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*To*: xianming.wei@pi.csiro.au*Subject*: Re: question related to the TAG paper*From*: Arthur Gilmour <gilmoua@ornsun.agric.nsw.gov.au>*Date*: Mon, 30 Aug 1999 11:19:18 +1000 (EST)*Cc*: asreml@chiswick.anprod.csiro.au*Reply-To*: Arthur Gilmour <gilmoua@ornsun.agric.nsw.gov.au>*Sender*: asreml-owner@ram.chiswick.anprod.csiro.au

> Dear Arthur, > > I don't know if you've read the paper I wrote its title and source in the > cover of your workshop in the Uni Melb. I need your help to answer a > question related to this paper: > > Is this valid to directly estimate (co)variance components hence > heritability and genetic correlation for categorial traits without > transforming them? What's the consequence if this is not? > Dear Xianming, At last I have scanned the paper and got back to your question. My answer is YES it is valid but it may not be fully optimal or efficient. A few quick comments follow. I will send this to the list incase others wish to comment. Categorical traits are of various sorts. Usually they are regarded as an approximation to an underlying variable of interest. The basic assumption is that there is a monotonic relationship. Presumably the categorical trait arises becasue it is not possible to measure the real variable of interest. So, provided the relationship is monotonic, it will be approximately linear (to an unknown degree depending on the trait) and so normal analysis of the score will usually give a good idea of what is happening. The usual problem is that there may be a mean/variance relationship. This applies when the distribution is most skewed. In other words, the more 'normal' the distribution of scores, the happier you will be with a normal analysis. Problems occur with binomial data if the mean incidence is quite variable across groups including some with extreme (outside .10 .90 interval) values. ASREML can fit an animal model using GLMM (Schall type) assumptions to binomial data. The more categories present the less important the need to transform to the underlying scale. I have not introduced multple threshold models into ASREML largely becasue it is messy and the gain is usually small. Threhold models imply extra assumptions which usually can not be validated with data. So my conclusion is Analyses of ordered categorical data as if normal is valid and is usually quite efficient. If the data is severly skewed, empiracal transformation may be appropriate. Use of animal models in binomial GLMM's sometimes give problems with the genetic component blowing up as the effects are mapped onto an underlying scale. It is common for 'undelying scale' estimates of heritability to be higher than observed scale estimates becasue there is a loss of information associated with truncating a continuous variable to a categorical variable. However, if you ever only have the categorical trait, then the higher value for the underlying scale is academic. The more data you have, the better the estimates of variance even on the categorical scale. In sire models, genetic correlations are generally quite consistent between the variaous scales. Arthur > Thanks. > > Xianming > > --------------------------------------------- > Xianming WEI > CSIRO Plant Industry > Horticultural Unit > PMB Merbein VIC 3505 > Australia > > Ph: +61 3 50513170 > Fax: +61 3 50513111 > -------------------------------------------- <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> Arthur Gilmour PhD mailto:Arthur.Gilmour@agric.nsw.gov.au Senior Research Scientist (Biometrics) fax: <61> 2 6391 3899 NSW Agriculture <61> 2 6391 3922 Orange Agricultural Institute telephone work: <61> 2 6391 3815 Forest Rd, ORANGE, 2800, AUSTRALIA home: <61> 2 6362 0046 ASREML is still free by anonymous ftp from pub/aar on ftp.res.bbsrc.ac.uk or point your web browser at ftp://ftp.res.bbsrc.ac.uk/pub/aar/ To join the asreml discussion list, send the message subscribe mailto:asreml-request@chiswick.anprod.CSIRO.au To send messages to the list, mailto:asreml@chiswick.anprod.CSIRO.au Asreml list archive: http://www.chiswick.anprod.csiro.au/lists/asreml <> <> <> <> <> <> <> "Christ Jesus came into the world to save sinners" I Timothy 1:15. <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><> -- Asreml mailinglist archive: http://www.chiswick.anprod.csiro.au/lists/asreml

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