Genomic newborn screening for rare diseases

Stark, Zornitza; Scott, Richard

doi:10.1038/s41576-023-00621-w

Cited by 49 publications

(43 citation statements)

References 137 publications

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“…Genetic screening is increasingly applied in clinical practice, leading to the identification of a rapidly growing number of genetic variations 47–49 . One of the challenges in modern biology is decoding the functional implications of these genetic variations at the molecular level 37,38,40,50–55 .…”

Section: Discussionmentioning

confidence: 99%

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Kwon,

Safer,

Nguyen

et al. 2024

Preprint

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Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types – to “map” variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

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Section: Discussionmentioning

confidence: 99%

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Kwon,

Safer,

Nguyen

et al. 2024

Preprint

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“…At least 34 groups worldwide are exploring genomic sequencing as a way to expand newborn screening and identify children at high risk for hundreds of actionable genetic disorders. 1 Stakeholders, including diverse groups of parents, 2,3 rare disease specialists, 4 primary care physicians, 5 genetic counselors, 4,6 and the public 7 support the implementation of genomic newborn screening for at least some genetic disorders. However, many clinical, ethical, and technological barriers to implementation remain.…”

Section: Introductionmentioning

confidence: 99%

Determining the characteristics of genetic disorders that predict inclusion in newborn genomic sequencing programs

Minten,

Gold,

Bick

et al. 2024

Preprint

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Over 30 international research studies and commercial laboratories are exploring the use of genomic sequencing to screen apparently healthy newborns for genetic disorders. These programs have individualized processes for determining which genes and genetic disorders are queried and reported in newborns. We compared lists of genes from 26 research and commercial newborn screening programs and found substantial heterogeneity among the genes included. A total of 1,750 genes were included in at least one newborn genome sequencing program, but only 74 genes were included on >80% of gene lists, 16 of which are not associated with conditions on the Recommended Uniform Screening Panel. We used a linear regression model to explore factors related to the inclusion of individual genes across programs, finding that a high evidence base as well as treatment efficacy were two of the most important factors for inclusion. We applied a machine learning model to predict how suitable a gene is for newborn sequencing. As knowledge about and treatments for genetic disorders expand, this model provides a dynamic tool to reassess genes for newborn screening implementation. This study highlights the complex landscape of gene list curation among genomic newborn screening programs and proposes an empirical path forward for determining the genes and disorders of highest priority for newborn screening programs.

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“…Most rare diseases have a Mendelian basis and are the result of variation in one or at most a small number of genes [2]. Next-generation sequencing (NGS) has revolutionized the diagnosis of Mendelian diseases and whole exome and genome sequencing can identify genetic variants in individuals which may cause the disorder; the number of these variants ranges from around 20-40,000 in the whole exome to over a million in whole genome sequencing [3, 4].…”

Section: Introductionmentioning

confidence: 99%

The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients

Kafkas,

Abdelhakim,

Althagafi

et al. 2023

Preprint

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Computational methods for identifying gene–disease associations can use both genomic and phenotypic information to prioritize genes and variants that may be associated with genetic diseases. Phenotype-based methods commonly rely on comparing phenotypes observed in a patient with a database of genotype-to-phenotype associations using a measure of semantic similarity, and are primarily limited by the quality and completeness of this database as well as the quality of phenotypes assigned to a patient. Genotype-to-phenotype associations used by these methods are largely derived from literature and coded using phenotype ontologies. Large Language Models (LLMs) have been trained on large amounts of text and have shown their potential to answer complex questions across multiple domains. Here, we demonstrate that LLMs can prioritize disease-associated genes as well, or better than, dedicated bioinformatics methods relying on calculated phenotype similarity. The LLMs use only natural language information as background knowledge and do not require ontology-based phenotyping or structured genotype-to-phenotype knowledge. We use a cohort of undiagnosed patients with rare diseases and show that LLMs can be used to provide diagnostic support that helps in identifying plausible candidate genes.

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Genomic newborn screening for rare diseases

Cited by 49 publications

References 137 publications

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Determining the characteristics of genetic disorders that predict inclusion in newborn genomic sequencing programs

The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients

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