Neural network non-linear modeling to predict hypospadias genotype-phenotype correlation

Introduction Hypospadias is an abnormal development of the urethral, ventral skin and corporeal bodies. Urethral meatus and ventral curvature have been historically the landmarks to define clinical severity. Genotyping has never been explored as a clinical predictor. Available reports have demonstrated a correlation between genetic mutations and syndromic hypospadias with poor surgical outcomes. We hypothesize that inclusion of genotyping can serve at classifying all types of hypospadias. We present the use of neural network algorithm to evaluate phenotype/genotype correlations and propose its potential clinical applicability. Methods A systematic review was performed from January 1974 to June 2022. Literature was retrieved from Medline, Embase, Web of Science and Google Scholar. Included manuscripts were those that had an explicit anatomical description of hypospadias phenotype (urethral meatus location following an anatomical description) and a defined genotype (genetic mutation) description. Cases with more than one variant/mutation were excluded. A comprehensive phenotype-genotype statistical analysis using neural network non-linear data modeling SPSS (TM) was performed. Results Genotype-Phenotype analysis was performed on 1731 subjects. Of those, 959 (55%) were distal and 772 (45%) proximal. 49 genes with mutations were identified. Neural network clustering predicted better for coronal (90%) and glanular (80%), and lowest for midshaft (22%) and perineal (45%). Using genes as predictor factor only, the model was able to highly and more accurately predict the phenotype for coronal and glanular hypospadias. The following genotypes showed association to a specific phenotype: AR gene n.2058G > A for glanular (p<0.0001), n.480C > T for coronal (p = 0.034), R840C for perineal (p = 0.002), MAMLD1 gene c.2960C > T for coronal (p< 0.0001), p. G289S for glanular (p<0.0001), gene SRD5A2 607G > A for scrotal (p<0.0001), c16C > T for penoscrotal (p<0.0001), c59 T > c for perineal (p = 0.042), V89L for midshaft and scrotal (p<0.0001, p = 0.041; respectively). Discussion Hypospadias phenotype has always been described from a purely anatomical perspective. Our results demonstrate that current phenotyping has poor correlation to the genotype. Higher genotype/phenotype correlation for distal hypospadias proves the clinical applicability of genotyping these cases. The concept and classification of differences in sexual development needs to be reconsidered given high positive yield reported for distal hypospadias. Given the better predictive value of genotyping in correlation to the phenotype, future efforts should be directed towards using the genotype. Conclusion Hypospadias has poor phenotype/genotype correlation. Sequencing all hypospadias phenotypes may add clinical value if used in association to other predictive variables. Neural network analysis may have the ability to combine all these variables for clinical prediction. [GRAPHICS] .

Automated summary

This study examines the correlation between genotype and phenotype in different types of hypospadias using a neural network algorithm and suggests the potential clinical applicability of this approach.