Genetics and Traumatic Brain Injury: Findings from an Exome-Based Study of a 50-Patient Case Series

Abstract

Traumatic brain injury (TBI) is the leading cause of global mortality and morbidity. Because TBI is accident-related, the role of genetics in predisposing to TBI has been largely unexplored. However, the likelihood of injury may not be entirely random and may be associated with certain physical and mental characteristics. In this study, we analyzed the exomes of 50 patients undergoing rehabilitation after TBI. Patients were divided into three groups according to rehabilitation outcome: improvement, no change, and deterioration/death. We focused on rare, potentially functional missense and high-impact variants in genes intolerant to these variants. The concordant results from the three independent groups of patients allowed for the suggestion of the existence of a genetic predisposition to TBI, associated with rare functional variations in intolerant genes, with a prevalent dominant mode of inheritance and neurological manifestations in the genetic phenotypes according to the OMIM database. Forty-four of the 50 patients had one or more rare, potentially deleterious variants in one or more neurological genes. Comparison of these results with those of a 50-sampled matched non-TBI cohort revealed significant differences: P = 2.6 × 10^-3, OR = 4.89 (1.77-13.47). There were no differences in the distribution of the genes of interest between the TBI patient groups. Our exploratory study provides new insights into the impact of genetics on TBI risk and is the first to address potential genetic susceptibility to TBI.

Keywords: exome sequencing; intolerant genes; moderate/severe traumatic brain injury (msTBI); nervous system disease-related genes; rare high-impact (HI) variants.

Figure 1

Violin plots for neurological scales and SOFA scale scores in three groups of patients at baseline and final measurements. The plots combine box plots common to both measurements (white dot is median) and kernel density plots showing the variation of the data across the distribution. Wider areas of the violin plot correspond to a higher probability that members of the sample will take the given value; the thinner areas correspond to a lower probability. The Kernel Density Estimation (KDE) plot smooths the distribution based on the trajectory of the data toward the convergence point. This explains values beyond the original min–max data. * In group 3, only one patient had a CRS-R score after treatment.

Figure 2

Exome data. (A) SNP density per chromosome. (B) A 100% stacked bar chart for the distribution of variants by allele frequency and functional consequences. (C) A 100% stacked bar chart for the distribution of variants by number of singletons. (D) Distribution of the number of HI/rare and potentially harmful rare missense variants (REVEL > 0.5) in the patient groups. (E) Venn diagram for the lists of intolerant genes with QVs (intolerant genes with HI/rare and missense (REVEL > 0.5)/rare variants in the patient groups. (F) Chromosome ideogram showing the cytogenetic band location of intolerant genes with QVs and the frequency of these genes in 50 patients.

Figure 3

Enrichment analysis of genes of interest in the series of 50 TBI patients. (A) Dual Y-axis bar plot for the number of enriched pathways within Reactome top-level pathways for all genes with rare HI and missense variants (REVEL > 0.5) (left panel) and intolerant genes with these variants (right panel). (B,C) REVIGO scatterplots for the cluster representatives of enriched GO terms for genes with rare HI and missense variants (REVEL > 0.5) (B) and intolerant genes with these variants (C). Bubble color indicates p-value (legend top right); size indicates frequency of GO term in underlying GOA database (bubbles of more general terms are larger). Functionally similar GO terms are close together.

Figure 4

Enrichment analysis of intolerant genes in three groups of TBI patients. (A) Enriched Reactome pathways. The pathways are grouped into eight top-level Reactome pathways, which are indicated by numbers in the vertical beige column to the right of the bar graph. The correspondence of the number to the top-level Reactome pathways is shown in the legend at the bottom left. (B) REVIGO scatterplots for cluster representatives of enriched GO terms for gene sets in three patient groups. Bubble color indicates p-value (legend top right); size indicates frequency of GO term in underlying GOA database (bubbles of more general terms are larger). Functionally similar GO terms are close together.

Figure 5

Overview of intolerant genes with QVs. (A) Pie chart for gene distribution according to OMIM phenotypes. (B) Word cloud for OMIM phenotypes associated with 76 genes. (C) Doughnut chart for OMIM gene distribution by mode of inheritance. (D) Box plots for the number of gene features considered per patient in the patient groups. (E) Comparison of the proportions of genes with neurological manifestations among all phenotype-associated genes in the OMIM database and in the patient groups. Odds ratios and horizontal bars indicating 95% confidence intervals are shown. (D,E) Complete gene sets were considered for each patient group (i.e., if a gene occurred in two patients, it was counted twice). R-HSA is a Reactome pathway.

Figure 6

Comparison of the distribution of QVs in patients with and without TBI. (A) Number of genes with QVs that have an OMIM phenotype. (B) Number of OMIM neurological genes with QVs among all OMIM genes. (C) Number of OMIM neurological genes with AD mode of inheritance. (D) Number of neurological genes with damaging missense variants and with HI variants. (E) Number of patients with and without QVs in neurological genes. p-values in panels (B,D,E) are significant after FDR correction. The 95% confidence intervals for the odds ratio are given in parentheses.