scholarly journals Loss-of-function of MFGE8 and protection against coronary atherosclerosis

2021 ◽  
Author(s):  
Sanni E Ruotsalainen ◽  
Ida Surakka ◽  
Nina Mars ◽  
Juha Karjalainen ◽  
Mitja Kurki ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Coronary Atherosclerosis ◽  
Association Studies ◽  
Meta Analysis ◽  
Significant Risk ◽  
Premature Death ◽  
Population History ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Loss Of Function

Cardiovascular diseases are the leading cause of premature death and disability worldwide, with both genetic and environmental determinants. While genome-wide association studies have identified multiple genetic loci associated with cardiovascular diseases, exact genes driving these associations remain mostly uncovered. Due to Finland's population history, many deleterious and high-impact variants are enriched in the Finnish population giving a possibility to find genetic associations for protein-truncating variants that likely tie the association to a gene and that would not be detected elsewhere. In FinnGen, a large Finnish biobank study, we identified an inframe insertion rs534125149 in MFGE8 to have protective effect against coronary atherosclerosis (OR = 0.75, p = 2.63E-16) and related endpoints. This variant is highly enriched in Finland (70-fold compared to Non-Finnish Europeans) with allele frequency of 3% in Finland. The protective association was replicated in meta-analysis of biobanks of Japan and Estonian (OR = 0.75, p = 5.41E-7). Additionally, we identified a splice acceptor variant rs201988637 in MFGE8, independent of the rs534125149 and similarly protective in relation to coronary atherosclerosis (OR = 0.72, p = 7.94E-06) and related endpoints, with no significant risk-increasing associations. The protein-truncating variant was also associated with lower pulse pressure, pointing towards a function of MFGE8 in arterial stiffness and aging also in humans in addition to previous evidence in mice. In conclusion, our results show that inhibiting the production of lactadherin could lower the risk for coronary heart disease substantially.

scholarly journals Genetic Variants behind Cardiovascular Diseases and Dementia

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1514
Author(s):  
Wei-Min Ho ◽  
Yah-Yuan Wu ◽  
Yi-Chun Chen
Keyword(s):  
White Matter ◽  
Cardiovascular Diseases ◽  
Genetic Variants ◽  
White Matter Hyperintensities ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Pubmed Database ◽  
Risk Snps

Cardiovascular diseases (CVDs) and dementia are the leading causes of disability and mortality. Genetic connections between cardiovascular risk factors and dementia have not been elucidated. We conducted a scoping review and pathway analysis to reveal the genetic associations underlying both CVDs and dementia. In the PubMed database, literature was searched using keywords associated with diabetes mellitus, hypertension, dyslipidemia, white matter hyperintensities, cerebral microbleeds, and covert infarctions. Gene lists were extracted from these publications to identify shared genes and pathways for each group. This included high penetrance genes and single nucleotide polymorphisms (SNPs) identified through genome wide association studies. Most risk SNPs to both diabetes and dementia participate in the phospholipase C enzyme system and the downstream nositol 1,4,5-trisphosphate and diacylglycerol activities. Interestingly, AP-2 (TFAP2) transcription factor family and metabolism of vitamins and cofactors were associated with genetic variants that were shared by white matter hyperintensities and dementia, and by microbleeds and dementia. Variants shared by covert infarctions and dementia were related to VEGF ligand–receptor interactions and anti-inflammatory cytokine pathways. Our review sheds light on future investigations into the causative relationships behind CVDs and dementia, and can be a paradigm of the identification of dementia treatments.

Genetics of systemic sclerosis

2020 ◽  
Vol 5 (3) ◽  
pp. 192-201 ◽  
Author(s):  
Yuki Ishikawa ◽  
Chikashi Terao
Keyword(s):  
Systemic Sclerosis ◽  
Disease Risk ◽  
Association Studies ◽  
Human Leukocyte ◽  
Premature Death ◽  
Microvascular Dysfunction ◽  
Candidate Gene Approach ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genetic Components

Systemic sclerosis is an autoimmune disease characterized by generalized fibrosis in connective tissues and internal organs as consequences of microvascular dysfunction and immune dysfunctions, which leads to premature death in affected individuals. The etiology of systemic sclerosis is complex and poorly understood, but as with most autoimmune diseases, it is widely accepted that both environmental and genetic factors contribute to disease risk. During the last decade, the number of genetic markers convincingly associated with systemic sclerosis has exponentially increased. In this article, we briefly mention the genetic components of systemic sclerosis. Then, we review the classical and novel genetic associations with systemic sclerosis, analyzing the firmest and replicated signals within non–human leukocyte antigen genes, identified by both candidate gene approach and genome-wide association studies. We also provide an insight into the future perspectives that will shed more light into the complex genetic background of the disease. Despite the remarkable advance of systemic sclerosis genetics during the last decade, the use of the new genetic technologies such as next-generation sequencing, as well as the deep phenotyping of the study cohorts, to fully characterize the genetic component of this disease is imperative to identify causal variants, which leads to more targeted and effective treatment of systemic sclerosis.

scholarly journals EnigmaVis: Online Interactive Visualization of Genome-Wide Association Studies of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium

2012 ◽  
Vol 15 (3) ◽  
pp. 414-418 ◽  
Author(s):  
Nic M. Novak ◽  
Jason L. Stein ◽  
Sarah E. Medland ◽  
Derrek P. Hibar ◽  
Paul M. Thompson ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Meta Analysis ◽  
Genome Wide Association ◽  
Intracranial Volume ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Neuroimaging Data ◽  
Neuroimaging Genetics

In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale, genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here, we present a freely available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/

scholarly journals Genetic Regulation of Biomarkers as Stress Proxies in Dairy Cows

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 534
Author(s):  
Marco Milanesi ◽  
Matilde Maria Passamonti ◽  
Katia Cappelli ◽  
Andrea Minuti ◽  
Valentino Palombo ◽  
...  
Keyword(s):  
Inflammatory Responses ◽  
Bos Taurus ◽  
Association Studies ◽  
Meta Analysis ◽  
Genetic Regulation ◽  
Chromosome 1 ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Physiological Level ◽  
Glutamyl Transferase

Stress in livestock reduces productivity and is a welfare concern. At a physiological level, stress is associated with the activation of inflammatory responses and increased levels of harmful reactive oxygen species. Biomarkers that are indicative of stress could facilitate the identification of more stress-resilient animals. We examined twenty-one metabolic, immune response, and liver function biomarkers that have been associated with stress in 416 Italian Simmental and 436 Italian Holstein cows which were genotyped for 150K SNPs. Single-SNP and haplotype-based genome-wide association studies were carried out to assess whether the variation in the levels in these biomarkers is under genetic control and to identify the genomic loci involved. Significant associations were found for the plasma levels of ceruloplasmin (Bos taurus chromosome 1—BTA1), paraoxonase (BTA4) and γ-glutamyl transferase (BTA17) in the individual breed analysis that coincided with the position of the genes coding for these proteins, suggesting that their expression is under cis-regulation. A meta-analysis of both breeds identified additional significant associations with paraoxonase on BTA 16 and 26. Finding genetic associations with variations in the levels of these biomarkers suggests that the selection for high or low levels of expression could be achieved rapidly. Whether the level of expression of the biomarkers correlates with the response to stressful situations has yet to be determined.

scholarly journals Best practices of multi-ancestry, meta-analytic transcriptome-wide association studies: lessons from the Global Biobank Meta-analysis Initiative

2021 ◽  
Author(s):  
Arjun Bhattacharya ◽  
Jibril B Hirbo ◽  
Dan Zhou ◽  
Wei Zhou ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Meta Analysis ◽  
Genomic Medicine ◽  
Genetic Association Studies ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Study Population

The Global Biobank Meta-analysis Initiative (GBMI), through its genetic and demographic diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ethnic study populations across biobanks, the GBMI provides a distinct set of challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWAS) are a popular tool to boost detection power for and provide biological context to genetic associations by integrating single nucleotide polymorphism to trait (SNP-trait) associations from genome-wide association studies (GWAS) with SNP-based predictive models of gene expression. TWAS presents unique challenges beyond GWAS, especially in a multi-biobank and meta-analytic setting like the GBMI. In this work, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity and meta-analytic strategies, as well as open challenges at every step of the framework. Our work provides a strong foundation for adding tissue-specific gene expression context to biobank-linked genetic association studies, allowing for ancestry-aware discovery to accelerate genomic medicine.

scholarly journals Trans-ethnic analysis reveals genetic and non-genetic associations with COVID-19 susceptibility and severity

2020 ◽  
Author(s):  
Janie F. Shelton ◽  
Anjali J. Shastri ◽  
Chelsea Ye ◽  
Catherine H. Weldon ◽  
Teresa Filshtein-Somnez ◽  
...  
Keyword(s):  
Association Studies ◽  
Economic Status ◽  
Strong Association ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Blood Type ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Wide Range

COVID-19 presents with a wide range of severity, from asymptomatic in some individuals to fatal in others. Based on a study of over one million 23andMe research participants, we report genetic and non-genetic associations with testing positive for COVID-19, respiratory symptoms, and hospitalization. Risk factors for hospitalization include advancing age, male sex, elevated body mass index, lower socio-economic status, non-European ancestry, and pre-existing cardio-metabolic and respiratory conditions. Using trans-ethnic genome-wide association studies, we identify a strong association between blood type and COVID-19 diagnosis, as well as a gene-rich locus on chr3p21.31 that is more strongly associated with outcome severity. While non-European ancestry was found to be a significant risk factor for hospitalization after adjusting for socio-demographics and pre-existing health conditions, we did not find evidence that these two primary genetic associations explain differences between populations in terms of risk for severe COVID-19 outcomes.

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.

Sign in / Sign up

Export Citation Format

Share Document