polygenic {GenABEL} | R Documentation |

Estimation of polygenic model

polygenic(formula, kinship.matrix, data, fixh2, starth2=0.3, trait.type="gaussian", opt.method="nlm", scaleh2=1, quiet=FALSE, steptol=1e-08, gradtol=1e-08, optimbou=8, fglschecks=TRUE, maxnfgls=8, maxdiffgls=1e-04, patchBasedOnFGLS=TRUE, ...)

`formula` |
Formula describing fixed effects to be used in analysis, e.g. y ~ a + b means that outcome (y) depends on two covariates, a and b. If no covariates used in analysis, skip the right-hand side of the equation. |

`kinship.matrix` |
Kinship matrix, as provided by e.g. ibs(,weight="freq"), or estimated outside of GenABEL from pedigree data. |

`data` |
An (optional) object of `gwaa.data-class` or a data frame with
outcome and covariates |

`fixh2` |
Optional value of heritability to be used, instead of maximisation. The uses of this option are two-fold: (a) testing significance of heritability and (b) using a priori known heritability to derive the rest of MLEs and var.-cov. matrix. |

`starth2` |
Starting value for h2 estimate |

`trait.type` |
"gaussian" or "binomial" |

`opt.method` |
"nlm" or "optim". These two use dirrerent optimisation functions.
We suggest using the default `nlm` , though
`optim` may give better results in some situations |

`scaleh2` |
Only relevant when "nlm" optimisation function is used. "scaleh2" is the heritability scaling parameter, regulating how "big" are parameter changes in h2 with the respect to changes in other parameters. As other parameters are estimated from previous regression, these are expected to change little from the initial estimate. The default value of 1000 proved to work rather well under a range of conditions. |

`quiet` |
If FALSE (default), details of optimisation process are reported. |

`steptol` |
steptal parameter of "nlm" |

`gradtol` |
gradtol parameter of "nlm" |

`optimbou` |
fixed effects boundary scale parameter for 'optim' |

`fglschecks` |
additional check for convergance on/off (convergence between estimates obtained and that from FGLS) |

`maxnfgls` |
number of fgls checks to perform |

`maxdiffgls` |
max difference allowed in fgls checks |

`patchBasedOnFGLS` |
if FGLS checks not passed, 'patch' fixed effect estimates based on FGLS expectation |

`...` |
Optional arguments to be passed to `nlm` or (`optim` )
minimisation function |

This function maximises the likelihood of the data under polygenic model with covariates an reports twice negative maximum likelihood estimates and the inverse of variance-covariance matrix at the point of ML.

One of the major use of this function is to estimate residuals of the
trait and the inverse of the variance-covariance matrix for
further use in analysis with `mmscore`

and
`grammar`

.

Also, it can be used for a variant of GRAMMAR analysis, which
allows for permutations for GW significance by use of
environmental residuals as an analysis trait with `qtscore`

.

"Environmental residuals" (not to be mistaken with just "residuals") are the residual where both the effect of covariates AND the estimated polygenic effect (breeding values) are factored out. This thus provides an estimate of the trait value contributed by environment (or, turning this other way around, the part of trait not explained by covariates and by the polygene). Polygenic residuals are estimated as

*
σ^2 V^{-1} (Y - (\hat{μ} + \hat{β} C_1 + ...))
*

where *sigma^2* is the residual variance, *V^{-1}* is the
InvSigma (inverse of the var-cov matrix at the maximum of
polygenic model) and
*(Y - (\hat{μ} + \hat{β} C_1 + ...))* is the trait
values adjusted for covariates (also at at the maximum of
polygenic model likelihood).

It can also be used for heritability analysis. If you want to test significance of heritability, estimate the model and write down the function minimum reported at "h2an" element of the output (this is twice negative MaxLikleihood). Then do next round of estimation, but set fixh2=0. The difference between you function minima gives a test distribued as chi-squared with 1 d.f.

The way to compute the likleihood is partly based on the paper of Thompson (see refs), namely instead of taking inverse of var-cov matrix every time, eigenvectors of the inverse of G (taken only once) are used.

A list with values

`h2an` |
A list supplied by the `nlm` minimisation routine.
Of particular interest are elements "estimate" containing parameter
maximal likelihood estimates (MLEs) (order: mean, betas for covariates,
heritability, (polygenic + residual variance)). The value of
twice negative maximum log-likelihood
is returned as h2an\$minimum. |

`residualY` |
Residuals from analysis, based on covariate effects only; NOTE: these are NOT grammar "environmental residuals"! |

`esth2` |
Estimate (or fixed value) of heritability |

`pgresidualY` |
Environmental residuals from analysis, based on covariate effects and predicted breeding value. |

`InvSigma` |
Inverse of the variance-covariance matrix, computed at the
MLEs – these are used in `mmscore` and `grammar`
functions. |

`call` |
The details of call |

`measuredIDs` |
Logical values for IDs who were used in analysis (traits and all covariates measured) == TRUE |

`convFGLS` |
was convergence achieved according to FGLS criterionE |

Presence of twins may complicate your analysis. Check kinship matrix for
singularities, or rather use `check.marker`

for identification
of twin samples. Take special care in interpretation.

If a trait (no covarites) is used, make sure that order of IDs in kinship.matrix is exactly the same as in the outcome

Please note that there is alternative to 'polygenic',
`polygenic_hglm`

, which is faster than
'polygenic'.

Yurii Aulchenko

Thompson EA, Shaw RG (1990) Pedigree analysis for quantitative traits: variance components without matrix inversion. Biometrics 46, 399-413.

Aulchenko YS, de Koning DJ, Haley C. Genomewide rapid association using mixed model and regression: a fast and simple method for genome-wide pedigree-based quantitative trait loci association analysis. Genetics. 2007 177(1):577-85.

Amin N, van Duijn CM, Aulchenko YS. A genomic background based method for association analysis in related individuals. PLoS ONE. 2007 Dec 5;2(12):e1274.

`polygenic_hglm`

,
`mmscore`

,
`grammar`

# note that procedure runs on CLEAN data data(ge03d2ex.clean) gkin <- ibs(ge03d2ex.clean,w="freq") h2ht <- polygenic(height ~ sex + age,kin=gkin,ge03d2ex.clean) # estimate of heritability h2ht$esth2 # other parameters h2ht$h2an # the minimum twice negative log-likelihood h2ht$h2an$minimum # twice maximum log-likelihood -h2ht$h2an$minimum #for binary trait (experimental) h2dm <- polygenic(dm2 ~ sex + age,kin=gkin,ge03d2ex.clean,trait="binomial") # estimated parameters h2dm$h2an

[Package *GenABEL* version 1.6-7 Index]