===================== INSTRUCTIONS ============================== This document contains instructions for installing and using the famLBL R package. 1. Install For Linux users, download the compiled R package file: famLBL_1.0.tar.gz Use the following function in R to install the package >install.packages(pkgs="filename") where "filename" should be replaced with the corresponding file name (famLBL_1.0.tar.gz) with the correct path or using setwd to get to the directory that contains the downloaded file before installing the package. Alternatively, at the Linux command line, you may type "R CMD INSTALL PACKAGE_DIRECTORY/famLBL_1.0.tar.gz" where PACKAGE_DIRECTORY is the folder containing famLBL_1.0.tar.gz. 2. Usage #Load the packages into R > library(famLBL) #Read the details about the package >?famLBL #Rad the details about the function and an example for runing the function. >?LBL 3. Example Using the example data file: data.ex The input format is as follows: Genotype data for each trio are coded in three separate lines, one for each individual. The first 5 columns provide family id, individual id, father id, mother id, and sex (1=male, 2=female). The 6th columns codes the affection status (1=unaffected, 2=affected). The rest of the columns code the SNP data. If the data are coded in "allelic" format, then each SNP is represented by two columns. If the data are coded in "genotypic" format, then each SNP is represented by a single column. #load the example dataset that is included in the package > data(data.ex) #You are now ready to run LBL to make inference on association of haplotypes with the disease. #Note 1: famLBL uses hapassoc to find all compatible haplotypes. #Note 2: The defaul setting are burn.in = 10000, num.it = 40000 in the LBL function, which takes about 12 seconds to run. However, for a quick check to make sure that the package is loaded properly, the example uses much smaller numbers. > out.LBL<-LBL(data.ex, burn.in=100, num.it=1000) #Note the results indicate that haplotypes h10011 and h10100 (both rare), are associated with the disease (CI of OR deosn't include and BF is large). The numerical values of your results may be quite different from tose shown below because of different random seeds and small number of iterations. > last<-length(out.LBL$freq) > ans <-data.frame(out.LBL$freq[1:(last-1)],out.LBL$OR,out.LBL$OR.CI,out.LBL$BF) > rownames(ans)=out.LBL$haplotypes[1:(last-1)] > names(ans) <- c("Freq", "OR", "Lower", "Upper", "BF") > ans Freq OR Lower Upper BF h01100 0.245945585 0.8888696 0.7119094 1.0959156 0.135491378078772 h10011 0.005518758 2.8660444 1.3173611 7.1866739 46.7322806367995 h10100 0.007726756 2.9527504 1.4442202 8.6300105 >100 h11011 0.174054320 0.7505082 0.5950480 0.9411337 1.97753526079999 h11100 0.279087832 0.9380714 0.7830914 1.1281489 0.0736662990390889 4. References Wang, M. and Lin, S. FamLBL: Detecting causal rare haplotypes based on common SNPs using family data. Manuscript. Burkett K, Graham J and McNeney B (2006). hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes. Journal of Statistical Software, *16(2)*:1-19 ----------------------------------------------------------------- If you have any questions, please email Meng Wang at wang.1357@osu.edu, or Shili Lin at shili@stat.osu.edu