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Proceedings of the National Academy of... Aug 2020The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's...
The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.
Topics: Genetic Variation; Genome-Wide Association Study; Humans; Models, Genetic; Multifactorial Inheritance; Phenotype; Quantitative Trait, Heritable
PubMed: 32753378
DOI: 10.1073/pnas.2005634117 -
European Journal of Epidemiology Sep 2023Physical activity (PA), aerobic fitness, and cardiometabolic diseases (CMD) are highly heritable multifactorial phenotypes. Shared genetic factors may underlie the...
Physical activity (PA), aerobic fitness, and cardiometabolic diseases (CMD) are highly heritable multifactorial phenotypes. Shared genetic factors may underlie the associations between higher levels of PA and better aerobic fitness and a lower risk for CMDs. We aimed to study how PA genotype associates with self-reported PA, aerobic fitness, cardiometabolic risk factors and diseases. PA genotype, which combined variation in over one million of gene variants, was composed using the SBayesR polygenic scoring methodology. First, we constructed a polygenic risk score for PA in the Trøndelag Health Study (N = 47,148) using UK Biobank single nucleotide polymorphism-specific weights (N = 400,124). The associations of the PA PRS and continuous variables were analysed using linear regression models and with CMD incidences using Cox proportional hazard models. The results showed that genotypes predisposing to higher amount of PA were associated with greater self-reported PA (Beta [B] = 0.282 MET-h/wk per SD of PRS for PA, 95% confidence interval [CI] = 0.211, 0.354) but not with aerobic fitness. These genotypes were also associated with healthier cardiometabolic profile (waist circumference [B = -0.003 cm, 95% CI = -0.004, -0.002], body mass index [B = -0.002 kg/m, 95% CI = -0.004, -0.001], high-density lipoprotein cholesterol [B = 0.004 mmol/L, 95% CI = 0.002, 0.006]) and lower incidence of hypertensive diseases (Hazard Ratio [HR] = 0.97, 95% CI = 0.951, 0.990), stroke (HR = 0.94, 95% CI = 0.903, 0.978) and type 2 diabetes (HR = 0.94, 95 % CI = 0.902, 0.970). Observed associations were independent of self-reported PA. These results support earlier findings suggesting small pleiotropic effects between PA and CMDs and provide new evidence about associations of polygenic inheritance of PA and intermediate cardiometabolic risk factors.
Topics: Humans; Cardiometabolic Risk Factors; Diabetes Mellitus, Type 2; Exercise; Hypertension; Multifactorial Inheritance; Genetic Risk Score
PubMed: 37603226
DOI: 10.1007/s10654-023-01029-w -
Journal of Child Psychology and... May 2020Polygenic scores estimate an individual's genetic liability for a particular disorder or trait. They are based on current knowledge of the trait's genetic architecture...
Polygenic scores estimate an individual's genetic liability for a particular disorder or trait. They are based on current knowledge of the trait's genetic architecture and focus on common genetic variants. In this editorial, I will discuss some of the strengths, weaknesses, opportunities and threats (SWOT) to polygenic scores within the context of child and adolescent psychiatry. I consider how the potential application of polygenic scores in health settings has some parallels with existing practices, but that polygenic scores also undoubtedly raise unique challenges. This SWOT analysis is accompanied by discussion of some new findings using polygenic scores in this issue of Journal of Child Psychology and Psychiatry.
Topics: Adolescent; Adolescent Psychiatry; Child; Child Psychiatry; Genetic Predisposition to Disease; Humans; Multifactorial Inheritance
PubMed: 32304105
DOI: 10.1111/jcpp.13246 -
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 -
Trends in Genetics : TIG May 2024Lennon et al. recently proposed a clinical polygenic score (PGS) pipeline as part of the Electronic Medical Records and Genomics (eMERGE) network initiative. In this... (Review)
Review
Lennon et al. recently proposed a clinical polygenic score (PGS) pipeline as part of the Electronic Medical Records and Genomics (eMERGE) network initiative. In this spotlight article we discuss the broader context for the use of PGS in preventive medicine and highlight key limitations and challenges facing their inclusion in prediction models.
Topics: Multifactorial Inheritance; Humans; Genomics; Genetic Predisposition to Disease; Genome-Wide Association Study; Electronic Health Records; Preventive Medicine
PubMed: 38643035
DOI: 10.1016/j.tig.2024.04.002 -
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 -
Immunology Sep 2018High-throughput sequencing of the DNA/RNA encoding antibody heavy- and light-chains is rapidly transforming the field of adaptive immunity. It can address key questions,... (Review)
Review
High-throughput sequencing of the DNA/RNA encoding antibody heavy- and light-chains is rapidly transforming the field of adaptive immunity. It can address key questions, including: (i) how the B-cell repertoire differs in health and disease; and (ii) if it does differ, the point(s) in B-cell development at which this occurs. The advent of technologies, such as whole-genome sequencing, offers the chance to link abnormalities in the B-cell antibody repertoire to specific genomic variants and polymorphisms. Here, we discuss the current research using B-cell antibody repertoire sequencing in three polygenic autoimmune diseases where there is good evidence for a pathological role for B-cells, namely systemic lupus erythematosus, multiple sclerosis and rheumatoid arthritis. These autoimmune diseases exhibit significantly skewed B-cell receptor repertoires compared with healthy controls. Interestingly, some common repertoire defects are shared between diseases, such as elevated IGHV4-34 gene usage. B-cell clones have effectively been characterized and tracked between different tissues and blood in autoimmune disease. It has been hypothesized that these differences may signify differences in B-cell tolerance; however, the mechanisms and implications of these defects are not clear.
Topics: Antibodies; Autoimmune Diseases; High-Throughput Nucleotide Sequencing; Humans; Multifactorial Inheritance
PubMed: 29574826
DOI: 10.1111/imm.12927 -
Cell Metabolism Mar 2017Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic... (Review)
Review
Except in rare cases, obesity tends to be a consequence of both an unhealthy lifestyle and a genetic susceptibility to gain weight. With more than 200 common genetic variants identified, there is a growing interest in developing personalized preventive and treatment strategies to predict an individual's obesity risk. We review the literature on the prediction of obesity and show that models based on the established genetic variants have poorer predictive ability than traditional predictors, such as family history of obesity and childhood obesity. Current findings suggest that opportunities for precision medicine in common obesity may be limited.
Topics: Genetic Predisposition to Disease; Genetic Variation; Humans; Life Style; Multifactorial Inheritance; Obesity; Risk Factors
PubMed: 28273476
DOI: 10.1016/j.cmet.2017.02.013