1. GenABEL: an R package for Genome Wide Association Analysis
Younghun Han
Department of Epidemiology
UT MD Anderson Cancer Center

2. Outline Introduction GeneABEL: GWAA.data class
Importing data to GenABEL
Genetic data QC
GWA association analysis

3. Introduction
GenABEL is an R library developed to facilitate Genome-Wide Association analysis of binary and quantitative traits.
Features of GenABEL :
specific facilities for storage and manipulation of large data QC
Maximum Likelihood estimation of linear, logistic and Cox regression on Genome-wide scale
Specific functions to analyze and display the results

4. GeneABEL: GWAA.data class

5. GWAA.data class
>library(GenABEL) >load(“lung2291.Rdata”)

6. GWAA.data class

7. Importing data to GenABEL
Need a phenotypic and genotypic data
Example of a phenotype file :
Example of a genotypic data (PLINK tped files)
TPED- file TFAM -file

8. Importing data to GenABEL
Convert the data to GenABEL raw format :
> convert.snp.affymetrix()
> convert.snp.illumina()
> convert.snp.mach()
> convert.snp.ped()
> convert.snp.text()
> convert.snp.tped()
Load the data into GenABEL
>load.gwaa.data()
Example :
> convert.snp.tped(tped="lung2291.tped",
tfam="lung2291.tfam",out="lung2291.raw", strand=“u")
> lung2291<- load.gwaa.data(phe="pheno.txt", gen="lung2291.raw",force = T)

9. Genetic data QC
summary.snp.data() : Number of observed genotypes, allelic frequency, genotypic distrbution, P-value of the exact test for HWE
check.trait() : summary of phenotypic data check for outliers (using False Discovery Rate framework) and plots the raw data
check.marker() : The major genetic data QC function of GenABEL
HWE.show() : showing HWE tables, Chi2 and exact HWE P-values
perid.summary() : call rate and heterozygosity per person
ibs() : matrix of average IBS for a group of people
hom() : average homozygosity (inbreeding) for a set of people, across multiple markers
EXAMPLE of QC

10. GWA association analysis
Descriptives of the phenotypic and marker data :
descriptives.trait(lung2291)
descriptives.trait(lung2291, by=case_control)
descriptives.marker(lung2291)
descriptives.marker(lung2291,ids=(case_control==0))
Score test
an0 <- qtscore(case_control, data=lung2291, trait = "binomial")
an1 <- qtscore(case_control~sex, data=lung2291, trait = "binomial")
Chi-squre test for binary trait
an2 <- ccfast("case_control", data=lung2291)
SNP association test using glm in R library
Scan.glm(“case_control~sex+CRSNP”, family=binomial)
Scan.glm(“case_control~sex*CRSNP”, family=binomial) # no G*E test
Scan.glm.2D(“case_control~sex+CRSNP”, family=binomial) # 2-snp interaction scan
Note : formula must contain CRSNP variable to be replaced with the analysis SNPs

11. GWA association analysis
GWAS results from qtscore, ccfast, scan.glm
P1df : P-values of 1-d.f. (additive or allelic) test
P2df : P-values of 2-d.f. (genotypic) test for association
Pc1df : P-values of 1-d.f. test: the statistics is corrected for possible inflation
effB: Effect of the B allele(second allele from coding) in allelic test (OR for ccfast)
effAB : Effect of the AB
effBB : Effect of the BB
map : list of map positions of the SNPs
chromosome : list of chromosomes the SNPs belong to
idnames : list of people used in analysis
lambda : inflation factor estimate
formula : formula/function call was used to compute P-values
family : family of the link function

12. Example of descriptives.trait() and descriptives.marker()

13. Example of qtscore() and ccfast()

14. Example of scan.glm() and scan.glm.2D()

19. Thank you!!!