News | Can Genes Predict Miscarriage and IVF Failure? Rutgers Study Points to a New Approach
Genes determine eye color, height, and body type, and may also hold hidden information about female fertility. Research from Rutgers University suggests that analyzing specific parts of a woman's genome may predict the risk of early miscarriage and in vitro fertilization (IVF) failure, offering a potential new direction in fertility care.
Published in Human Genetics on June 14, 2022, the study found that a technique combining whole exome sequencing and machine-learning algorithms could predict the likelihood of embryo loss caused by chromosomal abnormalities in eggs, medically known as oocyte aneuploidy.
Oocyte aneuploidy: A major cause of infertility and miscarriage
About 12% of women of reproductive age in the United States experience infertility. Many early miscarriages and IVF failures are closely associated with an abnormal number of chromosomes in the egg. A normal human egg contains 23 chromosomes, but genetic abnormalities may cause some eggs to have too many or too few, compromising embryo development from its earliest stages.
Age is a known risk factor for oocyte aneuploidy, but egg quality varies greatly among women of the same age, showing that age is not the only determining factor.
Genetic prediction model: Moving toward precision reproductive medicine
To investigate the underlying genetics, Rutgers associate professor of genetics Jinchuan Xing and his team worked with Reproductive Medicine Associates of New Jersey to analyze genetic samples from women undergoing IVF. They used whole exome sequencing, focusing on protein-coding regions of the human genome to identify variants associated with aneuploidy more precisely.
The researchers then built a machine-learning model that independently identified patterns in large genetic datasets and learned which genetic characteristics were strongly associated with aneuploidy risk.
The study produced an individualized chromosomal abnormality risk score that could be used to predict each woman's reproductive risk.
Key genes identified: MCM5, FGGY, and DDX60L
The team also identified three new genes—MCM5, FGGY, and DDX60L—whose specific mutations were associated with a significantly higher likelihood of chromosomal abnormalities in eggs.
If these mutations are detected, doctors may eventually be able to plan more appropriate embryo screening or treatment before IVF, reducing repeated transfer failure, emotional stress, and financial and time costs.
From trial and error to data-informed fertility decisions
“I prefer to call it the arrival of genomic medicine,” Professor Xing said. “In the future, a woman could provide her genomic data at a reproductive center, and doctors could use the algorithm to predict aneuploidy risk and tailor fertility treatment to each patient.”
The study offers insight into genetic mechanisms of female infertility and signals a shift toward more personalized, data-driven reproductive medicine. Compared with past trial-and-error approaches, women and their doctors may gain greater control over treatment decisions.
News | Can Genes Predict Miscarriage and IVF Failure? Rutgers Study Points to a New Approach
News | Can Genes Predict Miscarriage and IVF Failure? Rutgers Study Points to a New Approach
Genes determine eye color, height, and body type, and may also hold hidden information about female fertility. Research from Rutgers University suggests that analyzing specific parts of a woman's genome may predict the risk of early miscarriage and in vitro fertilization (IVF) failure, offering a potential new direction in fertility care.
Published in Human Genetics on June 14, 2022, the study found that a technique combining whole exome sequencing and machine-learning algorithms could predict the likelihood of embryo loss caused by chromosomal abnormalities in eggs, medically known as oocyte aneuploidy.
Oocyte aneuploidy: A major cause of infertility and miscarriage
About 12% of women of reproductive age in the United States experience infertility. Many early miscarriages and IVF failures are closely associated with an abnormal number of chromosomes in the egg. A normal human egg contains 23 chromosomes, but genetic abnormalities may cause some eggs to have too many or too few, compromising embryo development from its earliest stages.
Age is a known risk factor for oocyte aneuploidy, but egg quality varies greatly among women of the same age, showing that age is not the only determining factor.
Genetic prediction model: Moving toward precision reproductive medicine
To investigate the underlying genetics, Rutgers associate professor of genetics Jinchuan Xing and his team worked with Reproductive Medicine Associates of New Jersey to analyze genetic samples from women undergoing IVF. They used whole exome sequencing, focusing on protein-coding regions of the human genome to identify variants associated with aneuploidy more precisely.
The researchers then built a machine-learning model that independently identified patterns in large genetic datasets and learned which genetic characteristics were strongly associated with aneuploidy risk.
The study produced an individualized chromosomal abnormality risk score that could be used to predict each woman's reproductive risk.
Key genes identified: MCM5, FGGY, and DDX60L
The team also identified three new genes—MCM5, FGGY, and DDX60L—whose specific mutations were associated with a significantly higher likelihood of chromosomal abnormalities in eggs.
If these mutations are detected, doctors may eventually be able to plan more appropriate embryo screening or treatment before IVF, reducing repeated transfer failure, emotional stress, and financial and time costs.
From trial and error to data-informed fertility decisions
“I prefer to call it the arrival of genomic medicine,” Professor Xing said. “In the future, a woman could provide her genomic data at a reproductive center, and doctors could use the algorithm to predict aneuploidy risk and tailor fertility treatment to each patient.”
The study offers insight into genetic mechanisms of female infertility and signals a shift toward more personalized, data-driven reproductive medicine. Compared with past trial-and-error approaches, women and their doctors may gain greater control over treatment decisions.
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