A GWAS approach identifies Dapp1 as a determinant of air pollution-induced airway hyperreactivity

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

BACKGROUND

The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

RESULTS

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

CONCLUSIONS

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Gene-environment interactions that influence CVD, lipid traits, obesity, diabetes, and hypertension appear to be able to influence gene therapy

Most mind boggling diseases are accepted to be impacted by both genetic and environmental elements. As of late, there has been a flood in the improvement of different methodologies, concentrate on plans, and measurable and logical techniques to examine gene-environment cooperations (G × Es) in enormous scope studies including human populaces. The many-sided exchange between genetic elements and environmental openings has long charmed the consideration of clinicians and researchers looking to grasp the complicated starting points of diseases. While single variables can add to disease, the blend of genetic variations and environmental openings frequently decides disease risk. The fundamental point of this paper is to talk about the Gene-Environment Associations That Impact CVD, Lipid Characteristics, Obesity, Diabetes, and Hypertension Have all the earmarks of being Ready to Impact Gene Therapy. This survey paper investigates the meaning of gene-environment collaborations (G × E) in disease advancement. The intricacy of genetic and environmental communications in disease causation is explained, underlining the multifactorial idea of many circumstances. The job of gene-environment cooperations in cardiovascular disease, lipid digestion, diabetes, obesity, and hypertension is investigated. This audit fixates on Gene by Environment (G × E) collaborations, investigating their importance in disease etiology.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Gene-environment interactions and their impact on human health

The molecular processes underlying human health and disease are highly complex. Often, genetic and environmental factors contribute to a given disease or phenotype in a non-additive manner, yielding a gene-environment (G × E) interaction. In this work, we broadly review current knowledge on the impact of gene-environment interactions on human health. We first explain the independent impact of genetic variation and the environment. We next detail well-established G × E interactions that impact human health involving environmental toxicants, pollution, viruses, and sex chromosome composition. We conclude with possibilities and challenges for studying G × E interactions.

Transcriptomic and metabolomic associations with exposures to air pollutants among young adults with childhood asthma history

Ambient air pollutants are well-known risk factors for childhood asthma and asthma exacerbation. It is unknown whether different air pollutants individually or jointly affect pathophysiological mechanisms of asthma. In this study, we aim to integrate transcriptome and untargeted metabolome to identify dysregulated genetic and metabolic pathways that are associated with exposures to a mixture of ambient and traffic-related air pollutants among adults with asthma history. In this cross-sectional study, 102 young adults with childhood asthma history were enrolled from southern California in 2012. Whole blood transcriptome was measured with 20,869 expression signatures, and serum untargeted metabolomics including 937 metabolites were analyzed by Metabolon, Inc. Participants' exposures to regional air pollutants (NO₂, O₃, PM₁₀, PM_2.5) and near-roadway air pollutants averaged at one month and one year before study visit were estimated based on residential addresses. xMWAS network analysis and joint-pathway analysis were performed to identify subnetworks and genetic and metabolic pathways that were associated with exposure to air pollutants adjusted for socio-characteristic covariates. Network analysis found that exposures to air pollutants mixture were connected to 357 gene markers and 92 metabolites. One-year and one-month averaged PM_2.5 and NO₂ were associated with several amino acids related to serine, glycine, and beta-alanine metabolism. Lower serum levels of carnosine and aspartate, which are involved in the beta-alanine metabolic pathway, as well as choline were also associated with worse asthma control (p < 0.05). One-year and one-month averaged PM₁₀ and one-month averaged O₃ were associated with higher gene expression levels of HSPA5, LGMN, CTSL and HLA-DPB1, which are involved in antigen processing and presentation. These results indicate that exposures to various air pollutants are associated with altered genetic and metabolic pathways that affect anti-oxidative capacity and immune response and can potentially contribute to asthma-related pathophysiology.

Gene-Environment Interactions for Cardiovascular Disease

PURPOSE OF REVIEW

We provide an overview of recent findings with respect to gene-environment (GxE) interactions for cardiovascular disease (CVD) risk and discuss future opportunities for advancing the field.

RECENT FINDINGS

Over the last several years, GxE interactions for CVD have mostly been identified for smoking and coronary artery disease (CAD) or related risk factors. By comparison, there is more limited evidence for GxE interactions between CVD outcomes and other exposures, such as physical activity, air pollution, diet, and sex. The establishment of large consortia and population-based cohorts, in combination with new computational tools and mouse genetics platforms, can potentially overcome some of the limitations that have hindered human GxE interaction studies and reveal additional association signals for CVD-related traits. The identification of novel GxE interactions is likely to provide a better understanding of the pathogenesis and genetic liability of CVD, with significant implications for healthy lifestyles and therapeutic strategies.

Genome-wide analysis highlights contribution of immune system pathways to the genetic architecture of asthma

Asthma is a chronic and genetically complex respiratory disease that affects over 300 million people worldwide. Here, we report a genome-wide analysis for asthma using data from the UK Biobank and the Trans-National Asthma Genetic Consortium. We identify 66 previously unknown asthma loci and demonstrate that the susceptibility alleles in these regions are, either individually or as a function of cumulative genetic burden, associated with risk to a greater extent in men than women. Bioinformatics analyses prioritize candidate causal genes at 52 loci, including CD52, and demonstrate that asthma-associated variants are enriched in regions of open chromatin in immune cells. Lastly, we show that a murine anti-CD52 antibody mimics the immune cell-depleting effects of a clinically used human anti-CD52 antibody and reduces allergen-induced airway hyperreactivity in mice. These results further elucidate the genetic architecture of asthma and provide important insight into the immunological and sex-specific relevance of asthma-associated risk variants.

Mouse Systems Genetics as a Prelude to Precision Medicine

Mouse models have been instrumental in understanding human disease biology and proposing possible new treatments. The precise control of the environment and genetic composition of mice allows more rigorous observations, but limits the generalizability and translatability of the results into human applications. In the era of precision medicine, strategies using mouse models have to be revisited to effectively emulate human populations. Systems genetics is one promising paradigm that may promote the transition to novel precision medicine strategies. Here, we review the state-of-the-art resources and discuss how mouse systems genetics helps to understand human diseases and to advance the development of precision medicine, with an emphasis on the existing resources and strategies.