HOPE: A multi-site research study seeking to find explanations for miscarriage.

HOPE, Harnessing multiple Opportunities for Pregnancy loss Exploration, is a national research study that seeks to find explanations of pregnancy loss and predict future pregnancy outcomes. 

While more than 3000 human genes are conserved and likely essential for early development remarkably little is known about their contribution to recurrent pregnancy loss and current genetic databases are essentially devoid of these entries. We believe that through genetic and molecular profiling of trios (mom, dad, and pregnancy or pregnancy loss), we will be able to confirm which genes contribute to pregnancy loss and discover novel variants in genes and gene pathways essential to human development. The HOPE project is pursuing 4 aims to build a database of knowledge on the various genetic and molecular pathways that may contribute to recurrent pregnancy loss. 

Aim 1

Build a national registry and biobank of patients who have experienced one or more pregnancy loss, that can be used to accelerate discovery and identify genetic and clinical predictors of future pregnancy outcomes.  

Aim 2

Analyze whole-genome sequences of recurrent pregnancy loss trios. 

Aim 3

Use molecular testing to clarify how gene expression results in biological pathways/mechanisms that lead to miscarriage

Aim 4

Engage in machine learning to improve the prediction of pregnancy loss. 

The Science 

We aim to understand what biological factors (genes or chromosome regions and molecular profiles) are associated with unexplained pregnancy loss. The human genome contains 3 billion letters (base pairs) that makeup approximately 100,000 genes. Performing exploratory genetic testing on multiple members of the same family allows us to better determine which genes and gene changes (mutations) are associated with diseases and which are associated with healthy outcomes.

A trio analysis is a type of genetic analysis where both parents and a pregnancy (or baby) are analyzed together to discover inheritance and gene changes that cause the disease in the affected pregnancy. The combination of medical history and genetic information helps us understand the relationship between new genetic markers with unexplained medical conditions such as infertility, miscarriage, and pregnancy loss. The novel informatics approaches used in this study will uncover genes and gene pathways previously unrecognized with standard genetic testing approaches. The genetic markers found in this research will help us understand the causes of recurrent pregnancy loss and eventually lead to new tests and treatments for families with the diagnosis of unexplained pregnancy loss.  

The Data 

Machine learning is a powerful computational technique for recognizing and identifying patterns hidden in large amounts of complex data. With the advent of high-throughput sequencing and lower cost of computation, machine learning is becoming more popular to exploit those data for pinpointing the molecular bases of complex human diseases, paving the way to precision medicine for pre-screening, diagnosis, and treatment. We plan to use novel machine learning approaches to discover risk genes and pathways underpinning the various presentations (gestational ages, number of losses, obstetric history) of recurrent pregnancy loss and to predict future pregnancy loss.