### Post by Jian Yang on Dec 13, 2015 1:57:23 GMT

--reml

Perform a REML (restricted maximum likelihood) analysis. This option is usually followed by the option --grm (one GRM) or --mgrm (multiple GRMs) to estimate the variance explained by the SNPs that were used to estimate the GRM.

--reml-priors 0.45 0.55

Specify the starting values for REML iterations. The number of starting values specified should NOT be smaller than the number of variance components in the model. By default, GCTA will use equal variances of all the components as the starting values if this option is not specified.

--reml-alg 0

Specify the algorithm to run REML iterations, 0 for average information (AI), 1 for Fisher-scoring and 2 for EM. The default option is 0, i.e. AI-REML, if this option is not specified.

--reml-no-constrain

By default, if an estimate of variance component escapes from the parameter space (i.e. negative value), it will be set to be a small positive value i.e. Vp * 10-6 with Vp being the phenotypic variance. If the estimate keeps escaping from the parameter space, the estimate will be constrained to be Vp * 10-6. If the option --reml-no-constrain is specified, the program will allow an estimate of variance component to be negative, which may result in the estimate of proportion variance explained by all the SNPs > 100%.

--reml-maxit 100

Specify the maximum number of iterations. The default number is 100 if this option is not specified.

--pheno test.phen

Input phenotype data from a plain text file, e.g. test.phen. If the phenotypic value is coded as 0 or 1, then it will be recognized as a case-control study (0 for controls and 1 for cases). Missing value should be represented by "-9" or "NA".

test.phen (no header line; columns are family ID, individual ID and phenotypes)

--mpheno 2

If the phenotype file contains more than one trait, by default, GCTA takes the first trait for analysis (the third column of the file) unless this option is specified. For example, --mpheno 2 tells GCTA to take the second trait for analysis (the fourth column of the file).

--gxe test.gxe

Input an environmental factor from a plain text file, e.g. test.gxe. Apart from estimating the genetic variance, this command tells GCTA to estimate the variance of genotype-environment (GE) interaction. You can fit multiple environmental factors simultaneously. The main effects of an environmental factor will be included in the model as fixed effects and the GE interaction effects will be treated as random effects. NOTE: the design matrix of the overall mean in the model (which is a vector of all ones) is always a linear combination of the design matrix of a discrete environmental factor so that not all the main effects (fixed effects) are estimable. GCTA will always constrain the main effect of the first level to be zero and the main effect of any other level represents its difference in effect compared to the first level. For example, if you fit sex as an environmental factor, GCTA will fit only one main effect in the model, i.e. the mean difference between males and females.

test.gxe (no header line; columns are family ID, individual ID and environmental factors)

--covar test.covar

Input discrete covariates from a plain text file, e.g. test.covar. Each discrete covariate is recognized as a categorical factor with several levels. The levels of each factor can be represented by a single character, word or numerical number. NOTE: the design matrix of the mean in the model (which is a vector of all ones) is always a linear combination of the design matrix of a discrete covariate so that not all the effects of the levels (or classes, e.g. male and female) of a discrete covariate are estimable. GCTA will always constrain the effect of the first level to be zero and the effect of any other level represents its difference in effect compared to the first level.

test.covar (no header line; columns are family ID, individual ID and discrete covariates)

--qcovar test.qcovar

Input quantitative covariates from a plain text file, e.g. test.qcovar. Each quantitative covariate is recognized as a continuous variable.

test.qcovar (no header line; columns are family ID, individual ID and quantitative covariates)

--reml-lrt 1

Calculate the log likelihood of a reduce model with one or multiple genetic variance components dropped from the full model and calculate the LRT and p-value. By default, GCTA will always calculate and report the LRT for the first genetic variance component, i.e. --reml-lrt 1, unless you re-specify this option, e.g. --reml-lrt 2 assuming there are a least two genetic variance components included in the analysis. You can also test multiple components simultaneously, e.g. --reml-lrt 1 2 4. See FAQ #1 for more details.

--reml-no-lrt

Turn off the LRT.

--prevalence 0.01

Specify the disease prevalence for a case-control study. Once this option is specified, GCTA will transform the estimate of variance explained, V(1)/Vp, on the observed scale to that on the underlying scale, V(1)/Vp_L. The prevalence should be estimated from a general population in literatures rather than that estimated from the sample.

1. You do not have to have exactly the same individuals in these files. GCTA will find the individuals in common in the files and sort the order of the individuals.

2. Please be aware that if the GRM is estimated from the imputed SNPs (either “best guess” or “dosage score”), the estimate of variance explained by the SNPs will depend on the imputation-R2 cutoff used to select SNPs because the imputation-R2 is correlated with MAF, so that selection on imputation-R2 will affect the MAF spectrum and thus affect the estimate of variance explained by the SNPs.

3. For a case-control study, the phenotypic values of cases and controls should be specified as 1 and 0 (or 2 and 1, compatible with PLINK), respectively.

4. Any missing value (either phenotype or covariate) should be represented by “-9” or “NA”.

5. The summary result of REML analysis will be saved in a plain text file (*.hsq).

test.hsq (rows are

header line;

name of genetic variance, estimate and standard error (SE);

residual variance, estimate and SE;

phenotypic variance, estimate and SE;

ratio of genetic variance to phenotypic variance, estimate and SE;

log-likelihood;

sample size). If there are multiple GRMs included in the REML analysis, there will be multiple rows for the genetic variance (as well as their ratios to phenotypic variance) with the names of V(1), V(2), … .

--reml-est-fix

Output the estimates of fixed effects on the screen.

--reml-pred-rand

Predict the random effects by the BLUP (best linear unbiased prediction) method. This option is actually to predict the total genetic effect (called “breeding value” in animal genetics) of each individual attributed by the aggregative effect of the SNPs used to estimate the GRM. The total genetic effects of all the individuals will be saved in a plain ext file *.indi.blp.

Output file format

test.indi.blp (no header line; columns are family ID, individual ID, an intermediate variable, the total genetic effect, another intermediate variable and the residual effect.

If there are multiple GRMs fitted in the model, each GRM will insert additional two columns, , i.e. an intermediate variable (the intermediate variable =

--blup-snp test.indi.blp

Calculate the BLUP solutions for the SNP effects (you have to specify the option --bfile to read the genotype data). This option takes the output of the option --reml-pred-rand as input (*.indi.blp file) and transforms the BLUP solutions for individuals to the BLUP solutions for the SNPs, which can subsequently be used to predict the total genetic effect of individuals in an independent sample by PLINK --score option. Note that for the ease of using the BLUP solutions in a PLINK-score analysis, the BLUP effects are scaled by sqrt[2p(1-p)] (please see pages 77 and 78 of Yang et al. 2011 AJHG for details).

test.snp.blp (columns are SNP ID, reference allele and BLUP of SNP effect; if there are multiple GRMs fitted in the model, each GRM will add an additional column to the file; the last column is for the residual effect)

NOTE: if your GRMs files were generated by the --grm-bin option (i.e. saved in binary format, *.grm.bin), you could simply replace the --grm option by the --grm-bin option in the examples below.

# Without GRM (fitting the model under the null hypothesis that the additive genetic variance is zero)

# One GRM (quantitative traits)

# One GRM (case-control studies)

# GxE interaction (LRT test for the significance of GxE)

# Multiple GRMs

# BLUP solutions for the SNP effects

Perform a REML (restricted maximum likelihood) analysis. This option is usually followed by the option --grm (one GRM) or --mgrm (multiple GRMs) to estimate the variance explained by the SNPs that were used to estimate the GRM.

--reml-priors 0.45 0.55

Specify the starting values for REML iterations. The number of starting values specified should NOT be smaller than the number of variance components in the model. By default, GCTA will use equal variances of all the components as the starting values if this option is not specified.

--reml-alg 0

Specify the algorithm to run REML iterations, 0 for average information (AI), 1 for Fisher-scoring and 2 for EM. The default option is 0, i.e. AI-REML, if this option is not specified.

--reml-no-constrain

By default, if an estimate of variance component escapes from the parameter space (i.e. negative value), it will be set to be a small positive value i.e. Vp * 10-6 with Vp being the phenotypic variance. If the estimate keeps escaping from the parameter space, the estimate will be constrained to be Vp * 10-6. If the option --reml-no-constrain is specified, the program will allow an estimate of variance component to be negative, which may result in the estimate of proportion variance explained by all the SNPs > 100%.

--reml-maxit 100

Specify the maximum number of iterations. The default number is 100 if this option is not specified.

--pheno test.phen

Input phenotype data from a plain text file, e.g. test.phen. If the phenotypic value is coded as 0 or 1, then it will be recognized as a case-control study (0 for controls and 1 for cases). Missing value should be represented by "-9" or "NA".

**Input file format**test.phen (no header line; columns are family ID, individual ID and phenotypes)

`011 0101 0.98 `

012 0102 -0.76

013 0103 -0.06

……

--mpheno 2

If the phenotype file contains more than one trait, by default, GCTA takes the first trait for analysis (the third column of the file) unless this option is specified. For example, --mpheno 2 tells GCTA to take the second trait for analysis (the fourth column of the file).

--gxe test.gxe

Input an environmental factor from a plain text file, e.g. test.gxe. Apart from estimating the genetic variance, this command tells GCTA to estimate the variance of genotype-environment (GE) interaction. You can fit multiple environmental factors simultaneously. The main effects of an environmental factor will be included in the model as fixed effects and the GE interaction effects will be treated as random effects. NOTE: the design matrix of the overall mean in the model (which is a vector of all ones) is always a linear combination of the design matrix of a discrete environmental factor so that not all the main effects (fixed effects) are estimable. GCTA will always constrain the main effect of the first level to be zero and the main effect of any other level represents its difference in effect compared to the first level. For example, if you fit sex as an environmental factor, GCTA will fit only one main effect in the model, i.e. the mean difference between males and females.

**Input file format**test.gxe (no header line; columns are family ID, individual ID and environmental factors)

`01 0101 F smoker`

02 0203 M nonsmoker

03 0305 F smoker

……

--covar test.covar

Input discrete covariates from a plain text file, e.g. test.covar. Each discrete covariate is recognized as a categorical factor with several levels. The levels of each factor can be represented by a single character, word or numerical number. NOTE: the design matrix of the mean in the model (which is a vector of all ones) is always a linear combination of the design matrix of a discrete covariate so that not all the effects of the levels (or classes, e.g. male and female) of a discrete covariate are estimable. GCTA will always constrain the effect of the first level to be zero and the effect of any other level represents its difference in effect compared to the first level.

**Input file format**test.covar (no header line; columns are family ID, individual ID and discrete covariates)

`01 0101 F Adult 0`

02 0203 M Adult 0

03 0305 F Adolescent 1

……

--qcovar test.qcovar

Input quantitative covariates from a plain text file, e.g. test.qcovar. Each quantitative covariate is recognized as a continuous variable.

**Input file format**test.qcovar (no header line; columns are family ID, individual ID and quantitative covariates)

`01 0101 -0.024 0.012`

02 0203 0.032 0.106

03 0305 0.143 -0.056

……

--reml-lrt 1

Calculate the log likelihood of a reduce model with one or multiple genetic variance components dropped from the full model and calculate the LRT and p-value. By default, GCTA will always calculate and report the LRT for the first genetic variance component, i.e. --reml-lrt 1, unless you re-specify this option, e.g. --reml-lrt 2 assuming there are a least two genetic variance components included in the analysis. You can also test multiple components simultaneously, e.g. --reml-lrt 1 2 4. See FAQ #1 for more details.

--reml-no-lrt

Turn off the LRT.

--prevalence 0.01

Specify the disease prevalence for a case-control study. Once this option is specified, GCTA will transform the estimate of variance explained, V(1)/Vp, on the observed scale to that on the underlying scale, V(1)/Vp_L. The prevalence should be estimated from a general population in literatures rather than that estimated from the sample.

**NOTE:**1. You do not have to have exactly the same individuals in these files. GCTA will find the individuals in common in the files and sort the order of the individuals.

2. Please be aware that if the GRM is estimated from the imputed SNPs (either “best guess” or “dosage score”), the estimate of variance explained by the SNPs will depend on the imputation-R2 cutoff used to select SNPs because the imputation-R2 is correlated with MAF, so that selection on imputation-R2 will affect the MAF spectrum and thus affect the estimate of variance explained by the SNPs.

3. For a case-control study, the phenotypic values of cases and controls should be specified as 1 and 0 (or 2 and 1, compatible with PLINK), respectively.

4. Any missing value (either phenotype or covariate) should be represented by “-9” or “NA”.

5. The summary result of REML analysis will be saved in a plain text file (*.hsq).

**Output file format**test.hsq (rows are

header line;

name of genetic variance, estimate and standard error (SE);

residual variance, estimate and SE;

phenotypic variance, estimate and SE;

ratio of genetic variance to phenotypic variance, estimate and SE;

log-likelihood;

sample size). If there are multiple GRMs included in the REML analysis, there will be multiple rows for the genetic variance (as well as their ratios to phenotypic variance) with the names of V(1), V(2), … .

`Source Variance SE`

V(1) 0.389350 0.161719

V(e) 0.582633 0.160044

Vp 0.971984 0.031341

V(1)/Vp 0.400573 0.164937

The estimate of variance explained on the observed scale is transformed to that on the underlying scale:

(Proportion of cases in the sample = 0.5; User-specified disease prevalence = 0.1)

V(1)/Vp_L 0.657621 0.189123

logL -945.65

logL0 -940.12

LRT 11.06

Pval 4.41e-4

n 2000

--reml-est-fix

Output the estimates of fixed effects on the screen.

--reml-pred-rand

Predict the random effects by the BLUP (best linear unbiased prediction) method. This option is actually to predict the total genetic effect (called “breeding value” in animal genetics) of each individual attributed by the aggregative effect of the SNPs used to estimate the GRM. The total genetic effects of all the individuals will be saved in a plain ext file *.indi.blp.

Output file format

test.indi.blp (no header line; columns are family ID, individual ID, an intermediate variable, the total genetic effect, another intermediate variable and the residual effect.

If there are multiple GRMs fitted in the model, each GRM will insert additional two columns, , i.e. an intermediate variable (the intermediate variable =

**Py**, please see Yang et al. 2011 AJHG for the definitions of**P**and**y**) and a total genetic effect, in front of the last two columns)`01 0101 -0.012 -0.014 -0.010 -0.035`

02 0203 0.021 0.031 -0.027 -0.031

03 0305 0.097 0.102 -0.026 -0.041

……

--blup-snp test.indi.blp

Calculate the BLUP solutions for the SNP effects (you have to specify the option --bfile to read the genotype data). This option takes the output of the option --reml-pred-rand as input (*.indi.blp file) and transforms the BLUP solutions for individuals to the BLUP solutions for the SNPs, which can subsequently be used to predict the total genetic effect of individuals in an independent sample by PLINK --score option. Note that for the ease of using the BLUP solutions in a PLINK-score analysis, the BLUP effects are scaled by sqrt[2p(1-p)] (please see pages 77 and 78 of Yang et al. 2011 AJHG for details).

**Output file format**test.snp.blp (columns are SNP ID, reference allele and BLUP of SNP effect; if there are multiple GRMs fitted in the model, each GRM will add an additional column to the file; the last column is for the residual effect)

`rs103645 A 0.00312 0.00451`

rs175292 G -0.00021 0.00139

……

**Examples**NOTE: if your GRMs files were generated by the --grm-bin option (i.e. saved in binary format, *.grm.bin), you could simply replace the --grm option by the --grm-bin option in the examples below.

# Without GRM (fitting the model under the null hypothesis that the additive genetic variance is zero)

`gcta64 --reml --pheno test.phen --out test_null`

gcta64 --reml --pheno test.phen --keep test.indi.list --out test_null

# One GRM (quantitative traits)

`gcta64 --reml --grm test --pheno test.phen --reml-pred-rand –qcovar test_10PCs.txt --out test`

gcta64 --reml --grm test --pheno test.phen --grm-adj 0 --grm-cutoff 0.05 --out test

gcta64 --reml --grm test --pheno test.phen --keep test.indi.list --grm-adj 0 --out test

# One GRM (case-control studies)

`gcta64 --reml --grm test --pheno test_cc.phen --prevalence 0.01 --out test_cc`

gcta64 --reml --grm test --pheno test_cc.phen --prevalence 0.01 --qcovar test_10PCs.txt --out test_cc

# GxE interaction (LRT test for the significance of GxE)

`gcta64 --reml --grm test --pheno test.phen --gxe test.gxe --reml-lrt 2 --out test`

# Multiple GRMs

`gcta64 --reml --mgrm multi_grm.txt --pheno test.phen --reml-no-lrt --out test_mgrm `

gcta64 --reml --mgrm multi_grm.txt --pheno test.phen --keep test.indi.list --reml-no-lrt --out test_mgrm

# BLUP solutions for the SNP effects

`gcta64 --bfile test --blup-snp test.indi.blp --out test`

**References****Method for estimating the variance explained by all SNPs:**Yang et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 42(7): 565-9. [PubMed ID: 20562875]**Method for estimating the variance explained by all SNPs using case-control data:**Lee et al. (2011) Estimating Missing Heritability for Disease from Genome-wide Association Studies. Am J Hum Genet. 88(3): 294-305. [PubMed ID: 21376301]**Method for partitioning the genetic variance captured by all SNPs onto chromosomes and genomic segments:**Yang et al. (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet. 43(6): 519-525. [PubMed ID: 21552263]