-
International Journal of Molecular... Mar 2020Brugada syndrome (BrS) is diagnosed by a coved-type ST-segment elevation in the right precordial leads on the electrocardiogram (ECG), and it is associated with an... (Review)
Review
Brugada syndrome (BrS) is diagnosed by a coved-type ST-segment elevation in the right precordial leads on the electrocardiogram (ECG), and it is associated with an increased risk of sudden cardiac death (SCD) compared to the general population. Although BrS is considered a genetic disease, its molecular mechanism remains elusive in about 70-85% of clinically-confirmed cases. Variants occurring in at least 26 different genes have been previously considered causative, although the causative effect of all but the gene has been recently challenged, due to the lack of systematic, evidence-based evaluations, such as a variant's frequency among the general population, family segregation analyses, and functional studies. Also, variants within a particular gene can be associated with an array of different phenotypes, even within the same family, preventing a clear genotype-phenotype correlation. Moreover, an emerging concept is that a single mutation may not be enough to cause the BrS phenotype, due to the increasing number of common variants now thought to be clinically relevant. Thus, not only the complete list of genes causative of the BrS phenotype remains to be determined, but also the interplay between rare and common multiple variants. This is particularly true for some common polymorphisms whose roles have been recently re-evaluated by outstanding works, including considering for the first time ever a polygenic risk score derived from the heterozygous state for both common and rare variants. The more common a certain variant is, the less impact this variant might have on heart function. We are aware that further studies are warranted to validate a polygenic risk score, because there is no mutated gene that connects all, or even a majority, of BrS cases. For the same reason, it is currently impossible to create animal and cell line genetic models that represent all BrS cases, which would enable the expansion of studies of this syndrome. Thus, the best model at this point is the human patient population. Further studies should first aim to uncover genetic variants within individuals, as well as to collect family segregation data to identify potential genetic causes of BrS.
Topics: Animals; Brugada Syndrome; Humans; Ion Channels; Multifactorial Inheritance; Mutation; Sarcomeres
PubMed: 32121523
DOI: 10.3390/ijms21051687 -
Human Genomics Jul 2021Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics... (Review)
Review
Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs.
Topics: Genetic Diseases, Inborn; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Phenotype; Risk Factors
PubMed: 34284826
DOI: 10.1186/s40246-021-00339-y -
Cold Spring Harbor Perspectives in... Sep 2021Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason...
Causation has multiple distinct meanings in genetics. One reason for this is meaning slippage between two concepts of the gene: Mendelian and molecular. Another reason is that a variety of genetic methods address different kinds of causal relationships. Some genetic studies address causes of traits in individuals, which can only be assessed when single genes follow predictable inheritance patterns that reliably cause a trait. A second sense concerns the causes of trait differences within a population. Whereas some single genes can be said to cause population-level differences, most often these claims concern the effects of many genes. Polygenic traits can be understood using heritability estimates, which estimate the relative influences of genetic and environmental differences to trait differences within a population. Attempts to understand the molecular mechanisms underlying polygenic traits have been developed, although causal inference based on these results remains controversial. Genetic variation has also recently been leveraged as a randomizing factor to identify environmental causes of trait differences. This technique-Mendelian randomization-offers some solutions to traditional epidemiological challenges, although it is limited to the study of environments with known genetic influences.
Topics: Gene-Environment Interaction; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Phenotype
PubMed: 33753353
DOI: 10.1101/cshperspect.a040519 -
Journal of the American Academy of... Jun 2021Understanding the genetic architecture of psychiatric disorders is paramount to linking psychopathologies to their genetic underpinnings. In turn, this knowledge can...
Understanding the genetic architecture of psychiatric disorders is paramount to linking psychopathologies to their genetic underpinnings. In turn, this knowledge can inform strategies for identifying high-risk individuals, early intervention, and development of personalized treatment approaches. Over the past 2 decades, owing to lowering per capita costs and relative ease of analysis, a plethora of studies have used single nucleotide polymorphism genotyping and genome-wide association studies (GWASs) to unravel common and rare risk loci underlying psychiatric disorders and their endophenotypes. In contrast to the single allele focus of classical Mendelian inheritance, mental illnesses are often polygenic in nature with multiple common genetic variants, each contributing a small, but meaningful added risk. By interrogating the entire genome, GWASs have allowed the functional assessment of promising candidate genes in in vivo as well as in vitro models of psychiatric disease. Further, these findings have spawned the approach of calculating polygenic risk scores, a promising strategy for inferring genetic susceptibility to the development of psychopathology by taking into account the polygenic structure of psychiatric disorders.
Topics: Child; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Mental Disorders; Multifactorial Inheritance; Polymorphism, Single Nucleotide
PubMed: 33385509
DOI: 10.1016/j.jaac.2020.12.031 -
Genetic Epidemiology Oct 2022As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise... (Review)
Review
As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family- and genome-wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.
Topics: Genome-Wide Association Study; Humans; Linkage Disequilibrium; Models, Genetic; Multifactorial Inheritance; Phenotype; Polymorphism, Single Nucleotide
PubMed: 35652173
DOI: 10.1002/gepi.22459 -
Nature Human Behaviour Sep 2023Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have...
Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association studies of the Big 5 personality traits and cognitive function on our multivariate findings, boosting genetic discovery in other personality traits and improving polygenic prediction. These findings advance our understanding of the polygenic architecture of these complex mental traits, indicating a prominence of pleiotropic genetic effects across higher order domains of mental function such as personality and cognitive function.
Topics: Humans; Genome-Wide Association Study; Personality; Phenotype; Multifactorial Inheritance; Cognition
PubMed: 37365406
DOI: 10.1038/s41562-023-01630-9 -
Annual Review of Medicine Jan 2023Polygenic scores quantify inherited risk by integrating information from many common sites of DNA variation into a single number. Rapid increases in the scale of genetic... (Review)
Review
Polygenic scores quantify inherited risk by integrating information from many common sites of DNA variation into a single number. Rapid increases in the scale of genetic association studies and new statistical algorithms have enabled development of polygenic scores that meaningfully measure-as early as birth-risk of coronary artery disease. These newer-generation polygenic scores identify up to 8% of the population with triple the normal risk based on genetic variation alone, and these individuals cannot be identified on the basis of family history or clinical risk factors alone. For those identified with increased genetic risk, evidence supports risk reduction with at least two interventions, adherence to a healthy lifestyle and cholesterol-lowering therapies, that can substantially reduce risk. Alongside considerable enthusiasm for the potential of polygenic risk estimation to enable a new era of preventive clinical medicine is recognition of a need for ongoing research into how best to ensure equitable performance across diverse ancestries, how and in whom to assess the scores in clinical practice, as well as randomized trials to confirm clinical utility.
Topics: Humans; Coronary Artery Disease; Risk Factors; Multifactorial Inheritance; Genome-Wide Association Study; Genetic Predisposition to Disease
PubMed: 36315649
DOI: 10.1146/annurev-med-042921-112629 -
Annual Review of Biomedical Data Science Aug 2022Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants... (Review)
Review
Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
Topics: Biomarkers; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Risk Factors
PubMed: 35576555
DOI: 10.1146/annurev-biodatasci-111721-074830 -
European Journal of Psychotraumatology 2023Although trauma exposure (TE) is a transdiagnostic risk factor for many psychiatric disorders, not everyone who experiences TE develops a psychiatric disorder....
Although trauma exposure (TE) is a transdiagnostic risk factor for many psychiatric disorders, not everyone who experiences TE develops a psychiatric disorder. Resilience may explain this heterogeneity; thus, it is critical to understand the etiologic underpinnings of resilience. The present study sought to examine the genetic underpinnings of psychiatric resilience using genome-wide association studies (GWAS), genome-wide complex trait analysis (GCTA), and polygenic risk score (PRS) analyses. Participants were 6,634 trauma exposed college students attending a diverse, public university in the Mid Atlantic. GWAS and GCTA analyses were conducted, and using GWAS summary statistics from large genetic consortia, PRS analyses examined the shared genetic risk between resilience and various phenotypes. Results demonstrate that nine single-nucleotide polymorphisms (SNPs) met the suggestive of significance threshold, heritability estimates for resilience were non-significant, and that there is genetic overlap between resilience and AD, as well as resilience and PTSD. Mixed findings from the present study suggest additional research to elucidate the etiological underpinnings of resilience, ideally with larger samples less biased by variables such as heterogeneity (i.e. clinical vs. population based) and population stratification. Genetic investigations of resilience have the potential to elucidate the molecular bases of stress-related psychopathology, suggesting new avenues for prevention and intervention efforts.
Topics: Humans; Genetic Predisposition to Disease; Genome-Wide Association Study; Mental Disorders; Multifactorial Inheritance; Risk Factors
PubMed: 37052082
DOI: 10.1080/20008066.2023.2178762 -
Journal of Child Psychology and... Oct 2022The increasing availability of genotype data in longitudinal population- and family-based samples provides opportunities for using polygenic scores (PGS) to study... (Review)
Review
The increasing availability of genotype data in longitudinal population- and family-based samples provides opportunities for using polygenic scores (PGS) to study developmental questions in child and adolescent psychology and psychiatry. Here, we aim to provide a comprehensive overview of how PGS can be generated and implemented in developmental psycho(patho)logy, with a focus on longitudinal designs. As such, the paper is organized into three parts: First, we provide a formal definition of polygenic scores and related concepts, focusing on assumptions and limitations. Second, we give a general overview of the methods used to compute polygenic scores, ranging from the classic approach to more advanced methods. We include recommendations and reference resources available to researchers aiming to conduct PGS analyses. Finally, we focus on the practical applications of PGS in the analysis of longitudinal data. We describe how PGS have been used to research developmental outcomes, and how they can be applied to longitudinal data to address developmental questions.
Topics: Adolescent; Child; Genotype; Humans; Multifactorial Inheritance
PubMed: 35354222
DOI: 10.1111/jcpp.13611