2017
DOI: 10.1038/srep39943
|View full text |Cite
|
Sign up to set email alerts
|

Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm

Abstract: A genetic risk score could be beneficial in assisting clinical diagnosis for complex diseases with high heritability. With large-scale genome-wide association (GWA) data, the current study constructed a genetic risk model with a machine learning approach for bipolar disorder (BPD). The GWA dataset of BPD from the Genetic Association Information Network was used as the training data for model construction, and the Systematic Treatment Enhancement Program (STEP) GWA data were used as the validation dataset. A ra… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
27
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
4
3
1

Relationship

0
8

Authors

Journals

citations
Cited by 31 publications
(27 citation statements)
references
References 53 publications
0
27
0
Order By: Relevance
“…Total cholesterol, HDL and diabetes local polygenic risk associated with BD overlapped in two neighboring regions on chromosome 3 (3p14.1-p12.3), a region previously found to be associated with hypertension (Koivukoski et al, 2004) that harbors multiple genes known to be involved in HDL and cholesterol traits. This region has also been associated with bipolar disorder, depression and schizophrenia in multiple studies (Chuang and Kuo, 2017;Goes et al, 2015;Rudd et al, 2015; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Total cholesterol and LDL local polygenic risk associated with BD overlapped in one region on chromosome 20 (20q11.23-q12), a region previously associated with hypertension and diabetes, although not found to be consistently associated with bipolar disorder.…”
Section: Identifying Pleiotropic Regions Of the Genomementioning
confidence: 99%
“…Total cholesterol, HDL and diabetes local polygenic risk associated with BD overlapped in two neighboring regions on chromosome 3 (3p14.1-p12.3), a region previously found to be associated with hypertension (Koivukoski et al, 2004) that harbors multiple genes known to be involved in HDL and cholesterol traits. This region has also been associated with bipolar disorder, depression and schizophrenia in multiple studies (Chuang and Kuo, 2017;Goes et al, 2015;Rudd et al, 2015; Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). Total cholesterol and LDL local polygenic risk associated with BD overlapped in one region on chromosome 20 (20q11.23-q12), a region previously associated with hypertension and diabetes, although not found to be consistently associated with bipolar disorder.…”
Section: Identifying Pleiotropic Regions Of the Genomementioning
confidence: 99%
“…Artificial intelligence algorithms, especially machine learning, are being increasingly employed to examine biological data . We started utilizing these for pharmacometric analyses almost 10 years ago .…”
Section: Discussionmentioning
confidence: 99%
“…These results are also compared with the existing related works in order to evaluate the performance of this work, given briefly in Table 4. Machine Learning empowers wide range of applications such as weather forecasting [19] [26], sports success prediction [27] etc. More effective methods improve the predictability of the learning models.…”
Section: Rhinoceros Search Algorithmmentioning
confidence: 99%
“…Two risk genes are identified namely NOG and CTBP1[26]. A genetic risk score model is developed using the RF algorithm to classify between normal and abnormal genes[27].…”
mentioning
confidence: 99%