scholarly journals Genetic polymorphisms of key enzymes in folate metabolism affect the efficacy of folate therapy in patients with hyperhomocysteinaemia

2018 ◽  
Vol 119 (8) ◽  
pp. 887-895 ◽  
Author(s):  
Binghui Du ◽  
Huizi Tian ◽  
Dandan Tian ◽  
Chengda Zhang ◽  
Wenhua Wang ◽  
...  
Keyword(s):  
Folic Acid ◽  
Interaction Model ◽  
Folate Metabolism ◽  
Efficacy Rate ◽  
Environment Interaction ◽  
Optimal Model ◽  
Acid Therapy ◽  
Gene Environment ◽  
History Of ◽  
Factor Interaction

AbstractThe aim of this study is to analyse the efficacy rate of folate for the treatment of hyperhomocysteinaemia (HHcy) and to explore how folate metabolism-related gene polymorphisms change its efficacy. This study also explored the effects of gene–gene and gene–environment interactions on the efficacy of folate. A prospective cohort study enrolling HHcy patients was performed. The subjects were treated with oral folate (5 mg/d) for 90 d. We analysed the efficacy rate of folate for the treatment of HHcy by measuring homocysteine (Hcy) levels after treatment. Unconditioned logistic regression was conducted to analyse the association between SNP and the efficacy of folic acid therapy for HHcy. The efficacy rate of folate therapy for HHcy was 56·41 %. The MTHFR rs1801133 CT genotype, TT genotype and T allele; the MTHFR rs1801131 AC genotype, CC genotype and C allele; the MTRR rs1801394 GA genotype, GG genotype and G allele; and the MTRR rs162036 AG genotype and AG+GG genotypes were associated with the efficacy of folic acid therapy for HHcy (P<0·05). No association was seen between other SNP and the efficacy of folic acid. The optimal model of gene–gene interactions was a two-factor interaction model including rs1801133 and rs1801394. The optimal model of gene–environment interaction was a three-factor interaction model including history of hypertension, history of CHD and rs1801133. Folate supplementation can effectively decrease Hcy level. However, almost half of HHcy patients failed to reach the normal range. The efficacy of folate therapy may be genetically regulated.

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

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.

scholarly journals Developmental behavioral genetics research on school achievement is missing vulnerable children, to our detriment

2021 ◽  
Author(s):  
LaTasha R Holden ◽  
Rasheda Haughbrook ◽  
Sara Ann Hart
Keyword(s):  
Behavioral Genetics ◽  
Scientific Evidence ◽  
School Achievement ◽  
Interaction Model ◽  
Environment Interaction ◽  
White Children ◽  
Gene By Environment Interaction ◽  
Genetics Research ◽  
P Gene ◽  
Gene Environment

Gene–environment processes tell us how genes and environments work together to influence children in schools. One type of gene–environment process that has been extensively studied using behavioral genetics methods is a gene-by-environment interaction. A gene-by-environment interaction shows us when the effect of your context differs depending on your genes, or vice versa, when the effect of your genes differs depending on your context. Developmental behavioral geneticists interested in children’s school achievement have examined many different contexts within the gene-by-environment interaction model, including contexts measured from within children’s home and school environments. However, this work has been overwhelmingly focused on White children, leaving us with non-inclusive scientific evidence. This can lead to detrimental outcomes when we overgeneralize this non-inclusive scientific evidence to racialized groups. We conclude with a call to include racialized children in more research samples.

Biometric and developmental gene–environment interactions: Looking back, moving forward

2007 ◽  
Vol 19 (4) ◽  
pp. 961-976 ◽  
Author(s):  
James Tabery
Keyword(s):  
Complex Traits ◽  
Environment Interaction ◽  
Developmental Gene ◽  
Statistical Interaction ◽  
Gene Environment Interaction ◽  
History Of Research ◽  
Gene Environment ◽  
Lancelot Hogben ◽  
History Of ◽  
Looking Back

AbstractA history of research on gene–environment interaction (G × E) is provided in this article, revealing the fact that there have actually been two distinct concepts of G × E since the very origins of this research. R. A. Fisher introduced what I call the biometric concept of G × E (G × EB), whereas Lancelot Hogben introduced what I call the developmental concept of G × E (G × ED). Much of the subsequent history of research on G × E has largely consisted of the separate legacies of these separate concepts, along with the (sometimes acrimonious) disputes that have arisen time and again when employers of each have argued over the appropriate way to conceptualize the phenomenon. With this history in place, more recent attempts to distinguish between different concepts of G × E are considered, paying particular attention to the commonly made distinction between “statistical interaction” and “interactionism,” and Michael Rutter's distinction between statistical interaction and “the biological concept of interaction.” I argue that the history of the separate legacies of G × EB and G × ED better supports Rutter's analysis of the situation and that this analysis best paves the way for an integrative relationship between the various scientists investigating the place of G × E in the etiology of complex traits.

A Gene–Environment Interaction Model of Stress-Induced Hypertension

2005 ◽  
Vol 5 (2) ◽  
pp. 109-132 ◽  
Author(s):  
Ikhide G. Imumorin ◽  
Yanbin Dong ◽  
Haidong Zhu ◽  
Joseph C. Poole ◽  
Gregory A. Harshfield ◽  
...  
Keyword(s):  
Interaction Model ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Induced Hypertension
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