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Most previous research on the genetic basis of disease has focused on the 20,000 known genes and the surrounding sections of DNA that regulate those genes.However, even this enormous amount of genetic information makes up only slightly more than one per cent of the 3.2 billion chemical pairs in the human genome.The other 99 per cent has conventionally been thought of as "dark" or "junk," although recent research has begun to disrupt that idea.In their new finding, detailed in the journal Nature Genetics, the research team offers a method to make sense of this vast array of genomic data.The system uses an AI technique called deep learning in which an algorithm performs successive layers of analysis to learn about patterns that would otherwise be impossible to discern.The algorithm teaches itself how to identify biologically relevant sections of DNA and predicts whether those snippets play a role in any of more than 2,000 protein interactions that are known to affect the regulation of genes."This method provides a framework for doing this analysis with any disease," said Olga Troyanskaya, Professor at Princeton University in the US.The approach could be particularly helpful for neurological disorders, cancer, heart disease and many other conditions that have eluded efforts to identify genetic causes.In the case of autism, the researchers analysed the genomes of 1,790 families with "simplex" autism spectrum disorder, meaning the condition is apparent in one child but not in other members of the family.The method sorted among 120,000 mutations to find those that affect the behaviour of genes in people with autism.Among this sample, fewer than 30 per cent of the people affected by autism spectrum disorder had a previously identified genetic cause.The newly found mutations are likely to significantly increase that fraction, the researchers said.( With inputs from IANS )

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