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  Feedback and Ideas  ~  Fit at(Tr,1) and permanent environmental effects

emheu
Posted: Thu Aug 11, 2016 11:46 am Reply with quote
Joined: 25 Mar 2015 Posts: 5
Dear all,

I am currently struggling with two things in my analyses:
1. If I analyse a bivariate model with several random effects and I want e.g. the first variate to be only analyzed with random effect 1 and the second with random effect 2, how do I define the G structures? In the model line I will write for example:

Tr1 Tr2 ~ Trait Trait.par Trait.hys Trait.sex mv !r Trait.animal Trait.dam, at(Tr,1).ide(litter) at(Tr,2).ide(dam)

1 2 4
0
2 0 US !GFPP
V1
Cov V2
Trait.animal 2
Trait 0 US
V1
Cov V2
animal 0 AINV
Trait.dam 2
Trait 0 US
V1
Cov V2
dam 0 AINV
at(Tr,2).ide(litter) 2
2 0 US what do I have to put here?
V1 And how many variances do I need? I would have
guessed 1 only
ide(litter)
at(Tr,2).ide(dam) 2
2 0 US what do I have to put here?
V2
And how many variances do I need? I would have
guessed 1 only
ide(dam)

2. I did uni- and bivariate analyses using the ide(dam) as maternal permanent environment effect, this worked really fine and got me results I expected. But working with pigs having several parities I tried an analysis including a litter effect as random (ide(litter)) which is built by the id of the dam and the number of the parity added, respectively. Unfortunately I got very different results as heritability, maternal genetic and maternal permanent environment effect, whilst we expected them not to be that different. Does perhaps anyone have an idea how these deferrals might occur?

Thank you so much in advance for any help and ideas.
Best regards,
Esther
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Arthur
Posted: Fri Aug 12, 2016 8:49 pm Reply with quote
Joined: 05 Aug 2008 Posts: 407 Location: Orange, NSW
Dear Esther,
1) Since at(Tr,1).ide(litter) only has 1 parameter, you do not need to formally specify a variance structure for it, or for at(Tr,2).ide(dam)
so you can just write

1 2 2
...

But you should exclude 'mv' from this model

If you are using ASReml 4, you can write it all as
Tr1 Tr2 ~ Trait Trait.par Trait.hys Trait.sex !r us(Trait).animal us(Trait).dam, at(Tr,1).ide(litter) at(Tr,2).ide(dam)
residual units.us(Trait)

2) Comparing models

Tr1 ~ mu parity sex !r animal dam ide(dam)
and
Tr1 ~ mu parity sex !r animal dam ide(dam) parity.ide(dam)

where parity is a factor

will may have large effects on the components, depending on the number
of parities and the relative size of the litter component.

If you think the differences are unreasonable, send me the two analyses (.asr files) and I will comment further. (either on the forum of direct to arthur@vsni.co.uk)

_________________
Arthur Gilmour

Retired Principal Research Scientist (Biometrics)
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emheu
Posted: Fri Aug 19, 2016 8:11 am Reply with quote
Joined: 25 Mar 2015 Posts: 5
Dear Mr. Gilmour,

thank you so much for your helpful reply . I will try everything, if it does not work or I have further questions I will get back to you.

Have a nice day!
Best regards,
Esther
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