Total number of 3290 exomes by predominant phenotypes.

Total number of 3290 exomes by predominant phenotypes.</p

Phenogenon profiling workflow.

A) The distribution of frequency vs CADD Phred score for variants of a single gene were binned according to empirically chosen cut-offs. B) Variants within each binned area are further analysed. Individuals carrying these variants are identified and then filtered on the basis of whether they have a selected HPO term. C) Fisher’s Exact test is then used to determine the significance of the gene-phenotype relationship. D) A Phenogenon heatmap is produced using the Fisher Exact P-Values for each binned area. E) Fisher Exact Scores for each of the binned area in the first column are collapsed into a single HPO goodness of fit score (HGF) using a Scaled Stouffer transformation.</p

Top-ranked gene-phenotype-MOI relations reported by phenogenon.

Top-ranked gene-phenotype-MOI relations reported by phenogenon.</p

Known HPO-gene-MOI relationships used to benchmark Phenogenon.

Known HPO-gene-MOI relationships used to benchmark Phenogenon.</p

Using phenogenon to predict gene-HPO-mode of inheritance (MOI) relationships for the 12 known genes.

A. Examples of using Phenogenon to profile known relationships: ABCA4—Macular dystrophy (HP:0007754) -recessive, and SCN1A—Seizures (HP:0001250)—dominant. The color scales represent the HGF score. The majority of high-scoring bins are for rare variants (HGF < 0.00025). B. Error rate in predicting HPO when number of patients selected per gene is higher than ‘HPO NP cut-off’. The lines give the trend of error rates for each prediction model. C. Error rate for MOI when HPO selected per gene is higher than HGF cut-off. The lines give the trend of error rates for each prediction model. Orange line: model using gnomAD allele frequency instead of estimated homozygote frequency for recessive MOI; Red line: model using HGF for both HPO association and MOI prediction; Blue line: model using Fisher method to combine p values; Green line: our current model for Phenogenon.</p

Metadata record for: The Personal Genome Project-UK, an open access resource of human multi-omics data

This dataset contains key characteristics about the data described in the Data Descriptor The Personal Genome Project-UK, an open access resource of human multi-omics data. Contents: 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format 3. machine readable metadata file in ISA-Tab format (zipped folder)</div

DataSheet1_Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation.DOCX

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.</p

Table1_Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation.DOCX

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.</p