Meta-Analysis Revives Genome-Wide Association Studies in Epilepsy

Concordance of genetic variation that increases risk for anxiety disorders and posttraumatic stress disorders and that influences their underlying neurocircuitry

BACKGROUND

There have been considerable recent advances in understanding the genetic architecture of anxiety disorders and posttraumatic stress disorder (PTSD), as well as the underlying neurocircuitry of these disorders. However, there is little work on the concordance of genetic variations that increase risk for these conditions, and that influence subcortical brain structures. We undertook a genome-wide investigation of the overlap between the genetic influences from single nucleotide polymorphisms (SNPs) on volumes of subcortical brain structures and genetic risk for anxiety disorders and PTSD.

METHOD

We obtained summary statistics of genome-wide association studies (GWAS) of anxiety disorders (N_cases= 7016, N_controls= 14,745), PTSD (European sample; N_cases= 2424, N_controls= 7113) and of subcortical brain structures (N = 13,171). SNP Effect Concordance Analysis (SECA) and Linkage Disequilibrium (LD) Score Regression were used to examine genetic pleiotropy, concordance, and genome-wide correlations respectively. SECAs conditional false discovery was used to identify specific risk variants associated with anxiety disorders or PTSD when conditioning on brain related traits.

RESULTS

For anxiety disorders, we found evidence of significant concordance between increased anxiety risk variants and variants associated with smaller amygdala volume. Further, by conditioning on brain volume GWAS, we identified novel variants that associate with smaller brain volumes and increase risk for disorders: rs56242606 was found to increase risk for anxiety disorders, while two variants (rs6470292 and rs683250) increase risk for PTSD, when conditioning on the GWAS of putamen volume.

LIMITATIONS

Despite using the largest available GWAS summary statistics, the analyses were limited by sample size.

CONCLUSIONS

These preliminary data indicate that there is genome wide concordance between genetic risk factors for anxiety disorders and those for smaller amygdala volume, which is consistent with research that supports the involvement of the amygdala in anxiety disorders. It is notable that a genetic variant that contributes to both reduced putamen volume and PTSD plays a key role in the glutamatergic system. Further work with GWAS summary statistics from larger samples, and a more extensive look at the genetics underlying brain circuits, is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.

Developing DNA methylation-based diagnostic biomarkers

An emerging paradigm shift for disease diagnosis is to rely on molecular characterization beyond traditional clinical and symptom-based examinations. Although genetic alterations and transcription signature were first introduced as potential biomarkers, clinical implementations of these markers are limited due to low reproducibility and accuracy. Instead, epigenetic changes are considered as an alternative approach to disease diagnosis. Complex epigenetic regulation is required for normal biological functions and it has been shown that distinctive epigenetic disruptions could contribute to disease pathogenesis. Disease-specific epigenetic changes, especially DNA methylation, have been observed, suggesting its potential as disease biomarkers for diagnosis. In addition to specificity, the feasibility of detecting disease-associated methylation marks in the biological specimens collected noninvasively, such as blood samples, has driven the clinical studies to validate disease-specific DNA methylation changes as a diagnostic biomarker. Here, we highlight the advantages of DNA methylation signature for diagnosis in different diseases and discuss the statistical and technical challenges to be overcome before clinical implementation.

Ankyrin-G isoform imbalance and interneuronopathy link epilepsy and bipolar disorder

ANK3, encoding the adaptor protein Ankyrin-G (AnkG), has been implicated in bipolar disorder by genome-wide association studies. ANK3 has multiple alternative first exons, and a bipolar disorder-associated ANK3 variant has been shown to reduce the expression of exon 1b. Here we identify mechanisms through which reduced ANK3 exon 1b isoform expression disrupts neuronal excitation-inhibition balance. We find that parvalbumin (PV) interneurons and principal cells differentially express ANK3 first exon subtypes. PV interneurons express only isoforms containing exon 1b, whereas excitatory principal cells express exon 1e alone or both 1e and 1b. In transgenic mice deficient for exon 1b, PV interneurons lack voltage-gated sodium channels at their axonal initial segments and have increased firing thresholds and diminished action potential dynamic range. These mice exhibit an Ank3 gene dosage-dependent phenotype including behavior changes modeling bipolar disorder, epilepsy and sudden death. Thus ANK3's important association with human bipolar susceptibility may arise from imbalance between AnkG function in interneurons and principal cells and resultant excessive circuit sensitivity and output. AnkG isoform imbalance is a novel molecular endophenotype and potential therapeutic target.

Genetic regulation of gene expression in the epileptic human hippocampus

Epilepsy is a serious and common neurological disorder. Expression quantitative loci (eQTL) analysis is a vital aid for the identification and interpretation of disease-risk loci. Many eQTLs operate in a tissue- and condition-specific manner. We have performed the first genome-wide cis-eQTL analysis of human hippocampal tissue to include not only normal (n = 22) but also epileptic (n = 22) samples. We demonstrate that disease-associated variants from an epilepsy GWAS meta-analysis and a febrile seizures (FS) GWAS are significantly more enriched with epilepsy-eQTLs than with normal hippocampal eQTLs from two larger independent published studies. In contrast, GWAS meta-analyses of two other brain diseases associated with hippocampal pathology (Alzheimer's disease and schizophrenia) are more enriched with normal hippocampal eQTLs than with epilepsy-eQTLs. These observations suggest that an eQTL analysis that includes disease-affected brain tissue is advantageous for detecting additional risk SNPs for the afflicting and closely related disorders, but not for distinct diseases affecting the same brain regions. We also show that epilepsy eQTLs are enriched within epilepsy-causing genes: an epilepsy cis-gene is significantly more likely to be a causal gene for a Mendelian epilepsy syndrome than to be a causal gene for another Mendelian disorder. Epilepsy cis-genes, compared to normal hippocampal cis-genes, are more enriched within epilepsy-causing genes. Hence, we utilize the epilepsy eQTL data for the functional interpretation of epilepsy disease-risk variants and, thereby, highlight novel potential causal genes for sporadic epilepsy. In conclusion, an epilepsy-eQTL analysis is superior to normal hippocampal tissue eQTL analyses for identifying the variants and genes underlying epilepsy.