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. 6. CORE GREML (see chapter 15 in the manual and example 12) can estiamte correaltion between two random effects in the phenotypic analysis where the covariance structure between the random effects are not pre-defined, e.g. genome-transcriptome corerlation in the phenotypic analysis of a complex trait. Zhou, Im and Lee (2020) CORE GREML: Estimating covariance between random effects in linear mixed models for genomic analyses of complex traits. Nature Communications 11: 4208.
7. GxEsum GxEsum script, README and example Genetic variation in response to the environment is fundamental in biology and has been described as genotype-by-environment interaction (GxE), reaction norm or phenotypic plasticity. In the genomic era, there has been increasing interest in estimating GxE, using genome-wide SNPs, e.g. a whole-genome reaction norm model (RNM) that can estimate unbiased genome-wide GxE. However, the existing approach is computationally demanding and infeasible to handle large-scale biobank data. Here we introduce GxEsum, a model for estimating GxE based on GWAS summary statistics, which can be applied to a large sample size. In simulations, we show that GxEsum can control type I error rate and produce unbiased estimates in general. We apply GxEsum to UK Biobank to estimate genome-wide GxE for BMI and hypertension, and find that the computational efficiency of GxEsum is thousands of times higher than existing whole-genome GxE methods such as RNM. Because of its computational efficiency, GxEsum can achieve a higher precision (i.e. power) from a larger sample size. As the scale of available resources has been increased, GxEsum may be an efficient tool to estimate GxE that can be applied to large-scale data across multiple complex traits and diseases. Shin and Lee (2020) GxEsum: genotype-by-environment interaction model based on summary statistics. bioRxiv preprint doi: https://doi.org/10.1101/2020.05.31.122549. The algorithms, theory, coalescence simulation functions are implemented in MTG2
software that can be downloaded from the link below. There are manual and
examples. 8. Integrative analysis of genomic and exposomic data (IGE) IGE is a whole-genome approach to the estimation of heritability and g x e interactions, which models variances explained by additive effects of exposomic variables, by exposome x exposome interactions, and by exposome x covariate (such as demographics) interactions; and covariance between genetic effects and exposomic effects (Table 3). Further, bivariate or multivariate IGE (i.e., simultaneously including two or more traits) can be feasibly performed using mtg2 version 2.18. Please see section 17 in the manual and exampleIGE below, which can be also found in the IGE GitHub. 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. 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 |