It is widely believed that risks of many complex diseases are determined by genetic susceptibilities,
including environmental exposures, and their interaction. Chatterjee and Carroll
(2005) have recently developed an efficient retrospective maximum-likelihood method for
analysis of case-control studies that exploits an assumption of gene-environment independence
and leaves the distribution of the environmental covariates to be completely nonparametric.
We generalize the semiparametric maximum-likelihood approach to situations
when some of the environmental covariates are measured with error and allow genetic information
to be missing on some subjects, e.g., unphased haplotypes. Profile likelihood
techniques and an EM algorithm are developed, resulting in a relatively simple procedure
for parameter estimation. We prove consistency and derive the resulting asymptotic covariance
matrix of parameter estimates when variance of measurement error is known and when
it is estimated using replications. The performance of the proposed method is illustrated
using simulation studies emphasizing the case when genetic information is in the form of
a haplotype and missing data arises from haplotype-phase ambiguity and missing genetic
data. Inference is performed via a likelihood-ratio type procedure, one that we show has
better small-sample performance thanWald-type inferences. An application of this method
is illustrated using a case-control study of an association of calcium intake with early stages
of colorectal tumor development.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1026 |
| Date | 15 May 2009 |
| Creators | Lobach, Iryna |
| Contributors | Carroll, R.J. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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