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

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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Construction of an exome-wide risk score for schizophrenia based on a weighted burden test

10.1101/145961 ◽

2017 ◽

Cited By ~ 3

Author(s):

David Curtis

Keyword(s):

Risk Score ◽

Rare Variants ◽

Risk Scores ◽

Individual Risk ◽

Data Sets ◽

Weighted Sum ◽

Polygenic Risk ◽

Whole Exome ◽

Sequencing Studies ◽

Burden Test

SummaryPolygenic risk scores obtained as a weighted sum of associated variants can be used to explore association in additional data sets and to assign risk scores to individuals. The methods used to derive polygenic risk scores from common SNPs are not suitable for variants detected in whole exome sequencing studies. Rare variants which may have major effects are seen too infrequently to judge whether they are associated and may not be shared between training and test subjects. A method is proposed whereby variants are weighted according to their frequency, their annotations and to the genes they affect. A weighted sum across all variants provides an individual risk score. Scores constructed in this way are used in a weighted burden test and are shown to be significantly different between schizophrenia cases and controls using a five-way cross validation procedure. This approach represents a first attempt to summarise exome sequence variation into a summary risk score, which could be combined with risk scores from common variants and from environmental factors. It is hoped that the method could be developed further.

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Contribution of common risk variants to multiple sclerosis in Orkney and Shetland

European Journal of Human Genetics ◽

10.1038/s41431-021-00914-w ◽

2021 ◽

Author(s):

Catriona L. K. Barnes ◽

Caroline Hayward ◽

David J. Porteous ◽

Harry Campbell ◽

Peter K. Joshi ◽

...

Keyword(s):

Multiple Sclerosis ◽

Risk Allele ◽

Risk Scores ◽

Data Sets ◽

Common Variants ◽

Risk Allele Frequency ◽

Polygenic Risk ◽

Risk Variants ◽

Generation Scotland ◽

High Prevalence

AbstractOrkney and Shetland, the population isolates that make up the Northern Isles of Scotland, are of particular interest to multiple sclerosis (MS) research. While MS prevalence is high in Scotland, Orkney has the highest global prevalence, higher than more northerly Shetland. Many hypotheses for the excess of MS cases in Orkney have been investigated, including vitamin D deficiency and homozygosity: neither was found to cause the high prevalence of MS. It is possible that this excess prevalence may be explained through unique genetics. We used polygenic risk scores (PRS) to look at the contribution of common risk variants to MS. Analyses were conducted using ORCADES (97/2118 cases/controls), VIKING (15/2000 cases/controls) and Generation Scotland (30/8708 cases/controls) data sets. However, no evidence of a difference in MS-associated common variant frequencies was found between the three control populations, aside from HLA-DRB1*15:01 tag SNP rs9271069. This SNP had a significantly higher risk allele frequency in Orkney (0.23, p value = 8 × 10–13) and Shetland (0.21, p value = 2.3 × 10–6) than mainland Scotland (0.17). This difference in frequency is estimated to account for 6 (95% CI 3, 8) out of 150 observed excess cases per 100,000 individuals in Shetland and 9 (95% CI 8, 11) of the observed 257 excess cases per 100,000 individuals in Orkney, compared with mainland Scotland. Common variants therefore appear to account for little of the excess burden of MS in the Northern Isles of Scotland.

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Validity and Prognostic Value of a Polygenic Risk Score for Parkinson’s Disease

Genes ◽

10.3390/genes12121859 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1859

Author(s):

Sebastian Koch ◽

Björn-Hergen Laabs ◽

Meike Kasten ◽

Eva-Juliane Vollstedt ◽

Jos Becktepe ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Prognostic Value ◽

Later Life ◽

Risk Scores ◽

Polygenic Risk Score ◽

Data Sets ◽

Data Set ◽

Polygenic Risk ◽

Individual Level

Idiopathic Parkinson’s disease (PD) is a complex multifactorial disorder caused by the interplay of both genetic and non-genetic risk factors. Polygenic risk scores (PRSs) are one way to aggregate the effects of a large number of genetic variants upon the risk for a disease like PD in a single quantity. However, reassessment of the performance of a given PRS in independent data sets is a precondition for establishing the PRS as a valid tool to this end. We studied a previously proposed PRS for PD in a separate genetic data set, comprising 1914 PD cases and 4464 controls, and were able to replicate its ability to differentiate between cases and controls. We also assessed theoretically the prognostic value of the PD-PRS, i.e., its ability to predict the development of PD in later life for healthy individuals. As it turned out, the PD-PRS alone can be expected to perform poorly in this regard. Therefore, we conclude that the PD-PRS could serve as an important research tool, but that meaningful PRS-based prognosis of PD at an individual level is not feasible.

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Role of Genetic Interactions in Alzheimer’s Disease: Lessons from Long Life Family Study (LLFS)

Innovation in Aging ◽

10.1093/geroni/igaa057.1589 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 491-491

Author(s):

Anatoliy Yashin ◽

Dequing Wu ◽

Konstantin Arbeev ◽

Eric Stallard ◽

Qihua Tan ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Family Study ◽

Interaction Analysis ◽

Specific Interaction ◽

Interaction Effects ◽

Genetic Interactions ◽

Risk Scores ◽

Long Life

Abstract Experimental and clinical studies of Alzheimer’s disease (AD) provide plentiful evidence of AD heterogeneity and involvement of many interacting genes and pathways in regulation of AD-related traits. However, detailed mechanisms of genetic interactions (GxG) involved in AD remain largely unknown. Uncovering hidden patterns of such interactions from human data will help better understand the nature of AD heterogeneity and find new targets for AD prevention. In this paper, we applied a newly developed method of evaluating joint GxG effects on AD to analysis of the Long Life Family Study data. The analysis included several steps: (i) selecting candidate genes from stress response pathways that are thought to be involved in AD; (ii) estimating interaction effects of SNP-pairs on AD risk, and selecting the top interacting SNPs; (iii) running GWAS-like interaction analysis for SNP-pairs, with one SNP fixed; (iv) using characteristics of the detected SNP-pairs interactions to construct the SNP-specific Interaction Polygenic Risk Scores (IPRS); and (v) evaluating the effects of IPRSs on AD. We found that SNP-specific IPRS have highly significant effects on AD risk. For most SNPs involved in the significant interaction effects on AD, their individual effects were statistically not significant. Male and female analyses yielded different subsets of the top interacting SNPs. These results support major role of genetic interactions in heterogeneity of AD, and indicate that AD mechanisms can involve different combinations of the interacting genetic variants in males and females, which may point to different pathways of resistance/response to stressors in two genders.

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The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

10.1101/449520 ◽

2018 ◽

Author(s):

Saman Amini ◽

Annika Jacobsen ◽

Olga Ivanova ◽

Philip Lijnzaad ◽

Jaap Heringa ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Molecular Mechanisms ◽

Genetic Interaction ◽

Genetic Diseases ◽

Mechanistic Explanation ◽

Genetic Interactions ◽

Interaction Patterns ◽

And Inversion ◽

Complex Genetic Diseases

AbstractGenetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several putative molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker’s yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.Author SummaryThe relationship between genotype and phenotype is one of the major challenges in biology. While many previous studies have identified genes involved in complex genetic diseases, there is still a gap between genotype and phenotype. One of the difficulties in filling this gap has been attributed to genetic interactions. Large-scale studies have revealed that genetic interactions are widespread in model organisms such as baker’s yeast. Several molecular mechanisms have been proposed for different genetic interaction types. However, differences in occurrence and underlying molecular mechanism of genetic interactions have not yet been compared between gene classes of different function. Here, we compared genetic interaction patterns identified using gene expression profiling for two classes of genes: gene specific transcription factors and signaling related genes. We modelled all possible molecular networks to unravel putative molecular differences underlying different genetic interaction patterns. Our study proposes a new mechanistic explanation for a certain genetic interaction pattern that is more strongly associated with transcription factors compared to signaling related genes. Overall, our findings and the computational methodologies implemented here can be valuable for understanding the molecular mechanisms underlying genetic interactions.

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

Current Biotechnology ◽

10.2174/2211550109999201008125800 ◽

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.

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