8 research outputs found
Π‘Π²ΡΠ·Ρ ΠΏΠΎΠ»ΠΈΠΌΠΎΡΡΠΈΠ·ΠΌΠ° Π³Π΅Π½ΠΎΠ² Π‘ΠΠΠ’, DRD2/ANKK1, MTHFR, MIR137, DNMT3B Ρ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΡΠΎΡΠ²Π»Π΅Π½ΠΈΡΠΌΠΈ ΡΠΈΠ·ΠΎΡΡΠ΅Π½ΠΈΠΈ Π² ΠΎΡΡΡΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅ ΠΈ Π² ΡΠΎΡΡΠΎΡΠ½ΠΈΠΈ ΡΠ΅ΠΌΠΈΡΡΠΈΠΈ
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,Β DRD2/ANKK1 rs1800497, MTHFR rs1801133, DNMT3B rs2424913, Π‘ΠΠΠ’ 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 Π‘ΠΠΠ’ rs4680 (Ρ = 0.008) and the Π‘/Π‘-genotype of MTHFR rs1801133 (Ρ = 0.02) are associated with the risk of schizophrenia among Belarusians. The T-allele of MTHFR rs1801133 is a risk factor of positive symptoms (Ρ = 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 Π‘ΠΠΠ’ rs4680 (Ρ < 0.05) and the combination of Π‘ΠΠΠ’ rs4680 + DRD2/ANKK1 rs1800497 (Ρ = 0.005) as well as MTHFR rs1801133 + DNMT3B rs2424913 (Ρ = 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 Π’/Π’homozygotes (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.ΠΠΎ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠΌ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡΠΌ, ΠΏΠ°ΡΠΈΠ΅Π½Ρ, ΡΡΡΠ°Π΄Π°ΡΡΠΈΠΉ ΡΠΈΠ·ΠΎΡΡΠ΅Π½ΠΈΠ΅ΠΉ, ΡΠ²Π»ΡΠ΅ΡΡΡ, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, Π½ΠΎΡΠΈΡΠ΅Π»Π΅ΠΌ ΠΎΠ΄Π½ΠΎΠΉ ΠΈΠ»ΠΈ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΡΠ΅Π΄ΠΊΠΈΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ Ρ Π²ΡΡΠΎΠΊΠΈΠΌ ΡΡΡΠ΅ΠΊΡΠΎΠΌ ΠΈ ΡΡΠ΄Π° ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ Ρ ΠΌΠ°Π»ΡΠΌΠΈ ΡΡΡΠ΅ΠΊΡΠ°ΠΌΠΈ. Π‘ΠΎΠ²ΠΌΠ΅ΡΡΠ½ΠΎΠ΅ Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΡΠ΅Π°Π»ΠΈΠ·ΡΠ΅ΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠΌΠΈ Π½Π΅ΠΉΡΠΎΠ±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΡΡΡΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΏΠΎΡΠΎΠΆΠ΄Π°ΡΡ ΡΠΏΠ΅ΠΊΡΡ Π½Π΅ΠΉΡΠΎΡΠΈΠ·ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π½Π°ΡΡΡΠ΅Π½ΠΈΠΉ, Π½Π°Π±Π»ΡΠ΄Π°Π΅ΠΌΡΡ
ΠΏΡΠΈ ΡΠΈΠ·ΠΎΡΡΠ΅Π½ΠΈΠΈ. Π Ρ
ΠΎΠ΄Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ»Π° ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π° ΡΠ²ΡΠ·Ρ ΠΏΠΎΠ»ΠΈΠΌΠΎΡΡΠ½ΡΡ
ΡΠ°ΠΉΡΠΎΠ² MIR137 rs1625579, DRD2/ANKK1 rs1800497, MTHFR rs1801133, DNMT3B rs2424913, Π‘ΠΠΠ’ rs4680 Ρ ΡΠΈΡΠΊΠΎΠΌ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ ΡΠΈΠ·ΠΎΡΡΠ΅Π½ΠΈΠΈ ΡΡΠ΅Π΄ΠΈ Π±Π΅Π»ΠΎΡΡΡΠΎΠ², ΡΡΠ΅ΠΏΠ΅Π½ΡΡ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΡΡΠΈ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ² ΠΏΠΎ ΡΠΊΠ°Π»Π΅ PANSS Π² ΠΎΡΡΡΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅ ΠΈ Π² ΡΠ°Π·Π΅ ΡΠ΅ΠΌΠΈΡΡΠΈΠΈ, ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΡΠΌΠΈ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΎΠΉ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΈΠΌΠΏΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ°ΡΡΠΈΠ½Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠΈΠ·ΠΎΡΡΠ΅Π½ΠΈΠ΅ΠΉ Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΈΠ²Π°ΡΡΠ΅ΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ Π°Π½ΡΠΈΠΏΡΠΈΡ
ΠΎΡΠΈΠΊΠ°ΠΌΠΈ. Π‘ΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΌ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ, Π³Π΅Π½ΠΎΡΠΈΠΏ A/AΠ‘ΠΠΠ’ rs4680 (Ρ = 0,008) ΠΈ Π³Π΅Π½ΠΎΡΠΈΠΏ Π‘/Π‘ MTHFR rs1801133 (Ρ = 0,02) ΡΠ²Π»ΡΡΡΡΡ ΡΠ°ΠΊΡΠΎΡΠ°ΠΌΠΈ ΡΠΈΡΠΊΠ° ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΈΠ·ΠΎΡΡΠ΅Π½ΠΈΠΈ ΡΡΠ΅Π΄ΠΈ ΠΌΡΠΆΡΠΈΠ½ Π±Π΅Π»ΠΎΡΡΡΡΠΊΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ. ΠΡΡΠ²Π»Π΅Π½Ρ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΡΠ²ΡΠ·ΠΈ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
Π»ΠΎΠΊΡΡΠΎΠ² Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ Π²ΠΈΠ΄Π°ΠΌΠΈ ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ² ΠΈ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ. T-Π°Π»Π»Π΅Π»Ρ MTHFR rs1801133 ΡΠ²ΡΠ·Π°Π½ Ρ ΡΠΈΡΠΊΠΎΠΌ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΡ
ΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ² (Ρ = 0,02). Π‘ΠΎΡΠ΅ΡΠ°Π½ΠΈΠ΅ Π³Π΅Π½ΠΎΡΠΈΠΏΠ° C/C (DNMT3B rs2424913) ΠΈ G-Π°Π»Π»Π΅Π»Ρ (COMT rs4680) ΡΠ²ΡΠ·Π°Π½ΠΎ ΡΠΎ Π·Π½Π°ΡΠΈΠΌΡΠΌΠΈ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΌΠΈ Π² ΡΡΠΎΠ²Π½Π΅ Π½Π΅Π³Π°ΡΠΈΠ²Π½ΡΡ
ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ² ΠΌΠ΅ΠΆΠ΄Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌΠΈ ΠΌΡΠΆΡΠΊΠΎΠ³ΠΎ ΠΈ ΠΆΠ΅Π½ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠ»Π° (p = 0,00009). ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π²ΡΡΠ°ΠΆΠ΅Π½Π° ΡΠ²ΡΠ·Ρ Π»ΠΎΠΊΡΡΠ° Π‘ΠΠΠ’ rs4680 (Ρ < 0,05) ΠΈ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΠΈ Π»ΠΎΠΊΡΡΠΎΠ² Π‘ΠΠΠ’ rs4680 + DRD2/ANKK1 rs1800497 (Ρ = 0,005) Ρ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ (ΠΎΡΠ΅Π½ΠΊΠ° ΠΏΠΎ ΠΠΈΡΠΊΠΎΠ½ΡΠΈΠ½ΡΠΊΠΎΠΌΡ ΡΠ΅ΡΡΡ ΡΠΎΡΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΊΠ°ΡΡΠΎΡΠ΅ΠΊ β ΠΠ’Π‘Π), Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΠΈ Π»ΠΎΠΊΡΡΠΎΠ² MTHFR rs1801133 + DNMT3B rs24 24913 (Ρ = 0,006) Ρ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠΌ ΡΠ΅ΡΡΠ° Π‘ΡΡΡΠΏΠ°. Π’Π°ΠΊΠΆΠ΅ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΎ, ΡΡΠΎ ΠΏΠ°ΡΠΈΠ΅Π½ΡΡ Ρ G-Π°Π»Π»Π΅Π»Π΅ΠΌ MIR137 rs1625579 Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΡΡ Π±ΠΎΠ»Π΅Π΅ Π±Π»Π°Π³ΠΎΠΏΡΠΈΡΡΠ½ΡΡ Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π΅Π³Π°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠΈΠΌΠΏΡΠΎΠΌΠ°ΡΠΈΠΊΠΈ (F = 2,2, p = 0,03) Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π’/Π’Π³ΠΎΠΌΠΎΠ·ΠΈΠ³ΠΎΡΠ°ΠΌΠΈ. ΠΠ±Π½Π°ΡΡΠΆΠ΅Π½Ρ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡ Π² ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π΅Π³Π°ΡΠΈΠ²Π½ΠΎΠΉ (F = 2,2, p = 0,03) ΠΈ ΠΎΠ±ΡΠ΅ΠΉ ΠΏΡΠΈΡ
ΠΎΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΠΌΠΏΡΠΎΠΌΠ°ΡΠΈΠΊΠΈ (F = 4,3, p = 0,0001) Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌΠΈ ΠΌΡΠΆΡΠΊΠΎΠ³ΠΎ ΠΈ ΠΆΠ΅Π½ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠ»Π° Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π° ΠΌΠΈΠ½ΠΎΡΠ½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΏΠΎ ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΠΌ ΠΏΠΎΠ»ΠΈΠΌΠΎΡΡΠ½ΡΠΌ Π²Π°ΡΠΈΠ°Π½ΡΠ°ΠΌ
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
A primer on the use of machine learning to distil knowledge from data in biological psychiatry.
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or 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 polymorphismβbased 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 polymorphismβbased heritability was significantly different from zero for remission (h2 = 0.132, SE = 0.056) but not for percentage improvement (h2 = β0.018, 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