With genome analysis, scientists can now predict if you will have a miscarriage

fetus in the womb

The researchers discovered three genes, MCM5, FGGY and DDX60L, that are strongly linked to the risk of developing eggs with an abnormal number of chromosomes when the genes mutate.

To elucidate the genetic cause of female infertility, Rutgers researchers combined genome sequencing with machine learning techniques

According to research from Rutgers University, a specialized analysis of a woman’s genome can be used to predict her likelihood of suffering one of the most common forms of miscarriage.

According to scientists, this knowledge could help patients and doctors make more informed judgments about their reproductive options and fertility treatment strategies.

Rutgers researchers, in a study recently published in the journal, describe a technique that combines genome sequencing with machine learning methods to predict a woman’s likelihood of miscarrying due to egg aneuploidy — a term describing a human egg with an abnormal number of chromosomes — to predict human genetics.

Infertility is a serious reproductive health disorder that affects approximately 12% of women of childbearing age in the United States. Aneuploidy in human oocytes causes early miscarriage and in vitro fertilization (IVF) failure and is responsible for a large percentage of infertility.

Recent research has shown that some genes predispose certain females to aneuploidy, although the exact genetic origins of aneuploid egg production remain unknown. The Rutgers research is the first to assess how strongly certain genetic variants in the mother’s genome predict a woman’s risk of infertility.

“The aim of our project was to understand the genetic cause of female infertility and to develop a method to improve the clinical prognosis of patients’ aneuploidy risk,” said Jinchuan Xing, study author and associate professor in the Department of Genetics at the Rutgers School of Arts and Sciences. “Based on our work, we have shown that the risk of embryonic aneuploidy in female IVF patients can be highly predicted[{” attribute=””>accuracy with the patients’ genomic data. We also have identified several potential aneuploidy risk genes.”

Working with Reproduction Medicine Associates of New Jersey, an IVF clinic in Basking Ridge, N.J., the scientists were able to examine genetic samples of patients using a technique called “whole exome sequencing,” which allows researchers to home in on the protein-coding sections of the vast human genome. Then they created software using machine learning, an aspect of artificial intelligence in which programs can learn and make predictions without following specific instructions. To do so, the researchers developed algorithms and statistical models that analyzed and drew inferences from patterns in the genetic data.

As a result, the scientists were able to create a specific risk score based on a woman’s genome. The scientists also identified three genes – MCM5, FGGY, and DDX60L – that, when mutated, are highly associated with a risk of producing eggs with aneuploidy.

While age is a predictive factor for aneuploidy, it is not a highly accurate gauge because aneuploidy rates within individuals of the same age can vary dramatically. Identifying genetic variations with more predictive power arms women and their treating clinicians with better information, Xing said.

“I like to think of the coming era of genetic medicine when a woman can enter a doctor’s office or, in this case, perhaps, a fertility clinic with her genomic information, and have a better sense of how to approach treatment,” Xing said. “Our work will enable such a future.”

The study was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute of General Medical Sciences, and the National Institute of Mental Health. 

Reference: “Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing” by Siqi Sun, Maximilian Miller, Yanran Wang, Katarzyna M. Tyc, Xiaolong Cao, Richard T. Scott Jr., Xin Tao, Yana Bromberg, Karen Schindler and Jinchuan Xing, 26 March 2022, Human Genetics.
DOI: 10.1007/s00439-022-02450-z