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- | ====== BLUPF90TEST ====== | ||
- | |||
- | UNDER CONSTRUCTION | ||
- | |||
- | ===== Summary ===== | ||
- | This is a combined program of blupf90 and airemlf90. | ||
- | \\ | ||
- | \\ | ||
- | See PREGSF90 with genotypes (SNP) for options. | ||
- | |||
- | ===== Options ===== | ||
- | <file> | ||
- | OPTION method VCE (default BLUP) | ||
- | </file> | ||
- | Run airemlf90 (default running blupf90) | ||
- | <file> | ||
- | OPTION conv_crit 1d-12 | ||
- | </file> | ||
- | Convergence criterion (default 1d-10). | ||
- | <file> | ||
- | OPTION maxrounds 1000 | ||
- | </file> | ||
- | Maximum rounds (default 5000). When the number = 0, the program calculates BLUP without iterating REML and some statistics (-2logL, AIC, SE for (co)variances, ...). | ||
- | <file> | ||
- | OPTION EM-REML 10 | ||
- | </file> | ||
- | Run EM-REML (REMLF90) for first 10 rounds to get initial variances within the parameter space (default 0). | ||
- | <file> | ||
- | OPTION use_yams | ||
- | </file> | ||
- | Run the program with YAMS (modified FSPAK). The computing time can be dramatically improved. | ||
- | <file> | ||
- | OPTION tol 1d-12 | ||
- | </file> | ||
- | Tolerance (or precision) (default 1d-14) for positive definite matrix and g-inverse subroutines.\\ | ||
- | Convergence may be much faster by changing this value. | ||
- | <file> | ||
- | OPTION sol se | ||
- | </file> | ||
- | Store solutions and those standard errors. | ||
- | <file> | ||
- | OPTION store_pev_pec 6 | ||
- | </file> | ||
- | Store triangular matrices of standard errors and its covariances for correlated random effects such as direct-maternal effects and random-regression effects in "pev_pec_bf90". | ||
- | <file> | ||
- | OPTION residual | ||
- | </file> | ||
- | y-hat and residuals will be included in "yhat_residual". | ||
- | <file> | ||
- | OPTION missing -999 | ||
- | </file> | ||
- | Specify the missing value (default 0) in integer. | ||
- | <file> | ||
- | OPTION constant_var 5 1 2 ... | ||
- | </file> | ||
- | 5: effect number\\ | ||
- | 1: first trait number\\ | ||
- | 2: second trait number\\ | ||
- | implying the covariance between traits 1 and 2 for effect 5. | ||
- | |||
- | **Heterogeneous residual variances for a single trait** | ||
- | <file> | ||
- | OPTION hetres_pos 10 11 | ||
- | </file> | ||
- | Specify the position of covariables. | ||
- | <file> | ||
- | OPTION hetres_pol 4.0 0.1 0.1 | ||
- | </file> | ||
- | Initial values of coefficients for heterogeneous residual variances using //ln//(a0, a1, a2, ...) to make these values. | ||
- | |||
- | **Heterogeneous residual variances for multiple traits**\\ | ||
- | Convergence will be very slow with multiple trait heterogeneous residual variances | ||
- | <file> | ||
- | OPTION hetres_pos 10 10 11 11 | ||
- | </file> | ||
- | or | ||
- | <file> | ||
- | OPTION hetres_pos 10 11 12 13 | ||
- | </file> | ||
- | Specify the position of covariables (trait first). | ||
- | "10 10" or "10 11" could be linear for first and second traits.\\ | ||
- | "11 11" or "12 13" could be quadratic. | ||
- | <file> | ||
- | OPTION hetres_pol 4.0 4.0 0.1 0.1 0.01 0.01 | ||
- | </file> | ||
- | Initial values of coefficients for heterogeneous residual variances using //ln//(a0, a1, a2, ...) to make these values (trait first).\\ | ||
- | "4.0 4.0" are intercept for first and second traits.\\ | ||
- | "0.1 0.1" could be linear and "0.01 0.01" could be quadratic.\\ | ||
- | To transform back to the original scale, use exp(a0+a1*X1+a2*X2). | ||
- | <file> | ||
- | OPTION SNP_file snp | ||
- | </file> | ||
- | Specify the SNP file name to use genotype data. | ||
- | |||
- | <file>OPTION se_covar_function <label> <function></file> | ||
- | As an alternative of SE, calculate SD for function of (co)variances by repeated sampling of parameters estimates from their asymptotic multivariate normal distribution, following ideas presented by Meyer and Houle 2013.\\ | ||
- | \\ | ||
- | ''<label>''\\ | ||
- | A name for a particular function (e.g., ''P1'' for phenotypic variance of trait 1, ''H2_1'' for heritability for trait 1, ''rg12'' for genetic correlation between traits 1 and 2, …).\\ | ||
- | \\ | ||
- | ''<function>''\\ | ||
- | A formula to calculate a function of (co)variances to estimate SD. All terms of the function should be written with no spaces.\\ | ||
- | \\ | ||
- | Each term of the function corresponds to (co)variance elements and could include any random effects (G) and residual (R) (co)variances.\\ | ||
- | \\ | ||
- | Notation is with reference to the effect number and the trait number (''G_eff1_eff2_trt1_trt2'') that indicate the element of the (co)variance matrix for random effect ''eff1'' and ''eff2'' and ''trt1'' and ''trt2'',\\ | ||
- | where ''eff1'' and ''eff2'' are effect numbers 1 and 2, and ''trt1'' and ''trt2'' are trait numbers 1 and 2.\\ | ||
- | ''R_trt1_trt1'' indicates the element of the residual (co)variance matrix for traits 1 and 2.\\ | ||
- | \\ | ||
- | Several functions could be added, with one OPTION line per function.\\ | ||
- | \\ | ||
- | Examples:\\ | ||
- | \\ | ||
- | ''OPTION se_covar_function P G_2_2_1_1+G_2_3_1_1+G_3_3_1_1+G_4_4_1_1+R_1_1''\\ | ||
- | |||
- | ''OPTION se_covar_function H2d G_2_2_1_1/(G_2_2_1_1+G_2_3_1_1+G_3_3_1_1+G_4_4_1_1+R_1_1)''\\ | ||
- | |||
- | ''OPTION se_covar_function H2t (G_2_2_1_1+1.5*G_2_3_1_1+0.5*G_3_3_1_1)/(G_2_2_1_1+G_2_3_1_1+G_3_3_1_1+G_4_4_1_1+R_1_1)''\\ | ||
- | |||
- | ''OPTION se_covar_function rg12 G_2_2_1_2/(G_2_2_1_1*G_2_2_2_2)**0.5''\\ | ||
- | \\ | ||
- | The first function calculates the SD for the total variance for a maternal model with permanent maternal effect, where 2 and 3 are the effect number for the direct and maternal additive genetic effects respectively, and 4 is the effect number for the maternal permanent random effect. | ||
- | |||
- | The second function calculates the heritability for the direct component. | ||
- | |||
- | The third function the total heritability. | ||
- | |||
- | The fourth function calculates the SD of the genetic correlation between traits 1 and 2 for the direct genetic effect (effect number 2) | ||
- | |||
- | <file>OPTION samples_se_covar_function <n></file> | ||
- | Set the number of samples to calculate SE for function of (co)variances.\\ | ||
- | default value 10000 | ||
- | <file>OPTION out_se_covar_function</file> | ||
- | Indicate to store in file samples of (co)variances function for postprocessing (histogram, etc.) | ||
- | |||
- | ===== Tricks ===== | ||
- | When the covariance matrix is close to non-positive definite, the AIREMLF90 may not converge. | ||
- | There are two options you might want to try: | ||
- | |||
- | 1. change the tolerance value (xx) in the option: | ||
- | |||
- | OPTION tol xx | ||
- | |||
- | to a very strict value (e.g., 1d-20) or a lenient value (1d-06). | ||
- | |||
- | 2. use an option to use EM-REML inside AI-REML: | ||
- | |||
- | OPTION EM-REML xx | ||
- | |||
- | where xx is the number of iterations for EM-REML you expect to get a good starting value for AI-REML. After running xx rounds with EM-REML, the AIREMLF90 program will automatically switch from EM-REML to AI-REML using the last estimate from EM-REML as a starting value for AI-REML. | ||