update details

mtg2 version 2.01

Binary file for linux (Mar/16)

Delta function added (section 5) (Mar/16)

Product matrix for random variable to fit random effects (section 4) (Mar/16)

Spline, -spl with –eig and -rrme 1 (residual covariance) checked and confirmed (Mar/16)

Estimating GRM added (section 6) (Apr/16)

Fixed a bug when fitting class variable as fixed effects (Apr/16)

Multivariate random regression model (section 1.26, 1.27 and 1.28) (Apr/16)

Reliability for BLUP (section 2) (Apr/16)

Binary file for window (Apr/16)

mtg2 version 2.02

gz format GRM from GCTA or PLINK1.9 can be used (section 1.1, and 2) (May/16)

Search a better starting values in an initial iteration for MVLMM (May/16)

Effective number of chromosome segments (section 7) (May/16)

Variance of relationship estimation (section 8) (May/16)

Prediction accuracy theory (section 9) (May/16)

Coalescence simulation and phenotype simulation based on given genotype data (section 10) (May/16)

Transform h2 between observed scale and liability scale (section 2) (May/16)

Transform genetic correlation to co-heritability on the liability scale (section 2) (May/16)

mtg2 version 2.04

Constrain some parameters during REML (section 11) (Dec/16)

# knots in spline function in univariate RRM can varied across different random effects (Jan/17)

In estimating predicted accuracy, the input parameter should now have # SNPs (section 9) (Jan/17)

mtg2 version 2.05

Section 12. H matrix added

mtg2 version 2.06

Section 9. Prediction accuracy revised

Section 6. Weighted GRM added

mtg2 version 2.08

Section 1.4. Reaction norm model

mtg2 version 2.09

Version 2.09 has fixed or improved a few things.

1. The ID order does not have to be the same between the fam file and phenotypic data file. But, the ID order between phenotypic data file and other covariate files still have to be the same.

2. Some memory allocation problems have been fixed especially for BLUP output part for the multivariate random regression model.

Version 2.17 has been optimised for the computing speed of multivariate linear mixed models (REML) that is > 10 times faster than earlier versions when fitting many levels of covariates.

Version 2.18 has now BLUP SNP (providing SNP effect, its SE, reliability, Wald test p-value, i.e. GWAS summary stats). It can supports univariate as well as multivariate models (see section 16 in the manual and example13 and 13-2).

To do list

Reliability for BLUP (GPA) (when using -eig or -rrm)

Weighting residual structure

Snp_blup (considering multiple inputs, e.g. snpvn)

*.py output when using -rrm or -spl

Search a better starting values in an initial iteration for random regression

Spline function for multivariate random regression