Integrated whole exome sequencing and functional approach delineate genetic heterogeneity in cerebellar ataxias

Abstract

PurposeDisease deconvolution in heterogeneous cerebellar ataxias (CAs) needs a focussed approach to overcome the diagnostic challenges. A diverse clinical presentation with over 100 reported genetic loci, in addition to the various challenges associated with genotype-phenotype correlation complicate the genetic diagnosis in 40-60% of the CA cases that remain uncharacterized. We present here an integrated whole exome sequencing combined with a functional validation approach to delineate the genetic etiology in Indian CA patients.MethodA total of 50 familial and sporadic progressive CA families (negative for CNG expansion) including 101 subjects were recruited for this study. Index patients from 50 families were subjected to singleton whole exome sequencing (S-WES). Family-based WES (F-WES) was carried out for seven S-WES selected families. Protein simulation and docking studies were performed for seven genetic variants identified through WES. A Cell line-based model was used to assess disease signatures for variants in KCNC3 and a new candidate gene, SPTB.ResultsClinically relevant variants identified in 70% (35/50) of the selected families. We achieved a 50% (25/50) definitive diagnostic yield and 14% (7/50) probable diagnostic yield while 6% (3/50) of the families showed variants of uncertain significance. We prioritized compound heterozygous variants in a candidate gene, SPTB for cerebellar ataxia with hereditary spherocytosis. Lymphoblastoid cell line derived from a patient with a KCNC3 variant showed altered disease signatures with induced ROS and elevated unfolded protein response markers at the basal level.ConclusionOur results highlight an extensive experimental design for the genetic diagnosis of CA. Through targeted analysis of ataxia phenotype-derived gene panel in S-WES, new gene identification through F-WES, and revaluation of unsolved families’ WES data, we identified novel, reported and other clinically relevant variants in CA patients. Bioinformatic protein modeling along with the cellular insights into the pathogenicity of novel variants enabled delineation of genetic diagnostics and enhanced the mechanistic understanding of CAs.