Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases

Krämer, A.; Shah, Sohela; Rebres, R.; Tang, Susan; Richards, Daniel R.

doi:10.1186/s12864-017-3910-4

Cited by 11 publications

(10 citation statements)

References 27 publications

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“…Phenotype-driven prioritization of candidate genes and diseases is a well-established approach towards genomic diagnostics in rare disease. [1][2][3][4][5][6][7][8][9][10][11][12] Most current approaches use the Human Phenotype Ontology (HPO) for annotating the set of phenotypic abnormalities observed in the individual being investigated by exome or genome sequencing (WES/WGS). The HPO contains 14,813 terms arranged as a directed acyclic graph in which edges represent subclass relations; 13,182 of these terms represent phenotypic abnormalities.…”

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confidence: 99%

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Robinson

Ravanmehr

Jacobsen

et al. 2020

Preprint

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Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result. However, given that the overall success rate of genomic diagnostics is only around 25-50% or less in many cohorts, a good ranking cannot be taken to imply that the gene or disease at rank one is necessarily a good candidate. Likelihood ratios (LR) are statistics for summarizing diagnostic accuracy, providing a measure of how much more (or less) a patient with a disease has a particular test result compared to patients without the disease. Here, we present an approach to genomic diagnostics that exploits the LR framework to provide an estimate of (1) the posttest probability of candidate diagnoses; (2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes. LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) placed the correct diagnosis within the first three ranks in 92.9% of 384 cases reports comprising 262 Mendelian diseases, with the correct diagnosis having a mean posttest probability of 67.3%. Simulations show that LIRICAL is robust to many typically encountered forms of genomic and phenomic noise. In summary, LIRICAL provides accurate, clinically interpretable results for phenotype-driven genomic diagnostics.

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confidence: 99%

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Robinson

Ravanmehr

Jacobsen

et al. 2020

Preprint

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“…Microbiome data will be analyzed with software tools such as BCL2FASTQ, VSEARCH, Python libraries, diet analysis software [13], algorithms developed for analysis of noisy data [14–17], networks and metabolic pathways [18,19].…”

Section: Research Proposalmentioning

confidence: 99%

Dynamics of the Gut Microbiota in MEBO and PATM conditions: Protocol of a fully remote clinical study

Gabashvili¹

2020

Preprint

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Human odor-prints, mostly owing to the microbiome, have proven their value as biomarkers of health and environmental exposures. In recent years, microbial networks responsible for localized malodors - such as intra-oral halitosis, neck, foot and axillary odor - have been mapped by using next generation sequencing approaches. Intestinal microbes responsible for psychologically debilitating systemic malodor (whole-body including extraoral halitosis), however, remain to be identified. Even a relatively straightforward disorder of choline metabolism trimethylaminuria (TMAU) is thought to exhibit complex host-gene microbiome interactions and its microbial dynamics has not been sufficiently studied. Mapping gut microbiome is needed to understand human metabolic disfunction, make proper dietary recommendations and develop targeted treatments such as microbial therapies. Our preliminary analysis of culture-, PCR- and 16S-RNA-based data found several Operational Taxonomic Units (OTUs) potentially linked to systemic malodor. Proposed controlled pilot study will provide a more comprehensive evaluation and, combined with our prior data, will help to develop new therapies and treatments.

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“…Phenotype-driven prioritization of candidate genes and diseases is a well-established approach to genomic diagnostics in rare disease. [1][2][3][4][5][6][7][8][9][10][11][12] Most current approaches use the Human Phenotype Ontology (HPO) for annotating the set of phenotypic abnormalities observed in the individual being investigated by whole-exome or whole-genome sequencing. The HPO contains 14,813 terms arranged as a directed acyclic graph in which edges represent subclass relations; 13,182 of these terms represent phenotypic abnormalities.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Robinson

Ravanmehr

Jacobsen

et al. 2020

The American Journal of Human Genetics

View full text Add to dashboard Cite

Human Phenotype Ontology (HPO)-based analysis has become standard for genomic diagnostics of rare diseases. Current algorithms use a variety of semantic and statistical approaches to prioritize the typically long lists of genes with candidate pathogenic variants. These algorithms do not provide robust estimates of the strength of the predictions beyond the placement in a ranked list, nor do they provide measures of how much any individual phenotypic observation has contributed to the prioritization result. However, given that the overall success rate of genomic diagnostics is only around 25%-50% or less in many cohorts, a good ranking cannot be taken to imply that the gene or disease at rank one is necessarily a good candidate. Here, we present an approach to genomic diagnostics that exploits the likelihood ratio (LR) framework to provide an estimate of (1) the posttest probability of candidate diagnoses, (2) the LR for each observed HPO phenotype, and (3) the predicted pathogenicity of observed genotypes. LIkelihood Ratio Interpretation of Clinical AbnormaLities (LIRICAL) placed the correct diagnosis within the first three ranks in 92.9% of 384 case reports comprising 262 Mendelian diseases, and the correct diagnosis had a mean posttest probability of 67.3%. Simulations show that LIRICAL is robust to many typically encountered forms of genomic and phenomic noise. In summary, LIRICAL provides accurate, clinically interpretable results for phenotype-driven genomic diagnostics.

show abstract

Leveraging network analytics to infer patient syndrome and identify causal genes in rare disease cases

Cited by 11 publications

References 27 publications

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

Dynamics of the Gut Microbiota in MEBO and PATM conditions: Protocol of a fully remote clinical study

Interpretable Clinical Genomics with a Likelihood Ratio Paradigm

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