Cornell researchers have developed an experimental strategy to identify infertility-causing mutations found in human populations. These mutations are known as single nucleotide polymorphisms, or SNPs, and are the most common type of genetic variation among people. Each SNP represents a difference in a single DNA building block, called a nucleotide.
“If we figure out whether a SNP is truly deleterious, then in the future when patients come in they can have their genomes sequenced to determine which SNPs they have. If we know which variation is good or bad, doctors will be able make a genetic diagnosis,” said John Schimenti, director of the Center for Vertebrate Genomics at Cornell and the paper’s senior author. Priti Singh, a postdoctoral fellow in Schimenti’s lab, is the first author of the study published Aug. 3 in the Proceedings of the National Academy of Sciences.
The standard way to identify disease-causing SNPs involves comparing the genomes of healthy and affected people to narrow down their chromosomal locations, then using algorithms to predict which SNPs are harmful. But because fertility is such a complex process involving many genes, this method has not worked.
So Schimenti and Singh developed a new strategy: They took a list of all the known infertility genes in mice – which have been well-established through experimentation that cannot be done with humans – then computationally identified the equivalent SNPs in humans through databases of human genetic variation.
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