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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 -
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 -
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 -
Nature Human Behaviour May 2019Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic...
Genetic correlations estimated from genome-wide association studies (GWASs) reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modelling (genomic SEM): a multivariate method for analysing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and single-nucleotide polymorphism heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores and identify loci that cause divergence between traits. We demonstrate several applications of genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent single-nucleotide polymorphisms not previously identified in the contributing univariate GWASs. Polygenic scores from genomic SEM consistently outperform those from univariate GWASs. Genomic SEM is flexible and open ended, and allows for continuous innovation in multivariate genetic analysis.
Topics: Factor Analysis, Statistical; Genome-Wide Association Study; Genomics; Humans; Latent Class Analysis; Mental Disorders; Multifactorial Inheritance; Multivariate Analysis; Polymorphism, Single Nucleotide
PubMed: 30962613
DOI: 10.1038/s41562-019-0566-x -
American Journal of Human Genetics Jan 2019Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method...
Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.
Topics: Calibration; Databases, Genetic; Datasets as Topic; False Positive Reactions; Genome-Wide Association Study; Humans; Multifactorial Inheritance; Polymorphism, Single Nucleotide; Probability; Reproducibility of Results; United Kingdom
PubMed: 30595370
DOI: 10.1016/j.ajhg.2018.11.008 -
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 -
Nature Genetics Sep 2022The genetic etiology of autism spectrum disorder (ASD) is multifactorial, but how combinations of genetic factors determine risk is unclear. In a large family sample, we...
The genetic etiology of autism spectrum disorder (ASD) is multifactorial, but how combinations of genetic factors determine risk is unclear. In a large family sample, we show that genetic loads of rare and polygenic risk are inversely correlated in cases and greater in females than in males, consistent with a liability threshold that differs by sex. De novo mutations (DNMs), rare inherited variants and polygenic scores were associated with various dimensions of symptom severity in children and parents. Parental age effects on risk for ASD in offspring were attributable to a combination of genetic mechanisms, including DNMs that accumulate in the paternal germline and inherited risk that influences behavior in parents. Genes implicated by rare variants were enriched in excitatory and inhibitory neurons compared with genes implicated by common variants. Our results suggest that a phenotypic spectrum of ASD is attributable to a spectrum of genetic factors that impact different neurodevelopmental processes.
Topics: Autism Spectrum Disorder; Autistic Disorder; Child; Family; Female; Genetic Predisposition to Disease; Humans; Male; Multifactorial Inheritance
PubMed: 35654974
DOI: 10.1038/s41588-022-01064-5 -
Proceedings of the National Academy of... Aug 2023Autism spectrum disorder (ASD) has a complex genetic architecture involving contributions from both de novo and inherited variation. Few studies have been designed to...
Autism spectrum disorder (ASD) has a complex genetic architecture involving contributions from both de novo and inherited variation. Few studies have been designed to address the role of rare inherited variation or its interaction with common polygenic risk in ASD. Here, we performed whole-genome sequencing of the largest cohort of multiplex families to date, consisting of 4,551 individuals in 1,004 families having two or more autistic children. Using this study design, we identify seven previously unrecognized ASD risk genes supported by a majority of rare inherited variants, finding support for a total of 74 genes in our cohort and a total of 152 genes after combined analysis with other studies. Autistic children from multiplex families demonstrate an increased burden of rare inherited protein-truncating variants in known ASD risk genes. We also find that ASD polygenic score (PGS) is overtransmitted from nonautistic parents to autistic children who also harbor rare inherited variants, consistent with combinatorial effects in the offspring, which may explain the reduced penetrance of these rare variants in parents. We also observe that in addition to social dysfunction, language delay is associated with ASD PGS overtransmission. These results are consistent with an additive complex genetic risk architecture of ASD involving rare and common variation and further suggest that language delay is a core biological feature of ASD.
Topics: Child; Humans; Autism Spectrum Disorder; Multifactorial Inheritance; Parents; Whole Genome Sequencing; Language Development Disorders; Genetic Predisposition to Disease
PubMed: 37506195
DOI: 10.1073/pnas.2215632120 -
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 -
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