Whole-genome sequencing reveals individual and cohort level insights into chromosome 9p syndromes.
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Wang Yingxi, Sams Eleanor I, Slaugh Rachel, Crocker Sandra, Hurtado Emily Cordova, Tracy Sophia, Hou Ying-Chen Claire, Markovic Christopher, Valle Kostandin, Tate Victoria, Belhassan Khadija, Appelbaum Elizabeth, Akinwe Titilope, Starosta Rodrigo T, Cao Yang, Neilson Amber, Liu Yu, Jensen Nathaniel, Ghasemi Reza, Lindsay Tina, Manuel Juana, Couteranis Sophia, Kremitzki Milinn, Ustanik Jack, Antonacci Thomas, Ng Jeffrey K, Emory Andrew, Metz Laura, DeLuca Tracie, Lyons Katherine N, Sinnwell Toni, Thomeczek Brianne, Wang Kymme, Sisneros Nick, Muraleedharan Megha, Kethireddy Anantha, Corbo Marco, Gowda Harsha, King Katherine A, Gurnett Christina A, Dutcher Susan K, Gooch Catherine, Li Yang E, Mitchell Matthew W, Peterson Kevin A, Horani Amjad, Rosenfeld Jill A, Bi Weimin, Stankiewicz Pawel, Chao Hsiao-Tuan, Posey Jennifer E, Grochowski Christopher M, Dardas Zain, Puffenberger Erik G, Pearson Christopher E, Kooy Frank, Annear Dale, Innes A Micheil, Heinz Michael, Head Richard, Fulton Robert, Toutain Stephan, 9P-ARCH, Antonacci-Fulton Lucinda, Cui Xiaoxia, Mitra Robi D, Cole F Sessions, Neidich Julie, Dickson Patricia I, Milbrandt Jeffrey, Turner Tychele N
Аннотация
METHODS: Through the newly formed 9P-ARCH (Advanced Research in Chromosomal Health: Genomic, Phenotypic, and Functional Aspects of 9p-Related syndromes) research network, we assembled a cohort of individuals from families with chromosome 9p syndromes. WGS was applied to 100 individuals, and other genomic technologies were applied to a subset of individuals. To prioritize genes on 9p, we utilized two independent approaches: statistical analyses of genomic data and spatial transcriptomic profiling of embryonic mouse tissue. To assess the enrichment of DNVs within genomic regions, we developed a computational tool, DiamondsDenovo ( https://github.com/TNTurnerLab/DiamondsDenovo ).
RESULTS: Unlike previous low-resolution studies, we analyzed the genomic architecture of chromosome 9p syndromes, highlighting fundamental features and their commonalities and differences across individuals. A machine-learning model was developed to predict 9p deletion syndrome based on gene copy number estimates using WGS data. We identified two late-replicating regions containing most structural variant breakpoints in 9p deletion syndrome, pointing to replication-based issues as a potential cause of structural variant formation in most individuals and structural rearrangements in some individuals. Genes on 9p were prioritized based on statistical assessment of human genomic variation and through spatial transcriptomics, with 24 genes (AK3, BRD10, CD274, CDC37L1, DMRT1, DMRT2, DMRT3, DOCK8, GLIS3, JAK2, KANK1, KDM4C, PLPP6, PTPRD, PUM3, RANBP6, RCL1, RFX3, RIC1, SLC1A1, SMARCA2, UHRF2, VLDLR, and ZNG1A) identified as important for the majority (83%) of individuals with 9p deletion syndrome. Testing of the mitochondrial genome revealed excess copy number in individuals with 9p deletion syndrome.
CONCLUSIONS: This study introduces the 9P-ARCH research network that is actively pursuing genomic, phenotypic, and functional aspects of 9p-related syndromes. We advanced the study of 9p-related syndromes both at the individual level and across the cohort through the largest, most comprehensive genomic analysis of 9p-related syndromes to date.