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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. [ABSTRACT FROM AUTHOR]

All of these animals have a functioning visual system, so how does the operation of the visual circuit change, given twice as many horizontal cells or retinal ganglion cells? Geneticists, or perhaps more accurately genomicists, are used to big science, enormous projects that take years to complete and consume vast sums of money: sequencing mammalian genomes, genotyping a million people, obtaining ever more extensive catalogs of epigenetic marks, and so on. Yet some mouse strains have nearly twice as many horizontal cells or retinal ganglion cells than others; strains with high numbers of cells of one type do not necessarily also have high numbers of the other [[31]]. Entitled "The mind of a mouse", a position paper in Cell described a visionary project to construct the map of the mouse brain down to the level of the synapse, requiring electron microscopy to obtain the necessary resolution [[1]]. [Extracted from the article]

It's generally agreed that the successful application of genome-wide association studies (GWAS) to the genetic dissection of psychiatric disease, resulting in the identification of hundreds of susceptibility loci for schizophrenia [[1]] and smaller numbers for depression [[2]], autism [[4]], bipolar disorder [[5]], and anorexia [[6]], hasn't been matched by equal success in working out the biology of the same conditions. To get at biological mechanism, one thing that defenders of the GWAS methodology generally propose and instantiate in their papers is a description of relevant molecular pathways and interaction networks-a.k.a network analysis-which takes as its starting point risk loci occurring at or close to genes. In short, 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. As with other complex traits, most of the risk loci for psychiatric disease lie in regulatory regions of the genome [[9]], which means that rather than altering protein structure they alter mRNA production or mRNA stability via transcriptional networks. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

The article focuses on how to understand the neurophysiology of phenotype and strength of the genetic approach is the unbiased nature of the screen. It mentions specific molecular stimulus identified the receptor and the afferent pathway, we are well on the way to creating a laboratory system, in vitro, that recapitulates the elements of the behavior. It also mentions chemical stimulus could be replaced by a recording electrode that detects an action potential from a neuron innervating muscle.

Background: Anxiety and depression display frequent comorbidity. Individuals with comorbid disorders also often have more extreme symptomatology than those with single disorders. This correlation between comorbidity and severity poses an interesting question: Are comorbid forms of anxiety and depression essentially just more severe versions of the pure disorders?Methods: In a large major depression (MD) case-control sample of individuals from the China, Oxford and VCU Experimental Research on Genetic Epidemiology project, we examined the patterns of lifetime anxiety comorbidity (including generalized anxiety disorder--GAD, panic disorder, and five phobia subtypes) among MD cases (N = 5,864) in this population. Binary and multinomial logistic regression was used to estimate associations between risk factors and outcomes including MD as well as latent class membership, which were compared using continuation ratios.Results: We found a five-class solution to fit best, and each resulting class had a distinct pattern of association with the tested risk factors. The use of continuation ratios suggests that a class characterized by high endorsement of GAD is comparable to a more severely affected "pure MD" group. The other three classes (characterized by agoraphobia, various specific phobias, and by high endorsement of all comorbid anxiety disorders, respectively) appear to differ meaningfully from MD alone.Conclusions: Risk for MD resulting from environmental and psychosocial factors may also predispose individuals to GAD, and less consistently, other anxiety disorders. Presentations of MD with certain phobias display distinguishably different patterns of risk, however, and are therefore likely qualitatively distinct. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Intermediate phenotypes are traits positioned somewhere between genetic variation and disease. They represent a target for attempts to find disease-associated genetic variants and elucidation of mechanisms. Psychiatry has been particularly enamoured with intermediate phenotypes, due to uncertainty about disease aetiology, inconclusive results in early psychiatric genetic studies, and their appeal relative to traditional diagnostic categories. In this review, we argue that new genetic findings are relevant to the question of the utility of these constructs. In particular, results from genome-wide association studies of psychiatric disorders now allow an assessment of the potential role of particular intermediate phenotypes. Based on such an analysis, as well as other recent results, we conclude that intermediate phenotypes are likely to be most valuable in understanding mechanism. [ABSTRACT FROM AUTHOR]

Major depression is the commonest psychiatric disorder and in the U.S. has the greatest impact of all biomedical diseases on disability. Here we review evidence of the genetic contribution to disease susceptibility and the current state of molecular approaches. Genome-wide association and linkage results provide constraints on the allele frequencies and effect sizes of susceptibility loci, which we use to interpret the voluminous candidate gene literature. We consider evidence for the genetic heterogeneity of the disorder and the likelihood that subtypes exist that represent more genetically homogenous conditions than have hitherto been analyzed. [ABSTRACT FROM AUTHOR]

Progress in complex trait mapping in mice has been accelerated by the development of new populations suited to high-resolution mapping and by statistical methodologies that control for population structure. When combined with newly acquired catalogs of sequence variation in inbred strains, the genetic architecture of these new populations makes it possible to dissect complex traits down to the level of single variants. These analyses have shown not only that complex traits are caused by multiple contributing loci but also that each locus is likely due to the combined effects of multiple causal DNA variants. In combination with new rapid methods for producing transgenic mice that make it efficient to test candidate genes and variants, these advances significantly enhance the mouse genetics toolbox for dissecting quantitative traits. [ABSTRACT FROM AUTHOR]

In this review we discuss recent developments in psychiatric genetics: on the one hand, studies using whole genome approaches (genome-wide association studies (GWAS) and exome sequencing) are coming close to finding genes and molecular variants that contribute to disease susceptibility; on the other candidate genes, such as the serotonin transporter, continue to dominate in genetic studies of brain imaging phenotypes and in protracted searches for gene by environment interactions. These two areas intersect, in that new information about genetic effects from whole genome approaches, should (but does not always) inform the single locus analyses. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches. [ABSTRACT FROM AUTHOR]

Thousands of loci that contribute to quantitative traits in outbred crosses of mice have been reported over the last two decades. In this review we discuss how outbred mouse populations can be used to map and identify the genes and sequence variants that give rise to quantitative variation. We discuss heterogeneous stocks, the diversity outbred, and commercially available outbred populations of mice. All of these populations are descended from a small number of progenitor strains. The availability of the complete sequence of laboratory strains means that in many cases it will be possible to reconstruct the genomes of the outbred animals so that in a genetic association study we can detect the effect of all variants, a situation that has so far eluded studies in completely outbred populations. These resources constitute a major advance and make it possible to progress from a quantitative trait locus to a gene at an unprecedented speed. [ABSTRACT FROM AUTHOR]

Abstract: This review summarizes the first clinical reports from the CONVERGE consortium: China, Oxford and VCU Experimental Research on Genetic Epidemiology. CONVERGE sets out to investigate the nature and mode of action of the genetic and environmental risk factors for major depressive disorder (MDD). CONVERGE aims to collect 6000 cases of recurrent MDD and 6000 controls. The consortium includes hospitals in 30 cities, all with populations exceeding 5million, and has collected over 2000 cases and controls. High quality phenotype data on MDD collected in China is now available to determine the source and nature of this highly heterogeneous condition. Analyses reported in a series of papers indicate that the clinical features and risk factors of MDD are sufficiently similar to those in the West that we can confidently predict that the results of subsequent analyses will be widely applicable. [Copyright &y& Elsevier]

The first candidate gene studies of human personality promised much but, in the fifteen years since their publication, have delivered little in the way of clear evidence for the contribution of specific genetic variants to observed variation in personality traits. This is most likely due to the very small effects conferred by individual loci. The advent of genome-wide association studies has brought growing awareness that high levels of statistical stringency, very large sample sizes, and independent replication will be minimum requirements for future genetic studies of personality. At the same time, evidence from other fields indicates that the genetic architecture of personality is likely to consist of the combined effect of many hundreds, if not thousands, of small effect loci. [ABSTRACT FROM AUTHOR]

Abstract: In 1999 a meeting took place at the Jackson Laboratory, a large mouse research centre in Bar Harbor, Maine, to consider the value of systematically collecting phenotypes on inbred strains of mice (Paigen and Eppig (2000) ). The group concluded that cataloguing the extensive phenotypic diversity present among laboratory mice, and in particular providing the research community with data from cohorts of animals, phenotyped according to standardized protocols, was essential if we were to take advantage of the possibilities of mouse genetics. Beginning with the collection of basic physiological, biochemical and behavioral data on nine commonly used inbred strains, the project has expanded so that by the beginning of 2010 data for 178 strains had been collected, with 105 phenotype projects yielding over 2000 different measurements (Bogue et al. (2007) . [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Studies of the genetic basis of behaviour in mice are at a turning point. Soon, new resources will enable the behavioural function of all genes to be tested and the networks of genes, messenger RNAs and proteins involved in a particular behaviour to be identified. Using these resources, scientists will be able to analyse mouse behaviour at an unprecedented level of detail. Interpreting the new data, however, will require a shift in focus from gene-based approaches to network-based approaches. [ABSTRACT FROM AUTHOR]

Animal models of psychiatric diseases are useful tools for screening new drugs and for investigating the mechanisms of those disorders. Despite the difficulties inherent in modelling human psychiatric phenotypes in animals, there has been recent success identifying mutations in mice that give rise to some of the characteristic features of anxiety, depression, schizophrenia, autism, obsessive-compulsive disorder and bipolar disorder. In some cases these models have the additional strength that drugs used to treat the human condition alleviate the symptoms in mice. Robust genetic evidence of the involvement of multiple susceptibility genes in psychiatric disease will enable future studies to move from single-gene models to models with multiple modified loci, with the promise of better representing the complexity of the human diseases. [Copyright &y& Elsevier]

Abstract: The identification of human quantitative trait genes underlying variation in emotionality has proved to be both difficult and expensive. Whilst several QTL have been consistently identified and independently replicated across both phenotypes and populations, little progression has been made towards the identification of underlying genes or even variants. In this review we discuss the potential for using cross-species QTL concordance as a tool for QTL dissection in behaviour genetics, using an affect-related locus mapped to human chromosome 1 as an example. [Copyright &y& Elsevier]

The last decade has witnessed a steady expansion in the number of quantitative trait loci (QTL) mapped for complex phenotypes. However, despite this proliferation, the number of successfully cloned QTL has remained surprisingly low, and to a great extent limited to large effect loci. In this review, we follow the progress of one complex trait locus; a low magnitude moderator of murine emotionality identified some 10 years ago in a simple two-strain intercross, and successively resolved using a variety of crosses and fear-related phenotypes. These experiments have revealed a complex underlying genetic architecture, whereby genetic effects fractionate into several separable QTL with some evidence of phenotype specificity. Ultimately, we describe a method of assessing gene candidacy, and show that given sufficient access to genetic diversity and recombination, progression from QTL to gene can be achieved even for low magnitude genetic effects.European Journal of Human Genetics (2006) 14, 721–728. doi:10.1038/sj.ejhg.5201569 [ABSTRACT FROM AUTHOR]

It has been suggested that the collaborative cross, a large set of recombinant inbred strains derived from eight inbred mouse strains, would be a powerful resource for the dissection of complex phenotypes. Here we use simulation to investigate the power of the collaborative cross to detect and map small genetic effects. We show that for a fixed population of 1000 individuals, 500 RI lines bred using a modified version of the collaborative cross design are adequate to map a single additive locus that accounts for 5% of the phenotypic variation to within 0.96 cM. In the presence of strong epistasis more strains can improve detection, but 500 lines still provide sufficient resolution to meet most goals of the collaborative cross. However, even with a very large panel of RILs, mapping resolution may not be sufficient to identify single genes unambiguously. Our results are generally applicable to the design of RILs in other species. [ABSTRACT FROM AUTHOR]

Over the past 15 years, more than 2,000 quantitative trait loci (QTLs) have been identified in crosses between inbred strains of mice and rats, but less than 1% have been characterized at a molecular level. However, new resources, such as chromosome substitution strains and the proposed Collaborative Cross, together with new analytical tools, including probabilistic ancestral haplotype reconstruction in outbred mice, Yin-Yang crosses and in silico analysis of sequence variants in many inbred strains, could make QTL cloning tractable. We review the potential of these strategies to identify genes that underlie QTLs in rodents. [ABSTRACT FROM AUTHOR]

Meta-analysis, a statistical tool for combining results across studies, is becoming popular as a method for resolving discrepancies in genetic association studies. Persistent difficulties in obtaining robust, replicable results in genetic association studies are almost certainly because genetic effects are small, requiring studies with many thousands of subjects to be detected. In this article, we describe how meta-analysis works and consider whether it will solve the problem of underpowered studies or whether it is another affliction visited by statisticians on geneticists. We show that meta-analysis has been successful in revealing unexpected sources of heterogeneity, such as publication bias. If heterogeneity is adequately recognized and taken into account, meta-analysis can confirm the involvement of a genetic variant, but it is not a substitute for an adequately powered primary study. [Copyright &y& Elsevier]

Gray has drawn upon genetic evidence to argue for the existence of rodent emotionality, a model of human neuroticism. With the advent of molecular mapping techniques it has become possible to test this hypothesis. Here I review the progress that has been made, largely in animal genetic studies, demonstrating that a common set of genes act pleiotropically on measures of emotionality. More recently, evidence has emerged supporting the view that the same genes influence variation in both rodent and human phenotypes. [Copyright &y& Elsevier]

Recent advances in methodologies for testing epistatic interactions, combined with several successes in demonstrating genetic interaction effects in animal and human genetics, have rekindled interest in the role of epistatic influences on complex traits. It has even been suggested that the unacknowledged presence of epistasis vitiates the genetic dissection of human and animal behavior. Here we report a genome-wide interaction analysis of 1636 F2 mice to show that epistasis is of minimal importance in an animal model of anxiety. By using a sufficiently large sample of F2 animals, we provide evidence that interaction effects between any two loci contribute less than 5% to the total phenotypic variance in multiple tests of anxiety. We conclude that interactions between loci do not necessarily vitiate the genetic analysis of behavior in at least one animal model of anxiety. [ABSTRACT FROM AUTHOR]

We compared strategies to fine-map Quantitative Trait Loci (QTL) in mice with heterogeneous stocks (HS). We showed that a panel of about 100 Recombinant Inbred Lines (RIL) derived from an HS, and which we called an RIHS, was ideally suited to fine-map QTL to very high resolution, without the cost of additional genotyping. We also investigated a strategy based on in vitro fertilization of large numbers of F1 offspring of HS males crossed with an inbred line (IVHS). This method required some additional genotyping but avoided the breeding delays and costs associated with the construction of an RI panel. We showed that QTL detection was higher by using RIHS than with IVHS and that it was independent of the number of RI lines, provided the total number of animals phenotyped was constant. However, fine-mapping accuracy was slightly better with IVHS. We also investigated the effects of varying the number of HS generations and using multiallelic microsatellites instead of SNPs. We found that quite modest generation times of 10–20 generations were optimal. Microsatellites were superior to SNPs only when the generation time was 30 or more and when the markers were widely spaced. [ABSTRACT FROM AUTHOR]

Repeatability of many genetic analysis experiments can be improved by the application of signal processing and image-processing tools. Most of the experiments in genetics result in some sort of digital signal or image patterns that are subjectively analyzed by the geneticists. This paper presents an example project where image-processing techniques are applied for automation of most of the routine processes of agarose gel image analysis. Result of applying this program has shown that the time taken for the analysis has been reduced by about 70% and the result is reproducible. [Copyright &y& Elsevier]

Idiopathic mental retardation is a common condition the origins of which are poorly understood. Following initial reports that small chromosomal rearrangements affecting telomeres could be an important aetiological contributor, several new methods for screening patients have been developed. Over the past few years, 22 studies have reported results from 2585 patients. The prevalence of abnormalities in the entire group is 5.1%; but the figure is higher (6.8%) in individuals with moderate to severe mental retardation. About half the cases are caused by a de novo deletion, and about half by a balanced translocation segregating in a patient’s family. Despite the large sample size available, it is still not clear whether a combination of thorough clinical examination and assiduous cytogenetic investigation might not be as effective at detecting subtelomeric anomalies as molecular assays. [Copyright &y& Elsevier]

Genetic approaches to psychiatric illness need appropriate animal models both for investigating how genetic variants give rise to behavioural disorder and for identifying genes that may be important in human conditions. Yet the relevance of many animal models to psychiatric illness is often not clear. Here I discuss how genetic approaches can be used to validate animal models of anxiety, an approach which is applicable to other animal models. One drawback of genetic validation is the difficulty inherent in identifying the molecular variants that influence the phenotype. I review genetic approaches that have the potential to overcome this problem. [Copyright &y& Elsevier]

Genetic effects on behavioural measures thought to model anxiety have been reported on 15 mouse chromosomes. In general the individual effect from each locus is small, contributing to 10% or less of the total variation, but through use of crosses between inbred rodents the power to detect such effects is high: 39 loci have been reported at stringent levels of significance. Novel multivariate analyses of these data go some way to characterizing the genetic architecture of anxiety and also to validating the tests that are used for its measurement. However, we are still some way from finding the molecular variants that explain the heritability of the trait. [Copyright &y& Elsevier]

Examines the use of heterogenous stock method in the simultaneous detection and fine mapping of quantitative trait loci in mice. Employment of the dynamic-programming technique; Modifiers affecting mouse models of human diseases; Determination of alleles in inbred and outbred animals.

Understanding the molecular basis of quantitative genetic variation is a principal goal for biomedicine. Although the complex genetic architecture of quantitative traits has so far largely frustrated attempts to identify genes in humans by standard linkage methodologies, quantitative trait loci (QTL) have been mapped in plants, insects and rodents. However, identifying the molecular bases of QTL remains a challenge. Here, we discuss why this is and how new experimental strategies and analytical techniques, combined with the fruits of the genome projects, are beginning to identify candidate genes for QTL studies in several model organisms. [ABSTRACT FROM AUTHOR]

Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases. Our method, the Pearson-Aitken Family Genetic Risk Score, estimates genetic liability in a proband from patterns of disease outcomes in their relatives. The method is flexible, and the resulting scores can be used for genetic classification, describing genetic etiology, and improving power for gene mapping. [ABSTRACT FROM AUTHOR]

Characteristic behavioural pattern (including cognitive processes and social interactions) have been reported in a number of syndromes arising from genetic or chromosomal abnormalities, suggesting that molecular analysis of the underlying defect could reveal the biological basis of the behavioural phenotype. Because of the rarity of many of the syndromes, and the complexity of their genetic basis, there are great difficulties in establishing The validity of the association between syndrome and behavioural phenotypes. Nevertheless, evidence from animal studies with relevance to human behavioural phenotypes shows that the pathway from genotype to phenotype may be accessible by careful delineation of behavioural phenotypes. [ABSTRACT FROM AUTHOR]

Studies the genetic basis for emotionality in laboratory mice. Covariation in activity and defecation in a novel environment and emergence into the open arms of an elevated plus maze; Mapping; Relation to human susceptibility to anxiety and neuroticism.

A major challenge in genetic studies of complex diseases is to determine how the action of risk genes is restricted to a tissue or cell type. Here, we investigate tissue specificity of gene action using CRISPR screens from 786 cancer cell lines originating from 24 tissues. We find that the expression pattern of the gene across tissues explains only a minority of cases of tissue-specificity (9%), while gene amplification and the expression levels of paralogs account for 39.5% and 15.5%, respectively. In addition, the transfer of small molecules to mutant cells explains tissue-specific gene action in blood. The tissue-specific genes we found are not specific just for human cancer cell lines: we found that the tissue-specific genes are intolerant to functional mutations in the human population and are associated with human diseases more than genes that are essential across all cell types. Our findings offer important insights into genetic mechanisms for tissue specificity of human diseases. [ABSTRACT FROM AUTHOR]

The article discusses how the progress in whole-genome association analysis applied to human case-control studies will affect complex trait analysis in the mouse. It highlights the three key issues that the mouse complex genetics community must address for the understanding of human biology and disease. The article comments on the need for comprehensive annotation of the gene networks in the mouse.

Speech-based automatic depression detection systems have been extensively explored over the past few years. Typically, each speaker is assigned a single label (Depressive or Non-depressive), and most approaches formulate depression detection as a speech classification task without explicitly considering the non-uniformly distributed depression pattern within segments, leading to low generalizability and robustness across different scenarios. However, depression corpora do not provide fine-grained labels (at the phoneme or word level) which makes the dynamic depression pattern in speech segments harder to track using conventional frameworks. To address this, we propose a novel framework, Speechformer-CTC, to model non-uniformly distributed depression characteristics within segments using a Connectionist Temporal Classification (CTC) objective function without the necessity of input–output alignment. Two novel CTC-label generation policies, namely the Expectation-One-Hot and the HuBERT policies, are proposed and incorporated in objectives on various granularities. Additionally, experiments using Automatic Speech Recognition (ASR) features are conducted to demonstrate the compatibility of the proposed method with content-based features. Our results show that the performance of depression detection, in terms of Macro F1-score, is improved on both DAIC-WOZ (English) and CONVERGE (Mandarin) datasets. On the DAIC-WOZ dataset, the system with HuBERT ASR features and a CTC objective optimized using HuBERT policy for label generation achieves 83.15% F1-score, which is close to state-of-the-art without the need for phoneme-level transcription or data augmentation. On the CONVERGE dataset, using Whisper features with the HuBERT policy improves the F1-score by 9.82% on CONVERGE1 (in-domain test set) and 18.47% on CONVERGE2 (out-of-domain test set). These findings show that depression detection can benefit from modeling non-uniformly distributed depression patterns and the proposed framework can be potentially used to determine significant depressive regions in speech utterances. • Depression patterns in speech signals can be manifested in a non-uniform manner. • Using CTC can resolve the challenge of non-uniformly distributed depression patterns. • Two CTC-label generation policies are proposed: E-One-Hot, and HuBERT policies. • Proposed methods achieve close-to-SOTA or SOTA on DAIC-WOZ and CONVERGE. [ABSTRACT FROM AUTHOR]

B Abstract: b Attempts to identify the genetic loci contributing to the risk to major depressive disorder have been frustrated by the heterogeneity of the condition and the need to obtain relevant phenotypic information on large sample sizes. I discuss approaches to this problem and propose the use of digital phenotyping as a way forward to phenotype at scale. [Extracted from the article]

Speech signals are valuable biomarkers for assessing an individual's mental health, including identifying Major Depressive Disorder (MDD) automatically. A frequently used approach in this regard is to employ features related to speaker identity, such as speaker-embeddings. However, over-reliance on speaker identity features in mental health screening systems can compromise patient privacy. Moreover, some aspects of speaker identity may not be relevant for depression detection and could serve as a bias factor that hampers system performance. To overcome these limitations, we propose disentangling speaker-identity information from depression-related information. Specifically, we present four distinct disentanglement methods to achieve this — adversarial speaker identification (SID)-loss maximization (ADV), SID-loss equalization with variance (LEV), SID-loss equalization using Cross-Entropy (LECE) and SID-loss equalization using KL divergence (LEKLD). Our experiments, which incorporated diverse input features and model architectures, have yielded improved F1 scores for MDD detection and voice-privacy attributes, as quantified by Gain in Voice Distinctiveness (G V D) and De-Identification Scores (DeID). On the DAIC-WOZ dataset (English), LECE using ComparE16 features results in the best F1-Scores of 80% which represents the audio-only SOTA depression detection F1-Score along with a G V D of −1.1 dB and a DeID of 85%. On the EATD dataset (Mandarin), ADV using raw-audio signal achieves an F1-Score of 72.38% surpassing multi-modal SOTA along with a G V D of −0.89 dB dB and a DeID of 51.21%. By reducing the dependence on speaker-identity-related features, our method offers a promising direction for speech-based depression detection that preserves patient privacy. [ABSTRACT FROM AUTHOR]

The genetic influence on human vocal pitch in tonal and non-tonal languages remains largely unknown. In tonal languages, such as Mandarin Chinese, pitch changes differentiate word meanings, whereas in non-tonal languages, such as Icelandic, pitch is used to convey intonation. We addressed this question by searching for genetic associations with interindividual variation in median pitch in a Chinese major depression case-control cohort and compared our results with a genome-wide association study from Iceland. The same genetic variant, rs11046212-T in an intron of the ABCC9 gene, was one of the most strongly associated loci with median pitch in both samples. Our meta-analysis revealed four genome-wide significant hits, including two novel associations. The discovery of genetic variants influencing vocal pitch across both tonal and non-tonal languages suggests the possibility of a common genetic contribution to the human vocal system shared in two distinct populations with languages that differ in tonality (Icelandic and Mandarin). A GWAS finds genetic link in ABCC9 gene affecting vocal pitch in both Mandarin and Icelandic speakers, suggesting a shared genetic influence on human vocal systems across populations. [ABSTRACT FROM AUTHOR]

It is now clear that almost all complex traits have a highly polygenic component; that is, their genetic basis consists of relatively frequent risk alleles at a very large number of loci, each making a small contribution to variation, or disease susceptibility. This general conclusion appears to hold for intermediate phenotypes. Therefore, we should not expect these phenotypes to be associated with substantially larger effect sizes than conventional phenotypes. Instead, their usefulness is likely to lie in understanding the mechanism underpinning associations identified via genome-wide association studies of conventional phenotypes. [ABSTRACT FROM AUTHOR]

The article discusses a paper from the Schizophrenia Working Group of the Psychiatric Genomics Consortium on genome-wide analysis of mental disorder schizophrenia. It mentions that the analysis revealed over 100 genetic regions associated with schizophrenia that contribute to disease risk. It also mentions that existence of 108 loci that contribute to susceptibility of disease has been confirmed by the Psychiatric Genomics Consortium.

The article discusses a report within the issue regarding a genome-wide association study (GWAS) by C. A. Rietveld et al. that found possible human genetic variants that influence behavior linked to educational attainment. Topics include the controversy over the legitimacy of determining the heritability of socioeconomic achievement measures, the use of GWAS in measuring nonbehavioral traits, the limitation of GWAS in measuring behavioral traits, and Rietveld et al.'s research methodology. Also discussed is the use GWAS to investigate the genetic aspects of smoking behavior.

The article offers information on psychiatric disease. It is attributable either to disordered circuitry or to disordered activity in brain circuitry. It mentions that genetic strategies converge, as a Genome study can only be turned into a confirmed gene by finding an excess of rare deleterious mutations in it, which means eventually searching for rare variants. With next-generation sequencing possiblity, DNA technology is mature to find structural variants and coding mutations.

A preface is presented to articles on human genetics published in this periodical.