Most individuals with rare diseases initially consult their primary care physician. For a subset of rare diseases, efficient diagnostic pathways are available. However, ultra-rare diseases often require both expert clinical knowledge and comprehensive genetic diagnostics, which poses structural challenges for public healthcare systems. To address these challenges within Germany, a novel structured diagnostic concept, based on multidisciplinary expertise at established university hospital centers for rare diseases (CRDs), was evaluated in the three year prospective study TRANSLATE NAMSE. A key goal of TRANSLATE NAMSE was to assess the clinical value of exome sequencing (ES) in the ultra-rare disease population. The aims of the present study were to perform a systematic investigation of the phenotypic and molecular genetic data of TRANSLATE NAMSE patients who had undergone ES in order to determine the yield of both ultra-rare diagnoses and novel gene-disease associations; and determine whether the complementary use of machine learning and artificial intelligence (AI) tools improved diagnostic effectiveness and efficiency. ES was performed for 1,577 patients (268 adult and 1,309 pediatric). Molecular genetic diagnoses were established in 499 patients (74 adult and 425 pediatric). A total of 370 distinct molecular genetic causes were established. The majority of these concerned known disorders, most of which were ultra-rare. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were delineated, mainly in individuals with neurodevelopmental disorders. To determine the likelihood that ES will lead to a molecular diagnosis in a given patient, based on the respective clinical features only, we developed a statistical framework called YieldPred. The genetic data of a subcohort of 224 individuals that also gave consent to the computer-assisted analysis of their facial images were processed with the AI tool Prioritization of Exome Data by Image Analysis (PEDIA) and showed superior performance in variant prioritization. The present analyses demonstrated that the novel structured diagnostic concept facilitated the identification of ultra-rare genetic disorders and novel gene-disease associations on a national level and that the machine learning and AI tools improved diagnostic effectiveness and efficiency for ultra-rare genetic disorders.
Pseudohypoparathyroidism 1A (PHP1A) consists of signs of Albright hereditary osteodystrophy (AHO) and multiple, variable hormonal resistances. Elevated PTH levels are the biochemical hallmark of the disease. Short stature in PHP1A may be caused by a form of accelerated chondrocyte differentiation leading to premature growth plate closure, possibly in combination with GH deficiency in some patients. Treatment of short stature with recombinant growth hormone (rhGH) in pediatric patients may improve final height if started during childhood. The 10 11/12-year-old boy with clinical signs of AHO presented for evaluation of short stature [height standard deviation score (SDS) −2.72]. Clinically his mother was affected by AHO as well. A heterozygous mutation c.505G>A (p.E169K) in exon 6 of the GNAS gene confirmed a diagnosis of PHP1A in the boy. However, hormonal assessment was unremarkable except for low serum IGF-1 (SDS −2.67). On follow-up, GH deficiency due to GHRH resistance was suspected and confirmed by clonidine and arginine stimulation tests. Treatment with rhGH (0.035 mg/kg) for 2 years resulted in catch-up growth (height SDS −1.52). At age 15 years the PTH levels and bone age of the patient remain within the normal range. In patients with PHP1A, short stature is caused by the effects of Gs-α deficiency on the growth plate. However, resistance to GHRH and the resulting GH deficiency might also contribute. Recombinant GH treatment increases growth in these patients. Diagnostic workup for GH deficiency as a factor contributing to short stature is recommended even in the absence of other hormonal resistances.
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