TY - JOUR
T1 - Systematic analysis and prediction of genes associated with monogenic disorders on human chromosome X
AU - Leitao, E.
AU - Schroder, C.
AU - Parenti, I.
AU - Dalle, C.
AU - Rastetter, A.
AU - Kuhnel, T.
AU - Kuechler, A.
AU - Kaya, S.
AU - Gerard, B.
AU - Schaefer, E.
AU - Nava, C.
AU - Drouot, N.
AU - Engel, C.
AU - Piard, J.
AU - Duban-Bedu, B.
AU - Villard, L.
AU - Stegmann, A.P.A.
AU - Vanhoutte, E.K.
AU - Verdonschot, J.A.J.
AU - Kaiser, F.J.
AU - Mau-Them, F.T.
AU - Scala, M.
AU - Striano, P.
AU - Frints, S.G.M.
AU - Argilli, E.
AU - Sherr, E.H.
AU - Elder, F.
AU - Buratti, J.
AU - Keren, B.
AU - Mignot, C.
AU - Heron, D.
AU - Mandel, J.L.
AU - Gecz, J.
AU - Kalscheuer, V.M.
AU - Horsthemke, B.
AU - Piton, A.
AU - Depienne, C.
N1 - Funding Information:
The authors thank the patients with CDK16 and TRPC5 pathogenic variants and their family for their participation in this study. We kindly thank Mrs Céline Cuny for Sanger sequencing analysis of the patient with TRPC5 missense variant. Electrophysiological experiments were carried out at the electrophysiology core facility of ICM funded from the program “Investissements d’avenir” ANR-10-IAIHU-06. We thank Universitätsklinikum Essen, University Duisburg-Essen, the Deutsche Forschungsgemeinschaft (DFG), the Tom-Wahlig-Stiftung (TWS), and the Deutsche Stiftung Neurologie (DSN) for their financial support to the research studies conducted by the authors. E.L. and F.K. are associated with the FOR 2488 project (DFG, Project number 287074911). Sequencing and analysis (Paris cohort) were supported by Assistance Publique des Hôpitaux de Paris (APHP). Sequencing and analysis (UCSF cohort) were provided by the Broad Institute of MIT and Harvard Center for Mendelian Genomics (Broad CMG) and was funded by the National Human Genome Research Institute (grant number: R01NS058721 to E.H.S.). This study makes use of data generated by the DECIPHER community. A full list of centres who contributed to the generation of the data is available from https://deciphergenomics.org/about/stats and via email from [email protected]. Funding for the DECIPHER project was provided by Wellcome. E.A., E.H.S., C.M., D.H., and C.De. are members of the IRC5consortium.
Funding Information:
The authors thank the patients with CDK16 and TRPC5 pathogenic variants and their family for their participation in this study. We kindly thank Mrs Céline Cuny for Sanger sequencing analysis of the patient with TRPC5 missense variant. Electrophysiological experiments were carried out at the electrophysiology core facility of ICM funded from the program “Investissements d’avenir” ANR-10-IAIHU-06. We thank Universitätsklinikum Essen, University Duisburg-Essen, the Deutsche Forschungsgemeinschaft (DFG), the Tom-Wahlig-Stiftung (TWS), and the Deutsche Stiftung Neurologie (DSN) for their financial support to the research studies conducted by the authors. E.L. and F.K. are associated with the FOR 2488 project (DFG, Project number 287074911). Sequencing and analysis (Paris cohort) were supported by Assistance Publique des Hôpitaux de Paris (APHP). Sequencing and analysis (UCSF cohort) were provided by the Broad Institute of MIT and Harvard Center for Mendelian Genomics (Broad CMG) and was funded by the National Human Genome Research Institute (grant number: R01NS058721 to E.H.S.). This study makes use of data generated by the DECIPHER community. A full list of centres who contributed to the generation of the data is available from https://deciphergenomics.org/about/stats and via email from [email protected]. Funding for the DECIPHER project was provided by Wellcome. E.A., E.H.S., C.M., D.H., and C.De. are members of the IRC consortium.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/11/2
Y1 - 2022/11/2
N2 - 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.Discovering disease genes on the X chromosome can be particularly challenging. Here, the authors use features of known disease genes and machine learning to predict genes that remain to be associated with disorders on this chromosome.
AB - 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.Discovering disease genes on the X chromosome can be particularly challenging. Here, the authors use features of known disease genes and machine learning to predict genes that remain to be associated with disorders on this chromosome.
KW - CPG DENSITY
KW - EXPRESSION
KW - FRAMEWORK
KW - INACTIVATION
KW - INTELLECTUAL DISABILITY
KW - LANDSCAPE
KW - MUTATIONS
KW - R/BIOCONDUCTOR PACKAGE
KW - VARIANTS
KW - VERTEBRATE
U2 - 10.1038/s41467-022-34264-y
DO - 10.1038/s41467-022-34264-y
M3 - Article
C2 - 36323681
SN - 2041-1723
VL - 13
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 6570
ER -