Improving prenatal diagnosis through standards and aggregation

Abstract

Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype-driven tools.

Figure 1:. Semantic Search Algorithms Improve with Comprehensive Phenotypes:

Prenatal phenotypes found in two fetuses diagnosed postnatally with Cornelia de Lange Syndrome, and matching phenotypes associated with the diagnosis. Semantic search algorithms attempt to match phenotypic feature profiles (encoded as terms from ontologies) from patients to phenotypic profiles associated with a particular disease. A growing amount of comprehensive prenatal phenotype-gene-disease correlation data will increase the sensitivity of these algorithms and permit training of machine learning models. Cases adapted from Clark, et al. (93). Circles with dot represent perfect HPO term match, circles with line represent partial HPO match.

Figure 2:. Expansion of the prenatal HPO:

Since 2020, over 90 new prenatal-relevant terms were added to the HPO. This is demonstrated in the evolving fetal phenotype of a fetus with a PTPN11 variant, adapted from Malniece, et al. (73). Newly added HPO terms, including onset terms, are bolded and italicized.

Figure 3:. Summary of prenatal and adult mouse and human phenotypes associated with pathogenic variation in the Ndufs7/NDUFS7 gene.

Shown are the abnormal phenotypes associated with the homozygous and heterozygous mouse Ndufs7 gene knockout from the IMPC at different life stages and some of the overlapping phenotypes/physiological systems reported for the autosomal recessive disorder associated with the corresponding human gene orthologue (HOM: homozygote, HET: heterozygote; AR: autosomal recessive; E: embryonic day; IMPC DR19.1; HPO v2023-10-09).

Figure 4:. Workflow of the Repository of the International Fetal Genomics Consortium (RIFGC).

Genotype and phenotype data are uploaded to the cloud and access is given to the repository. For exome and genome sequencing data, standardized genomic pipelines generate small and structural variant (SV) callsets. Phenotype information is extracted and used to populate a Phenopacket. Genomic variants are then filtered and interpreted and associated with a Phenopacket. The Beacon v2 application programming interface (API) permits search by phenotype or variant and the possibility to connect to additional prenatal data and other databases through the interface.