[GenABEL-dev] faster polygenic

[Yurii Aulchenko]

re: [BAD]

This is really strange -- the links work well for me! definitely we need to
figure out what is the problem and how to solve that -- we need to share
links to repository!

To start with -- Gulya, Lennart, et al., can you follow the links I have
send? (see original mail below)

re: [GOOD]

These are interesting news! Can someone (Gulya?) run quick checks comparing
the speed and accuracy of what Xia suggested versus standard polygenic?

Gulya: if you did not do this already, can you please subscribe to the
genabel-devel list (
http://lists.r-forge.r-project.org/mailman/listinfo/genabel-devel) -- we
want to keep the discussion open and documented, unless things discussed are
too sensitive.

Yurii

On Wed, Feb 16, 2011 at 2:11 PM, Xia Shen <xia.shen at lcb.uu.se> wrote:

> Hi Yurii,
>
> Bad news and good news:
>
> [BAD]
> I cannot open the two links that you sent...
>
> [GOOD]
> One of my colleague happens to be playing with your 'polygenic' function,
> and has got stuck by some binary trait because the numerical algorithm
> didn't converge. However, treating the trait as normal works. To fit the
> same mixed model, I tried my 'hglm' package (published on the recent issue
> of The R Journal), it worked pretty well, and fitting binary, poisson,
> gamma, ... traits will all work.
>
> The way to fit hglm using GenABEL results is straightforward:
>
> 1. create the IBS matrix G, has to be symmetric, just need to replicate the
> lower triangle of the IBS from GenABEL and overwrite the upper triangle
> 2. create L that satisfies LL' = G by
>    s <- svd(G)
>    L <- s$u%*%diag(sqrt(s$d))
> 3. call hglm like
>    hglm(y = phdata(...)$..., X = design matrix of fixed effects, Z = L,
> family = gaussian())
>    where NA has to be removed before calling, gaussian() can be changed to
> binomial(link = 'logit'), etc.
>
> The hglm function will return all the parameter estimates (fixed effects,
> variance components) and random effects predicts and residuals, etc. The
> algorithm is based on inter-connected GLMs (h-likelihood theory, the work by
> Lee & Nelder in the last decade), efficient and stable, without establishing
> the likelihood and calling numeric algorithm like FGLS.
>
> I think the residuals from the mixed model is the most interesting stuff
> that will be passed into the next GWAS in GenABEL.
>
> Hopefully this can serve some help :)
>
> Cheers
>
> Xia
>
>
> *
>
> o \o/ _ o _| \ / |_ o_ \o/ o /|\ | /\ _\o \o | o/ o/_ /\ | /|\ / \ / \ | \
> /) | ( \ /o\ / ) | (\ / | / \ / \
> ---------------------------------------------------------------
>
> -------
>
> Xia Shen - PhD student in Bioinformatics, Uppsala University.
>
>           - Computational Genetics Group
>
> : www.computationalgenetics.se
>
>           - Personal URL:
>
> www.19850911.com
>
>
> **
>
>  All judgements are, in their rationale, STATISTICS.        - C.R.Rao
>
> ---------------------------------------------------------------
>
> -------
>
> *
>
> ********
>
>
> On Feb 10, 2011, at 3:35 PM, Yurii Aulchenko wrote:
>
> Hi Gulya,
>
> As I mentioned, I am also very bothered by the fact that 'polygenic'
> works so slowly. I cc to Xia who mentioned he had some idea on how to
> make it working faster -- Xia, would be great if you could contribute
> to the discussion! I also cc to GA devel list, so people know what is
> going on.
>
> Here I will describe how polygenic works currently (very far from optimal!)
>
> 'polygenic'
>
>
> https://r-forge.r-project.org/scm/viewvc.php/pkg/GenABEL/R/polygenic.R?view=markup&revision=623&root=genabel
>
> is numerically maximizing likelihood function computed by procedure
> 'polylik'
>
>
> https://r-forge.r-project.org/scm/viewvc.php/pkg/GenABEL/R/polylik.R?view=markup&revision=545&root=genabel
>
> For maximization, standard R procedures 'nlm' or 'optim' can be used
> -- see what happens in 'polygenic' code between lines 246-300.
>
> Any ideas on how we can speed it up are very welcome!
>
> Yurii
>
>
>
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