Limits...
Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity.

Ruklisa D, Ware JS, Walsh R, Balding DJ, Cook SA - Genome Med (2015)

Bottom Line: We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors.The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions.Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making.

View Article: PubMed Central - PubMed

Affiliation: UCL Genetics Institute, London, UK.

ABSTRACT

Background: With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation.

Methods: We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual's disease. We present models for genes causing inherited cardiac conditions, though the framework is transferable to other genes and syndromes.

Results: Our models report a probability of pathogenicity, rather than a categorisation into pathogenic or benign, which captures the inherent uncertainty of the prediction. We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors. The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions. Though the probability scale is continuous, and innately interpretable, performance summaries based on thresholds are useful for comparisons. Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making. A web tool APPRAISE [http://www.cardiodb.org/APPRAISE] provides access to these models and predictions.

Conclusions: Our Bayesian framework provides a transparent, flexible and robust framework for the analysis and interpretation of rare genetic variants. Models tailored to specific genes outperform genome-wide approaches, and can be sufficiently accurate to inform clinical decision-making.

No MeSH data available.


Related in: MedlinePlus

Graphical representation of the three prediction models for a single syndrome. The logistic regression models are represented as the three rectangles on the right, for a radical variant (top), an inframe indel (middle) and a missense substitution (bottom). Ellipses describe model predictors. Each model is additive on a logistic scale. Multiple arrows emerging from an ellipse indicate that the parameter is shared across the models indicated by the destinations of the arrows. This diagram represents the model for one syndrome.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4308924&req=5

Fig2: Graphical representation of the three prediction models for a single syndrome. The logistic regression models are represented as the three rectangles on the right, for a radical variant (top), an inframe indel (middle) and a missense substitution (bottom). Ellipses describe model predictors. Each model is additive on a logistic scale. Multiple arrows emerging from an ellipse indicate that the parameter is shared across the models indicated by the destinations of the arrows. This diagram represents the model for one syndrome.

Mentions: Our Bayesian logistic regression model is represented diagrammatically in Figure 2, and in BUGS format in Additional file 2. For each variant, the logistic transform of the probability for it to be pathogenic is calculated as a sum over contributions from the predictor variables described above. The contributions from quantitative predictors are either linear or quadratic.Figure 2


Bayesian models for syndrome- and gene-specific probabilities of novel variant pathogenicity.

Ruklisa D, Ware JS, Walsh R, Balding DJ, Cook SA - Genome Med (2015)

Graphical representation of the three prediction models for a single syndrome. The logistic regression models are represented as the three rectangles on the right, for a radical variant (top), an inframe indel (middle) and a missense substitution (bottom). Ellipses describe model predictors. Each model is additive on a logistic scale. Multiple arrows emerging from an ellipse indicate that the parameter is shared across the models indicated by the destinations of the arrows. This diagram represents the model for one syndrome.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4308924&req=5

Fig2: Graphical representation of the three prediction models for a single syndrome. The logistic regression models are represented as the three rectangles on the right, for a radical variant (top), an inframe indel (middle) and a missense substitution (bottom). Ellipses describe model predictors. Each model is additive on a logistic scale. Multiple arrows emerging from an ellipse indicate that the parameter is shared across the models indicated by the destinations of the arrows. This diagram represents the model for one syndrome.
Mentions: Our Bayesian logistic regression model is represented diagrammatically in Figure 2, and in BUGS format in Additional file 2. For each variant, the logistic transform of the probability for it to be pathogenic is calculated as a sum over contributions from the predictor variables described above. The contributions from quantitative predictors are either linear or quadratic.Figure 2

Bottom Line: We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors.The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions.Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making.

View Article: PubMed Central - PubMed

Affiliation: UCL Genetics Institute, London, UK.

ABSTRACT

Background: With the advent of affordable and comprehensive sequencing technologies, access to molecular genetics for clinical diagnostics and research applications is increasing. However, variant interpretation remains challenging, and tools that close the gap between data generation and data interpretation are urgently required. Here we present a transferable approach to help address the limitations in variant annotation.

Methods: We develop a network of Bayesian logistic regression models that integrate multiple lines of evidence to evaluate the probability that a rare variant is the cause of an individual's disease. We present models for genes causing inherited cardiac conditions, though the framework is transferable to other genes and syndromes.

Results: Our models report a probability of pathogenicity, rather than a categorisation into pathogenic or benign, which captures the inherent uncertainty of the prediction. We find that gene- and syndrome-specific models outperform genome-wide approaches, and that the integration of multiple lines of evidence performs better than individual predictors. The models are adaptable to incorporate new lines of evidence, and results can be combined with familial segregation data in a transparent and quantitative manner to further enhance predictions. Though the probability scale is continuous, and innately interpretable, performance summaries based on thresholds are useful for comparisons. Using a threshold probability of pathogenicity of 0.9, we obtain a positive predictive value of 0.999 and sensitivity of 0.76 for the classification of variants known to cause long QT syndrome over the three most important genes, which represents sufficient accuracy to inform clinical decision-making. A web tool APPRAISE [http://www.cardiodb.org/APPRAISE] provides access to these models and predictions.

Conclusions: Our Bayesian framework provides a transparent, flexible and robust framework for the analysis and interpretation of rare genetic variants. Models tailored to specific genes outperform genome-wide approaches, and can be sufficiently accurate to inform clinical decision-making.

No MeSH data available.


Related in: MedlinePlus