Systematic analysis and prediction of genes associated with monogenic disorders on human chromosome X

Elsa Leitão , Christopher Schröder , Ilaria Parenti , Carine Dalle , Agnès Rastetter , Theresa Kühnel , Alma Kuechler , Sabine Kaya , Bénédicte Gérard , Elise Schaefer , Caroline Nava , Nathalie Drouot , Camille Engel , Juliette Piard , Bénédicte Duban-Bedu , Laurent Villard (1, 2) , Alexander P A Stegmann , Els K Vanhoutte , Job a J Verdonschot , Frank J Kaiser , Frédéric Tran Mau-Them , Marcello Scala , Pasquale Striano , Suzanna G M Frints , Emanuela Argilli , Elliott H Sherr , Fikret Elder , Julien Buratti , Boris Keren , Cyril Mignot , Delphine Héron , Jean-Louis Mandel , Jozef Gecz , Vera M Kalscheuer , Bernhard Horsthemke , Amélie Piton , Christel Depienne

1 MMG - Marseille medical genetics - Centre de génétique médicale de Marseille
2 Département de génétique médicale [Hôpital de la Timone - APHM]

Disease gene discovery on chromosome (chr) X is challenging owing to its unique modes of inheritance. We undertook a systematic analysis of human chrX genes. We observe a higher proportion of disorder-associated genes and an enrichment of genes involved in cognition, language, and seizures on chrX compared to autosomes. We analyze gene constraints, exon and promoter conservation, expression, and paralogues, and report 127 genes sharing one or more attributes with known chrX disorder genes. Using machine learning classifiers trained to distinguish disease-associated from dispensable genes, we classify 247 genes, including 115 of the 127, as having high probability of being disease-associated. We provide evidence of an excess of variants in predicted genes in existing databases. Finally, we report damaging variants in CDK16 and TRPC5 in patients with intellectual disability or autism spectrum disorders. This study predicts large-scale gene-disease associations that could be used for prioritization of X-linked pathogenic variants.

Domaines: Sciences du Vivant [q-bio]; Génétique humaine; Médecine humaine et pathologie; Machine Learning [stat.ML]

Elsa Leitão, Christopher Schröder, Ilaria Parenti, Carine Dalle, Agnès Rastetter, et al.. Systematic analysis and prediction of genes associated with monogenic disorders on human chromosome X. Nature Communications, 2022, 13 (1), pp.6570. ⟨10.1038/s41467-022-34264-y⟩. ⟨hal-03959490⟩