Revolutionary research combining computational biology with gene editing to understand the genetic basis of infertility
For the one in six couples worldwide struggling with infertility, the dream of having a child often becomes a heartbreaking medical odyssey. What many don't realize is that nearly half of these cases may stem from invisible genetic factors—tiny spelling mistakes in our DNA that disrupt the intricate dance of reproduction.
Worldwide are affected by infertility, with genetic factors playing a significant role in nearly half of cases.
Single-nucleotide polymorphisms are minute genetic variations that can silently interfere with fertility.
Revolutionary approach aims to build a comprehensive map of infertility-causing genetic variants.
Single-nucleotide polymorphisms (SNPs, pronounced "snips") represent the most common type of genetic variation among people. Each SNP represents a difference in a single DNA building block called a nucleotide. Imagine your DNA as a massive recipe book with 3 billion letters—a SNP would be like changing one letter in one word, potentially altering the recipe's outcome 2 .
While most SNPs have no effect on health, some can significantly influence how genes function, especially when they occur in genes crucial for reproduction. The reproductive system relies on thousands of genes working in perfect harmony 1 5 .
For decades, scientists have struggled to pinpoint exactly which SNPs cause infertility. Traditional methods like genome-wide association studies (GWAS) have faced significant challenges because infertility is genetically complex and heterogeneous—meaning different genetic defects can lead to similar symptoms 1 7 .
| Gene | Function | Associated Infertility Type |
|---|---|---|
| CDK2 | Cell cycle regulation, meiosis | Male infertility (spermatogonial stem cell maintenance) |
| MTHFR | Folate metabolism | Unexplained female infertility, embryo implantation |
| WNT4 | Reproductive organ development | Multiple female infertility categories |
| TEX11 | Meiotic chromosome pairing | Azoospermia (no sperm in semen) |
| INHBB | Hormone regulation (inhibin B) | Anovulatory infertility (failure to ovulate) |
In 2015, researchers developed a groundbreaking strategy that bypassed the limitations of traditional genetic association studies. Their innovative approach consisted of two key phases: first, using computational methods to identify potential infertility-causing SNPs from human genetic databases, and second, experimentally validating these candidates by recreating them in mouse models using CRISPR/Cas9 genome editing 1 .
The research team focused on four meiosis genes—CDK2, MLH1, SMC1B, and TEX15—that are essential for proper sperm and egg production. Using eight different computational algorithms, they identified specific SNPs in these genes that met strict criteria 1 .
| Gene | SNP ID | Amino Acid Change | Prediction Algorithms Indicating Deleterious (out of 8) |
|---|---|---|---|
| CDK2 | rs3087335 | Y15S | 7 |
| MLH1 | rs63750447 | V384D | 7 |
| SMC1B | rs61735519 | F1055L | 5 |
| TEX15 | rs147871035 | T2181I | 5 |
Researchers designed single-guide RNAs (sgRNAs) specifically targeting the regions of interest in the mouse versions of the four selected genes. These sgRNAs would act as molecular address tags, directing the Cas9 enzyme to the exact DNA locations that needed editing.
For each target, scientists created single-stranded oligodeoxynucleotides (ssODNs)—short DNA sequences containing the desired human SNP changes, flanked by regions identical to the mouse DNA. These would serve as repair templates when the Cas9 enzyme cut the DNA.
The research team performed delicate microinjections on single-celled mouse embryos, introducing three components: mRNA encoding the Cas9 protein (the DNA-cutting tool), the specific sgRNA (the address tag), and the ssODN donor (the repair template with human SNP).
The microinjected embryos were then transferred to surrogate mothers. The resulting offspring were screened for successful incorporation of the human SNPs, creating what researchers call "humanized" mouse models—mice carrying human genetic variants in their DNA 1 .
The revolutionary work on modeling infertility-causing SNPs relies on a sophisticated array of research reagents and technologies. Each component plays a critical role in the process, from initial genetic analysis to functional validation.
Function in SNP Modeling: Precise genome editing to introduce specific SNP variants
Application in Infertility Research: Creating mouse models with human infertility SNPs
Function in SNP Modeling: Serve as repair templates during DNA repair
Application in Infertility Research: Introducing exact nucleotide changes to mimic human SNPs
Function in SNP Modeling: Predicting functional consequences of genetic variants
Application in Infertility Research: Prioritizing which SNPs to test experimentally
Function in SNP Modeling: Cataloging human genetic variation
Application in Infertility Research: Identifying candidate infertility SNPs in populations
The experimental results brought a significant surprise: of the four SNPs predicted to be deleterious by multiple computational algorithms, only one—the CDK2 Y15S mutation corresponding to human SNP rs3087335—actually caused infertility in the mouse models. Mice with this mutation were completely infertile due to a previously unknown role for CDK2 in maintaining spermatogonial stem cells, the foundation of sperm production 1 .
This finding was particularly insightful because it revealed a novel biological function for a well-studied gene. The CDK2 Y15S mutation alters an inhibitory phosphorylation site, effectively putting the brakes on a crucial regulatory mechanism in the testis.
This discrepancy between computational prediction and biological reality highlights a critical lesson in genetics: we cannot rely solely on algorithms to understand the functional impact of genetic variations. The complex physiology of living organisms involves compensation mechanisms, redundant pathways, and contextual factors that computer models cannot fully capture 1 .
The research demonstrated that segregating infertility alleles—genetic variations that cause infertility but persist in populations—do exist. This challenges the assumption that strong infertility-causing mutations would be rapidly eliminated from the gene pool. The study also provided crucial insights for personalized reproductive genetics by distinguishing between benign genetic variations and those truly responsible for infertility 1 .
Subsequent research has continued to reveal the complex landscape of infertility genetics. A 2020 study examining thrombophilia (blood clotting) SNPs found that interactions between multiple SNPs—specifically in MTHFR and Factor V Leiden genes—created a higher risk for unexplained infertility than any single SNP alone. This epistatic interaction (where the effect of one gene depends on the presence of other genes) illustrates that infertility often results from a combination of genetic factors rather than single mutations 6 .
Similarly, a 2025 study exploring SNPs in mitochondrial genes MT-ND3, MT-ND4L, and MT-ND4 found significant associations with both primary and secondary male infertility. The research revealed that specific mitochondrial SNPs could influence sperm function over time, potentially explaining why some men who have previously fathered children later develop infertility issues 2 .
Screening for multiple confirmed infertility SNPs
Based on a patient's specific genetic profile
For couples struggling with infertility
Addressing underlying molecular causes
Researchers at Oregon Health & Science University have developed a technique called "mitomeiosis" that can turn human skin cells into eggs. Though still in early stages with significant limitations, this technology could eventually help create gametes for people unable to produce their own eggs or sperm 3 8 .
Combining genomic data with transcriptomic, proteomic, and epigenomic information provides a more comprehensive view of the molecular pathways disrupted in infertility.
Improvements in CRISPR technology allow for more precise genetic modifications, including single-base editing without creating double-strand breaks in DNA.
Using induced pluripotent stem cells (iPSCs) derived from infertile patients to create in vitro models of germ cell development.
As with any reproductive technology, these advances raise important ethical questions that society must address. The ability to create artificial gametes, edit reproductive genes, and screen embryos for genetic variants requires careful regulation and broad public discussion. Researchers emphasize that current technologies are still in the proof-of-concept stage and likely require a decade or more of additional research before clinical application 3 8 .
The future of infertility research lies in integrating knowledge from multiple approaches—from large-scale genomic studies to functional validation in models to clinical observations. Each level of investigation informs the others, creating a virtuous cycle of discovery that ultimately benefits patients.
The development of in vitro platforms to model infertility-causing SNPs represents more than just a technical achievement—it signifies a fundamental shift in how we approach the genetic basis of reproduction. By combining computational power with biological validation, scientists are moving from simply observing genetic associations to understanding functional consequences.
This research reminds us that the path from genetic blueprint to living organism is filled with complexity, redundancy, and surprise. The fact that only one of four predicted deleterious SNPs actually caused infertility underscores how much we have yet to learn about the exquisite choreography of human reproduction.
As these scientific advances continue, they offer not just hope for new treatments, but a deeper appreciation of the intricate genetic ballet that makes human life possible. For the millions struggling with infertility, this growing understanding of our fundamental biological architecture represents the possibility of turning the dream of parenthood into reality.