scholarly journals A replication study separates polymorphisms behind migraine with and without depression

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261477
Author(s):  
Peter Petschner ◽  
Daniel Baksa ◽  
Gabor Hullam ◽  
Dora Torok ◽  
Andras Millinghoffer ◽  
...  
Keyword(s):  
Neural Network ◽  
Genome Wide Association Study ◽  
Effect Analysis ◽  
Analysis Methods ◽  
The Neural Network ◽  
Genome Wide ◽  
Risk Effect ◽  
Relevance Analysis ◽  
Advanced Analysis ◽  
Candidate Loci

The largest migraine genome-wide association study identified 38 candidate loci. In this study we assessed whether these results replicate on a gene level in our European cohort and whether effects are altered by lifetime depression. We tested SNPs of the loci and their vicinity with or without interaction with depression in regression models. Advanced analysis methods such as Bayesian relevance analysis and a neural network based classifier were used to confirm findings. Main effects were found for rs2455107 of PRDM16 (OR = 1.304, p = 0.007) and five intergenic polymorphisms in 1p31.1 region: two of them showed risk effect (OR = 1.277, p = 0.003 for both rs11209657 and rs6686879), while the other three variants were protective factors (OR = 0.4956, p = 0.006 for both rs12090642 and rs72948266; OR = 0.4756, p = 0.005 for rs77864828). Additionally, 26 polymorphisms within ADGRL2, 2 in REST, 1 in HPSE2 and 33 mostly intergenic SNPs from 1p31.1 showed interaction effects. Among clumped results representing these significant regions, only rs11163394 of ADGRL2 showed a protective effect (OR = 0.607, p = 0.002), all other variants were risk factors (rs1043215 of REST with the strongest effect: OR = 6.596, p = 0.003). Bayesian relevance analysis confirmed the relevance of intergenic rs6660757 and rs12128399 (p31.1), rs1043215 (REST), rs1889974 (HPSE2) and rs11163394 (ADGRL2) from depression interaction results, and the moderate relevance of rs77864828 and rs2455107 of PRDM16 from main effect analysis. Both main and interaction effect SNPs could enhance predictive power with the neural network based classifier. In summary, we replicated p31.1, PRDM16, REST, HPSE2 and ADGRL2 genes with classic genetic and advanced analysis methods. While the p31.1 region and PRDM16 are worthy of further investigations in migraine in general, REST, HPSE2 and ADGRL2 may be prime candidates behind migraine pathophysiology in patients with comorbid depression.

scholarly journals Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

2020 ◽  
Author(s):  
Nam D Nguyen ◽  
Ting Jin ◽  
Daifeng Wang
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Molecular Mechanisms ◽  
Genome Wide Association Study ◽  
Disease Risk ◽  
Supplementary Information ◽  
Neural Network Architecture ◽  
Risk Variants ◽  
Genome Wide

Abstract Summary Population studies such as genome-wide association study have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g. gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics, is still challenging. To address this problem, we developed an interpretable deep learning method, Varmole, to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via biological drop-connect without needing prior feature selections. Availability and implementation Varmole is available as a Python tool on GitHub at https://github.com/daifengwanglab/Varmole. Supplementary information Supplementary data are available at Bioinformatics online.

An approach based on a genome-wide association study reveals candidate loci for narcolepsy

2010 ◽  
Vol 128 (4) ◽  
pp. 433-441 ◽  
Author(s):  
Mihoko Shimada ◽  
Taku Miyagawa ◽  
Minae Kawashima ◽  
Susumu Tanaka ◽  
Yutaka Honda ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide ◽  
A Genome ◽  
Candidate Loci

A genome-wide association study identifies novel loci associated with susceptibility to chronic myeloid leukemia

Blood ◽  
2011 ◽  
Vol 117 (25) ◽  
pp. 6906-6911 ◽  
Author(s):  
Dong Hwan (Dennis) Kim ◽  
Seung-Tae Lee ◽  
Hong-Hee Won ◽  
Seonwoo Kim ◽  
Min-Ji Kim ◽  
...  
Keyword(s):  
Chronic Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Genome Wide Association Study ◽  
European Descent ◽  
P Values ◽  
Genome Wide Analysis ◽  
Control Subjects ◽  
Genome Wide ◽  
A Genome ◽  
Candidate Loci

Abstract In the current study, we identified 2 genetic markers for susceptibility to chronic myeloid leukemia (CML) using a genome-wide analysis. A total of 2744 subjects (671 cases and 2073 controls) were included, with 202 Korean CML patients and 497 control subjects enrolled as a discovery set. Significant findings in the discovery set were validated in a second Korean set of 237 patients and 1000 control subjects and in an additional Canadian cohort of European descent, including 232 patients and 576 control subjects. Analysis revealed significant associations of 2 candidate loci, 6q25.1 and 17p11.1, with CML susceptibility, with the lowest combined P values of 2.4 × 10−6 and 1.3 × 10−12, respectively. Candidate genes in those regions include RMND1, AKAP12, ZBTB2, and WSB1. The locus 6q25.1 was validated in both Korean and European cohorts, whereas 17p11.1 was validated only in the Korean cohort. These findings suggest that genetic variants of 6q25.1 and 17p11.1 may predispose one to the development of CML.

Genome-wide association study identifies candidate loci associated with postoperative fentanyl requirements after laparoscopic-assisted colectomy

2016 ◽  
Vol 17 (2) ◽  
pp. 133-145 ◽  
Author(s):  
Tsutomu Mieda ◽  
Daisuke Nishizawa ◽  
Hideyuki Nakagawa ◽  
Miki Tsujita ◽  
Hirokazu Imanishi ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide ◽  
Laparoscopic Assisted ◽  
Candidate Loci

scholarly journals Novel Genome-Wide Association Study–Based Candidate Loci for Differentiated Thyroid Cancer Risk

2014 ◽  
Vol 99 (10) ◽  
pp. E2084-E2092 ◽  
Author(s):  
Gisella Figlioli ◽  
Aleksandra Köhler ◽  
Bowang Chen ◽  
Rossella Elisei ◽  
Cristina Romei ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Cancer Risk ◽  
Association Study ◽  
Differentiated Thyroid Cancer ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide ◽  
Thyroid Cancer Risk ◽  
Candidate Loci

scholarly journals Application of intellectual data analysis methods for digital educational platform

2020 ◽  
pp. 1-5
Author(s):  
Kristina Zhatkina ◽  
Oksana Kreider
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Services ◽  
Data Mining Techniques ◽  
Analysis Methods ◽  
The Neural Network ◽  
Competency Based ◽  
Using Data ◽  
Data Analysis Methods

This article describes the possibility of using data mining techniques. In order to join new carpet participants, it is necessary to understand that the system of interaction with them is public educational services. To implement digital educational platforms, it is proposed to create an agent that collects information about sites, and also selects and tests the architecture of the neural network to build an individual trajectory that is trained using the competency-based model.

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