Major depressive disorder (MDD), one of the most frequently encountered forms of mental illness and a leading cause of disability worldwide1, poses a major challenge to genetic analysis. To date no robustly replicated genetic loci have been identified 2, despite analysis of more than 9,000 cases3. Using low coverage genome sequence of 5,303 Chinese women with recurrent MDD selected to reduce phenotypic heterogeneity, and 5,337 controls screened to exclude MDD, we identified and replicated two genome-wide significant loci contributing to risk of MDD on chromosome 10: one near the SIRT1 gene (P-value = 2.53×10−10) the other in an intron of the LHPP gene (P = 6.45×10−12). Analysis of 4,509 cases with a severe subtype of MDD, melancholia, yielded an increased genetic signal at the SIRT1 locus. We attribute our success to the recruitment of relatively homogeneous cases with severe illness.

SummaryAdversity, particularly in early life, can cause illness. Clues to the responsible mechanisms may lie with the discovery of molecular signatures of stress, some of which include alterations to an individual’s somatic genome. Here, using genome sequences from 11,670 women, we observed a highly significant association between a stress-related disease, major depression, and the amount of mtDNA (p = 9.00 × 10−42, odds ratio 1.33 [95% confidence interval [CI] = 1.29–1.37]) and telomere length (p = 2.84 × 10−14, odds ratio 0.85 [95% CI = 0.81–0.89]). While both telomere length and mtDNA amount were associated with adverse life events, conditional regression analyses showed the molecular changes were contingent on the depressed state. We tested this hypothesis with experiments in mice, demonstrating that stress causes both molecular changes, which are partly reversible and can be elicited by the administration of corticosterone. Together, these results demonstrate that changes in the amount of mtDNA and telomere length are consequences of stress and entering a depressed state. These findings identify increased amounts of mtDNA as a molecular marker of MD and have important implications for understanding how stress causes the disease.

Two algorithms based on machine learning neural networks are proposed—the shallow learning (S‐L) and deep learning (D‐L) algorithms—that can potentially be used in atmosphere‐only typhoon forecast models to provide flow‐dependent typhoon‐induced sea surface temperature cooling (SSTC) for improving typhoon predictions. The major challenge of existing SSTC algorithms in forecast models is how to accurately predict SSTC induced by an upcoming typhoon, which requires information not only from historical data but more importantly also from the target typhoon itself. The S‐L algorithm composes of a single layer of neurons with mixed atmospheric and oceanic factors. Such a structure is found to be unable to represent correctly the physical typhoon‐ocean interaction. It tends to produce an unstable SSTC distribution, for which any perturbations may lead to changes in both SSTC pattern and strength. The D‐L algorithm extends the neural network to a 4 × 5 neuron matrix with atmospheric and oceanic factors being separated in different layers of neurons, so that the machine learning can determine the roles of atmospheric and oceanic factors in shaping the SSTC. Therefore, it produces a stable crescent‐shaped SSTC distribution, with its large‐scale pattern determined mainly by atmospheric factors (e.g., winds) and small‐scale features by oceanic factors (e.g., eddies). Sensitivity experiments reveal that the D‐L algorithms improve maximum wind intensity errors by 60–70% for four case study simulations, compared to their atmosphere‐only model runs.

Background Despite substantial research, uncertainty remains about the clinical and etiological heterogeneity of major depression (MD). Can meaningful and valid subtypes be identified and would they be stable cross-culturally? Method Symptoms at their lifetime worst depressive episode were assessed at structured psychiatric interview in 6008 women of Han Chinese descent, age ≥30 years, with recurrent DSM-IV MD. Latent class analysis (LCA) was performed in Mplus. Results Using the nine DSM-IV MD symptomatic A criteria, the 14 disaggregated DSM-IV criteria and all independently assessed depressive symptoms (n=27), the best LCA model identified respectively three, four and six classes. A severe and non-suicidal class was seen in all solutions, as was a mild/moderate subtype. An atypical class emerged once bidirectional neurovegetative symptoms were included. The non-suicidal class demonstrated low levels of worthlessness/guilt and hopelessness. Patterns of co-morbidity, family history, personality, environmental precipitants, recurrence and body mass index (BMI) differed meaningfully across subtypes, with the atypical class standing out as particularly distinct. Conclusions MD is a clinically complex syndrome with several detectable subtypes with distinct clinical and demographic correlates. Three subtypes were most consistently identified in our analyses: severe, atypical and non-suicidal. Severe and atypical MD have been identified in multiple prior studies in samples of European ethnicity. Our non-suicidal subtype, with low levels of guilt and hopelessness, may represent a pathoplastic variant reflecting Chinese cultural influences.