Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis

Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes

publisher: Elsevier articletitle: Genomic Dissection of Bipolar Disorder and Schizophrenia, Including 28 Subphenotypes journaltitle: Cell articlelink: https://doi.org/10.1016/j.cell.2018.05.046 content_type: article copyright: © 2018 Elsevier Inc

Predictors of compassionate and polite prosocial behavior: Moral reasoning, relaxation, self -reflection, and spirituality

Recent public opinion polls have indicated that Americans feel there is less civility and prosocial behavior today in this country than in the past. To understand what predicts prosocial behavior so as to understand how to promote it, this study examined predictors of prosocial behaviors based on the work of Colby and Damon (1992) and Oliner and Oliner (1988). These researchers studied exemplary prosocial individuals who behaved in a prosocial manner that involved significant personal effort and commitment. Psychological characteristics found consistently in these people included strong commitments to personal, authoritative, or moral principles and a strong sense of empathy and connection with those they had helped. A previous pilot study (Jankowski & Higgins-D\u27Alessandro, 2003) had found that spiritual feelings, moral reasoning and time spent in relaxation predicted more frequent compassionate prosocial behaviors as listed on the Self-report Altruism Scale (SRA; Rushton, Chrisjohn, & Fekken, 1981). The current study found support for the division of the SRA scale into two subscales of compassionate and polite prosocial behaviors based on item correlations with moral reasoning as determined in the pilot study. In this study, spiritual transcendence, quiet relaxation time, self-reflection during relaxation and gender all predicted compassionate prosocial behaviors in a multiple regression. Polite prosocial behaviors were predicted by income and quiet relaxation time. Moral reasoning scores in this study were not significantly related to prosocial behaviors, although prosocial behaviors did increase slightly across three consecutively higher moral reasoning stages. These findings identify the importance of the following in understanding and promoting prosocial behaviors in adults: a perception of a universal connection to others, an understanding and appreciation of a reality that is greater than the one that is seen, and quiet relaxation time where these perceptions and understandings can be reflected upon and coalesce. The demographic characteristics of this current sample call for some caution when generalizing the findings of this study to other populations, as it was mainly comprised of community dwelling adults who had higher yearly incomes and educational levels than adults in the general population. Implications and directions for future research are discussed

Adapting Psychosocial Intervention Research to Urban Primary Care Environments: A Case Example

PURPOSE We wanted to describe the unique issues encountered by our research team in testing an intervention to reduce perinatal depression in real-world community health centers. METHOD We used a case study of an experience in conducting a randomized controlled trial designed to test the effectiveness of a low-cost multimodal psychosocial intervention to reduce prenatal and postpartum depression. Low-income minority women (N = 187) with low-risk pregnancies were randomly assigned to the intervention or treatment as usual. Outcomes of interest were depressive symptoms and social support assessed at 3 months’ postpartum. RESULTS Our intervention was not associated with changes in depressive symptoms or social support. Challenges in implementation were related to participant retention and intervention delivery. Turnover of student therapists affected continuity in participant-therapist relationships and created missed opportunities to deliver the intervention. The academic-community partnership that was formed also required more involvement of health center personnel to facilitate ownership at the site level, especially for fidelity monitoring. While attentive to cultural sensitivity, the project called for more collaboration with participants to define common goals and outcomes. Participatory research strategies could have anticipated barriers to uptake of the intervention and achieved a better match between outcomes desired by researchers and those of participants. CONCLUSION Several criteria for future research planning emerged: assessing what the population is willing and able to accept, considering what treatment providers can be expected to implement, assessing the setting’s capacity to accommodate intervention research, and collecting and using emerging unanticipated data

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers