Genetic Interactions Reveal that Specific Defects of Chloroplast Translation are Associated with the Suppression of var2 -Mediated Leaf Variegation

2013 ◽  
Vol 55 (10) ◽  
pp. 979-993 ◽  
Author(s):  
Xiayan Liu ◽  
Mengdi Zheng ◽  
Rui Wang ◽  
Ruijuan Wang ◽  
Lijun An ◽  
...  
Keyword(s):  
Genetic Interactions ◽  
Chloroplast Translation ◽  
Leaf Variegation

scholarly journals Chloroplast translation initiation factors regulate leaf variegation and development

2016 ◽  
pp. pp.02040.2015 ◽  
Author(s):  
Mengdi Zheng ◽  
Xiayan Liu ◽  
Shuang Liang ◽  
Shiying Fu ◽  
Yafei Qi ◽  
...  
Keyword(s):  
Translation Initiation ◽  
Initiation Factors ◽  
Translation Initiation Factors ◽  
Chloroplast Translation ◽  
Leaf Variegation

scholarly journals Mutations in SUPPRESSOR OF VARIEGATION1, a Factor Required for Normal Chloroplast Translation, Suppress var2-Mediated Leaf Variegation in Arabidopsis

2008 ◽  
Vol 20 (7) ◽  
pp. 1786-1804 ◽  
Author(s):  
Fei Yu ◽  
Xiayan Liu ◽  
Muath Alsheikh ◽  
Sungsoon Park ◽  
Steve Rodermel
Keyword(s):  
Chloroplast Translation ◽  
Leaf Variegation

scholarly journals Balance between Cytosolic and Chloroplast Translation Affects Leaf Variegation

2017 ◽  
Vol 176 (1) ◽  
pp. 804-818 ◽  
Author(s):  
Ruijuan Wang ◽  
Jun Zhao ◽  
Min Jia ◽  
Ni Xu ◽  
Shuang Liang ◽  
...  
Keyword(s):  
Chloroplast Translation ◽  
Leaf Variegation

Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

2020 ◽  
Vol 9 (3) ◽  
pp. 177-191
Author(s):  
Sridharan Priya ◽  
Radha K. Manavalan
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiovascular Diseases ◽  
Computational Models ◽  
Genetic Interaction ◽  
Association Studies ◽  
Genetic Interactions ◽  
Computational Techniques ◽  
Genome Wide Association Studies ◽  
Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

scholarly journals Stress Mediating Genes in Aging, Health, and Longevity Traits: Effects of Multiple Interactions

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 286-286
Author(s):  
Anatoliy Yashin ◽  
Dequing Wu ◽  
Konstantin Arbeev ◽  
Arseniy Yashkin ◽  
Galina Gorbunova ◽  
...  
Keyword(s):  
Strong Interaction ◽  
Genetic Interaction ◽  
Interaction Effects ◽  
Genetic Interactions ◽  
Risk Scores ◽  
Data Sets ◽  
Interaction Patterns ◽  
Polygenic Risk ◽  
Drug Candidates ◽  
Aging Health

Abstract Persistent stress of external or internal origin accelerates aging, increases risk of aging related health disorders, and shortens lifespan. Stressors activate stress response genes, and their products collectively influence traits. The variability of stressors and responses to them contribute to trait heterogeneity, which may cause the failure of clinical trials for drug candidates. The objectives of this paper are: to address the heterogeneity issue; to evaluate collective interaction effects of genetic factors on Alzheimer’s disease (AD) and longevity using HRS data; to identify differences and similarities in patterns of genetic interactions within two genders; and to compare AD related genetic interaction patterns in HRS and LOADFS data. To reach these objectives we: selected candidate genes from stress related pathways affecting AD/longevity; implemented logistic regression model with interaction term to evaluate effects of SNP-pairs on these traits for males and females; constructed the novel interaction polygenic risk scores for SNPs, which showed strong interaction potential, and evaluated effects of these scores on AD/longevity; and compared patterns of genetic interactions within the two genders and within two datasets. We found there were many genes involved in highly significant interactions that were the same and that were different within the two genders. The effects of interaction polygenic risk scores on AD were strong and highly statistically significant. These conclusions were confirmed in analyses of interaction effects on longevity trait using HRS data. Comparison of HRS to LOADFS data showed that many genes had strong interaction effects on AD in both data sets.

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