Estimating temporal trends in additive genetic variance

Dear all,

I was wondering if anyone had any thoughts on the following problem:

I wish to test whether the additive genetic variance of a trait is changing over time. I have a study population where for the last 30 years, all of the individuals born each year are measured for the trait at birth. I have a pedigree for all individuals within the population.

I've set up a model as follows:

BYR is a 30 level factor for birth year included as a fixed effect to control for temporal trends in environmental conditions.
sBYR is a standardized linear covariate of birth year centred on the mean value and ranging from -1 to 1.
ANIMAL is the identity of the individual linked through the pedigree as a pedigree factor (!P)
leg(sBYR,1).ANIMAL fits ANIMAL as a first order polynomial function of the standardized birth year.

Trait ~ mu BYR !r leg(sBYR,1).ANIMAL

From this model I think I get estimates for the intercept, slope, and covariance between the two from the random term:
int
cov slope

I can then run the model again changing the random term to leg(sBYR,0).ANIMAL and compare the log-likelihoods to see whether the additive genetic variance is changing over the study period.

I can also back transform the value I get onto the values of the polynomial to get estimates of additive genetic variance for each birth year and estimate the approximate standard errors using the matrix from the vvp file, which I can then use to plot a graph of changing additive genetic variance over time.

I see this as a potential way to test for the effects of selection acting upon the trait to reduce additive genetic variance over time.

Would this work?

Thank-you all very much for your time.
Best wishes
Matt

*****************************************************
Matt Robinson

Institute of Evolutionary Biology
The University of Edinbrugh
Room 102, Ashworth Laboratories,
King's Building's Campus,
West Mains Road,
Edinburgh.
EH9 3JT

Tel: +44 (0)131 650 5462

e-mail: matthew.r.robinson_at_ed.ac.uk

http://homepages.ed.ac.uk/loeske/matt.html

********************************************************
  ----- Original Message -----
  From: vivi noviati
  To: ASREML-L_at_AGRIC.NSW.GOV.AU
  Sent: Thursday, June 07, 2007 6:56 AM
  Subject: GIV matrix - additional information

  Dear All,

  I sent you the subset data (data file, as file, giv file, and asr file) so you can take a look and please tell me if you find something wrong. Because it is a small job and 2GB memory should be enough to run it, so I suspect I did something wrong.

  For the larger data set, the data (BLUEs) has been centered (substract the trial mean), so there is no variability in trials (ie. occ).

  Thank you.

  Regards,
  Vivi

  vivi noviati <vivi_pmt34_at_yahoo.com> wrote:
    Date: Wed, 6 Jun 2007 21:42:48 -0700 (PDT)
    From: vivi noviati <vivi_pmt34_at_yahoo.com>
    Subject: GIV matrix
    To: ASREML-L_at_AGRIC.NSW.GOV.AU

    Dear All,

    I need some assitance for running ASREML job. I tried to fit GIV matrix into ASREML. However, it always said that it failed to form G structure and I should increase memory. Currently, my computer only able to allocate 2048MB. The GIV matrix is a lower triangular matrix from 686 x 686 entry. It has three column: row, colomn, and value.

    I also try to run the job under unix, it can allocate 4096MB (s9), but when it allocate that memory, it failed to read my data (it able to read my data when I used less memory).

    This is the sample of my as file:
    ------------------------------------------------------------------------------------------------------------------------------------
    ESWYT yield combine data
     tid 1 !A
     yea 25 !I
     occ 208 !I
     loc 386 !A
     ent 50 !I
     gen 686 !I
     BLUE
     wt

    C:\DATA\YLD\stage_2\GIV_browse.giv !skip 1
    C:\DATA\YLD\stage_2\yld_cent.txt !skip 1 !AISING

    BLUE !WT wt ~ mu !r gen yea.occ.gen

    1 1 1
    0 0 0 !s2== 0.443036672875

    gen 1
    gen 0 GIV
    -------------------------------------------------------------------------------------------------------------------------------------

    I also try to subset the data and only run with 30 genotype and 17 location. But it still failed to form G scores (memory allocated 2048MB).

    However, if I use giv(gen) instead of fitted G structure model, it will converge. Does giv() give the same result as fitted G structure?

    Thank you.

    Regards,
    Vivi Arief
    University of Queensland

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Received on Sat Jun 12 2007 - 18:15:42 EST