Increasing sample sizes in genetic studies may help better estimate risk for depression

Increasing sample sizes in genetic studies, regardless of the depth of phenotyping, may help better estimate risk for depression, according to results of a case-control polygenic risk score analysis published in JAMA Psychiatry.

“The proliferation of large, population-based health studies with genomic information and the increasing availability of administrative health data with diagnostic codes for depression might facilitate valuable insights into the cause of depression,” Brittany L. Mitchell, MSc, of the QIMR Berghofer Medical Research Institute in Australia, and colleagues wrote. “However, the extent to which genetic findings from depression defined by minimal phenotyping extend to clinical diagnoses of depression using diagnostic questionnaires or interviews is a key issue that will inform the interpretation and design of future studies.”

The researchers used the Australian Genetics of Depression Study (AGDS), a cross-sectional, population-based online study of the genetic underpinnings of depression, to evaluate how polygenic risk scores that are created according to varying definitions of depression and meta-analyses that encompass multiple definitions map to certain features of clinical depression. They included patients who met diagnostic criteria for a major depressive disorder diagnosis (n = 12,106; 71% women; mean age, 42.3 years) from the AGDS, as well as control participants with no history of psychiatric disorders (n = 12,621; 55% women; mean age, 60.9 years) from QSkin, a population-based cohort study. Further, they assessed polygenic risk scores for estimation of MDD in and within individuals with MDD for an association with age at onset, adverse childhood experiences, comorbid psychiatric and somatic disorders and current physical and mental health.

Results showed the polygenic risk score had a proportional effect size to the discovery sample size. The largest study had the largest effect size with the OR for MDD (1.75; 95% CI, 173-1.77) per standard deviation of polygenic risk score and the polygenic risk score derived from ICD-10 codes documented via hospitalization records among a population health cohort having the lowest OR (1.14; 95% CI, 1.12-1.16). Upon accounting for sample size differences, the researchers found that the polygenic risk score from a genome-wide association study of patients who met diagnostic criteria for MDD and control participants was the best estimator of MDD; however, it was not the best among those with self-reported depression nor among those who had associations with increased risk for childhood adverse experiences and measures of somatic distress.

“Results of this case-control study suggest that increasing sample sizes by including patients defined in numerous ways is essential to enhancing our understanding of genetic risk for depression and generating more accurate PRSs for use in research and clinical settings,” Mitchell and colleagues wrote. “However, to see a complete picture of the biological characteristics of depression, large, well-phenotyped cohorts that are enriched for clinical depression are needed. The AGDS demonstrates that it is feasible to establish large genetically informative cohorts with in-depth online phenotyping that can provide meaningful insights into the cause of depression.”