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Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders.
Genetics in Medicine ( IF 6.6 ) Pub Date : 2018-12-20 , DOI: 10.1038/s41436-018-0404-y
Roos van der Donk 1, 2 , Sandra Jansen 2 , Janneke H M Schuurs-Hoeijmakers 2 , David A Koolen 2 , Lia C M J Goltstein 2 , Alexander Hoischen 2 , Han G Brunner 2 , Patrick Kemmeren 1 , Christoffer Nellåker 3, 4, 5 , Lisenka E L M Vissers 2 , Bert B A de Vries 2 , Jayne Y Hehir-Kwa 1
Affiliation  

PURPOSE The interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance. METHODS Facial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes (PACS1, PPM1D, and PHIP) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population. RESULTS We validated our model using images from 71 individuals with Koolen-de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 (p = 8 × 10-4), PPM1D (p = 4.65 × 10-2), and PHIP (p = 6.3 × 10-3). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients (p < 1.52 × 10-2). CONCLUSION Our results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.

中文翻译:

在罕见的基因组神经发育障碍中使用计算机视觉算法进行下一代表型分析。

目的 在智力障碍 (ID) 等异质性疾病中,全基因组分析后对遗传变异的解释是复杂的。我们研究是否可以使用算法来检测三种新型 ID 综合征是否存在面部格式塔,以及这些技术是否可以帮助解释意义不确定的变体。方法 使用模拟人类面部畸形和面部识别的算法,从携带三种新 ID 基因(PACS1、PPM1D 和 PHIP)致病变异的 ID 患者的照片中提取面部特征。将得到的特征组合成一个混合模型,以将三个群组与背景 ID 人群进行比较。结果 我们使用来自 71 名 Koolen-de Vries 综合征患者的图像验证了我们的模型,然后显示具有 PACS1 (p = 8 × 10-4)、PPM1D (p = 4.65 × 10-2) 和 PHIP (p = 6.3 × 10-3) 致病变异的个体存在面部完形。此外,在 PHIP 中具有不确定意义的从头错义变体的两个个体与 PHIP 患者的预期面部表型具有显着相似性 (p < 1.52 × 10-2)。结论 我们的结果表明,面部照片分析可用于检测以前未知的新 ID 综合征的面部格式塔,这将有助于罕见和新综合征的临床和分子诊断。1.52 × 10-2)。结论 我们的结果表明,面部照片分析可用于检测以前未知的新 ID 综合征的面部格式塔,这将有助于罕见和新综合征的临床和分子诊断。1.52 × 10-2)。结论 我们的结果表明,面部照片分析可用于检测以前未知的新 ID 综合征的面部格式塔,这将有助于罕见和新综合征的临床和分子诊断。
更新日期:2019-01-26
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