Using the optimal method—explained variance weighted genetic risk score to predict the efficacy of folic acid therapy to hyperhomocysteinemia

AbstractArtificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954–5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092–7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden’s index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden’s index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.

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Prediction model for the efficacy of folic acid therapy on hyperhomocysteinaemia based on genetic risk score methods

British Journal Of Nutrition ◽

10.1017/s0007114519000783 ◽

2019 ◽

Vol 122 (1) ◽

pp. 39-46

Author(s):

Binghui Du ◽

Chengda Zhang ◽

Limin Yue ◽

Bingnan Ren ◽

Qinglin Zhao ◽

...

Keyword(s):

Folic Acid ◽

Prediction Model ◽

Risk Score ◽

Genetic Risk ◽

Acid Treatment ◽

Genetic Risk Score ◽

Assessment Tools ◽

Acid Therapy ◽

Gene Environment ◽

History Of

AbstractNo risk assessment tools for the efficacy of folic acid treatment for hyperhomocysteinaemia (HHcy) have been developed. We aimed to use two common genetic risk score (GRS) methods to construct prediction models for the efficacy of folic acid therapy on HHcy, and the best gene–environment prediction model was screened out. A prospective cohort study enrolling 638 HHcy patients was performed. We used a logistic regression model to estimate the associations of two GRS methods with the efficacy. Performances were compared using area under the receiver operating characteristic curve (AUC). The simple count genetic risk score (SC-GRS) and weighted genetic risk score (wGRS) were found to be independently associated with the efficacy of folic acid treatment for HHcy. Using the SC-GRS, per risk allele increased with a 1·46-fold increased failure risk (P < 0·001) after adjustment for traditional risk factors, including age, sex, BMI, smoking, alcohol consumption, history of diabetes, history of hypertension, history of hyperlipidaemia, history of stroke and history of CHD. When used the wGRS, the association was strengthened (OR = 2·08, P < 0·001). Addition of the SC-GRS and wGRS to the traditional risk model significantly improved the predictive ability by AUC (0·859). A precise gene–environment predictive model with good performance was developed for predicting the treatment failure rate of folic acid therapy for HHcy.

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A Weighted Genetic Risk Score Predicts Surgical Recurrence Independent of High-Risk Clinical Features in Dupuytren’s Disease

Plastic & Reconstructive Surgery ◽

10.1097/prs.0000000000005208 ◽

2019 ◽

Vol 143 (2) ◽

pp. 512-518 ◽

Cited By ~ 3

Author(s):

Sophie A. Riesmeijer ◽

Oliver W. G. Manley ◽

Michael Ng ◽

Ilja M. Nolte ◽

Dieuwke C. Broekstra ◽

...

Keyword(s):

High Risk ◽

Clinical Features ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Dupuytren’S Disease ◽

Dupuytren's Disease ◽

Surgical Recurrence ◽

Weighted Genetic Risk Score

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Evaluation of a weighted genetic risk score for the prediction of biomarkers of CYP2A6 activity

Addiction Biology ◽

10.1111/adb.12741 ◽

2019 ◽

Vol 25 (1) ◽

Cited By ~ 3

Author(s):

Ahmed El‐Boraie ◽

Taraneh Taghavi ◽

Meghan J. Chenoweth ◽

Koya Fukunaga ◽

Taisei Mushiroda ◽

...

Keyword(s):

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Weighted Genetic Risk Score

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Pregnancy does not modify the risk of MS in genetically susceptible women

Neurology Neuroimmunology & Neuroinflammation ◽

10.1212/nxi.0000000000000898 ◽

2020 ◽

Vol 7 (6) ◽

pp. e898

Author(s):

Cameron J. Adams ◽

Sean L. Wu ◽

Xiaorong Shao ◽

Patrick T. Bradshaw ◽

Edlin Gonzales ◽

...

Keyword(s):

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

The United States ◽

Sensitivity Analyses ◽

Risk Variants ◽

Inverse Variance ◽

Weighted Genetic Risk Score ◽

Meta Analyses ◽

Genetic Risk Variants

ObjectiveTo use the case-only gene-environment (G E) interaction study design to estimate interaction between pregnancy before onset of MS symptoms and established genetic risk factors for MS among White adult females.MethodsWe studied 2,497 female MS cases from 4 cohorts in the United States, Sweden, and Norway with clinical, reproductive, and genetic data. Pregnancy exposure was defined in 2 ways: (1) live birth pregnancy before onset of MS symptoms and (2) parity before onset of MS symptoms. We estimated interaction between pregnancy exposure and established genetic risk variants, including a weighted genetic risk score and both HLA and non-HLA variants, using logistic regression and proportional odds regression within each cohort. Within-cohort associations were combined using inverse variance meta-analyses with random effects. The case-only G × E independence assumption was tested in 7,067 individuals without MS.ResultsEvidence for interaction between pregnancy exposure and established genetic risk variants, including the strongly associated HLA-DRB1*15:01 allele and a weighted genetic risk score, was not observed. Results from sensitivity analyses were consistent with observed results.ConclusionOur findings indicate that pregnancy before symptom onset does not modify the risk of MS in genetically susceptible White females.

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Faculty Opinions recommendation of Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1587956.1078054 ◽

2010 ◽

Author(s):

Sreeram Ramagopalan ◽

Lahiru Handunnetthi

Keyword(s):

Risk Factors ◽

Multiple Sclerosis ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Genetic Risk Factors ◽

Clinical Algorithm ◽

Multiple Sclerosis Susceptibility ◽

Weighted Genetic Risk Score

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Burden of genetic risk variants in multiple sclerosis families in the Netherlands

Multiple Sclerosis Journal - Experimental Translational and Clinical ◽

10.1177/2055217316648721 ◽

2016 ◽

Vol 2 ◽

pp. 205521731664872 ◽

Cited By ~ 2

Author(s):

Julia Y Mescheriakova ◽

Linda Broer ◽

Simin Wahedi ◽

André G Uitterlinden ◽

Cornelia M van Duijn ◽

...

Keyword(s):

Multiple Sclerosis ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Weighted Genetic Risk Score ◽

Sporadic Cases ◽

Multiplex Families ◽

The Difference ◽

Multiple Sclerosis Patients ◽

Genetic Burden

Background Approximately 20% of multiple sclerosis patients have a family history of multiple sclerosis. Studies of multiple sclerosis aggregation in families are inconclusive. Objective To investigate the genetic burden based on currently discovered genetic variants for multiple sclerosis risk in patients from Dutch multiple sclerosis multiplex families versus sporadic multiple sclerosis cases, and to study its influence on clinical phenotype and disease prediction. Methods Our study population consisted of 283 sporadic multiple sclerosis cases, 169 probands from multiplex families and 2028 controls. A weighted genetic risk score based on 102 non-human leukocyte antigen loci and HLA-DRB1*1501 was calculated. Results The weighted genetic risk score based on all loci was significantly higher in familial than in sporadic cases. The HLA-DRB1*1501 contributed significantly to the difference in genetic burden between the groups. A high weighted genetic risk score was significantly associated with a low age of disease onset in all multiple sclerosis patients, but not in the familial cases separately. The genetic risk score was significantly but modestly better in discriminating familial versus sporadic multiple sclerosis from controls. Conclusion Familial multiple sclerosis patients are more loaded with the common genetic variants than sporadic cases. The difference is mainly driven by HLA-DRB1*1501. The predictive capacity of genetic loci is poor and unlikely to be useful in clinical settings.

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THU0010 A Weighted Genetic Risk Score Using 46 Loci to Predict Rheumatoid Arthritis Risk

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2013-eular.538 ◽

2013 ◽

Vol 72 (Suppl 3) ◽

pp. A167.3-A168 ◽

Cited By ~ 3

Author(s):

A. Yarwood ◽

M. Lunt ◽

B. Han ◽

S. Raychaudhuri ◽

J. Bowes ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Rheumatoid Arthritis Risk ◽

Weighted Genetic Risk Score

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Alzheimer’s Genetic Risk Score linked to Incident Mild Behavioral Impairment

10.1101/200840 ◽

2017 ◽

Author(s):

Shea J. Andrews ◽

Zahinoor Ismail ◽

Kaarin J. Anstey ◽

Moyra Mortby

Keyword(s):

Cognitive Impairment ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score ◽

Neuropsychiatric Symptoms ◽

Later Life ◽

Behavioral Impairment ◽

Affective Dysregulation ◽

Weighted Genetic Risk Score ◽

Shared Risk

AbstractMild Behavioral Impairment (MBI) describes the emergence of later-life Neuropsychiatric Symptoms (NPS) as an at-risk state for cognitive decline and dementia and as a potential manifestation of prodromal dementia. How NPS mechanistically link to the development of Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD) is not fully understood. Potential mechanisms include either shared risk factors that are related to both NPS and cognitive impairment, or AD pathology promoting NPS. This is the first study to examine whether AD genetic loci, individually and as a genetic risk score, are a shared risk factor with MBI. 1377 older adults (aged 72-79; 738 males; 763 normal cognition) from the PATH Through Life project. MBI was assessed in accordance with Criterion 1 of the ISTAART-AA diagnostic criteria using the Neuropsychiatric Inventory. 25 LOAD risk loci were genotyped and a weighted genetic risk score (GRS) was constructed. Binomial logistic regression adjusting for age, gender, and education examined the association between LOAD GRS and MBI domains. An increase in the LOAD GRS and APOE*ε4 were associated with higher likelihood of Affective Dysregulation;MS4A4A-rs4938933*C andMS4A6A-rs610932*G were associated with a reduced likelihood of Affective Dysregulation;ZCWPW1-rs1476679*C was associated with a reduced likelihood of Social Inappropriateness and Abnormal Perception;BIN1-rs744373*G andEPHA1-rs11767557*C were associated with higher likelihood of Abnormal Perception;NME8-rs2718058*G was associated with a reduced likelihood Decreased Motivation. These findings suggest a common genetic etiology between MBI and traditionally recognized memory problems observed in AD and improve our understanding of the pathophysiological features underlying MBI.

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