Stroke Genetics: Turning Discoveries into Clinical Applications

Neuroprotective Agents ◽

Experimental Systems ◽

Genome Wide ◽

Rapid Pace

The field of medical and population genetics in stroke is moving at a rapid pace and has led to unanticipated opportunities for discovery and clinical applications. Genome-wide association studies have highlighted the role of specific pathways relevant to etiologically defined subtypes of stroke and to stroke as a whole. They have further offered starting points for the exploration of novel pathways and pharmacological strategies in experimental systems. Mendelian randomization studies continue to provide insights in the causal relationships between exposures and outcomes and have become a useful tool for predicting the efficacy and side effects of drugs. Additional applications that have emerged from recent discoveries include risk prediction based on polygenic risk scores and pharmacogenomics. Among the topics currently moving into focus is the genetics of stroke outcome. While still at its infancy, this field is expected to boost the development of neuroprotective agents. We provide a brief overview on recent progress in these areas.

The effect of liver enzymes on adiposity: a Mendelian randomization study

Scientific Reports ◽

10.1038/s41598-019-52489-8 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Junxi Liu ◽

Shiu Lun Au Yeung ◽

Man Ki Kwok ◽

June Yue Yan Leung ◽

Shi Lin Lin ◽

...

Keyword(s):

Liver Function ◽

Liver Enzymes ◽

Mendelian Randomization ◽

Association Studies ◽

Alanine Transaminase ◽

Genome Wide Association ◽

Genetic Predictors ◽

AbstractPoorer liver function is positively associated with diabetes in Mendelian randomization (MR) studies. Observationally, adiposity is associated with poorer liver function. To clarify the etiology, we assessed the association of liver enzymes with adiposity observationally and using two-sample MR for validation. In the “Children of 1997” birth cohort, we used multivariable linear regression to assess the associations of alanine transaminase (ALT) and alkaline phosphatase (ALP) at ~17.5 years with body mass index (BMI) (n = 3,458). Using MR, genetic predictors of ALT, ALP and gamma glutamyltransferase (GGT), were applied to genome-wide association studies of BMI (n = 681,275), waist circumference (WC) (n = 224,459) and waist-hip ratio (WHR) (n = 224,459) to obtain unconfounded estimates. Observationally, ALT was positively associated with BMI (0.10 kg/m2 per IU/L, 95% confidence interval (CI) 0.09 to 0.11). ALP was inversely associated with BMI (−0.018 kg/m2 per IU/L, 95% CI −0.024 to −0.012). Using MR, ALT was inversely associated with BMI (−0.14 standard deviation per 100% change in concentration, 95% CI −0.20 to −0.07), but not WC or WHR. ALP and GGT were unrelated to adiposity. Poorer liver function might not cause adiposity; instead higher ALT might reduce BMI, raising the question as to the role of ALT in body composition.

Genetic Contribution of Endometriosis to the Risk of Developing Hormone-Related Cancers

International Journal of Molecular Sciences ◽

10.3390/ijms22116083 ◽

2021 ◽

Vol 22 (11) ◽

pp. 6083

Author(s):

Aintzane Rueda-Martínez ◽

Aiara Garitazelaia ◽

Ariadna Cilleros-Portet ◽

Sergi Marí ◽

Rebeca Arauzo ◽

...

Keyword(s):

Mendelian Randomization ◽

Clear Cell ◽

Association Studies ◽

Epidemiological Data ◽

Epidemiological Studies ◽

Genome Wide ◽

Genomic Analyses ◽

Gynecological Disorder ◽

Robust Evidence

Endometriosis is a common gynecological disorder that has been associated with endometrial, breast and epithelial ovarian cancers in epidemiological studies. Since complex diseases are a result of multiple environmental and genetic factors, we hypothesized that the biological mechanism underlying their comorbidity might be explained, at least in part, by shared genetics. To assess their potential genetic relationship, we performed a two-sample mendelian randomization (2SMR) analysis on results from public genome-wide association studies (GWAS). This analysis confirmed previously reported genetic pleiotropy between endometriosis and endometrial cancer. We present robust evidence supporting a causal genetic association between endometriosis and ovarian cancer, particularly with the clear cell and endometrioid subtypes. Our study also identified genetic variants that could explain those associations, opening the door to further functional experiments. Overall, this work demonstrates the value of genomic analyses to support epidemiological data, and to identify targets of relevance in multiple disorders.

Genomics-driven drug discovery based on disease-susceptibility genes

Inflammation and Regeneration ◽

10.1186/s41232-021-00158-7 ◽

2021 ◽

Vol 41 (1) ◽

Author(s):

Kyuto Sonehara ◽

Yukinori Okada

Keyword(s):

Drug Discovery ◽

Drug Targets ◽

Disease Susceptibility ◽

Mendelian Randomization ◽

Association Studies ◽

Drug Repurposing ◽

Susceptibility Genes ◽

Genome Wide ◽

Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

Genetic Determinants of Paget’s Disease of Bone

Current Osteoporosis Reports ◽

10.1007/s11914-021-00676-w ◽

2021 ◽

Author(s):

Navnit S. Makaram ◽

Stuart H. Ralston

Keyword(s):

Genetic Factors ◽

Association Studies ◽

Paget’S Disease ◽

Paget's Disease ◽

Paget’S Disease Of Bone ◽

Paget's Disease Of Bone ◽

Genome Wide ◽

Family Based

Abstract Purpose of Review To provide an overview of the role of genes and loci that predispose to Paget’s disease of bone and related disorders. Recent Findings Studies over the past ten years have seen major advances in knowledge on the role of genetic factors in Paget’s disease of bone (PDB). Genome wide association studies have identified six loci that predispose to the disease whereas family based studies have identified a further eight genes that cause PDB. This brings the total number of genes and loci implicated in PDB to fourteen. Emerging evidence has shown that a number of these genes also predispose to multisystem proteinopathy syndromes where PDB is accompanied by neurodegeneration and myopathy due to the accumulation of abnormal protein aggregates, emphasising the importance of defects in autophagy in the pathogenesis of PDB. Summary Genetic factors play a key role in the pathogenesis of PDB and the studies in this area have identified several genes previously not suspected to play a role in bone metabolism. Genetic testing coupled to targeted therapeutic intervention is being explored as a way of halting disease progression and improving outcome before irreversible skeletal damage has occurred.

Challenges of Adjusting Single-Nucleotide Polymorphism Effect Sizes for Linkage Disequilibrium

Human Heredity ◽

10.1159/000513303 ◽

2021 ◽

pp. 1-11

Author(s):

Valentina Escott-Price ◽

Karl Michael Schmidt

Keyword(s):

Linkage Disequilibrium ◽

Association Studies ◽

Statistical Significance ◽

Ordinary Least Squares ◽

Effect Sizes ◽

Risk Scores ◽

Single Nucleotide ◽

Genome Wide ◽

Tikhonov Regularisation

Background: Genome-wide association studies (GWAS) were successful in identifying SNPs showing association with disease, but their individual effect sizes are small and require large sample sizes to achieve statistical significance. Methods of post-GWAS analysis, including gene-based, gene-set and polygenic risk scores, combine the SNP effect sizes in an attempt to boost the power of the analyses. To avoid giving undue weight to SNPs in linkage disequilibrium (LD), the LD needs to be taken into account in these analyses. Objectives: We review methods that attempt to adjust the effect sizes (β-coefficients) of summary statistics, instead of simple LD pruning. Methods: We subject LD adjustment approaches to a mathematical analysis, recognising Tikhonov regularisation as a framework for comparison. Results: Observing the similarity of the processes involved with the more straightforward Tikhonov-regularised ordinary least squares estimate for multivariate regression coefficients, we note that current methods based on a Bayesian model for the effect sizes effectively provide an implicit choice of the regularisation parameter, which is convenient, but at the price of reduced transparency and, especially in smaller LD blocks, a risk of incomplete LD correction. Conclusions: There is no simple answer to the question which method is best, but where interpretability of the LD adjustment is essential, as in research aiming at identifying the genomic aetiology of disorders, our study suggests that a more direct choice of mild regularisation in the correction of effect sizes may be preferable.

Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges

10.1101/126599 ◽

2017 ◽

Cited By ~ 4

Author(s):

Lavinia Paternoster ◽

Kate Tilling ◽

George Davey Smith

Keyword(s):

Genetic Epidemiology ◽

Mendelian Randomization ◽

Association Studies ◽

Disease Treatment ◽

The Past ◽

Genome Wide ◽

Perceived Benefit ◽

Progression Of Disease ◽

High Yields

The past decade has been proclaimed as a hugely successful era of gene discovery through the high yields of many genome-wide association studies (GWAS). However, much of the perceived benefit of such discoveries lies in the promise that the identification of genes that influence disease would directly translate into the identification of potential therapeutic targets (1-4), but this has yet to be realised at a level reflecting expectation. One reason for this, we suggest, is that GWAS to date have generally not focused on phenotypes that directly relate to the progression of disease, and thus speak to disease treatment.

A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

10.1101/563379 ◽

2019 ◽

Cited By ~ 2

Author(s):

Tom G Richardson ◽

Gibran Hemani ◽

Tom R Gaunt ◽

Caroline L Relton ◽

George Davey Smith

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Complex Traits ◽

Mendelian Randomization ◽

Drug Repositioning ◽

Association Studies ◽

Thyroid Tissue ◽

Tissue Specific ◽

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

Current knowledge in hypertension genetics: mosaic theory, candidate genes and genome-wide association studies

Arterial’naya Gipertenziya (Arterial Hypertension) ◽

10.18705/1607-419x-2020-26-5-490-500 ◽

2020 ◽

Vol 26 (5) ◽

pp. 490-500

Author(s):

A. O. Konradi

Keyword(s):

Candidate Genes ◽

High Performance ◽

New Technologies ◽

Current Knowledge ◽

Association Studies ◽

Genome Wide Association ◽

Modern Approach ◽

The article reviews monogenic forms of hypertension, data on the role of heredity of essential hypertension and candidate genes, as well as genome-wide association studies. Modern approach for the role of genetics is driven by implementation of new technologies and their productivity. High performance speed of new technologies like genome-wide association studies provide data for better knowledge of genetic markers of hypertension. The major goal nowadays for research is to reveal molecular pathways of blood pressure regulation, which can help to move from populational to individual level of understanding of pathogenesis and treatment targets.

Age-related late-onset disease heritability patterns and implications for genome-wide association studies

10.1101/349019 ◽

2018 ◽

Cited By ~ 1

Author(s):

Roman Teo Oliynyk

Keyword(s):

Old Age ◽

Cumulative Incidence ◽

Late Onset ◽

Association Studies ◽

Genome Wide Association ◽

Risk Scores ◽

Cerebral Stroke ◽

Polygenic Risk ◽

AbstractBackgroundGenome-wide association studies and other computational biology techniques are gradually discovering the causal gene variants that contribute to late-onset human diseases. After more than a decade of genome-wide association study efforts, these can account for only a fraction of the heritability implied by familial studies, the so-called “missing heritability” problem.MethodsComputer simulations of polygenic late-onset diseases in an aging population have quantified the risk allele frequency decrease at older ages caused by individuals with higher polygenic risk scores becoming ill proportionately earlier. This effect is most prominent for diseases characterized by high cumulative incidence and high heritability, examples of which include Alzheimer’s disease, coronary artery disease, cerebral stroke, and type 2 diabetes.ResultsThe incidence rate for late-onset diseases grows exponentially for decades after early onset ages, guaranteeing that the cohorts used for genome-wide association studies overrepresent older individuals with lower polygenic risk scores, whose disease cases are disproportionately due to environmental causes such as old age itself. This mechanism explains the decline in clinical predictive power with age and the lower discovery power of familial studies of heritability and genome-wide association studies. It also explains the relatively constant-with-age heritability found for late-onset diseases of lower prevalence, exemplified by cancers.ConclusionsFor late-onset polygenic diseases showing high cumulative incidence together with high initial heritability, rather than using relatively old age-matched cohorts, study cohorts combining the youngest possible cases with the oldest possible controls may significantly improve the discovery power of genome-wide association studies.

Increasing Sample Diversity in Psychiatric Genetics – Introducing a new Cohort of Patients with Schizophrenia and Controls from Vietnam – Results from a Pilot Study

10.1101/2021.04.21.21255615 ◽

2021 ◽

Author(s):

VT Nguyen ◽

A Braun ◽

J Kraft ◽

TMT Ta ◽

GM Panagiotaropoulou ◽

...

Keyword(s):

Pilot Study ◽

Data Collection ◽

Predictive Power ◽

Association Studies ◽

East Asian ◽

Genetic Research ◽

European Ancestry ◽

Risk Scores ◽

AbstractObjectivesGenome-Wide Association Studies (GWAS) of Schizophrenia (SCZ) have provided new biological insights; however, most cohorts are of European ancestry. As a result, derived polygenic risk scores (PRS) show decreased predictive power when applied to populations of different ancestries. We aimed to assess the feasibility of a large-scale data collection in Hanoi, Vietnam, contribute to international efforts to diversify ancestry in SCZ genetic research and examine the transferability of SCZ-PRS to individuals of Vietnamese Kinh ancestry.MethodsIn a pilot study, 368 individuals (including 190 SCZ cases) were recruited at the Hanoi Medical University’s associated psychiatric hospitals and outpatient facilities. Data collection included sociodemographic data, baseline clinical data, clinical interviews assessing symptom severity and genome-wide SNP genotyping. SCZ-PRS were generated using different training data sets: i) European, ii) East-Asian and iii) trans-ancestry GWAS summary statistics from the latest SCZ GWAS meta-analysis.ResultsSCZ-PRS significantly predicted case status in Vietnamese individuals using mixed-ancestry (R2 liability=4.9%, p=6.83*10−8), East-Asian (R2 liability=4.5%, p=2.73*10−7) and European (R2 liability=3.8%, p = 1.79*10−6) discovery samples.DiscussionOur results corroborate previous findings of reduced PRS predictive power across populations, highlighting the importance of ancestral diversity in GWA studies.