# Compute ALD. A function to compute asymmetric Linkage Disequilibrium measures (ALD) for polymorphic genetic data. These measures are identical to the correlation measure (r) for bi-allelic data. ```r compute.ALD(dat, tolerance = 0.01) ``` ## Arguments - `dat`: A data.frame with 5 required variables (having the names listed below): ||| |:--|:--| |`haplo.freq`|A numeric vector of haplotype frequencies.| |`locus1`|A character vector indentifying the first locus.| |`locus2`|A character vector indentifying the second locus.| |`allele1`|A character vector indentifying the allele at locus 1.| |`allele2`|A character vector indentifying the allele at locus 2.| - `tolerance`: A threshold for the sum of the haplotype frequencies. If the sum of the haplotype frequencies is greater than 1+tolerance or less than 1-tolerance an error is returned. The default is 0.01. ## Returns The return value is a dataframe with the following components: ||| |:--|:--| |`locus1`|The name of the first locus.| |`locus2`|The name of the second locus.| |`F.1`|Homozygosity (expected under HWP) for locus 1.| |`F.1.2`|Conditional homozygosity* for locus1 given locus2.| |`F.2`|Homozygosity (expected under HWP) for locus 2.| |`F.2.1`|Conditional homozygosity* for locus2 given locus1.| |`ALD.1.2`|Asymmetric LD for locus1 given locus2.| |`ALD.2.1`|Asymmetric LD for locus2 given locus1.| *Overall weighted haplotype-specific homozygosity for the first locus given the second locus. ## Details A warning message is given if the sum of the haplotype frequencies is greater than 1.01 or less than 0.99 (regardless of the `tolerance` setting). The haplotype frequencies that are passed to the function are normalized within the function to sum to 1.0 by dividing each frequency by the sum of the passed frequencies. ## Examples ```r library(asymLD) # An example using haplotype frequencies from Wilson(2010) data(hla.freqs) hla.a_b <- hla.freqs[hla.freqs$locus1=="A" & hla.freqs$locus2=="B",] compute.ALD(hla.a_b) hla.freqs$locus <- paste(hla.freqs$locus1, hla.freqs$locus2, sep="-") compute.ALD(hla.freqs[hla.freqs$locus=="C-B",]) # Note: additonal columns on the input dataframe (e.g., "locus" above) are allowed, but # ignored by the function. # An example using genotype data from the haplo.stats package require(haplo.stats) data(hla.demo) geno <- hla.demo[,5:8] #DPB-DPA label <- unique(gsub(".a(1|2)", "", colnames(geno))) label <- paste("HLA*",label,sep="") keep <- !apply(is.na(geno) | geno==0, 1, any) em.keep <- haplo.em(geno=geno[keep,], locus.label=label) hapfreqs.df <- cbind(em.keep$haplotype, em.keep$hap.prob) #format dataframe for ALD function names(hapfreqs.df)[dim(hapfreqs.df)[2]] <- "haplo.freq" names(hapfreqs.df)[1] <- "allele1" names(hapfreqs.df)[2] <- "allele2" hapfreqs.df$locus1 <- label[1] hapfreqs.df$locus2 <- label[2] head(hapfreqs.df) compute.ALD(hapfreqs.df) # Note that there is substantially less variablity (higher ALD) for HLA*DPA1 # conditional on HLA*DPB1 than for HLA*DPB1 conditional on HLA*DPA1, indicating # that the overall variation for DPA1 is relatively low given specific DPB1 alleles # An example using SNP data where results are symmetric and equal to the ordinary # correlation measure (r) data(snp.freqs) snps <- c("rs1548306", "rs6923504", "rs4434496", "rs7766854") compute.ALD(snp.freqs[snp.freqs$locus1==snps[2] & snp.freqs$locus2==snps[3],]) snp.freqs$locus <- paste(snp.freqs$locus1, snp.freqs$locus2, sep="-") by(snp.freqs,list(snp.freqs$locus),compute.ALD) # SNP1 & SNP2 : the r correlation & ALD measures are equivalent due to symmetry for # bi-allelic SNPs p.AB <- snp.freqs$haplo.freq[1] p.Ab <- snp.freqs$haplo.freq[2] p.aB <- snp.freqs$haplo.freq[3] p.ab <- snp.freqs$haplo.freq[4] p.A <- p.AB + p.Ab p.B <- p.AB + p.aB r.squared <- (p.AB - p.A*p.B)^2 / (p.A*(1-p.A)*p.B*(1-p.B)) sqrt(r.squared) #the r correlation measure compute.ALD(snp.freqs[snp.freqs$locus1==snps[1] & snp.freqs$locus2==snps[2],]) ```

compute.ALD function