Performance of mutation pathogenicity prediction tools on missense variants associated with 46,XY differences of sex development.

OBJECTIVES: Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional in vitro studies. Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred. METHODS: We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17ß-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve. RESULTS: Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6 (Mutation Assessor, Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66). CONCLUSION: Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools (based on accuracy, precision, and specificity) were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies.

Performance of mutation pathogenicity prediction tools on missense variants associated with 46, XY differences of sex development

OBJECTIVES: Single nucleotide variants (SNVs) are the most common type of genetic variation among humans. High-throughput sequencing methods have recently characterized millions of SNVs in several thousand individuals from various populations, most of which are benign polymorphisms. Identifying rare disease-causing SNVs remains challenging, and often requires functional in vitro studies. Prioritizing the most likely pathogenic SNVs is of utmost importance, and several computational methods have been developed for this purpose. However, these methods are based on different assumptions, and often produce discordant results. The aim of the present study was to evaluate the performance of 11 widely used pathogenicity prediction tools, which are freely available for identifying known pathogenic SNVs: Fathmn, Mutation Assessor, Protein Analysis Through Evolutionary Relationships (Phanter), Sorting Intolerant From Tolerant (SIFT), Mutation Taster, Polymorphism Phenotyping v2 (Polyphen-2), Align Grantham Variation Grantham Deviation (Align-GVGD), CAAD, Provean, SNPs&GO, and MutPred. METHODS: We analyzed 40 functionally proven pathogenic SNVs in four different genes associated with differences in sex development (DSD): 17β-hydroxysteroid dehydrogenase 3 (HSD17B3), steroidogenic factor 1 (NR5A1), androgen receptor (AR), and luteinizing hormone/chorionic gonadotropin receptor (LHCGR). To evaluate the false discovery rate of each tool, we analyzed 36 frequent (MAF>0.01) benign SNVs found in the same four DSD genes. The quality of the predictions was analyzed using six parameters: accuracy, precision, negative predictive value (NPV), sensitivity, specificity, and Matthews correlation coefficient (MCC). Overall performance was assessed using a receiver operating characteristic (ROC) curve. RESULTS: Our study found that none of the tools were 100% precise in identifying pathogenic SNVs. The highest specificity, precision, and accuracy were observed for Mutation Assessor, MutPred, SNP, and GO. They also presented the best statistical results based on the ROC curve statistical analysis. Of the 11 tools evaluated, 6 (Mutation Assessor, Phanter, SIFT, Mutation Taster, Polyphen-2, and CAAD) exhibited sensitivity >0.90, but they exhibited lower specificity (0.42-0.67). Performance, based on MCC, ranged from poor (Fathmn=0.04) to reasonably good (MutPred=0.66). CONCLUSION: Computational algorithms are important tools for SNV analysis, but their correlation with functional studies not consistent. In the present analysis, the best performing tools (based on accuracy, precision, and specificity) were Mutation Assessor, MutPred, and SNPs&GO, which presented the best concordance with functional studies.

SHOX mutations in idiopathic short stature and Leri-Weill dyschondrosteosis: frequency and phenotypic variability.

OBJECTIVE: The frequency of SHOX mutations in children with idiopathic short stature (ISS) has been found to be variable. We analysed the SHOX gene in children with ISS and Leri-Weill dyschondrosteosis (LWD) and evaluated the phenotypic variability in patients harbouring SHOX mutations. PATIENTS: Sixty-three ISS, nine LWD children and 21 affected relatives. METHODS: SHOX gene deletion was evaluated by fluorescence in situ hybridization (FISH), Southern blotting and segregation study of polymorphic marker. Point mutations were assessed by direct DNA sequencing. RESULTS: None of the ISS patients presented SHOX deletions, but two (3.2%) presented heterozygous point mutations, including the novel R147H mutation. However, when ISS patients were selected by sitting height : height ratio (SH/H) for age > 2 SD, mutation frequency detection increased to 22%. Eight (89%) LWD patients had SHOX deletions, but none had point mutations. Analysis of the other relatives in the families carrying SHOX mutations identified 14 children and 17 adult patients. A broad phenotypic variability was observed in all families regarding short stature severity and Madelung deformities. However, the presence of disproportional height, assessed by SH/H, was observed in all children and 82% of adult patients, being the most common feature in our patients with SHOX mutations. CONCLUSION: Patients with SHOX mutations present a broad phenotypic variability. SHOX mutations are very frequent in LWD (89%), in opposition to ISS (3.2%) in our cohort. The use of SH/H SDS as a selection criterion increases the frequency of SHOX mutation detection to 22% and should be used for selecting ISS children to undergo SHOX mutation molecular studies.