Связь полиморфизма генов СОМТ, DRD2/ANKK1, MTHFR, MIR137, DNMT3B с клиническими проявлениями шизофрении в остром периоде и в состоянии ремиссии

Updated view of genetic features of schizophrenia based on rare SNPs/CNVs with a huge influence on a disease and common SNPs with a small effect of each allele is presented. Altogether these genetic factors are acting to create neuropathophysiological disturbances observed in schizophrenia. Association of five polymorphisms MIR137 rs1625579, DRD2/ANKK1 rs1800497, MTHFR rs1801133, DNMT3B rs2424913, СОМТ rs4680 with the risk of schizophrenia in the Belarusian population, the level of symptoms of schizophrenia patients assessed by PANSS in the acute stage and remission, cognitive impairments, and treatment trajectory of schizophrenia patients during antipsychotic treatment were analyzed. The A/A-genotype of СОМТ rs4680 (р = 0.008) and the С/С-genotype of MTHFR rs1801133 (р = 0.02) are associated with the risk of schizophrenia among Belarusians. The T-allele of MTHFR rs1801133 is a risk factor of positive symptoms (р = 0.02). Combining the C/C-genotype (DNMT3B rs2424913) and the G-allele (COMT rs4680) is associated with a significant difference in negative symptoms level between men and women. The polymorphism of СОМТ rs4680 (р < 0.05) and the combination of СОМТ rs4680 + DRD2/ANKK1 rs1800497 (р = 0.005) as well as MTHFR rs1801133 + DNMT3B rs2424913 (р = 0.006) are related to the cognitive parameters measured by the WCST and Stroop test respectively. Schizophrenia patients who are the G-allele carriers of MIR137 rs1625579 demonstrated a more favorable negative symptom trajectory in comparison to Т/Тhomozygotes (F = 2.2, p = 0.03). The trajectory of negative symptoms (F = 2.2, p = 0.03) and general psychopathological symptoms (F = 4.3, p = 0.0001) is different between men and women under antipsychotic treatment. These differences are associated with a minor amount of alleles of MIR137 rs1625579, DRD2/ANKK1 rs1800497, MTHFR rs1801133 polymorphic sites.По современным представлениям, пациент, страдающий шизофренией, является, как правило, носителем одной или нескольких редких аллелей с высоким эффектом и ряда распространенных аллелей с малыми эффектами. Совместное действие генетических факторов реализуется определенными нейробиологическими путями, которые порождают спектр нейрофизиологических нарушений, наблюдаемых при шизофрении. В ходе исследования была проанализирована связь полиморфных сайтов MIR137 rs1625579, DRD2/ANKK1 rs1800497, MTHFR rs1801133, DNMT3B rs2424913, СОМТ rs4680 с риском возникновения шизофрении среди белорусов, степенью выраженности симптомов по шкале PANSS в остром периоде и в фазе ремиссии, когнитивными нарушениями и динамикой изменения симптоматической картины пациентов с шизофренией в период поддерживающей терапии антипсихотиками. Согласно полученным результатам, генотип A/AСОМТ rs4680 (р = 0,008) и генотип С/С MTHFR rs1801133 (р = 0,02) являются факторами риска развития шизофрении среди мужчин белорусской популяции. Выявлены множественные связи исследуемых локусов с различными видами симптомов и когнитивными параметрами. T-аллель MTHFR rs1801133 связан с риском формирования психотических симптомов (р = 0,02). Сочетание генотипа C/C (DNMT3B rs2424913) и G-аллеля (COMT rs4680) связано со значимыми различиями в уровне негативных симптомов между пациентами мужского и женского пола (p = 0,00009). Наиболее выражена связь локуса СОМТ rs4680 (р < 0,05) и комбинации локусов СОМТ rs4680 + DRD2/ANKK1 rs1800497 (р = 0,005) с когнитивными параметрами (оценка по Висконсинскому тесту сортировки карточек – ВТСК), а также комбинации локусов MTHFR rs1801133 + DNMT3B rs24 24913 (р = 0,006) с параметром теста Струпа. Также обнаружено, что пациенты с G-аллелем MIR137 rs1625579 демонстрируют более благоприятную динамику изменения негативной симптоматики (F = 2,2, p = 0,03) в сравнении с Т/Тгомозиготами. Обнаружены достоверные различия в траектории изменения негативной (F = 2,2, p = 0,03) и общей психопатологической симптоматики (F = 4,3, p = 0,0001) в период терапии между пациентами мужского и женского пола в зависимости от количества минорных аллелей по исследуемым полиморфным вариантам

Signal from Noise: Using Machine Learning to Distil Knowledge from Data in Biological Psychiatry

Applications of machine learning (ML) in biomedical science are growing rapidly, spurred by interdisciplinary collaborations, aggregation of large datasets, accessibility of analytic routines, and availability of powerful computers. With this increased usage comes a responsibility for education, borne equally by data scientists plying their wares in medical research and biomedical scientists harnessing such methods to glean knowledge from data. This article provides a critical review of ML, covering common ML methods and historical trends of their use in psychiatry, and identifying areas of opportunity for future applications of ML in biological psychiatry. We also establish the ML in Psychiatry (MLPsych) Consortium, enumerate its objectives, and provide a set of standards (Guidelines for REporting ML Investigations in Neuropsychiatry [GREMLIN]) for designing and reporting studies that use ML. This review serves as a cautiously optimistic primer on ML for those on the precipice as they prepare to dive into the field, either as dedicated methodological practitioners or, at the very least, well-informed consumers

Identifying the Common Genetic Basis of Antidepressant Response

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods: Genome-wide analysis of remission (nremit = 1852, nnonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism–based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism–based heritability was significantly different from zero for remission (h2 = 0.132, SE = 0.056) but not for percentage improvement (h2 = −0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.</p