Genome-wide polygenic scoring for a 14-year long-term average depression phenotype
Glymour, M Maria
Koenen, Karestan C
Tchetgen, Eric J
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CitationChang, Shun-Chiao, M Maria Glymour, Stefan Walter, Liming Liang, Karestan C Koenen, Eric J Tchetgen, Marilyn C Cornelis, Ichiro Kawachi, Eric Rimm, and Laura D Kubzansky. 2014. “Genome-wide polygenic scoring for a 14-year long-term average depression phenotype.” Brain and Behavior 4 (2): 298-311. doi:10.1002/brb3.205. http://dx.doi.org/10.1002/brb3.205.
AbstractBackground: Despite moderate heritability estimates for depression-related phenotypes, few robust genetic predictors have been identified. Potential explanations for this discrepancy include the use of phenotypic measures taken from a single time point, rather than integrating information over longer time periods via multiple assessments, and the possibility that genetic risk is shaped by multiple loci with small effects. Methods: We developed a 14-year long-term average depression measure based on 14 years of follow-up in the Nurses' Health Study (NHS; N = 6989 women). We estimated polygenic scores (PS) with internal whole-genome scoring (NHS-GWAS-PS). We also constructed PS by applying two external PS weighting algorithms from independent samples, one previously shown to predict depression (GAIN-MDD-PS) and another from the largest genome-wide analysis currently available (PGC-MDD-PS). We assessed the association of all three PS with our long-term average depression phenotype using linear, logistic, and quantile regressions. Results: In this study, the three PS approaches explained at most 0.2% of variance in the long-term average phenotype. Quantile regressions indicated PS had larger impacts at higher quantiles of depressive symptoms. Quantile regression coefficients at the 75th percentile were at least 40% larger than at the 25th percentile in all three polygenic scoring algorithms. The interquartile range comparison suggested the effects of PS significantly differed at the 25th and 75th percentiles of the long-term depressive phenotype for the PGC-MDD-PS (P = 0.03), and this difference also reached borderline statistical significance for the GAIN-MDD-PS (P = 0.05). Conclusions: Integrating multiple phenotype assessments spanning 14 years and applying different polygenic scoring approaches did not substantially improve genetic prediction of depression. Quantile regressions suggested the effects of PS may be largest at high quantiles of depressive symptom scores, presumably among people with additional, unobserved sources of vulnerability to depression.
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