The Benefits of Marrying Next-Gene Genotyping and Next-Gene Phenotyping to Improve Ultra-Rare Diagnosis

Rare Daily Staff

Much has been made about the ability of next-generation genome sequencing to improve the diagnosis of patients with ultra-rare genetic diseases, but a new study suggests the speed and effectiveness of sequencing can be enhanced when combined with next-generation phenotyping and the use of artificial intelligence.

The study, which used a multidisciplinary approach, sought to improve the diagnosis of ultra-rare genetic diseases through a structured diagnostic approach. The three-year study in Germany, published in Nature Genetics, involved a multi-center study of 1,577 patients. Some 499 patients (32 percent) received a diagnosis through exome sequencing, which identified mutations in 370 different genes. Some 34 patients were diagnosed with previously unknown genetic diseases and the researchers identified an additional 23 candidate genotype-phenotype associations.

As part of the study, the researchers sought to determine if the addition of AI and machine learning approaches would facilitate diagnostic effectiveness and efficiency of exome sequencing. They found that the use of AI-powered next-generation phenotyping increases the efficacy of exome sequencing data analysis.

This included the use of a facial phenotyping program known as GestaltMatcher, which uses dysmorphology to identify people suspected of having a genetic disease by matching their features to people diagnosed with a genetic disease. The study used the sequence and image data of 224 people who had also consented to the computer-assisted analysis of their facial images, and it was shown that the AI-supported technology provides a clinical benefit.

It’s has long been recognized that even the most advanced sequencing technology leaves more patients without a diagnosis than those it is able to provide with a definitive answer. The study shows the benefit of combining both genotypic and phenotypic approaches and leveraging AI and machine learning to increase the speed and accuracy of diagnosis.