2020 Update on prenatal phenotyping
In prenatal phenotyping, understanding standardization of language, specific prenatal descriptions, and artificial intelligence may contribute toward the making of a diagnosis
Use of postnatal information facilitated diagnoses
Reanalysis of ES data using detailed postnatal findings revealed a possible diagnosis in 20% of cases. Each case in which a diagnosis was made, detailed below, highlights an important limitation in our current ability to make prenatal diagnoses.
Case 1. A fetus was diagnosed prenatally with arthrogryposis, plagiocephaly, and club feet. After birth, the infant also was found to have generalized muscle weakness, elevated creatine phosphokinase, and congenital hip dislocation.
Reanalysis of the ES data revealed compound heterozygous missense variants in the nebulin gene (NEB). Although classified as variants of uncertain significance (VUS), these are consistent with the phenotype, the authors argued, and with the diagnosis of autosomal recessive nemaline myopathy 2.
Case 2. Prenatal diagnosis was made of a right limb anomaly, tetralogy of Fallot, intrauterine growth restriction, ambiguous genitalia, and dextrocardia. Postnatal evaluation revealed absent pulmonary valve syndrome, right arm dysplasia, pectus carinatum deformity, and failure to thrive.
In this case, ES with the postnatal information revealed a VUS in the NOTCH1 gene, which has been associated with Adams-Oliver syndrome. Although by strict criteria this variant is also of uncertain significance, Adams-Oliver syndrome is characterized, in part, by transverse limb defects and congenital heart disease, as was found in the proband.
Case 3. Prenatal ultrasonography revealed microcephaly and absence of the septum pellucidum. Postnatal magnetic resonance imaging revealed semi-lobar holoprosencephaly. A holoprosencephaly-specific gene panel revealed a deletion in the ZIC2 gene, which is known to cause holoprosencephaly.
Careful re-examination of the ES data revealed some abnormality in the ZIC2 signal, which might have been studied in greater detail and thereby detected if the prenatal diagnosis of holoprosencephaly had been made.
Case 4. An ultrasound evaluation at 12 weeks’ gestation revealed a cystic hygroma, short long bones, and possible absent hand and fibula. A postnatal fetal autopsy at 14 weeks showed split-hand and split-foot malformations, which were not appreciated on ultrasonography.
In filtering the ES data with this information, a pathogenic variant in the PRCN gene was identified as causal, and the diagnosis of Goltz syndrome was made.
Challenges facing prenatal diagnosis
A case series is inherently limited by its small sample size. Nevertheless, the authors suggest 2 major challenges in our ability to make the above diagnoses in the prenatal setting:
1) the prenatal assessment being limited to major structural abnormalities, and 2) commercial laboratories not having enough experience or volume to interpret the limited information provided by prenatal imaging.
Prenatal genetic diagnosis often is limited by incomplete information about the features seen on ultrasonography. Although not all features are visible prenatally, more diagnoses can be made if laboratories are provided with detailed information about the structural abnormalities that are seen. Furthermore, if ES does not provide a prenatal diagnosis, the data should be reviewed postnatally if more detailed phenotypic information becomes available.
Can AI technology be incorporated to make a genetic diagnosis?
Hsieh TC, Mensah MA, Pantel JT, et al. PEDIA: prioritization of exome data by image analysis. Genet Med. 2019;21:2807-2814.
Increasingly, ES is used in all types of undiagnosed, rare genetic diseases. Although there is a high diagnostic yield in many populations, ES’s clinical utility is limited by the labor-intensive process of interpreting each variant in the context of detailed phenotypic information. The widespread use of HPO would be one step toward standardizing the information that is entered into the analysis of ES data, but even HPO cannot capture certain visual clues.
Hsieh and colleagues attempted to use artificial intelligence (AI) for “next-generation phenotyping” to assess facial dysmorphology and integrate the information into variant classification.5 The authors described their approach of incorporating AI as “prioritization of exome data by image analysis” (PEDIA).
Continue to: Designing dysmorphology machine learning...
