Poor In Vivo Validation May Cause Inaccurate Infertility Diagnoses
Infertility can have genetic causes, but identifying causative mutations is difficult because many genes control fertility and reproduction. These genes also carry many harmless but suspicious mutations in different people, making it difficult to detect truly harmful mutations.
In a study published July 17 in the Proceedings of the National Academy of Sciences, a team led by John Chimenti, professor of genetics in the Department of Biomedical Sciences in the College of Veterinary Medicine, tested the accuracy of existing methods for genetic prediction. . Variation that causes infertility.
Accurate interpretation of genetic variation is essential to provide patients with appropriate diagnosis and recommendations.
"Interpreting the functional effects of genetic variation is difficult, but is of great importance for clinical management and genetic counseling," Chimenti said.
When scientists want to identify the genetic mutations responsible for a trait, they use a combination of computational tools and molecular techniques. Complex algorithms typically analyze the patient's DNA sequence and rank the patient's genetic diversity based on the likelihood of causing disease.
Most variations in our DNA are classified as benign or variants of unknown significance (VUS).
"There will be a mutation that will cause sterility in candidate genes associated with many SUVs," Chimenti said. "It is difficult to say definitively that any single variable is responsible for infertility."
For many traits, such as rare diseases and cancer, computer predictions are reviewed by a panel of experts in specific disease areas. Experts are examining whether other evidence, such as published laboratory experiments, supports these predictions. This validation process increases the reliability of the clinical database of genetic variants.
However, there is no commission on infertility that requires the support and approval of the National Institutes of Health to establish. In the case of reproductive traits, most conclusions are based on algorithm predictions alone.
Chimenti and his team wanted to test whether computational methods alone provide accurate predictions about mutations associated with infertility. They conducted an experiment to study the fertility of mice engineered to carry human genetic variants in genes important to male reproduction. They focused on 11 genetic variants that the algorithms predicted would alter the function of these important fertility genes. Three of these 11 mutations were also observed in men with clinically diagnosed fertility problems.
Of the 11 mutations that the algorithm predicted to be harmful, the researchers found that 10 had no effect on the mice's fertility. A single genetic variant in a male infertility patient significantly reduced sperm production in mice.
One reason that in vivo observations don't match algorithmic predictions, Chimenti said, is that algorithms are trained on imprecise data sets; When models learn based on partially wrong data, their predictions are partially wrong.
"Several studies have shown that almost half of the rare mutations that the algorithm predicted would have a negative impact on health did not have the desired effect," he said.
Another possible reason, he says, is that computer predictions are not wrong, but that biological systems are resistant to mutations. "Living systems have robustness or redundancy that can mask small biochemical or structural defects in proteins," Chimenti said.
Some of these mutations are expected to affect gene function, but this alone is not enough to affect the organism's fertility. Sometimes genetic variants of a gene only affect the trait when combined with specific variants of other genes.
Chimenti also admits that his experiments tested human mutations in mouse models. "It is possible that mice are more tolerant of protein changes than humans," he said. "It is also possible that the consequences will only be felt throughout a person's life."
However, Chimenti's study shows that relying only on computer or laboratory experiments is not sufficient to be used as a diagnostic in the clinical setting. When used alone, these approaches misclassify harmless mutations as bad and fail to identify the genetic factors responsible for infertility in healthy patients.
"Computational predictions are only one part of the evidence, and if we don't look at the other parts, we're bound to make mistakes when interpreting genetic variants," Chimenti said.
For more information: Xinbao Ding et al., In vivo versus in silico evaluation of pathogenic missense variants in human reproductive genes, Proceedings of the National Academy of Sciences (2023). doi: 10.1073/pnas.2219925120
Citation : Poor in vivo validation may lead to inaccurate infertility diagnoses (August 25, 2023) Retrieved August 27, 2023 from https://medicalxpress.com/news/2023-08-08-poor-vivo-validation-inaccurate -infert .html
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