1. MTG is a computer program to provide Genomic Residual Maximum Likelihood
(GREML) estimates for genetic and environmental variance and covariance across
multiple traits. The program implements a multivariate linear mixed model and
can fit complex covariance structures that can be derived from genomic
information, i.e. multivariate version of GCTA GREML. The program also provides best
liner unbiased prediction (BLUP) of additive genetic effects; either breeding
values or predictions of genetic risk. MTG uses the direct average information
algorithm (Lee and van der Werf; Maier, R., et al. (2015) Joint analysis of psychiatric disorders
increases accuracy of risk prediction for schizophrenia, bipolar disorder and
major depression disorder. 2. We combined the direct AI algorithm with an
eigen-decomposition of the genomic relationship matrix, as first proposed by
Thompson and Shaw ( Lee, SH and van der Werf, JHJ (2016) MTG2: An efficient algorithm for
multivariate linear mixed model analysis based on genomic information. 3. We theoretically derived the relationship between the genomic prediction accuracy and population parameters, e.g. effective population size ( N, e.g. from 0.6 with _{e}N
=10,000 to 0.9 with _{e}N
=100.
It also shows that
the
top percentile of the estimated genetic profile scores had 23 times higher
proportion of cases than the general population (with _{e}N= 100), which increased from 2 times higher
proportion of cases (with _{e }N
= 10000). (also see section 7, 8, 9 and 10 in the manual)
_{e}Lee, S.H. et al. (2017) Using information of relatives in genomic prediction to apply effective stratified medicine. Scientific Reports 7: 42091. 4.
We present
a theoretical framework for genomic prediction accuracy when the reference data
consists of information sources with varying degrees of relationship to the
target individuals. A reference set can contain both close and distant
relatives as well as ‘unrelated’ individuals from the wider population. The various sources of information were modeled
as different populations with different effective population sizes (
Lee et. al. (2017) Estimation of genomic prediction accuracy
from reference populations with varying degrees of relationship. PLoS ONE 12(12):
e0189775. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189775 5. We have developed multivariate reaction norm model (MRNM) to tackle genotype–environment (G–E) correlation and interaction problems. It is well known that G–E correlation causes spurious G–E interaction signals although there is few statistical tools to correct this bias. MRNM implemented in mtg2 (section 1.4) can unbiasedly estimate G–E interaction in the presence of G–E correlation and even have higher power to detect the interaction, compared to existing methods. It is also notable that MRNM is efficient to detect significant heterogeneity in the estimated residual variances across different environmental or covariate levels. For more detail, please see the following paper. Ni et al. (2019) Genotype–covariate correlation and interaction
disentangled by a whole-genome multivariate
reaction norm model. Nature Communications 10: 2239. The algorithms, theory, coalescence simulation functions are implemented in MTG2
software that can be downloaded from the link below. There are manual and
examples. Version 2.09 has fixed or improved
a few things with some clarification. 1.
The ID order does not
have to be the same between the fam file and phenotypic data file for version 2.09. (But, the ID
order between phenotypic data file and other covariate files still have to be
the same.) For previous version (<= v2.08), the ID order has to be the same between the fam, phenotype and covariate files. 2.
Some memory allocation
problems have been fixed especially for BLUP output part for the multivariate
random regression model. mtg2 version 2.02 for window (Thank to Dr. Hawlader Al-mamun (Mamun) at UNE) mtg2 version 2.02 source code (fortran) The source codes are released under GNU General Public License v3.
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)
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)
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 6. Weighted GRM added
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.
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 |