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Current Protocols in Human Genetics Dec 2019Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical... (Review)
Review
Genome-wide variation data with millions of genetic markers have become commonplace. However, the potential for interpretation and application of these data for clinical assessment of outcomes of interest, and prediction of disease risk, is currently not fully realized. Many common complex diseases now have numerous, well-established risk loci and likely harbor many genetic determinants with effects too small to be detected at genome-wide levels of statistical significance. A simple and intuitive approach for converting genetic data to a predictive measure of disease susceptibility is to aggregate the effects of these loci into a single measure, the genetic risk score. Here, we describe some common methods and software packages for calculating genetic risk scores and polygenic risk scores, with focus on studies of common complex diseases. We review the basic information needed, as well as important considerations for constructing genetic risk scores, including specific requirements for phenotypic and genetic data, and limitations in their application. © 2019 by John Wiley & Sons, Inc.
Topics: Disease; Genetic Markers; Genetic Predisposition to Disease; Genotype; Humans; Multifactorial Inheritance; Phenotype; Risk Factors; Software
PubMed: 31765077
DOI: 10.1002/cphg.95 -
Genetic Epidemiology May 2016Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been... (Review)
Review
Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered.
Topics: Genetic Predisposition to Disease; Genotype; Humans; Linear Models; Linkage Disequilibrium; Models, Genetic; Molecular Epidemiology; Multifactorial Inheritance; Phenotype
PubMed: 27061411
DOI: 10.1002/gepi.21966 -
Cell Jun 2018The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up... (Review)
Review
The evidence that most adult-onset common diseases have a polygenic genetic architecture fully consistent with robust biological systems supported by multiple back-up mechanisms is now overwhelming. In this context, we consider the recent "omnigenic" or "core genes" model. A key assumption of the model is that there is a relatively small number of core genes relevant to any disease. While intuitively appealing, this model may underestimate the biological complexity of common disease, and therefore, the goal to discover core genes should not guide experimental design. We consider other implications of polygenicity, concluding that a focus on patient stratification is needed to achieve the goals of precision medicine.
Topics: Disease; Genome-Wide Association Study; Humans; Models, Genetic; Multifactorial Inheritance; Precision Medicine
PubMed: 29906445
DOI: 10.1016/j.cell.2018.05.051 -
Annual Review of Genomics and Human... Aug 2022Brugada syndrome is a heritable channelopathy characterized by a peculiar electrocardiogram (ECG) pattern and increased risk of cardiac arrhythmias and sudden death. The... (Review)
Review
Brugada syndrome is a heritable channelopathy characterized by a peculiar electrocardiogram (ECG) pattern and increased risk of cardiac arrhythmias and sudden death. The arrhythmias originate because of an imbalance between the repolarizing and depolarizing currents that modulate the cardiac action potential. Even if an overt structural cardiomyopathy is not typical of Brugada syndrome, fibrosis and structural changes in the right ventricle contribute to a conduction slowing, which ultimately facilitates ventricular arrhythmias. Currently, Mendelian autosomal dominant transmission is detected in less than 25% of all clinical confirmed cases. Although 23 genes have been associated with the condition, only , encoding the cardiac sodium channel, is considered clinically actionable and disease causing. The limited monogenic inheritance has pointed toward new perspectives on the possible complex genetic architecture of the disease, involving polygenic inheritance and a polygenic risk score that can influence penetrance and risk stratification.
Topics: Brugada Syndrome; Electrocardiography; Humans; Multifactorial Inheritance; NAV1.5 Voltage-Gated Sodium Channel; Sodium Channels
PubMed: 35567276
DOI: 10.1146/annurev-genom-112921-011200 -
Genetic Epidemiology Sep 2017Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk...
Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.
Topics: Case-Control Studies; Computer Simulation; Databases, Genetic; Humans; Models, Genetic; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Regression Analysis; Statistics as Topic
PubMed: 28480976
DOI: 10.1002/gepi.22050 -
Current Protocols May 2021As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings,...
As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.
Topics: Genome-Wide Association Study; Multifactorial Inheritance; Risk Factors; Software
PubMed: 33987971
DOI: 10.1002/cpz1.126 -
Cold Spring Harbor Perspectives in... Feb 2021Many exposures considered in Mendelian randomization (MR) studies are polygenic in that they are influenced by thousands of genetic variants. By using many... (Review)
Review
Many exposures considered in Mendelian randomization (MR) studies are polygenic in that they are influenced by thousands of genetic variants. By using many single-nucleotide polymorphisms (SNPs) as instrumental variables, more variation in the exposure is explained, increasing the precision of MR. Furthermore, methods can be designed that relax the assumptions of MR, especially concerning direct pleiotropic effects on the outcome. This article reviews the concepts and assumptions underlying the commonly used polygenic MR methods. Using a polygenic score as an instrument is equivalent to a weighted mean of individual SNP results, and the other fundamental averages, median and mode, may also be used to estimate causal effects. Outlier detection is useful for identifying pleiotropic SNPs to be excluded from analysis. Bayesian approaches are available to incorporate prior beliefs about pleiotropy. These methods each entail different assumptions, and together provide a set of sensitivity analyses to help triangulate evidence about causality.
Topics: Causality; Genetic Variation; Genome-Wide Association Study; Humans; Mendelian Randomization Analysis; Multifactorial Inheritance; Polymorphism, Single Nucleotide
PubMed: 32229610
DOI: 10.1101/cshperspect.a039586 -
Neuron Nov 2023Naomi Wray works at the interface of genetics, statistics and psychiatric disorders. With early training in quantitative genetics applied to livestock she brought to the...
Naomi Wray works at the interface of genetics, statistics and psychiatric disorders. With early training in quantitative genetics applied to livestock she brought to the field a perspective on the polygenic nature of common, complex disease. She advocates for experimental paradigms that exploit polygenicity to advance translational outcomes in psychiatry.
Topics: Female; Humans; Mental Disorders; Psychiatry; Multifactorial Inheritance
PubMed: 37918354
DOI: 10.1016/j.neuron.2023.09.001 -
Journal of the American Association of... Jul 2020Genetics is now known to play a substantial role in the predisposition to obesity and may contribute up to 70% risk for the disease. Over a hundred genes and gene...
Genetics is now known to play a substantial role in the predisposition to obesity and may contribute up to 70% risk for the disease. Over a hundred genes and gene variants related to excess weight have been discovered. Yet, genetic obesity risk does not always translate into actual obesity development, suggesting complex interactions between genetic, behavioral, and environmental influences and resulting epigenetic changes. Rare but serious forms of monogenic obesity typically appear in early childhood. Polygenic obesity is most common and demonstrates strong interplay between genes and the obesogenic environment. This review provides an overview of genetic causes of obesity, potential mechanisms of epigenetic changes, and environmental influences that should diminish obesity bias and offer hope for more effective obesity prevention and intervention strategies.
Topics: Alpha-Ketoglutarate-Dependent Dioxygenase FTO; Humans; Multifactorial Inheritance; Obesity; United States
PubMed: 32658169
DOI: 10.1097/JXX.0000000000000447 -
Molecular Psychiatry Jan 2022During the past decade, polygenic scores have become a fast-growing area of research in the behavioural sciences. The ability to directly assess people's genetic... (Review)
Review
During the past decade, polygenic scores have become a fast-growing area of research in the behavioural sciences. The ability to directly assess people's genetic propensities has transformed research by making it possible to add genetic predictors of traits to any study. The value of polygenic scores in the behavioural sciences rests on using inherited DNA differences to predict, from birth, common disorders and complex traits in unrelated individuals in the population. This predictive power of polygenic scores does not require knowing anything about the processes that lie between genes and behaviour. It also does not mandate disentangling the extent to which the prediction is due to assortative mating, genotype-environment correlation, or even population stratification. Although bottom-up explanation from genes to brain to behaviour will remain the long-term goal of the behavioural sciences, prediction is also a worthy achievement because it has immediate practical utility for identifying individuals at risk and is the necessary first step towards explanation. A high priority for research must be to increase the predictive power of polygenic scores to be able to use them as an early warning system to prevent problems.
Topics: Brain; Genome-Wide Association Study; Genotype; Humans; Multifactorial Inheritance; Phenotype
PubMed: 34686768
DOI: 10.1038/s41380-021-01348-y