Contribution of model organism phenotypes to the computational identification of human disease genes

Alghamdi, Sarah M.; Schofield, Paul N.; Hoehndorf, Robert

doi:10.1242/dmm.049441

Cited by 6 publications

(4 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although they are exceedingly powerful in aggregating diverse data from clinical and functional studies, any developmental or mechanistic connections for LPM-associated diseases, such as joint LPM origin of affected organs, remains to be determined by the database user. Further, drawing functional or causative conclusions from individual mutations or genetic variation is heavily influenced by insights gained in model organism studies ( Alghamdi et al, 2022 ), in particular, assessing the phenotypic impact of individual gene perturbations on structural birth defects. As nowhere near all orthologs and paralogs of human genes have been functionally tested in developmental model organisms ( Table 3 , Box 2 ), the community still faces a gap in gene ontology knowledge that restricts the sequence-based prediction of disease traits.…”

Section: Discussionmentioning

confidence: 99%

Lateral thinking in syndromic congenital cardiovascular disease

Kocere

Lalonde

Mosimann

et al. 2023

Disease Models &Amp; Mechanisms

View full text Add to dashboard Cite

Syndromic birth defects are rare diseases that can present with seemingly pleiotropic comorbidities. Prime examples are rare congenital heart and cardiovascular anomalies that can be accompanied by forelimb defects, kidney disorders and more. Whether such multi-organ defects share a developmental link remains a key question with relevance to the diagnosis, therapeutic intervention and long-term care of affected patients. The heart, endothelial and blood lineages develop together from the lateral plate mesoderm (LPM), which also harbors the progenitor cells for limb connective tissue, kidneys, mesothelia and smooth muscle. This developmental plasticity of the LPM, which founds on multi-lineage progenitor cells and shared transcription factor expression across different descendant lineages, has the potential to explain the seemingly disparate syndromic defects in rare congenital diseases. Combining patient genome-sequencing data with model organism studies has already provided a wealth of insights into complex LPM-associated birth defects, such as heart-hand syndromes. Here, we summarize developmental and known disease-causing mechanisms in early LPM patterning, address how defects in these processes drive multi-organ comorbidities, and outline how several cardiovascular and hematopoietic birth defects with complex comorbidities may be LPM-associated diseases. We also discuss strategies to integrate patient sequencing, data-aggregating resources and model organism studies to mechanistically decode congenital defects, including potentially LPM-associated orphan diseases. Eventually, linking complex congenital phenotypes to a common LPM origin provides a framework to discover developmental mechanisms and to anticipate comorbidities in congenital diseases affecting the cardiovascular system and beyond.

show abstract

Section: Discussionmentioning

confidence: 99%

Lateral thinking in syndromic congenital cardiovascular disease

Kocere

Lalonde

Mosimann

et al. 2023

Disease Models &Amp; Mechanisms

View full text Add to dashboard Cite

show abstract

“…As these methods require information about disease-associated phenotypes during training, they cannot generalize to entirely new cases, thereby limiting their application in identifying phenotype-associated genomic variants. Another limitation can be biases introduced by the neural network and the phenotypes annotations ( Alghamdi et al 2022 ) or similarity measure ( Kulmanov and Hoehndorf 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

Althagafi,

Zhapa-Camacho,

Hoehndorf

2024

Bioinformatics

Self Cite

View full text Add to dashboard Cite

Motivation Whole-exome and genome sequencing have become common tools in diagnosing patients with rare diseases. Despite their success, this approach leaves many patients undiagnosed. A common argument is that more disease variants still await discovery, or the novelty of disease phenotypes results from a combination of variants in multiple disease-related genes. Interpreting the phenotypic consequences of genomic variants relies on information about gene functions, gene expression, physiology, and other genomic features. Phenotype-based methods to identify variants involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been successfully applied to prioritizing variants, such methods are based on known gene–disease or gene–phenotype associations as training data and are applicable to genes that have phenotypes associated, thereby limiting their scope. In addition, phenotypes are not assigned uniformly by different clinicians, and phenotype-based methods need to account for this variability. Results We developed an Embedding-based Phenotype Variant Predictor (EmbedPVP), a computational method to prioritize variants involved in genetic diseases by combining genomic information and clinical phenotypes. EmbedPVP leverages a large amount of background knowledge from human and model organisms about molecular mechanisms through which abnormal phenotypes may arise. Specifically, EmbedPVP incorporates phenotypes linked to genes, functions of gene products, and the anatomical site of gene expression, and systematically relates them to their phenotypic effects through neuro-symbolic, knowledge-enhanced machine learning. We demonstrate EmbedPVP’s efficacy on a large set of synthetic genomes and genomes matched with clinical information. Availability and Implementation EmbedPVP and all evaluation experiments are freely available at https://github.com/bio-ontology-research-group/EmbedPVP. Supplementary information Supplementary data are available at Bioinformatics.

show abstract

“…Phenotypic data is critical for deciphering the biological pathways that cause a disease [ 2 ]. A formal ontological description of phenotype data can assist in identifying, interpreting, and inferring phenotypic features from experimental data in different species [ 3 – 6 ]. Many ontologies cover the phenotype domain for specific organisms, such as the Human Phenotype Ontology (HP) [ 7 ] and the Mammalian Phenotype Ontology (MP) [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Improving the classification of cardinality phenotypes using collections

Alghamdi,

Hoehndorf

2023

J Biomed Semant

Self Cite

View full text Add to dashboard Cite

Motivation Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena. Results We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.

show abstract

Contribution of model organism phenotypes to the computational identification of human disease genes

Cited by 6 publications

References 87 publications

Lateral thinking in syndromic congenital cardiovascular disease

Lateral thinking in syndromic congenital cardiovascular disease

Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning

Improving the classification of cardinality phenotypes using collections

Contact Info

Product

Resources

About