scholarly journals Existence and implications of population variance structure

2018 ◽  
Author(s):  
Shaila Musharoff ◽  
Danny Park ◽  
Andy Dahl ◽  
Joshua Galanter ◽  
Xuanyao Liu ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Direct Consequence ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Population Variance ◽  
Phenotypic Differences ◽  
Genetic And Environmental Factors ◽  
The Relationship ◽  
Variance Structure

AbstractIdentifying the genetic and environmental factors underlying phenotypic differences between populations is fundamental to multiple research communities. To date, studies have focused on the relationship between population and phenotypic mean. Here we consider the relationship between population and phenotypic variance, i.e., “population variance structure.” In addition to gene-gene and gene-environment interaction, we show that population variance structure is a direct consequence of natural selection. We develop the ancestry double generalized linear model (ADGLM), a statistical framework to jointly model population mean and variance effects. We apply ADGLM to several deeply phenotyped datasets and observe ancestry-variance associations with 12 of 44 tested traits in ~113K British individuals and 3 of 14 tested traits in ~3K Mexican, Puerto Rican, and African-American individuals. We show through extensive simulations that population variance structure can both bias and reduce the power of genetic association studies, even when principal components or linear mixed models are used. ADGLM corrects this bias and improves power relative to previous methods in both simulated and real datasets. Additionally, ADGLM identifies 17 novel genotype-variance associations across six phenotypes.

scholarly journals Carotid Artery Atherosclerosis: A Review on Heritability and Genetics

2018 ◽  
Vol 21 (5) ◽  
pp. 333-346 ◽  
Author(s):  
Bianka Forgo ◽  
Emanuela Medda ◽  
Anita Hernyes ◽  
Laszlo Szalontai ◽  
David Laszlo Tarnoki ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Intima Media Thickness ◽  
Carotid Intima Media Thickness ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Carotid Artery Atherosclerosis ◽  
Meta Analyses

Carotid atherosclerosis (CAS) is associated with increased cardiovascular risk, and therefore, assessing the genetic versus environmental background of CAS traits is of key importance. Carotid intima-media-thickness and plaque characteristics seem to be moderately heritable, with remarkable differences in both heritability and presence or severity of these traits among ethnicities. Although the considerable role of additive genetic effects is obvious, based on the results so far, there is an important emphasis on non-shared environmental factors as well. We aimed to collect and summarize the papers that investigate twin and family studies assessing the phenotypic variance attributable to genetic associations with CAS. Genes in relation to CAS markers were overviewed with a focus on genetic association studies and genome-wide association studies. Although the role of certain genes is confirmed by studies conducted on large populations and meta-analyses, many of them show conflicting results. A great focus should be on future studies elucidating the exact pathomechanism of these genes in CAS in order to imply them as novel therapeutic targets.

scholarly journals Statistical analysis of genetic interactions

2010 ◽  
Vol 92 (5-6) ◽  
pp. 443-459 ◽  
Author(s):  
NENGJUN YI
Keyword(s):  
Statistical Analysis ◽  
Complex Traits ◽  
Association Studies ◽  
Computer Software ◽  
Genetic Association Studies ◽  
Genetic Interactions ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genetic And Environmental Factors

SummaryMany common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene–gene and gene–environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.

scholarly journals The genomic architecture of blood metabolites based on a decade of genome-wide analyses

2019 ◽  
Author(s):  
Fiona A. Hagenbeek ◽  
René Pool ◽  
Jenny van Dongen ◽  
Harmen H.M. Draisma ◽  
Jouke Jan Hottenga ◽  
...  
Keyword(s):  
Organic Acids ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Phenotypic Differences ◽  
Degree Of Unsaturation ◽  
Genome Wide ◽  
Low Degree ◽  
Common Genetic Variants ◽  
Lipid Species ◽  
High Degree

AbstractMetabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes and lipid species. We performed a review of all genetic association studies, and identified > 800 class-specific metabolite loci that influence metabolite levels. In a twin-family cohort (N= 5,117), these metabolite loci were leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study revealed significant differences inh2Metabolite-hitsamong different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation had higherh2Metabolite-hitsestimates than phosphatidylcholines with a low degree of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes and lipid species.

A Perspective on Interaction Tests in Genetic Association Studies

2015 ◽  
Author(s):  
Hugues Aschard
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genetic Effect ◽  
Interaction Effects ◽  
Effect Estimate ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
New Perspective ◽  
Estimation Of Variance

The identification of gene-gene and gene-environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of some of the inherent characteristics of interaction effects. Here, I untangle several theoretical aspects of standard regression-based interaction tests in genetic association studies. In particular, I discuss variables coding scheme, interpretation of effect estimate, power, and estimation of variance explained in regard of various hypothetical interaction patterns. I show first that the simplest biological interaction models—in which the magnitude of a genetic effect depends on a common exposure—are among the most difficult to identify. Then, I demonstrate the demerits of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome these issues. Finally I explore the advantages and limitations of multivariate models when testing for interaction between multiple SNPs and/or multiple exposures, using either a joint test, or a test of interaction based on risk score. Theoretical and simulated examples presented along the manuscript demonstrate that the application of these methods can provide a new perspective on the role of interaction in multifactorial traits.

scholarly journals Integrating epidemiology and genetic association: the challenge of gene–environment interaction

2005 ◽  
Vol 360 (1460) ◽  
pp. 1609-1616 ◽  
Author(s):  
Peter Kraft ◽  
David Hunter
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Locus ◽  
Single Gene ◽  
Genetic Association Studies ◽  
Biologically Active ◽  
Marginal Effect ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment

Recent advances in human genomics have made it possible to better understand the genetic basis of disease. In addition, genetic association studies can also elucidate the mechanisms by which ‘non-genetic’ exogenous and endogenous exposures influence the risk of disease. This is true both of studies that assess the marginal effect of a single gene and studies that look at the joint effect of genes and environmental exposures. For example, gene variants that are known to alter enzyme function or level can serve as surrogates for long-term biomarker levels that are impractical or impossible to measure on many subjects. Evidence that genetic variants modify the effect of an established risk factor may help specify the risk factor's biologically active components. We illustrate these ideas with several examples and discuss design and analysis challenges, particularly for studies of gene–environment interaction. We argue that to increase the power to detect interaction effects and limit the number of false positive results, large sample sizes will be needed, which are currently only available through planned collaborative efforts. Such collaborations also ensure a common approach to measuring variation at a genetic locus, avoiding a problem that has led to difficulties when comparing results from genetic association studies.

scholarly journals Power and Sample Size Calculations for Genetic Association Studies in the Presence of Genetic Model Misspecification

2019 ◽  
Vol 84 (6) ◽  
pp. 256-271 ◽  
Author(s):  
Camille M. Moore ◽  
Sean A. Jacobson ◽  
Tasha E. Fingerlin
Keyword(s):  
Sample Size ◽  
Genetic Association ◽  
Genetic Model ◽  
Association Studies ◽  
Model Misspecification ◽  
Genetic Association Studies ◽  
Genetic Effects ◽  
Degree Of Freedom ◽  
Environment Interaction ◽  
Sample Size Calculations

<b><i>Introduction:</i></b> When analyzing data from large-scale genetic association studies, such as targeted or genome-wide resequencing studies, it is common to assume a single genetic model, such as dominant or additive, for all tests of association between a given genetic variant and the phenotype. However, for many variants, the chosen model will result in poor model fit and may lack statistical power due to model misspecification. <b><i>Objective:</i></b> We develop power and sample size calculations for tests of gene and gene × environment interaction, allowing for misspecification of the true mode of genetic susceptibility. <b><i>Methods:</i></b> The power calculations are based on a likelihood ratio test framework and are implemented in an open-source R package (“genpwr”). <b><i>Results:</i></b> We use these methods to develop an analysis plan for a resequencing study in idiopathic pulmonary fibrosis and show that using a 2-degree of freedom test can increase power to detect recessive genetic effects while maintaining power to detect dominant and additive effects. <b><i>Conclusions:</i></b> Understanding the impact of model misspecification can aid in study design and developing analysis plans that maximize power to detect a range of true underlying genetic effects. In particular, these calculations help identify when a multiple degree of freedom test or other robust test of association may be advantageous.

scholarly journals Allele Frequencies and the r2 Measure of Linkage Disequilibrium: Impact on Design and Interpretation of Association Studies

2005 ◽  
Vol 8 (2) ◽  
pp. 87-94 ◽  
Author(s):  
Naomi R. Wray
Keyword(s):  
Linkage Disequilibrium ◽  
Allele Frequency ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Maximum Difference ◽  
Functional Variant ◽  
Severe Constraint ◽  
The Difference ◽  
The Impact ◽  
The Relationship

AbstractThe design and interpretation of genetic association studies depends on the relationship between the genotyped variants and the underlying functional variant, often parameterized as the squared correlation or r2 measure of linkage disequilibrium between two loci. While it has long been recognized that placing a constraint on the r2 between two loci also places a constraint on the difference in frequencies between the coupled alleles, this constraint has not been quantified. Here, quantification of this severe constraint is presented. For example, for r2 ≥ .8, the maximum difference in allele frequency is ± .06 which occurs when one locus has allele frequency .5. For r2 ≥ .8 and allele frequency at one locus of .1, the maximum difference in allele frequency at the second locus is only ± .02. The impact on the design and interpretation of association studies is discussed.

scholarly journals A simple approximation to bias in gene-environment interaction estimates when a case might not be the case

2018 ◽  
Author(s):  
Iryna Lobach ◽  
Inyoung Kim ◽  
Alexander Alekseyenko ◽  
Siarhei Lobach ◽  
Li Zhang
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Parameter Estimates ◽  
Environment Interaction ◽  
Finite Samples ◽  
Gene Environment ◽  
Environmental Measures ◽  
Pathologic State

ABSTRACTCase-control genetic association studies are often used to examine the role of the genetic basis in complex diseases, such as cancer and neurodegenerative diseases. The role of the genetic basis might vary by non-genetic (environmental) measures, what is traditionally defined as gene-environment interactions (GxE). A commonly overlooked complication is that the set of clinically diagnosed cases might be contaminated by a subset with a nuisance pathologic state that presents with the same symptoms as the pathologic state of interest. The genetic basis of the pathologic state of interest might differ from that of the nuisance pathologic state. Often frequencies of the pathologically defined states within the clinically diagnosed set of cases vary by the environment. We derive a simple and general approximation to bias in GxE parameter estimates when presence of the nuisance pathologic state is ignored. We then perform extensive simulation studies to show that ignoring presence of the nuisance pathologic state can result in substantial bias in GxE estimates and that the approximation we derived is reasonably accurate in finite samples. We demonstrate the applicability of the proposed approximation in a study of Alzheimer’s disease.

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