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The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.

Families with multiple male children with intellectual disability (ID) are usually suspected of having disease due to a X-linked mode of inheritance and genetic studies focus on analysis of segregating variants in X-linked genes. However, the genetic cause of ID remains elusive in approximately 50% of affected individuals. Here, we report the analysis of next-generation sequencing data in 274 affected individuals from 135 families with a family history suggestive of X-linked ID. Genetic diagnoses were obtained for 19% (25/135) of the families, and 24% (33/135) had a variant of uncertain significance. In 12% of cases (16/135), the variants were not shared within the family, suggesting genetic heterogeneity and phenocopies are frequent. Of all the families with reportable variants (43%, 58/135), we observed that 55% (32/58) were in X-linked genes, but 38% (22/58) were in autosomal genes, while the remaining 7% (4/58) had multiple variants in genes with different modes on inheritance. This study highlights that in families with multiple affected males, X linkage should not be assumed, and both individuals should be considered, as different genetic etiologies are common in apparent familial cases.