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贝叶斯方法在确定致病性 SDH 变异体外显率中的应用。

Bayesian approach to determining penetrance of pathogenic SDH variants.

机构信息

Hormones and Cancer, Cancer Genetics Laboratory, Kolling Institute, Royal North Shore Hospital, St Leonards, New South Wales, Australia.

Department of Medicine, University of Sydney, Sydney, New South Wales, Australia.

出版信息

J Med Genet. 2018 Nov;55(11):729-734. doi: 10.1136/jmedgenet-2018-105427. Epub 2018 Sep 10.

Abstract

BACKGROUND

Until recently, determining penetrance required large observational cohort studies. Data from the Exome Aggregate Consortium (ExAC) allows a Bayesian approach to calculate penetrance, in that population frequencies of pathogenic germline variants should be inversely proportional to their penetrance for disease. We tested this hypothesis using data from two cohorts for succinate dehydrogenase subunits A, B and C () genetic variants associated with hereditary pheochromocytoma/paraganglioma (PC/PGL).

METHODS

Two cohorts were 575 unrelated Australian subjects and 1240 unrelated UK subjects, respectively, with PC/PGL in whom genetic testing had been performed. Penetrance of pathogenic variants was calculated by comparing allelic frequencies in cases versus controls from ExAC (removing those variants contributed by The Cancer Genome Atlas).

RESULTS

Pathogenic variants were identified in 106 subjects (18.4%) in cohort 1 and 317 subjects (25.6%) in cohort 2. Of 94 different pathogenic variants from both cohorts (seven in , 75 in and 12 in ), 13 are reported in ExAC (two in , nine in and two in ) accounting for 21% of subjects with variants. Combining data from both cohorts, estimated lifetime disease penetrance was 22.0% (95% CI 15.2% to 30.9%) for variants, 8.3% (95% CI 3.5% to 18.5%) for variants and 1.7% (95% CI 0.8% to 3.8%) for variants.

CONCLUSION

Pathogenic variants in are more penetrant than those in and . Our findings have important implications for counselling and surveillance of subjects carrying these pathogenic variants.

摘要

背景

直到最近,确定外显率还需要进行大规模的观察性队列研究。外显子组聚合联盟(ExAC)的数据使得贝叶斯方法能够计算外显率,因为人群中致病性种系变异的频率应该与其导致疾病的外显率成反比。我们使用与琥珀酸脱氢酶亚单位 A、B 和 C 相关的遗传变异相关的两个队列的数据来检验这一假设,这些遗传变异与遗传性嗜铬细胞瘤/副神经节瘤(PC/PGL)有关。

方法

两个队列分别是 575 名无血缘关系的澳大利亚受试者和 1240 名无血缘关系的英国受试者,他们患有 PC/PGL ,并进行了基因检测。通过比较 ExAC 中病例与对照的等位基因频率(从 The Cancer Genome Atlas 中去除那些变异)来计算致病性变异的外显率。

结果

在队列 1 中发现了 106 名受试者(18.4%)和队列 2 中 317 名受试者(25.6%)中存在致病性变异。来自两个队列的 94 种不同的致病性变异(七个在 ,75 个在 ,12 个在 )中,有 13 个在 ExAC 中报道(两个在 ,九个在 ,两个在 ),占携带 变异的受试者的 21%。合并两个队列的数据,估计终身疾病外显率为 22.0%(95%CI 15.2%至 30.9%)、 8.3%(95%CI 3.5%至 18.5%)和 1.7%(95%CI 0.8%至 3.8%)。

结论

在 中发现的致病性变异比在 中发现的更具外显率。我们的发现对携带这些致病性变异的受试者的咨询和监测具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad52/6252366/75a3c04cf1eb/jmedgenet-2018-105427f01.jpg

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