Rosenthal Elisabeth A, Ranola John Michael O, Shirts Brian H
Division of Medical Genetics, School of Medicine, University of Washington, Seattle, WA, USA.
Department of Laboratory Medicine, University of Washington Medical Center, Seattle, WA, USA.
Fam Cancer. 2017 Oct;16(4):611-620. doi: 10.1007/s10689-017-9989-6.
Rare and private variants of uncertain significance (VUS) are routinely identified in clinical panel, exome, and genome sequencing. We investigated the power of single family co-segregation analysis to aid classification of VUS. We simulated thousands of pedigrees using demographics in China and the United States, segregating benign and pathogenic variants. Genotypes and phenotypes were simulated using penetrance models for Lynch syndrome and breast/ovarian cancer. We calculated LOD scores adjusted for proband ascertainment (LODadj), to determine power to yield quantitative evidence for, or against, pathogenicity of the VUS. Power to classify VUS was higher for Chinese than United States pedigrees. The number of affected individuals explained the most variation in LODadj (21-38%). The distance to the furthest affected relative (FAR) from the proband explained 1-7% of the variation for the benign VUS and Lynch associated cancers. Minimum age of onset (MAO) explained 5-13% of the variation in families with pathogenic breast/ovarian cancer variants. Random removal of 50% of the phenotype/genotype data reduced power and the variation in LODadj was best explained by FAR followed by the number of affected individuals and MAO when the founder was only two generations from the proband. Power to classify benign variants was ~2x power to classify pathogenic variants. Affecteds-only analysis resulted in virtually no power to correctly classify benign variants and reduced power to classify pathogenic variants. These results can be used to guide recruitment efforts to classify rare and private VUS.
在临床基因检测板、外显子组和基因组测序中,经常会发现意义不明确的罕见和私人变异(VUS)。我们研究了单一家族共分离分析在辅助VUS分类方面的作用。我们利用中国和美国的人口统计学数据模拟了数千个家系,分离良性和致病性变异。使用林奇综合征和乳腺癌/卵巢癌的外显率模型模拟基因型和表型。我们计算了针对先证者确定情况进行调整的LOD分数(LODadj),以确定为支持或反对VUS的致病性提供定量证据的能力。中国家系对VUS进行分类的能力高于美国家系。受影响个体的数量解释了LODadj中最大的变异(21 - 38%)。与先证者距离最远的受影响亲属(FAR)的距离解释了良性VUS和林奇相关癌症变异中1 - 7%的变异。最小发病年龄(MAO)解释了携带致病性乳腺癌/卵巢癌变异的家系中5 - 13%的变异。随机去除50%的表型/基因型数据会降低分类能力,当奠基者与先证者仅相隔两代时,LODadj的变异最好由FAR解释,其次是受影响个体的数量和MAO。对良性变异进行分类的能力约为对致病性变异进行分类能力的两倍。仅分析受影响个体几乎没有能力正确分类良性变异,并且降低了对致病性变异进行分类的能力。这些结果可用于指导招募工作,以对罕见和私人VUS进行分类。