Understanding the Molecular, Cellular, and Structural Basis of Development
Highlighted Programs and Activities
NEW:NICHD resource program grants in bioinformatics This program supports the continued operation, maintenance, and dissemination of unique knowledge, data, and/or bioinformatics resources of major importance to the research community using animal models of embryonic developmental processes. These grants will support ongoing development and enhancement of the resources, user training and services, provision of community-generated data storage and curation, wide dissemination of the tools and/or resources, and expansion of interoperability with other NIH bioinformatics resources. Learn more: PAR-24-301.
NEW:New approaches for measuring brain changes across longer time spans NICHD has joined with other NIH institutes and centers to encourage multidisciplinary investigators to develop exploratory, highly novel new approaches or innovative applications of existing approaches to measure brain activity, connectivity, genomics, or other aspects across the age spectrum of neurodevelopment. Learn more: PAR-24-160 and PAR-24-161.
Gabriella Miller Kids First The NIH Common Fund established the Gabriella Miller Kids First Pediatric Research Program (Kids First) to develop a pediatric research data resource populated by genome sequence and phenotypic data that will be of high value for the communities of investigators who study the genetics of childhood cancers and/or structural congenital anomalies. NICHD administers and provides scientific expertise to the Kids First program, especially research on congenital anomalies, and supports analyses of Kids First data. Kids First has established and continues to develop a data resource, including a collection of curated genomic and phenotypic data from childhood cancer and structural congenital anomalies cohorts and a central portal where these data and analysis tools are accessible to the research community. Access to these data will promote comprehensive and cross-cutting research and collaboration leading to more refined diagnostic capabilities and ultimately more targeted therapies. Learn more: PAR-23-075.
ClinGen genomic curation expert panels NIH established a clinical genomics infrastructure to develop an openly accessible knowledge base that promotes data sharing and provides standardized infrastructure and tools for determining the clinical relevance of genetic variants through two initiatives: the Clinical Genomics Resource (ClinGen) and the Clinical Variant Database (ClinVar) of clinical variation. ClinGen defines the clinical relevance of genes and variants for use in precision medicine and research by standardizing clinical annotation and interpretation of variants and implementing evidence-based expert consensus assertions. NICHD has established expert panels to select genes and genomic variants associated with diseases or conditions of high-priority NICHD topic areas, and systematically determine their clinical significance for diagnosis and treatment of key diseases or conditions. Learn more: PAR-23-199.
Knockout mouse strains exhibiting embryonic or perinatal lethality or subviability NIH developed this program to encourage researchers to phenotype and/or perform research on embryonic lethal knockout mouse strains being generated through the International Mouse Phenotyping Consortium (IMPC), of which the NIH Knockout Mouse Phenotyping Program (KOMP2) is a member. IMPC's mission is to generate a comprehensive catalogue of mammalian gene function that will provide the foundation for functional analyses of human genetic variation. About 30% of these strains are expected to be either embryonic or perinatal lethal, or subviable. However, a large portion of homozygous lethal mutations are expected to have viable heterozygous phenotypes. Moreover, assessing these genes can be helpful for research into early human development processes, a priority for NICHD’s 2020 strategic plan. Learn more: PAR-23-074.
Selected Recent Advances
Developmental trajectories of autism (PMID: 37615073) Autism spectrum disorder (ASD) is a complex neurological and developmental disorder that affects how a person behaves, interacts with others, communicates, and learns. In California, all residents with ASD are offered support services, and this program incorporates regular surveys of communication and social functioning. Using records from more than 70,000 individuals with ASD born between 1992 and 2016, researchers used these surveys to look at how communication and social functioning in ASD changes over time. The scientists identified six developmental trajectories of communication skills and seven social skill trajectories. The results indicated that most individuals with ASD showed improvement as they age, with improvement in communication skills being more common than improvement in social functioning. Five percent of individuals followed a trajectory of adolescent decline, in which high social functioning in childhood declined during adolescence. Improvement was more prevalent in children from higher socioeconomic families or areas, and in children of White mothers compared to children of Hispanic, Black, Asian, or foreign-born mothers.
Artificial intelligence (AI) method shows promise in diagnosing genetic conditions (PMID: 38962029) Diagnosing genetic disorders requires extensive curation and interpretation of genetic variants, a labor-intensive task even for trained geneticists. Although AI shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success. Researchers developed a new AI system that incorporated a series of features designed to integrate expanded genetics approaches with clinical genetics knowledge to solve cases that were diagnostically challenging. The new AI tool was able to double the rate of accurate genetic diagnosis when tested in three distinct groups of individuals previously assessed using current methods. Moreover, the AI method can analyze thousands of samples within days, making it feasible and cost-effective. The new tool does have several limitations. Notably, it is not equipped to analyze structural variations or copy-number variations in the human genome, and it is subject to the limitations of current phenotyping tools that capture clinical features. Researchers are looking to address these limitations in the future, expanding the tool’s capabilities to make it more effective for clinical use.
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