Variant classification guidelines for animals to objectively evaluate genetic variant pathogenicity

Fréderique Boeykens, Marie Abitbol, Heidi Anderson, Iris Casselman, Caroline Dufaure de Citres, Jessica J. Hayward, Jens Häggström, Mark D Kittleson, Elvio Lepri, Ingrid Ljungvall, Maria Longeri, Leslie A Lyons, Åsa Ohlsson, Luc Peelman, Pascale Smets, Tommaso Vezzosi, Frank van Steenbeek, Bart J.G. Broeckx
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Abstract

Assessing the pathogenicity of a disease-associated variant in animals accurately is vital, both on a population and individual scale. At the population level, breeding decisions based on invalid DNA tests can lead to the incorrect exclusion of animals and compromise the long-term health of a population, and at the level of the individual animal, lead to incorrect treatment and even life-ending decisions. Criteria to determine pathogenicity are not standardized, hence no guidelines for animal variants are available. Here, we developed and optimized the animal variant classification guidelines, based on those developed for humans by The American College of Medical Genetics and Genomics, and demonstrated a superior classification in animals. We described methods to develop datasets for benchmarking the criteria and identified the most optimal in silico variant effect predictor tools. As the reproducibility was high, we classified 72 known disease-associated variants in cats and 40 other disease-associated variants in eight additional species.
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客观评估遗传变异致病性的动物变异分类指南
准确评估动物中与疾病相关的变异体的致病性对群体和个体都至关重要。在种群层面,根据无效的 DNA 检测结果做出的育种决定会导致错误的动物排斥,损害种群的长期健康;而在动物个体层面,则会导致错误的治疗决定,甚至会危及生命。确定致病性的标准并不统一,因此没有动物变异的指导原则。在此,我们以美国医学遗传学和基因组学学院为人类制定的变异分类指南为基础,制定并优化了动物变异分类指南,并证明了动物变异分类的优越性。我们介绍了开发数据集的方法,以便对标准进行基准测试,并确定了最理想的硅学变异效应预测工具。由于重现性很高,我们对猫的 72 个已知疾病相关变异和另外 8 个物种的 40 个其他疾病相关变异进行了分类。
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