Package ‘regent’ August 19, 2015
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| Package ‘REGENT’ August 19, 2015 Title Risk Estimation for Genetic and Environmental Traits Version 1.0.6 Date 2015-08-18 Author Daniel J.M. Crouch, Graham H.M. Goddard & Cathryn M. Lewis Maintainer Daniel Crouch Description Produces population distribution of disease risk and statistical risk categories, and pre- dicts risks for individuals with genotype information. Depends R (>= 2.14.0) License GPL NeedsCompilation no Repository CRAN Date/Publication 2015-08-19 13:49:39 R topics documented: REGENT-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 EnvironmentalA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 EnvironmentalB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 GeneticA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 GeneticB
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Inds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 REGENT.model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 REGENT.predict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Index
10 1
2 EnvironmentalA REGENT-package Risk Estimation for Genetic and Environmental Traits Description Provides risk estimation and categorisation for populations and individuals Details Package:
REGENT Type:
Package Version:
1.0.6 Date:
2015-08-18 License:
GPL LazyLoad: yes Author(s) Daniel Crouch, Graham Goddard & Cathryn Lewis. Maintainer: Daniel Crouch - djmcrouch@gmail.com References Crouch, Goddard & Lewis (2011) Goddard & Lewis, Risk categorization for complex disorders according to genotype relative risk and precision in parameter estimates (2010) See Also REGENT.model , REGENT.predict , GeneticA
, GeneticB
, EnvironmentalA , EnvironmentalB , Inds
EnvironmentalA Example file for single level environmental factors Description Example data for Crohns disease in correct input format. Also as text file "EnvironmentalA.txt" in the data folder for this package. data from Calkins, BM (1989). Usage
data("REGENT") EnvironmentalB 3 Format data frame References from Calkins, BM (1989) A meta-analysis of the role of smoking in inflammatory bowel disease EnvironmentalB Example file for single multiple level environmental factors Description Example data for Crohns disease in correct input format. Also as text file "EnvironmentalB.txt" in the data folder for this package. *PLEASE NOTE that the multilevel smoking data in Environmen- talB is entirely artificial.* Usage
data("REGENT") Format
data frame GeneticA
Example file for SNPs conferring multiplicative risks Description Example data for Crohns disease in correct input format. Also as text file "GeneticA.txt" in the data folder for this package. Data is from Franke et al (2010), Usage data("REGENT") Format data frame References Franke et al. (2010), Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci
4 Inds
GeneticB Example file for SNPs conferring additive risks Description Example data for Crohns disease in correct input format. Also as text file "GeneticB.txt" in the data folder for this package. Data is from Franke et al (2010), Usage
data("REGENT") Format
data frame References Franke et al. (2010), Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci Inds Example of individual file for REGENT.predict Description Columns for risk factors, rows for individuals. Entries refer to genotypes (number of risk alleles as defined in the locus file, ie can also be protective alleles) or exposure levels Usage
data("REGENT") Format
data frame REGENT.model 5 REGENT.model REGENT.model Description REGENT.model provides the population distribution of risk and proportion of the population in each risk category based on genetic(SNP) and environmental exposures. Usage REGENT.model(AnalysisName,LocusFile=NULL,EnvFile=NULL ,prev=0.001,cv=0.05,alpha=0.05,sims=100000 ,indsims=100000,SmallSampAdjust=0.5,BaseRange=0.01 ,PlotMax=5,Block=100) Arguments AnalysisName String, must be provided. Output files will be named according to this argument. Running multiple analyses with the same name will cause previous files to be overwritten. LocusFile File path string. Location of file containing table of SNP input data. Required columns should have headers SNP, MAF, Ncase, Ncontrol. Risks should ei- ther be provided in one column with header RR, or two columns with headers RR_het and RR_hom. Other columns may be present but will not be used in the analysis. Each SNP is a row. Additional columns may be provided but will be ignored. EnvFile
File path string. Location of file containing table of environmental risk data. Re- quired columns should have headers Factor, Exposure, RR, SE. If multiple ex- posure levels exist, then the columns should be named Factor, RR1, Exposure1, SE1, RR2, Exposure2, SE2, etc. Each factor is a row. Additional columns may be provided but will be ignored prev
Prevalance of the disease or trait. Default 0.001. cv Coefficient of variation. Default 0.05. alpha One minus the desired width of confidence intervals around multilocus risk es- timates. Default 0.05 giving 95 percent confidence intervals. sims
Number of simulations to perform for each single factor risk estimate, for ob- taining confidence intervals. Default 100000. indsims Number of individuals in the simulated population, for obtaining multilocus genotype frequencies. Default 100000 SmallSampAdjust Adjustment for small sample sizes, when calculating the standard error of ho- mozygous risk genotypes. Default 0.5 6 REGENT.model BaseRange Proportion of population used to calculate the baseline risk (the risk closest to the average in the population). This is to avoid choosing rare, uncertain risk estimates by chance. Default 0.01. PlotMax Value at which to truncate the Y-axis of risk distribution plots. High risks are typically rare and of less interest when assessing the distribution in the popula- tion. Default 5. Block Number of multilocus genotypes held in memory during confidence interval cal- culation. Higher values should decrease computation time. We advise increas- ing this substantially (10000+) on high performance systems. Default 100. Details 4 files are created by REGENT.model.A)All model details, inputs and log information are written to the main output file which is named after the argument provided to AnalysisName.B)Colour and C)greyscale plots of the risk distribution are also provided, and D)the raw data used to create these in a text file. See the example folder included in this package for the correct input file format. Value A list including elements categories Table giving upper and lower boundaries for each risk category: Reduced, Av- erage, Elevated and High. baseline
Single value specifying the baseline risk before rebasing; required when passing the object to REGENT.predict LocusFile Table of genetic data used for analysis. NULL if argument LocusFile was set to NULL. EnvFile
Table of environmental data used for analysis. NULL if argument EnvFile was set to NULL. Author(s) Graham Goddard, Daniel Crouch and Cathryn Lewis. Email: djmcrouch@gmail.com See Also REGENT.predict , GeneticA
, GeneticB
, EnvironmentalA , EnvironmentalB , Inds
Examples library(REGENT) #Load example data from package data("REGENT") write.table(GeneticA,file="GeneticA.txt")
REGENT.predict 7 write.table(GeneticB,file="GeneticB.txt") write.table(EnvironmentalA,file="EnvironmentalA.txt") write.table(EnvironmentalB,file="EnvironmentalB.txt") x=REGENT.model(AnalysisName="Example",LocusFile="GeneticA.txt",EnvFile="EnvironmentalA.txt") x REGENT.predict REGENT.predict Description REGENT.predict takes genotype and exposure information for individuals and calculates their ab- solute and relative risk of disease, and categorises them as reduced, average, elevated or high risk based on the risk categorisation model determined by REGENT.model. Usage
REGENT.predict(AnalysisName,model,ind,prev=0.001,cv=0.05,sims=100000,Block=100,alpha=0.05, SmallSampAdjust=0.5) Arguments AnalysisName String, must be provided. The output file will be named according to this ar- gument, with the suffix "_Predictions.txt". Running multiple analyses with the same name will cause previous files to be overwritten. model
Must be provided. Either a file path string giving the location of a file created by REGENT.model (the main file containing model information), or a variable containing the object returned by REGENT.model. ind
Must be provided. File path giving the location of individual file, which should have columns for each risk factor (with header of SNP names or Factor names as provided to REGENT.model) and a row for each individual. Genotypes are encoded 0, 1 or 2 describing the number of copies of the risk allele (as defined in the model). Environmental factors are encoded 0, 1, 2, 3 etc. depending on how many exposure levels were modelled. The row header contains individual names. prev
Prevalance of the disease or trait. Default 0.001. cv Coefficient of variation. Default 0.05. sims Number of simulations to perform for each single factor risk estimate, for ob- taining confidence intervals. Default 100000. Block
Number of multilocus genotypes held in memory during confidence interval cal- culation. Higher values should decrease computation time. We advise increas- ing this substantially (10000+) on high performance systems. Default 100.
8 REGENT.predict alpha One minus the desired width of confidence intervals around multilocus risk es- timates. Default 0.05 giving 95 percent confidence intervals. SmallSampAdjust Adjustment for small sample sizes, when calculating the standard error of ho- mozygous risk genotypes. Default 0.5. Details Email: djmcrouch@gmail.com One file is created by REGENT.predict, with the contents of the returned object and the input parameters/data, plus analysis log. See the example folder included in this package for the correct input file format. Value
Table with columns: Absolute risk, genotype relative risk, lower confidence interval, upper confi- dence interval, risk category, and borderline category status. Author(s) Graham Goddard, Daniel Crouch and Cathryn Lewis See Also REGENT.model , GeneticA
, GeneticB
, EnvironmentalA , EnvironmentalB , Inds
Examples #Load example data from package library(REGENT) data("REGENT") write.table(GeneticA,file="GeneticA.txt") write.table(GeneticB,file="GeneticB.txt") write.table(EnvironmentalA,file="EnvironmentalA.txt") write.table(EnvironmentalB,file="EnvironmentalB.txt") write.table(Inds,file="Inds.txt") #Create model x=REGENT.model(AnalysisName="Example",LocusFile="GeneticB.txt",EnvFile="EnvironmentalA.txt") #Option 1, read model from object y=REGENT.predict(AnalysisName="Example",model=x,ind="Inds.txt") #Option 2, read model from file REGENT.predict 9 y=REGENT.predict(AnalysisName="Example",model="Example.txt",ind="Inds.txt") Index ∗Topic
datasets EnvironmentalA , 2
, 3 GeneticA , 3 GeneticB , 4 Inds , 4 EnvironmentalA , 2 , 2 , 6 , 8 EnvironmentalB , 2 , 3 , 6 , 8 GeneticA , 2 , 3 , 6 , 8 GeneticB , 2 , 4 , 6 , 8 Inds , 2 , 4 , 6 , 8 REGENT (REGENT-package) , 2 REGENT-package , 2 REGENT.model , 2 , 5 , 8 REGENT.predict , 2
6 ,
10 Document Outline
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