scholarly journals Extension of the SIMLA Package for Generating Pedigrees with Complex Inheritance Patterns: Environmental Covariates, Gene-Gene and Gene-Environment Interaction

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Mike Schmidt ◽  
Elizabeth R Hauser ◽  
Eden R. Martin ◽  
Silke Schmidt
Keyword(s):  
Association Studies ◽  
Real Data ◽  
Environment Interaction ◽  
Data Generation ◽  
Environmental Covariates ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Linkage And Association Analysis ◽  
Complex Inheritance ◽  
Disease Loci

We have previously distributed a software package, SIMLA (SIMulation of Linkage and Association), which can be used to generate disease phenotype and marker genotype data in three-generational pedigrees of user-specified structure. To our knowledge, SIMLA is the only publicly available program that can simulate variable levels of both linkage (recombination) and linkage disequilibrium (LD) between marker and disease loci in general pedigrees. While the previous SIMLA version provided flexibility in choosing many parameters relevant for linkage and association mapping of complex human diseases, it did not allow for the segregation of more than one disease locus in a given pedigree and did not incorporate environmental covariates possibly interacting with disease susceptibility genes.Here, we present an extension of the simulation algorithm characterized by a much more general penetrance function, which allows for the joint action of up to two genes and up to two environmental covariates in the simulated pedigrees, with all possible multiplicative interaction effects between them. This makes the program even more useful for comparing the performance of different linkage and association analysis methods applied to complex human phenotypes. SIMLA can assist investigators in planning and designing a variety of linkage and association studies, and can help interpret results of real data analyses by comparing them to results obtained under a user-controlled data generation mechanism.A free download of the SIMLA package is available at http://wwwchg.duhs.duke.edu/software.

scholarly journals Estimating the effective sample size in association studies of quantitative traits

2021 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jihye Kim ◽  
Dmitry Prokopenko ◽  
Florian Privé ◽  
Fabien Laporte ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Effective Sample Size ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

Current Challenges in the Genetics of Psoriatic Arthritis: A Report from the GRAPPA 2009 Annual Meeting

2011 ◽  
Vol 38 (3) ◽  
pp. 564-566 ◽  
Author(s):  
PROTON RAHMAN
Keyword(s):  
Psoriatic Arthritis ◽  
Annual Meeting ◽  
Association Studies ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Novel Genes ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide

Psoriasis and psoriatic arthritis (PsA) are heterogeneous diseases. While both have a strong genetic basis, it is strongest for PsA, where fewer investigators are studying its genetics. Over the last year the number of independent genetic loci associated with psoriasis has substantially increased, mostly due to completion of multiple genome-wide association studies (GWAS) in psoriasis. At least 2 GWAS efforts are now under way in PsA to identify novel genes in this disease; a metaanalysis of genome-wide scans and further studies must follow to examine the genetics of disease expression, epistatic interaction, and gene-environment interaction. In the long term, it is anticipated that genome-wide sequencing is likely to generate another wave of novel genes in PsA. At the annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) in Stockholm, Sweden, in 2009, members discussed issues and challenges regarding the advancement of the genetics of PsA; results of those discussions are summarized here.

scholarly journals HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

2020 ◽  
Vol 21 (18) ◽  
pp. 6724
Author(s):  
Sungkyoung Choi ◽  
Sungyoung Lee ◽  
Iksoo Huh ◽  
Heungsun Hwang ◽  
Taesung Park
Keyword(s):  
Structural Component ◽  
Latent Variable ◽  
Association Studies ◽  
Component Analysis ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Missing Heritability Problem

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

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 Genetic Studies in the Nigerian Population Implicate an MSX1 Mutation in Complex Oral Facial Clefting Disorders

2011 ◽  
Vol 48 (6) ◽  
pp. 646-653 ◽  
Author(s):  
A. Butali ◽  
P.A. Mossey ◽  
W.L. Adeyemo ◽  
P.A. Jezewski ◽  
C.K. Onwuamah ◽  
...  
Keyword(s):  
Association Studies ◽  
Direct Sequencing ◽  
Environment Interaction ◽  
Orofacial Clefts ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Significant Difference ◽  
Hardy Weinberg Equilibrium ◽  
Nigerian Population ◽  
Set Up

Background Orofacial clefts are the most common malformations of the head and neck, with a worldwide prevalence of 1 in 700 births. They are commonly divided into CL(P) and CP based on anatomic, genetic, and embryologic findings. A Nigerian craniofacial anomalies study (NigeriaCRAN) was set up in 2006 to investigate the role of gene-environment interaction in the origin of orofacial clefts in Nigeria. Subjects and Methods DNA isolated from saliva from Nigerian probands was used for genotype association studies and direct sequencing of cleft candidate genes: MSX1, IRF6, FOXE1, FGFR1, FGFR2, BMP4, MAFB, ABCA4, PAX7, and VAX1, and the chromosome 8q region. Results A missense mutation A34G in MSX1 was observed in nine cases and four HapMap controls. No other apparent causative variations were identified. Deviation from Hardy Weinberg equilibrium (HWE) was observed in these cases ( p = .00002). A significant difference was noted between the affected side for unilateral CL ( p = .03) and bilateral clefts and between clefts on either side ( p = .02). A significant gender difference was also observed for CP ( p = .008). Conclusions Replication of a mutation previously implicated in other populations suggests a role for the MSX1 A34G variant in the development of CL(P).

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