The code from "Assoc_map_winbugs_1.txt" can be run as such but data needs to be given first. No separate data input files are needed! DATA STRUCTURE: Data can be given in, for example, with the list-command. To specify 200 individuals, 5 markers (with 3,4,4,4, and 6 segregating alleles) with intermarker distances (in Morgans) as : 0.01, 0.02, 0.001, and 0.01. You can give this specification with one line: list (n1=200, m=5, n2=c(3,4,4,4,6), n2max=7, d=c(0.01, 0.02, 0.001, 0.01)) To give genotype data, you can write for example: list (y=structure(.Data=c(1,2,...,), .Dim=c(200,10))) Here, number 200 corresponds to the number of individuals and number 10 means 5 markers and two alleles in each. Corresponding data is given as one vector so that first 10 numbers are marker alleles for the first individual (2 alleles at 5 markers), next 10 numbers for the second individuals and so on. Allele codes: it is required that alleles are coded in consecutive numbers starting from 1. The code for missing allele is NA. To give phenotypes you can write for example: list (z1=c(100,210,33,...)) Here, 200 consecutive numbers in data vector are the phenotypic values of all 200 individuals. To run the model, you can first "paint" the word and select "check model" from the menu. It may be that before model runs, WinBUGS sometimes needs good starting values for some of the parameters. INITIAL VALUES: Initial values can be given in using similar structure / format than data (with the list-command). It is OK with WinBUGS that you specify initial values only for some of the parameters. Other parameters can then be generated by using "GET INITS". However, you should not use 'NA' as initial value. Initial values must also be within prior limits (you should not give zero if it is not possible to get from the prior). Definition of convergence is that estimates are not depending on your initial values. Therefore, INITIAL values should only influence on the convergence speed but not to the estimates. If latter happens, it means that there are problems in convergence. OUTPUT: The variable 'beta' includes original allelic coefficients. 'bo' is allelic coefficients weighted with genetic variance. The latter is needed if the indicators and effects are confounded by strong LD in the region. In other cases, you can inspect 'beta' directly. However, remember that this is overparameterized model where number of estimable contrasts are one less than number of alleles. Because of this, coefficients themselves can have very arbitraty scale/level. Therefore, you should centralize the coefficients after estimation. For example, see Table II in Kilpikari and Sillanpaa (2003). The variable 'X2' includes information for posterior distribution of QTLs (markers having simultaneously indicator value one). It has (number of markers+1) elements so that X2[1] contains information for P(N_qtl=0 | data). X2[2] contains information for P(N_qtl=1 | data). x2[3] contains information for P(N_qtl=2 | data). ... .. Good luck!