scholarly journals Determinants of QTL mapping power in the realized Collaborative Cross

2018 ◽  
Author(s):  
Gregory R. Keele ◽  
Wesley L. Crouse ◽  
Samir N. P. Kelada ◽  
William Valdar
Keyword(s):  
Qtl Mapping ◽  
Power Analysis ◽  
Large Scale ◽  
Reference Population ◽  
R Package ◽  
Collaborative Cross ◽  
Mapping Study ◽  
General Power ◽  
Genetic Reference Population ◽  
Power Analyses

ABSTRACTThe Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses based on extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.

Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population

2019 ◽  
Vol 9 (4) ◽  
Author(s):  
Morris Soller ◽  
Hanifa J. Abu‐Toamih Atamni ◽  
Ilona Binenbaum ◽  
Aristotelis Chatziioannou ◽  
Fuad A. Iraqi
Keyword(s):  
Qtl Mapping ◽  
Reference Population ◽  
Collaborative Cross ◽  
Mapping Study ◽  
Genetic Reference Population

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals gQTL: A Web Application for QTL Analysis Using the Collaborative Cross Mouse Genetic Reference Population

2018 ◽  
Vol 8 (8) ◽  
pp. 2559-2562 ◽  
Author(s):  
Kranti Konganti ◽  
Andre Ehrlich ◽  
Ivan Rusyn ◽  
David W. Threadgill
Keyword(s):  
Qtl Analysis ◽  
Web Application ◽  
Reference Population ◽  
Collaborative Cross ◽  
Genetic Reference Population ◽  
Mouse Genetic

Epi2Loc: An R Package to Investigate Two-Locus Epistatic Models

2014 ◽  
Vol 17 (4) ◽  
pp. 272-278 ◽  
Author(s):  
Raymond K. Walters ◽  
Charles Laurin ◽  
Gitta H. Lubke
Keyword(s):  
Power Analysis ◽  
R Package ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Model Interpretation ◽  
Genome Wide ◽  
Using Data ◽  
Power Analyses

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP–SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

scholarly journals Simulation-Based Power Analysis for Factorial Analysis of Variance Designs

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592095150
Author(s):  
Daniël Lakens ◽  
Aaron R. Caldwell
Keyword(s):  
Analysis Of Variance ◽  
Power Analysis ◽  
Statistical Power ◽  
A Priori ◽  
R Package ◽  
Factorial Anova ◽  
Simulation Based ◽  
A Priori Power Analysis ◽  
Main Effects ◽  
Power Analyses

Researchers often rely on analysis of variance (ANOVA) when they report results of experiments. To ensure that a study is adequately powered to yield informative results with an ANOVA, researchers can perform an a priori power analysis. However, power analysis for factorial ANOVA designs is often a challenge. Current software solutions do not allow power analyses for complex designs with several within-participants factors. Moreover, power analyses often need [Formula: see text] or Cohen’s f as input, but these effect sizes are not intuitive and do not generalize to different experimental designs. We have created the R package Superpower and online Shiny apps to enable researchers without extensive programming experience to perform simulation-based power analysis for ANOVA designs of up to three within- or between-participants factors. Predicted effects are entered by specifying means, standard deviations, and, for within-participants factors, the correlations. The simulation provides the statistical power for all ANOVA main effects, interactions, and individual comparisons. The software can plot power across a range of sample sizes, can control for multiple comparisons, and can compute power when the homogeneity or sphericity assumption is violated. This Tutorial demonstrates how to perform a priori power analysis to design informative studies for main effects, interactions, and individual comparisons and highlights important factors that determine the statistical power for factorial ANOVA designs.

scholarly journals Collaborative Cross Mouse Populations as a Resource for the Study of Epilepsy

2019 ◽  
Author(s):  
Bin Gu ◽  
John R. Shorter ◽  
Lucy H. Williams ◽  
Timothy A. Bell ◽  
Pablo Hock ◽  
...  
Keyword(s):  
Animal Models ◽  
Critical Role ◽  
Reference Population ◽  
Collaborative Cross ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Unexpected Death ◽  
F2 Population ◽  
Complex Features ◽  
Genetic Reference Population

ABSTRACTEpilepsy is a neurological disorder with complex etiologies and genetic architecture. Animal models have a critical role in understanding the pathophysiology of epilepsy. Here we studied epilepsy utilizing a genetic reference population of Collaborative Cross (CC) mice with publicly available whole genome sequences. We measured multiple epilepsy traits in 35 CC strains, and we identified novel animal models that exhibit extreme outcomes in seizure susceptibility, seizure propagation, epileptogenesis, and sudden unexpected death in epilepsy. We performed QTL mapping in an F2 population and identified seven novel and one previously identified loci associated with seizure sensitivity. We combined whole genome sequence and hippocampal gene expression to pinpoint biologically plausible candidate genes and candidate variants associated with seizure sensitivity. These resources provide a powerful toolbox for studying complex features of seizures and for identifying genes associated with particular seizure outcomes, and hence will facilitate the development of new therapeutic targets for epilepsy.

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