Next-Generation Phenotyping in the Next-Generation Sequencing Era
Somya Srivastava Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, India Correspondence to: Dr Somya SrivastavaEmail:somyasrivastava18@gmail.com
1 Next-generation phenotyping using computer vision algorithms in rare genomic neurodevelopmental disorders (van
der Donk et al., 2019)
The authors used a novel algorithm by combining two computer algorithms: the Clinical Face Phenotype Space (CFPS)
for facial dysmorphism and OpenFace for facial recognition. Using them, they detected the facial gestalt in
three novel intellectual disability syndromes involving the genes PACS1, PPM1D, and PHIP. Significant
facial similarity for all three syndromes was found. Hence information contained in the face can be used to
delineate genetic entities including in novel ID syndromes with no previously known knowledge of a facial
phenotype.
2 Evaluating Face2Gene as a tool to identify Cornelia de Lange syndrome by facial phenotypes
(Latorre-Pellicer et al., 2020)
This study explored the sensitivity of artificial intelligence by means of Face2Gene technology for facial
recognition in a group of 49 patients with molecularly confirmed Cornelia de Lange syndrome with mutations
in NIPBL, SMC1A, HDAC8 and RAD21 genes. Cornelia de Lange, which can be diagnosed clinically but has features
that vary widely in range and severity, was the first diagnosis in 41/49 patients and one of the top five diagnosis in 47/49
cases giving a sensitivity of 83.7% and 97.9% respectively. The other top five diagnoses were KBG syndrome, CHARGE
syndrome, Rubinstein–Taybi syndrome and Moebius syndrome, with frequencies of 44.89% (22/49), 36.7% (18/49), 34.7%
(17/49), and 18.4% (9/49), respectively. Although substantial difference in sensitivity regarding the age at which facial
images were taken was not present, the sensitivity differed with the affected gene and presence of classical features
with high sensitivity noted in patients with NIBPL variants (97%) and those with the classical phenotype
(88.8%). Thus, each gene presented a different pattern recognition and this can be utilized for studying the
genotype–phenotype correlations and to differentiate between genetic subtypes. For example, it has been described
that thicker eyebrows are suggestive of a variation in SMC1A or SMC3, and females containing variants
in HDAC8 tend to have hypertelorism and a slightly bulbous nasal tip. Patients with NIPBL variants
show pronounced facial features, compared to patients with RAD21 variants who have less prominent
features.
3 Computer-aided facial analysis in diagnosing dysmorphic syndromes in Indian children (Narayanan et al., 2019)
This study used Face2Gene to assess its utility in predicting the diagnosis in 51 Indian children with obvious facial
dysmorphism and a definite molecular or cytogenetic diagnosis. A correct diagnosis as the first suggestion was found in 26
patients (50.9%) and as a part of the top ten suggestions was obtained in 37 patients (72.5%). This study highlights that
the results of the software can change based on the ethnicity as the software was unable to provide a diagnosis in easily
recognizable syndromes like Turner syndrome, Waardenburg syndrome and Wolf-Hirschhorn syndrome. Since Face2Gene
learns from every solved case, its sensitivity is likely to improve further with increasing use particularly in non-Caucasian
populations.
4 PEDIA: prioritization of exome data by image analysis (Hsieh et al., 2019)
This paper assessed the value added by computer assisted image analysis (DeepGestalt) to the diagnostic yield on a
cohort consisting of 679 individuals with 105 different monogenic disorders. For every case, scores from DeepGestalt were
used along with the clinical features and CADD score of the causative variant and a PEDIA score was generated. The
additional information from the photographs pushed the correct disease gene to the top 10 in 99% of all PEDIA cases
from less than 45% when only CADD scores were used. The accuracy rate for the top one gene rose from 36–74% without
DeepGestalt scores to 86–89% when artificial intelligence was used. The results were not affected by the ethnicity of the
patients, however low accuracy was seen in very rare disease due to limited training for those particular
genes.
5 Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging
(Hou et al., 2019)
A cohort of 1190 adult volunteers underwent whole genome sequencing followed by deep phenotyping by
metabolomics, advanced imaging, and clinical laboratory tests in addition to family/medical history. Integrating the
results of WGS with deep phenotyping, 11.5% individuals had a pathogenic variant, thereby providing
a plausible genetic cause for abnormal physiological measurements at the individual level of analysis. A
high percentage of genotype phenotype correlation was observed for dyslipidemia, cardiomyopathy and
arrhythmia, and diabetes and endocrine diseases. With deep phenotyping, heterozygous carriers of autosomal
recessive diseases were also found to exhibit detectable phenotypic changes. Sixty-nine (5.8%) individuals had
pathogenic/ likely pathogenic variants but did not have associated family history, medical history, or phenotypes
detected in tests. This could be because of reduced penetrance, variable expressivity, or late onset of disease
presentation.
References
1. Hsieh TC, et al. PEDIA: prioritization of exome data by image analysis. Genet Med 2019; 21: 2807–2814.
2. Hou YC, et al. Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and
advanced imaging. Proc Natl Acad Sci USA 2020; 117: 3053–3062.
3. Latorre-Pellicer A, et al. Evaluating Face2Gene as a tool to identify Cornelia de Lange syndrome by facial
phenotypes. Int J Mol Sci 2020; 21:1042.
4. Narayanan DL, et al. Computer-aided facial analysis in diagnosing dysmorphic syndromes in Indian
children. Indian Pediatr 2019; 56: 1017–1019.
5. van der Donk R, et al. Next-generation phenotyping using computer vision algorithms in rare genomic
neurodevelopmental disorders. Genet Med 2019; 21:1719–1725.