Association between maternal depression symptoms and child telomere length.

The biological mechanisms that explain how adverse early life events influence adult disease risk are poorly understood. One proposed mechanism is via the induction of accelerated biological aging, for which telomere length is considered a biomarker. We aimed to determine if maternal depression pre- and post-partum was associated with telomere length in children at 4 years of age (n = 4299). Mothers completed structured questionnaires assessing depression during pregnancy (Edinburgh Depression Scale), at 9 months (Edinburgh Depression Scale), and at 54 months postpartum (Patient Health Questionnaire 9). Regression methods were used to investigate the relationship between telomere length (DNA from saliva) and maternal depression score recorded at each stage. Significant covariates included in the final model were: maternal age at pregnancy; child sex; child ethnicity; gestational age group, and rurality group. Child telomere length was found to be longer if their mother had a higher depression score at both postpartum time points tested (9 months of age; coefficient 0.003, SE = 0.001, P = 0.01, 54 months of age; coefficient 0.003, SE = 0.002, P = 0.02). Although these findings seem paradoxical, increased telomere length may be an adaptive response to early life stressors. We propose several testable hypotheses for these results and to determine if the positive association between depression and telomere length is a developmental adaptation or an indirect consequence of environmental factors.

FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.

Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE .