hapassoc (version 1.2-1)

hapassoc: EM algorithm to fit maximum likelihood estimates of trait associations with SNP haplotypes

Description

This function takes a dataset of haplotypes in which rows for individuals of uncertain phase have been augmented by pseudo-individuals who carry the possible multilocus genotypes consistent with the single-locus phenotypes. For cohort or cross-sectional data, the EM algorithm is used to find MLE's for trait associations with covariates in generalized linear models. For case-control data, the algorithm solves a set of unbiased estimating equations (see Details).

Usage

hapassoc(form,haplos.list,baseline = "missing" ,family = binomial(),
design="cohort",disease.prob=NULL,freq = NULL, maxit = 50, tol = 0.001, 
start = NULL, verbose=FALSE)

Arguments

form

model equation in usual R format

haplos.list

list of haplotype data from pre.hapassoc

baseline

optional, haplotype to be used for baseline coding. Default is the most frequent haplotype according to the initial haplotype frequency estimates returned by pre.hapassoc.

family

binomial, poisson, gaussian or gamma are supported, default=binomial

design

study design. Default is cohort for cohort or cross-sectional sampling. Users may optionally specify cc for case-control or retrospective sampling of exposures (i.e. genotypes and non-genetic attributes) conditional on

disease.prob

marginal disease probability [P(D=1)] to use in the MPSE estimator, if design="cc". If disease.prob=NULL (the default value), a rare disease is assumed. This argument is ignored if design="cohort".

freq

initial estimates of haplotype frequencies, default values are calculated in pre.hapassoc using standard haplotype-counting (i.e. EM algorithm without adjustment for non-haplotype covariates)

maxit

maximum number of iterations of the EM algorithm; default=50

tol

convergence tolerance in terms of either the maximum difference in parameter estimates between interations or the maximum relative difference in parameter estimates between iterations, which ever is larger.

start

starting values for parameter estimates in the risk model

verbose

should the iteration number and value of the covergence criterion be printed at each iteration of the EM algorithm? Default=FALSE

Value

• it
  number of iterations of the EM algorithm
• beta
  estimated regression coefficients
• freq
  estimated haplotype frequencies
• fits
  fitted values of the trait
• wts
  final weights calculated in last iteration of the EM algorithm. These are estimates of the conditional probabilities of each multilocus genotype given the observed single-locus genotypes.
• var
  joint variance-covariance matrix of the estimated regression coefficients and the estimated haplotype frequencies
• dispersion
  maximum likelihood estimate of dispersion parameter (to get the moment estimate, use summary.hapassoc) if applicable, otherwise 1
• family
  family of the generalized linear model (e.g. binomial, gaussian, etc.)
• response
  trait value
• converged
  TRUE/FALSE indicator of convergence. If the algorithm fails to converge, only the converged indicator is returned.
• model
  model equation
• loglik
  the log-likelihood evaluated at the maximum likelihood estimates of all parameters if design="cohort", or NA if design="cc"
• call
  the function call

Details

See the hapassoc vignette, of the same name as the package, for details.

When the study design is case-control, i.e. genotypes and non-genetic attributes have been sampled retrospectively given disease status, naive application of prospective maximum likelihood methods can yield biased inference (Spinka et al., 2005, Chen, 2006). Therefore, when design="cc", the algorithm solves the modified prospective score equations or MPSE (Spinka et al. 2005) for regression and haplotype frequency parameters. The implementation in hapassoc is due to Chen (2006). In general, the MPSE approach requires that the marginal probability of disease, P(D=1), be known. An exception is when the disease is rare; hence, when disease.prob=NULL (the default) a rare disease is assumed. The variance-covariance matrix of the regression parameter and haplotype frequency estimators is approximated as described in Chen (2006). Limited simulations indicate that the resulting standard errors for regression parameters perform well, but not the standard errors for haplotype frequencies, which should be ignored. For case-control data, we hope to implement the variance-covariance estimator of Spinka et al. (2005) in a future version of hapassoc.

References

Burkett K, McNeney B, Graham J (2004). A note on inference of trait associations with SNP haplotypes and other attributes in generalized linear models. Human Heredity, 57:200-206

Burkett K, Graham J and McNeney B (2006). hapassoc: Software for Likelihood Inference of Trait Associations with SNP Haplotypes and Other Attributes. Journal of Statistical Software, 16(2):1-19

Chen, Z. (2006): Approximate likelihood inference for haplotype risks in case-control studies of a rare disease, Masters thesis, Statistics and Actuarial Science, Simon Fraser University, available at http://www.stat.sfu.ca/people/alumni/Theses/Chen-2006.pdf.

Spinka, C., Carroll, R. J. & Chatterjee, N. (2005). Analysis of case-control studies of genetic and environmental factors with missing genetic information and haplotype-phase ambiguity. Genetic Epidemiology, 29, 108-127.

See Also

pre.hapassoc,summary.hapassoc,glm,family.

Examples

data(hypoDat)
example.pre.hapassoc<-pre.hapassoc(hypoDat, 3)

example.pre.hapassoc$initFreq # look at initial haplotype frequencies
#      h000       h001       h010       h011       h100       h101       h110 
#0.25179111 0.26050418 0.23606001 0.09164470 0.10133627 0.02636844 0.01081260 
#      h111 
#0.02148268 


names(example.pre.hapassoc$haploDM)
# "h000"   "h001"   "h010"   "h011"   "h100"   "pooled"

# Columns of the matrix haploDM score the number of copies of each haplotype 
# for each pseudo-individual.

# Logistic regression for a multiplicative odds model having as the baseline 
# group homozygotes '001/001' for the most common haplotype

example.regr <- hapassoc(affected ~ attr + h000+ h010 + h011 + h100 + pooled,
                  example.pre.hapassoc, family=binomial())

# Logistic regression with separate effects for 000 homozygotes, 001 homozygotes 
# and 000/001 heterozygotes

example2.regr <- hapassoc(affected ~ attr + I(h000==2) + I(h001==2) +
                   I(h000==1 & h001==1), example.pre.hapassoc, family=binomial())