The identification of novel gene mutations for degenerative lumbar spinal stenosis using whole-exome sequencing in a Chinese cohort

Abstract

Background: Degenerative lumbar spinal stenosis (DLSS) is a common lumbar disease that requires surgery. Previous studies have indicated that genetic mutations are implicated in DLSS. However, studies on specific gene mutations are scarce. Whole-exome sequencing (WES) is a valuable research tool that identifies disease-causing genes and could become an effective strategy to investigate DLSS pathogenesis.

Methods: From January 2016 to December 2017, we recruited 50 unrelated patients with symptoms consistent with DLSS and 25 unrelated healthy controls. We conducted WES and exome data analysis to identify susceptible genes. Allele mutations firstly identified potential DLSS variants in controls to the patients' group. We conducted a site-based association analysis to identify pathogenic variants using PolyPhen2, SIFT, Mutation Taster, Combined Annotation Dependent Depletion, and Phenolyzer algorithms. Potential variants were further confirmed using manual curation and validated using Sanger sequencing.

Results: In this cohort, the major classification variant was missense_mutation, the major variant type was single nucleotide polymorphism (SNP), and the major single nucleotide variation was C > T. Multiple SNPs in 34 genes were identified when filtered allele mutations in controls to retain only patient mutations. Pathway enrichment analyses revealed that mutated genes were mainly enriched for immune response-related signaling pathways. Using the Novegene database, site-based associations revealed several novel variants, including HLA-DRB1, PARK2, ACTR8, AOAH, BCORL1, MKRN2, NRG4, NUP205 genes, etc., were DLSS related.

Conclusions: Our study revealed that deleterious mutations in several genes might contribute to DLSS etiology. By screening and confirming susceptibility genes using WES, we provided more information on disease pathogenesis. Further WES studies incorporating larger DLSS patient cohorts are required to comprehend the genetic landscape of DLSS pathophysiology fully.

Keywords: Degenerative lumbar spinal stenosis; Gene mutations; Single nucleotide polymorphisms; Susceptible genes; Whole-exome sequencing.

Fig. 1

Bioinformatics filtering pipeline showing how raw data were processed

Fig. 2

Candidate gene mutation frequencies in both groups

Fig. 3

Enriched pathways possibly related to DLSS

Fig. 4

ManhattanPlot Genome-wide spread of base-10 log of P values. Each signal is a gene-based − log10 (P value)