Gene family information facilitates variant interpretation and identification of disease-associated genes in neurodevelopmental disorders

Dennis Lal, Patrick May, Eduardo Perez-Palma, Kaitlin E. Samocha, Jack A. Kosmicki, Elise B. Robinson, Rikke S. Møller, Roland Krause, Peter Nürnberg, Sarah Weckhuysen, Peter De Jonghe, Renzo Guerrini, Lisa M. Niestroj, Juliana Du, Carla Marini, James S. Ware, Mitja Kurki, Padhraig Gormley, Sha Tang, Sitao WuSaskia Biskup, Annapurna Poduri, Bernd A. Neubauer, Bobby P.C. Koeleman, Katherine L. Helbig, Yvonne G. Weber, Ingo Helbig, Amit R. Majithia, Aarno Palotie, Mark J. Daly*, EuroEPINOMICS RES Consortium


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BACKGROUND: Classifying pathogenicity of missense variants represents a major challenge in clinical practice during the diagnoses of rare and genetic heterogeneous neurodevelopmental disorders (NDDs). While orthologous gene conservation is commonly employed in variant annotation, approximately 80% of known disease-associated genes belong to gene families. The use of gene family information for disease gene discovery and variant interpretation has not yet been investigated on a genome-wide scale. We empirically evaluate whether paralog-conserved or non-conserved sites in human gene families are important in NDDs. METHODS: Gene family information was collected from Ensembl. Paralog-conserved sites were defined based on paralog sequence alignments; 10,068 NDD patients and 2078 controls were statistically evaluated for de novo variant burden in gene families. RESULTS: We demonstrate that disease-associated missense variants are enriched at paralog-conserved sites across all disease groups and inheritance models tested. We developed a gene family de novo enrichment framework that identified 43 exome-wide enriched gene families including 98 de novo variant carrying genes in NDD patients of which 28 represent novel candidate genes for NDD which are brain expressed and under evolutionary constraint. CONCLUSION: This study represents the first method to incorporate gene family information into a statistical framework to interpret variant data for NDDs and to discover new NDD-associated genes.

TidsskriftGenome Medicine
Antal sider12
StatusUdgivet - 17. mar. 2020


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