Accounting for Multiple Comparisons in a Genome-Wide Association Study (GWAS)

Background As we enter an era when testing millions of SNPs in a single gene association study will become the standard, consideration of multiple comparisons is an essential part of determining statistical significance. Bonferroni adjustments can be made but are conservative due to the preponderance of linkage disequilibrium (LD) between genetic markers, and permutation testing is not always a viable option. Three major classes of corrections have been proposed to correct the dependent nature of genetic data in Bonferroni adjustments: permutation testing and related alternatives, principal components analysis (PCA), and analysis of blocks of LD across the genome. We consider seven implementations of these commonly used methods using data from 1514 European American participants genotyped for 700,078 SNPs in a GWAS for AIDS. Results A Bonferroni correction using the number of LD blocks found by the three algorithms implemented by Haploview resulted in an insufficiently conservative threshold, corresponding to a genome-wide significance level of α = 0.15 - 0.20. We observed a moderate increase in power when using PRESTO, SLIDE, and simpleℳ when compared with traditional Bonferroni methods for population data genotyped on the Affymetrix 6.0 platform in European Americans (α = 0.05 thresholds between 1 × 10-7 and 7 × 10-8). Conclusions Correcting for the number of LD blocks resulted in an anti-conservative Bonferroni adjustment. SLIDE and simpleℳ are particularly useful when using a statistical test not handled in optimized permutation testing packages, and genome-wide corrected p-values using SLIDE, are much easier to interpret for consumers of GWAS studies

Cerebrospinal fluid interferon alpha levels correlate with neurocognitive impairment in ambulatory HIV-Infected individuals

HIV-associated neurocognitive disorders (HANDs) continue to be common and are associated with increased morbidity and mortality. However, the underlying mechanisms in the combination antiretroviral therapy (cART) era are not fully understood. Interferon alpha (IFNα) is an antiviral cytokine found to be elevated in the cerebrospinal fluid (CSF) of individuals with advanced HIV-associated dementia in the pre-cART era. In this cross-sectional study, we investigated the association between IFNα and neurocognitive performance in ambulatory HIV-infected individuals with milder impairment. An eight-test neuropsychological battery representing six cognitive domains was administered. Individual scores were adjusted for demographic characteristics, and a composite neuropsychological score (NPT-8) was calculated. IFNα and CSF neurofilament light chain (NFL) levels were measured using enzyme-linked immunosorbent assay (ELISA). There were 15 chronically infected participants with a history of significant immunocompromise (median nadir CD4+ of 49 cells/μl). Most participants were neurocognitively impaired (mean global deficit score of 0.86). CSF IFNα negatively correlated with three individual tests (Trailmaking A, Trailmaking B, and Stroop Color-Word) as well as the composite NPT-8 score (r = −0.67, p = 0.006). These negative correlations persisted in multivariable analyses adjusting for chronic hepatitis B and C. Additionally, CSF IFNα correlated strongly with CSF NFL, a marker of neuronal damage (rho = 0.748, p = 0.0013). These results extend findings from individuals with severe HIV-associated dementia in the pre-cART era and suggest that IFNα may continue to play a role in HAND pathogenesis during the cART era. Further investigation into the role of IFNα is indicated

Reducing the rate and duration of Re-ADMISsions among patients with unipolar disorder and bipolar disorder using smartphone-based monitoring and treatment -- the RADMIS trials: study protocol for two randomized controlled trials

Abstract Background Unipolar and bipolar disorder combined account for nearly half of all morbidity and mortality due to mental and substance use disorders, and burden society with the highest health care costs of all psychiatric and neurological disorders. Among these, costs due to psychiatric hospitalization are a major burden. Smartphones comprise an innovative and unique platform for the monitoring and treatment of depression and mania. No prior trial has investigated whether the use of a smartphone-based system can prevent re-admission among patients discharged from hospital. The present RADMIS trials aim to investigate whether using a smartphone-based monitoring and treatment system, including an integrated clinical feedback loop, reduces the rate and duration of re-admissions more than standard treatment in unipolar disorder and bipolar disorder. Methods The RADMIS trials use a randomized controlled, single-blind, parallel-group design. Patients with unipolar disorder and patients with bipolar disorder are invited to participate in each trial when discharged from psychiatric hospitals in The Capital Region of Denmark following an affective episode and randomized to either (1) a smartphone-based monitoring system including (a) an integrated feedback loop between patients and clinicians and (b) context-aware cognitive behavioral therapy (CBT) modules (intervention group) or (2) standard treatment (control group) for a 6-month trial period. The trial started in May 2017. The outcomes are (1) number and duration of re-admissions (primary), (2) severity of depressive and manic (only for patients with bipolar disorder) symptoms; psychosocial functioning; number of affective episodes (secondary), and (3) perceived stress, quality of life, self-rated depressive symptoms, self-rated manic symptoms (only for patients with bipolar disorder), recovery, empowerment, adherence to medication, wellbeing, ruminations, worrying, and satisfaction (tertiary). A total of 400 patients (200 patients with unipolar disorder and 200 patients with bipolar disorder) will be included in the RADMIS trials. Discussion If the smartphone-based monitoring system proves effective in reducing the rate and duration of re-admissions, there will be basis for using a system of this kind in the treatment of unipolar and bipolar disorder in general and on a larger scale. Trial registration ClinicalTrials.gov, ID: NCT03033420 . Registered 13 January 2017. Ethical approval has been obtained

Diabetes mellitus type II as a risk factor for depression: a lower than expected risk in a general practice setting

The aim of the present study was to determine whether a diagnosis of diabetes mellitus (DM) in a primary setting is associated with an increased risk of subsequent depression. A retrospective cohort design was used based on the Registration Network Family Practice (RNH) database. Patients diagnosed with diabetes mellitus at or after the age of 40 and who were diagnosed between 01-01-1980 and 01-01-2007 (N = 6,140), were compared with age-matched controls from a reference group (N = 18,416) without a history of diabetes. Both groups were followed for an emerging first diagnosis of depression (and/or depressive feelings) until January 1, 2008. 2.0% of the people diagnosed with diabetes mellitus developed a depressive disorder, compared to 1.6% of the reference group. After statistical correction for confounding factors diabetes mellitus was associated with an increased risk of developing subsequent depression (HR 1.26; 95% CI: 1.12–1.42) and/or depressive feelings (HR 1.33; 95% CI: 1.18–1.46). After statistical adjustment practice identification code, age and depression preceding diabetes, were significantly related to a diagnosis of depression. Patients with diabetes mellitus are more likely to develop subsequent depression than persons without a history of diabetes. Results from this large longitudinal study based on a general practice population indicate that this association is weaker than previously found in cross-sectional research using self-report surveys. Several explanations for this dissimilarity are discussed

Influence of postpartum onset on the course of mood disorders

BACKGROUND: To ascertain the impact of postpartum onset (PPO) on the subsequent time course of mood disorders. METHODS: This retrospective study compared per year rates of excited (manic or mixed) and depressive episodes between fifty-five women with bipolar (N = 22) or major depressive (N = 33) disorders with first episode occurring postpartum (within four weeks after childbirth according to DSM-IV definition) and 218 non-postpartum onset (NPPO) controls. Such patients had a traceable illness course consisting of one or more episodes alternating with complete symptom remission and no additional diagnoses of axis I disorders, mental retardation or brain organic diseases. A number of variables reported to influence the course of mood disorders were controlled for as possible confounding factors RESULTS: Bipolar women with postpartum onset disorder had fewer excited episodes (p = 0.005) and fewer episodes of both polarities (p = 0.005) compared to non-postpartum onset subjects. No differences emerged in the rates of depressive episodes. All patients who met criteria for rapid cycling bipolar disorder (7 out of 123) were in the NPPO group. Among major depressives, PPO patients experienced fewer episodes (p = 0.016). With respect to clinical and treatment features, PPO-MDD subjects had less personality disorder comorbidity (p = 0.023) and were less likely to be on maintenance treatment compared to NPPO comparison subjects (p = 0.002) CONCLUSION: Such preliminary findings suggest that PPO mood disorders may be characterized by a less recurrent time course. Future research in this field should elucidate the role of comorbid personality disorders and treatment. Moreover it should clarify whether PPO disorders are also associated with a more positive outcome in terms of social functioning and quality of life

A Molecular Phylogeny of Living Primates

Comparative genomic analyses of primates offer considerable potential to define and understand the processes that mold, shape, and transform the human genome. However, primate taxonomy is both complex and controversial, with marginal unifying consensus of the evolutionary hierarchy of extant primate species. Here we provide new genomic sequence (~8 Mb) from 186 primates representing 61 (~90%) of the described genera, and we include outgroup species from Dermoptera, Scandentia, and Lagomorpha. The resultant phylogeny is exceptionally robust and illuminates events in primate evolution from ancient to recent, clarifying numerous taxonomic controversies and providing new data on human evolution. Ongoing speciation, reticulate evolution, ancient relic lineages, unequal rates of evolution, and disparate distributions of insertions/deletions among the reconstructed primate lineages are uncovered. Our resolution of the primate phylogeny provides an essential evolutionary framework with far-reaching applications including: human selection and adaptation, global emergence of zoonotic diseases, mammalian comparative genomics, primate taxonomy, and conservation of endangered species

Machine learning and big data analytics in bipolar disorder:A position paper from the International Society for Bipolar Disorders Big Data Task Force

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.Peer reviewe

Depression and sickness behavior are Janus-faced responses to shared inflammatory pathways

It is of considerable translational importance whether depression is a form or a consequence of sickness behavior. Sickness behavior is a behavioral complex induced by infections and immune trauma and mediated by pro-inflammatory cytokines. It is an adaptive response that enhances recovery by conserving energy to combat acute inflammation. There are considerable phenomenological similarities between sickness behavior and depression, for example, behavioral inhibition, anorexia and weight loss, and melancholic (anhedonia), physio-somatic (fatigue, hyperalgesia, malaise), anxiety and neurocognitive symptoms. In clinical depression, however, a transition occurs to sensitization of immuno-inflammatory pathways, progressive damage by oxidative and nitrosative stress to lipids, proteins, and DNA, and autoimmune responses directed against self-epitopes. The latter mechanisms are the substrate of a neuroprogressive process, whereby multiple depressive episodes cause neural tissue damage and consequent functional and cognitive sequelae. Thus, shared immuno-inflammatory pathways underpin the physiology of sickness behavior and the pathophysiology of clinical depression explaining their partially overlapping phenomenology. Inflammation may provoke a Janus-faced response with a good, acute side, generating protective inflammation through sickness behavior and a bad, chronic side, for example, clinical depression, a lifelong disorder with positive feedback loops between (neuro)inflammation and (neuro)degenerative processes following less well defined triggers