Towards a Gamete Matching Platform: Using Immunogenetics and Artificial Intelligence to Predict Recurrent Miscarriage

Abstract

The degree of Allele sharing of the Human Leukocyte Antigen (HLA) genes has been linked with recurrent miscarriage (RM). However, no clear genetic markers of RM have yet been identified, possibly because of the complexity of interactions between paternal and maternal genes. We propose a methodology to analyse HLA haplotypes from couples either with histories of successful pregnancies or RM. This article describes, for the first time, a method of RM genetic-risk calculation. Novel HLA representation techniques allowed us to create an algorithm (IMMATCH) to retrospectively predict RM with an AUC = 0.71 (p = 0.0035) thanks to high-resolution typing and the use of linear algebra on peptide binding affinity data. The algorithm features an adjustable threshold to increase either sensitivity or specificity. Combining immunogenetics with artificial intelligence could create personalized tools to better understand the genetic causes of unexplained infertility and a gamete matching platform that could increase pregnancy success rates.