene
Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Genome Med. 2024 Dec 3;16(1):143. doi: 10.1186/s13073-024-01392-7.
Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style functional data may help resolve variant classification disparities between populations, especially for Variants of Uncertain Significance (VUS).
We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN.
Using two orthogonal statistical approaches, we show a higher prevalence (p ≤ 5.95e - 06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation (p ≤ 2.5e - 05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were increased in individuals of European-like genetic ancestry (p ≤ 2.5e - 05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry (p = 9.1e - 03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency (p = 7.47e - 06) and computational predictor (p = 6.92e - 05) evidence codes for individuals of non-European-like genetic ancestry.
Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.
变异效应多重分析（MAVEs）可检测感兴趣基因中的所有可能单变异。由此产生的饱和式功能数据可能有助于解决不同人群之间的变异分类差异，尤其是对于意义未明变异（VUS）。
我们分析了来自“我们所有人”计划和基因组聚合数据库中213,663名欧洲样遗传血统个体与206,975名非欧洲样遗传血统个体的临床意义分类。然后，我们将经过临床校准的MAVE数据纳入临床基因组资源的变异管理专家小组规则，以自动对BRCA1、TP53和PTEN的VUS进行重新分类。
使用两种正交统计方法，我们发现在所有三个数据库中评估的所有医学专业中，非欧洲样遗传血统个体中VUS的患病率更高（p≤5.95e - 06）。此外，在非欧洲样遗传血统组中，许多医学专业中良性或可能良性以及无临床指定变异的发生率更高（p≤2.5e - 05），而致病性或可能致病性分类在欧洲样遗传血统个体中增加（p≤2.5e - 05）。使用MAVE数据，我们对非欧洲样遗传血统个体中的VUS进行重新分类的比率明显高于对欧洲样遗传血统个体中重新分类VUS的比率（p = 9.1e - 03），有效弥补了VUS差异。此外，基本代码分析表明，MAVE证据代码对非欧洲样遗传血统个体有公平影响，但等位基因频率（p = 7.47e - 06）和计算预测器（p = 6.92e - 05）证据代码有不公平影响。
生成饱和式MAVE数据应作为优先事项，以减少VUS差异，并为未来的计算预测器生成公平的训练数据。
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