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The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

Large-scale genomic investigations have just begun to illuminate the molecular genetic contributions to major psychiatric illnesses, ranging from small-effect-size common variants to larger-effect-size rare mutations. The findings provide causal anchors from which to understand their neurobiological basis. Although these studies represent enormous success, they highlight major challenges reflected in the heterogeneity and polygenicity of all of these conditions and the difficulty of connecting multiple levels of molecular, cellular, and circuit functions to complex human behavior. Nevertheless, these advances place us on the threshold of a new frontier in the pathophysiological understanding, diagnosis, and treatment of psychiatric disease.

[...]protein interaction networks are central to cell and tissue biology, otherwise there’d be no organization of proteins into large multimeric complexes, or co-localization in specific subcellular compartments. [...]metabolic networks provide a means by which changes in the levels or activities of enzymes or metabolites can propagate to affect the levels or activities of many others. [...]these many layers of networks attest to the fact that most genes don’t act in a vacuum, and thus to understand disease we need to know how individual effects alter larger biological processes modeled by networks. [...]even if we had accurate sets of genes from GWAS, we are still far from having complete interaction maps.

Key Points Low statistical power undermines the purpose of scientific research; it reduces the chance of detecting a true effect. Perhaps less intuitively, low power also reduces the likelihood that a statistically significant result reflects a true effect. Empirically, we estimate the median statistical power of studies in the neurosciences is between ∼8% and ∼31%. We discuss the consequences of such low statistical power, which include overestimates of effect size and low reproducibility of results. There are ethical dimensions to the problem of low power; unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established, but often ignored, methodological principles. We discuss how problems associated with low power can be addressed by adopting current best-practice and make clear recommendations for how to achieve this. Low-powered studies lead to overestimates of effect size and low reproducibility of results. In this Analysis article, Munafò and colleagues show that the average statistical power of studies in the neurosciences is very low, discuss ethical implications of low-powered studies and provide recommendations to improve research practices. A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.

Computational omics methods packaged as software have become essential to modern biological research. The increasing dependence of scientists on these powerful software tools creates a need for systematic assessment of these methods, known as benchmarking. Adopting a standardized benchmarking practice could help researchers who use omics data to better leverage recent technological innovations. Our review summarizes benchmarking practices from 25 recent studies and discusses the challenges, advantages, and limitations of benchmarking across various domains of biology. We also propose principles that can make computational biology benchmarking studies more sustainable and reproducible, ultimately increasing the transparency of biomedical data and results. Benchmarking studies are important for comprehensively understanding and evaluating different computational omics methods. Here, the authors review practices from 25 recent studies and propose principles to improve the quality of benchmarking studies.

Behavioral measurements in mice are critical tools used to evaluate the effects of interventions. Whilst mice are nocturnal animals, many studies conduct behavioral tests during the day. To better understand the effects of diurnal rhythm on mouse behaviors, we compared the results from behavioral tests conducted in the active and inactive phases. C57BL/6 mice were used in this study; we focus on sensorimotor performance, anxiety, learning and memory. Overall, our results show mice exhibit slightly higher cutaneous sensitivity, better long-term contextual memory, and a greater active avoidance escape response during the active phase. We did not observe significant differences in motor coordination, anxiety, or spatial learning and memory. Furthermore, apart from the elevated-O-maze, there was no remarkable sex effect among these tests. This study provides information on the effects of different diurnal phases on types of behavior and demonstrates the importance of the circadian cycle on learning and memory. Although we did not detect differences in anxiety and spatial learning/memory, diurnal rhythm may interact with other factors to influence these behaviors.

Seven leading geneticists express their views about where the unidentified components of the heritability for complex human diseases might lie and how this could affect the underlying genetic architecture, as well as offering suggestions of how genomic research could be targeted to address this key issue. Although recent genome-wide studies have provided valuable insights into the genetic basis of human disease, they have explained relatively little of the heritability of most complex traits, and the variants identified through these studies have small effect sizes. This has led to the important and hotly debated issue of where the 'missing heritability' of complex diseases might be found. Here, seven leading geneticists offer their opinion about where this heritability is likely to lie, what this could tell us about the underlying genetic architecture of common diseases and how this could inform research strategies for uncovering genetic risk factors.

A large proportion of Depression Disorder patients do not receive an effective diagnosis, which makes it necessary to find a more objective assessment to facilitate a more rapid and accurate diagnosis of depression. Speech data is easy to acquire clinically, its association with depression has been studied, although the actual predictive effect of voice features has not been examined. Thus, we do not have a general understanding of the extent to which voice features contribute to the identification of depression. In this study, we investigated the significance of the association between voice features and depression using binary logistic regression, and the actual classification effect of voice features on depression was re-examined through classification modeling. Nearly 1000 Chinese females participated in this study. Several different datasets was included as test set. We found that 4 voice features (PC1, PC6, PC17, PC24, P<0.05, corrected) made significant contribution to depression, and that the contribution effect of the voice features alone reached 35.65% (Nagelkerke's R2). In classification modeling, voice data based model has consistently higher predicting accuracy(F-measure) than the baseline model of demographic data when tested on different datasets, even across different emotion context. F-measure of voice features alone reached 81%, consistent with existing data. These results demonstrate that voice features are effective in predicting depression and indicate that more sophisticated models based on voice features can be built to help in clinical diagnosis.

“Roughly 1 million terabytes of data will need to be acquired”, and of course the project is only the beginning: unlike the identical connections that make up every worm brain (to date the only organism to have its connectome published), each mouse brain is unique, so “later work using the same brain mapping infrastructure will reveal aspects of neural circuits that are preserved from one animal to another, presumably based on inheritance, and importantly the ways in which connections vary between individuals, presumably based in part on different experiences” There was a time when the generation of what is sometimes euphemistically called genome resource generation projects, including large-scale genome-wide association studies of disease, were decried as ‘fishing trips’ and contrasted with supposedly more impactful hypothesis-driven research. There are many parallels between the Mind of a Mouse and the Human Genome Project: proof of principle experiments carried out in model organisms, the development of new and the improvement of old technologies, the realization that producing such vast amounts of data was going to place computational needs center stage, the promise of “discoveries … largely unexplainable in a previous era of investigation” [1] and community buy-in to protect the project from those who think the money would be better spent on other things. [...]the E–PG neurons are compass neurons, arranged appropriately as a compass [6,7]. In addition to the orientation and visual system examples, single behavior studies, combined with connectomics, have led to the discovery of a mechanism for sleep in flies [8], organizational principles governing how fruit flies groom their bodies [9], the identification of the neuronal basis of a distance-evaluation system [10] and given insights into the biology of aggression [11].