Infertility can have genetic causes, but it is difficult to identify the responsible mutations because many genes control fertility and reproduction. These genes carry many harmful but suspicious mutations in different people, making it difficult to identify truly harmful mutations.
In a study published July 17 in the Proceedings of the National Academy of Sciences, a team led by John Schimenti, professor of genetics in the Department of Biomedical Sciences at the School of Veterinary Medicine, examined the accuracy of existing methods for predict genetic variation. This leads to infertility.
Accurate interpretation of genetic variation is essential for accurate diagnoses and patient recommendations.
“Elucidating the functional consequences of genetic variation is a difficult task,” Schimenti said, “but extremely important for clinical treatment and genetic counseling.”
When scientists want to identify the genetic mutations responsible for a trait, they use a combination of computational tools and molecular techniques. Typically, sophisticated algorithms analyze a patient's DNA sequence and rank the patient's genetic changes based on their likelihood of causing disease.
Most variations in our DNA are classified as benign or “variants of unknown significance” (VUS).
“The mutation that causes infertility will be present in a candidate gene associated with multiple SUVs,” Schimenti said. "It's hard to unequivocally blame one variant of infertility."
For many characteristics, such as rare diseases and cancer, a group of experts in the relevant disease field then reviews the computer predictions. Experts are investigating whether other evidence, such as published lab experiments, supports these predictions. This validation process increases the reliability of clinical genetic variant databases.
However, there is no commission on infertility that requires the support and approval of the National Institutes of Health. For reproductive traits, most conclusions are based solely on algorithm predictions.
Scimenti and his team wanted to assess whether computational methods alone provide accurate predictions of mutations associated with infertility. They conducted an experiment in which they tested the fertility of mice carrying human genetic variants in genes necessary for male reproduction. They focused on 11 genetic variants that the algorithm found disrupted the function of these important fertility genes. Three of these 11 mutations have also been observed in men who have been clinically diagnosed with reproductive problems.
Of the 11 mutations that the algorithm predicted as harmful, the researchers found that 10 did not affect the mice's fertility. A single genetic variant found in a patient with male factor infertility dramatically reduces sperm production in mice.
Schimenti says one of the reasons that in vivo observations don't match computational predictions is that the algorithms are trained on incomplete datasets; When models are trained on partially incorrect data, their predictions are partially wrong.
"The study showed that about half of the rare mutations that the algorithms said had a negative effect on health, didn't have the intended effect," he said.
Another possible reason is not that computer predictions are wrong, but that biological systems are resistant to mutations. “Living systems have a robustness, or redundancy, that can mask small biochemical or structural flaws in proteins,” Schimenti said.
Some of these mutations can be expected to affect gene function, but this alone may not be enough to affect the fertility of the organism. Sometimes genetic variation in a gene only affects a trait when combined with specific changes in other genes.
Scimenti also admitted that his experiments tested human mutations in mouse models. "It's possible that mice are more tolerant of protein changes than humans," he said. "It's also possible that the effects only become noticeable during the longest human lifetime."
However, Schimenti's research shows that relying solely on computational or in vitro tests is not sufficient for diagnostic use in clinical settings. These techniques, used in isolation, falsely label deleterious mutations as deleterious and fail to identify the genetic factors responsible for infertility in real patients.
"Computational predictions are only part of the evidence," Schimenti said, "and if we don't look at other things, we are bound to make mistakes in interpreting genetic variation."
For more information, see Jinbao Ding et al., In vivo versus in silico assessment of potentially 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 diagnosis of infertility (August 25, 2023) Retrieved August 29, 2023 from https://medicalxpress.com/news/2023-08-poor-vivo-validation-inaccurate-infertility .html
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