R: Estimation of polygenic model

polygenic {GenABEL}

R Documentation

Estimation of polygenic model

Description

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

Usage

  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, llfun = "polylik_eigen",
    eigenOfRel, ...)

Arguments

`formula`	Formula describing fixed effects to be used in the analysis, e.g. y ~ a + b means that outcome (y) depends on two covariates, a and b. If no covariates used in the 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 different optimisation functions. We suggest using the default `nlm`, although `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 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 convergence 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
`llfun`	function to compute likelihood (default 'polylik_eigen', also available – but not recommended – 'polylik')
`eigenOfRel`	results of eigen(relationship matrix = 2*kinship.matrix). Passing this can decrease computational time substantially if multiple traits are analysed using the same kinship matrix. This option will not work if any NA's are found in the trait and/or covariates and if the dimensions of the 'eigen'-object, trait, covariates, kinship do not match.
`...`	Optional arguments to be passed to `nlm` or (`optim`) minimisation function

Details

One of the major uses 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 the 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 the "h2an" element of the output (this is twice the negative MaxLikelihood). Then do a next round of estimation, but set fixh2=0. The difference between your function minima gives a test distributed as chi-squared with 1 d.f.

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

Value

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.
`esth2`	Estimate (or fixed value) of heritability
`residualY`	Residuals from analysis, based on covariate effects only; NOTE: these are NOT grammar "environmental residuals"!
`pgresidualY`	Environmental residuals from analysis, based on covariate effects and predicted breeding value.
`grresidualY`	GRAMMAR+ transformed trait residuals
`grammarGamma`	list with GRAMMAR-gamma correction factors
`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

Note

Presence of twins may complicate your analysis. Check the kinship matrix for singularities, or rather use check.marker for identification of twin samples. Take special care in interpretation.

If a trait (no covariates) is used, make sure that the order of IDs in the 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() with the llfun='polylik' option, but slightly slower than the default polygenic().

Author(s)

Yurii Aulchenko, Gulnara Svischeva

References

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

for original GRAMMAR

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.

for GRAMMAR-GC

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.

for GRAMMAR-Gamma

Svischeva G, Axenovich TI, Belonogova NM, van Duijn CM, Aulchenko YS. Rapid variance components-based method for whole-genome association analysis. Nature Genetics. 2012 44:1166-1170. doi:10.1038/ng.2410

for GRAMMAR+ transformation

Belonogova NM, Svishcheva GR, van Duijn CM, Aulchenko YS, Axenovich TI (2013) Region-Based Association Analysis of Human Quantitative Traits in Related Individuals. PLoS ONE 8(6): e65395. doi:10.1371/journal.pone.0065395

Examples

# note that procedure runs on CLEAN data
require(GenABEL.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 1.8-0 Index]