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  ASReml  ~  could we run multivarite model for MET

Zhiqiang
Posted: Tue Sep 24, 2019 1:32 pm Reply with quote
Joined: 07 Dec 2014 Posts: 12
Hi all,

I was wondering if we could run a multivariate model for MET data as below

!PART 1
Adj_Hjd Adj_Dia ~ Trait Trait.Trial !f mv !r us(Trait).xfa1(Trial.Mum)
residual units.us(Trait)

As I know, we could run a univariate model for MET data as below

!PART 2
Adj_Hjd ~ mu Trait !r xfa1(Trial).Mum
residual at(Trial).units


Cheers
Chen
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yzhlinscau
Posted: Sun Sep 29, 2019 7:57 am Reply with quote
Joined: 27 Oct 2012 Posts: 8
Adj_Hjd Adj_Dia ~ Trait Trait.Trial !f mv !r us(Trait).xfa1(Trial).Mum
residual at(Trial).units.us(Trait)

would do work.

cheers



Zhiqiang wrote:
Hi all,

I was wondering if we could run a multivariate model for MET data as below

!PART 1
Adj_Hjd Adj_Dia ~ Trait Trait.Trial !f mv !r us(Trait).xfa1(Trial.Mum)
residual units.us(Trait)

As I know, we could run a univariate model for MET data as below

!PART 2
Adj_Hjd ~ mu Trait !r xfa1(Trial).Mum
residual at(Trial).units


Cheers
Chen
View user's profile Send private message
Zhiqiang
Posted: Wed Oct 02, 2019 9:41 am Reply with quote
Joined: 07 Dec 2014 Posts: 12
Hi Yuanzhen,

The model you suggested may not be the one I exactly want. As I want also to estimate genetic correlation between trials (MET) for each trait, and then consider multivariate models. When I run the model you suggested, I found I only get one XFA structure. Would we get two XFA structures? one for each trait?


Cheers
Chen


yzhlinscau wrote:
Adj_Hjd Adj_Dia ~ Trait Trait.Trial !f mv !r us(Trait).xfa1(Trial).Mum
residual at(Trial).units.us(Trait)

would do work.

cheers



Zhiqiang wrote:
Hi all,

I was wondering if we could run a multivariate model for MET data as below

!PART 1
Adj_Hjd Adj_Dia ~ Trait Trait.Trial !f mv !r us(Trait).xfa1(Trial.Mum)
residual units.us(Trait)

As I know, we could run a univariate model for MET data as below

!PART 2
Adj_Hjd ~ mu Trait !r xfa1(Trial).Mum
residual at(Trial).units


Cheers
Chen
View user's profile Send private message
yzhlinscau
Posted: Sun Oct 06, 2019 8:53 am Reply with quote
Joined: 27 Oct 2012 Posts: 8
I do not have real datasets, just as an example. The following codes could run in asreml-R 4.1, but does this model have real meaning? Although the model will get each xfa structure for each trait, I still doubt the model's meaning.

cheers

Yuanzhen


Code:

asreml.options(ai.sing=T)
run9<-asreml(cbind(yield,hh1)~trait+trait:Loc,
                    random=~at(trait):us(trait):fa(Loc,1):Genotype,
                    residual=~units:us(trait),
                    #residual=~dsum(~units:diag(trait)|Loc),
                    maxit=10,data=MET)

Var(run9)

                                                              component
at(trait, yield):trait:fa(Loc, 1):Genotype!trait_yield:yield  26.511314673
at(trait, yield):trait:fa(Loc, 1):Genotype!trait_hh1:yield     4.139143529
at(trait, yield):trait:fa(Loc, 1):Genotype!trait_hh1:hh1       6.208715293
at(trait, yield):trait:fa(Loc, 1):Genotype!1!var               0.000000000
at(trait, yield):trait:fa(Loc, 1):Genotype!2!var               0.087426566
at(trait, yield):trait:fa(Loc, 1):Genotype!3!var               0.090362905
at(trait, yield):trait:fa(Loc, 1):Genotype!4!var               0.261599691
at(trait, yield):trait:fa(Loc, 1):Genotype!5!var               0.000001600
at(trait, yield):trait:fa(Loc, 1):Genotype!6!var               0.000001600
at(trait, yield):trait:fa(Loc, 1):Genotype!1!fa1               0.407585698
at(trait, yield):trait:fa(Loc, 1):Genotype!2!fa1               0.394941929
at(trait, yield):trait:fa(Loc, 1):Genotype!3!fa1               0.255072194
at(trait, yield):trait:fa(Loc, 1):Genotype!4!fa1               0.375419515
at(trait, yield):trait:fa(Loc, 1):Genotype!5!fa1               0.301101708
at(trait, yield):trait:fa(Loc, 1):Genotype!6!fa1               0.104013083
at(trait, hh1):trait:fa(Loc, 1):Genotype!trait_yield:yield     6.208715293
at(trait, hh1):trait:fa(Loc, 1):Genotype!trait_hh1:yield       4.139143529
at(trait, hh1):trait:fa(Loc, 1):Genotype!trait_hh1:hh1         0.948916905
at(trait, hh1):trait:fa(Loc, 1):Genotype!1!var                 7.445363318
at(trait, hh1):trait:fa(Loc, 1):Genotype!2!var                 0.002515794
at(trait, hh1):trait:fa(Loc, 1):Genotype!3!var                 0.000000000
at(trait, hh1):trait:fa(Loc, 1):Genotype!4!var                 0.000000000
at(trait, hh1):trait:fa(Loc, 1):Genotype!5!var                 0.000001600
at(trait, hh1):trait:fa(Loc, 1):Genotype!6!var                 2.976343939
at(trait, hh1):trait:fa(Loc, 1):Genotype!1!fa1                -0.793744164
at(trait, hh1):trait:fa(Loc, 1):Genotype!2!fa1                 3.816775300
at(trait, hh1):trait:fa(Loc, 1):Genotype!3!fa1                 1.643122019
at(trait, hh1):trait:fa(Loc, 1):Genotype!4!fa1                -1.157603387
at(trait, hh1):trait:fa(Loc, 1):Genotype!5!fa1                 0.002461397
at(trait, hh1):trait:fa(Loc, 1):Genotype!6!fa1                 0.240477558
......



Zhiqiang wrote:
Hi Yuanzhen,

The model you suggested may not be the one I exactly want. As I want also to estimate genetic correlation between trials (MET) for each trait, and then consider multivariate models. When I run the model you suggested, I found I only get one XFA structure. Would we get two XFA structures? one for each trait?


Cheers
Chen


yzhlinscau wrote:
Adj_Hjd Adj_Dia ~ Trait Trait.Trial !f mv !r us(Trait).xfa1(Trial).Mum
residual at(Trial).units.us(Trait)

would do work.

cheers



Zhiqiang wrote:
Hi all,

I was wondering if we could run a multivariate model for MET data as below

!PART 1
Adj_Hjd Adj_Dia ~ Trait Trait.Trial !f mv !r us(Trait).xfa1(Trial.Mum)
residual units.us(Trait)

As I know, we could run a univariate model for MET data as below

!PART 2
Adj_Hjd ~ mu Trait !r xfa1(Trial).Mum
residual at(Trial).units


Cheers
Chen
Code:
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