Large genetic study finds no DNA variants predicting therapy response
Researchers analyzed genetic data from over 3,000 patients undergoing cognitive behavioral therapy for depression and anxiety but found no genetic markers that reliably predict who will improve. The null result suggests treatment response depends more on individual circumstances than inherited biology—reshaping how companies and clinicians should think about personalized mental healthcare.
Originaltitel: Genome-Wide Association Study of Symptom Change Following Cognitive Behavioral Therapy for Common Mental Disorders
<p>Cognitive behavioral therapy (CBT) is a well-established, evidence-based treatment for common mental disorders such as depression, anxiety disorders, and obsessive-compulsive disorder (OCD). However, treatment outcomes vary widely, and a substantial proportion of patients do not achieve sufficient improvement. Robust predictors of individual differences in symptom change are currently lacking. Genetic differences have been suggested to play a role, but existing evidence is inconclusive. This study investigated the extent to which common genetic variants-single nucleotide polymorphisms (SNPs)-contribute to variability in symptom change. The sample was derived from the MULTI-PSYCH and NORDiC cohorts, comprising 3113 adults and children treated with CBT for depression, panic disorder, social anxiety disorder, or OCD. We performed a genome-wide association study (GWAS) of symptom change following CBT and estimated the proportion of variance attributed to SNPs. Secondary analyses included GWAS and SNP-based heritability estimation of additional clinically relevant outcomes: pre- and post-treatment symptom severity and remission status. No variants reached genome-wide significance. We estimated SNP-based heritability of symptom change at h<sup>2</sup><sub>SNP</sub> = 0.221 (SE = 0.123. These results suggest that common genetic variation may contribute modestly to treatment outcomes. Much larger samples would be required to obtain more precise estimates and to detect genome-wide significant loci.</p>