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medRxivGenetic & Genomic MedicinePreprint

Whole Genome Sequencing in Adolescent Idiopathic Scoliosis Cohort Indicates Polygenic Disease Involving Multiple Biological Pathways

İslam Oğuz TuncayiD, Eun Kyoung LeeiD, Anxhela Gjyshi GustafsoniD, Yoonsuh Lee, Dawoon JungiD, June‐Young KohiD, Won‐Chul LeeiD, Sangmoon LeeiD, Kamran Shazand

PDFDOI

Abstract

Adolescent idiopathic scoliosis (AIS) is the most common nondegenerative spinal abnormality. Research indicates a strong correlation between genetics and AIS, with heritability estimates of 87.5%. However, the rarity of shared causative genes among patients, and the difficulty of replication between studies suggest that AIS is a highly complex polygenic disease. In this study, we utilized whole-genome sequencing (WGS) to comprehensively explore the genetic landscape of 119 AIS patients from 103 families. We implemented an automated WGS analysis pipeline powered by RareVisionTM consisting of automated algorithms and manual curation. We identified clinically relevant candidate variants in 20/119 (16.8%) patients and potentially relevant strong or moderate candidate variants in another 73/119 (61.3%) patients. Candidate variants included coding and noncoding point mutations, along with structural variants and large indels, showing the utility of WGS. Candidate genes included AIS-associated genes (e.g. CHD7, COL11A1/2, FBN1/2, HSPG2, KIF7), as well as genes associated with other musculoskeletal and developmental syndromes where scoliosis is a known symptom (e.g. RYR1, GJB2, MYH2, MYH7). Association analysis showed 4 known AIS single nucleotide polymorphisms (rs12946942, rs10756785, rs3904778, rs7633294) also correlated with AIS in our cohort. Finally, through gene set enrichment analysis, we were able to identify 3 gene clusters involved in skeletal muscle contraction, extracellular matrix composition, and gene expression regulation. In summary, through scalable WGS-based familial testing we were able to (1) find clinically relevant genetic variations in the majority of our patients and (2) create a large dataset that allowed us to identify biological pathways relevant to AIS etiopathogenesis.

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