PMID- 16355406
OWN - NLM
STAT- MEDLINE
DCOM- 20060403
LR  - 20071114
IS  - 0741-0395 (Print)
IS  - 0741-0395 (Linking)
VI  - 30
IP  - 1
DP  - 2006 Jan
TI  - Comparison of methods incorporating quantitative covariates into affected sib 
      pair linkage analysis.
PG  - 77-93
AB  - For complex traits, it may be possible to increase the power to detect linkage if 
      one takes advantage of covariate information. Several statistics have been 
      proposed that incorporate quantitative covariate information into affected sib 
      pair (ASP) linkage analysis. However, it is not clear how these statistics 
      perform under different gene-environment (G x E) interactions. We compare 
      representative statistics to each other on simulated data under three 
      biologically-plausible G x E models. We also compared their performance with a 
      model-free method and with quantitative trait locus (QTL) linkage approaches. The 
      statistics considered here are: (1) mixture model; (2) general 
      conditional-logistic model (LODPAL); (3) multinomial logistic regression models 
      (MLRM); (4) extension of the maximum-likelihood-binomial approach (MLB); (5) 
      ordered-subset analysis (OSA); and (6) logistic regression modeling (COVLINK). In 
      all three G x E models, most of these six statistics perform better when using 
      the covariate C1 associated with a G x E interaction effect than when using the 
      environmental risk factor C2 or the random noise covariate C3. Compared with a 
      model-free method without covariates (S(all)), the mixture model performs the 
      best when using C1, with the high-to-low OSA method also performing quite well. 
      Generally, MLB is the least sensitive to covariate choice. However, most of these 
      statistics do not provide better power than S(all). Thus, while inclusion of the 
      "correct" covariate can lead to increased power, careful selection of appropriate 
      covariates is vital for success.
CI  - Copyright 2005 Wiley-Liss, Inc.
FAU - Tsai, Hui-Ju
AU  - Tsai HJ
AD  - Department of Human Genetics, University of Pittsburgh, PA 15261, USA.
FAU - Weeks, Daniel E
AU  - Weeks DE
LA  - eng
GR  - R01DK055406/DK/NIDDK NIH HHS/United States
GR  - R01EY009859/EY/NEI NIH HHS/United States
GR  - RR03655/RR/NCRR NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - United States
TA  - Genet Epidemiol
JT  - Genetic epidemiology
JID - 8411723
SB  - IM
MH  - Analysis of Variance
MH  - Chromosome Mapping/*methods
MH  - Cytogenetic Analysis/*methods
MH  - Genetic Predisposition to Disease/genetics
MH  - *Genotype
MH  - Humans
MH  - Models, Genetic
MH  - Models, Statistical
MH  - Phenotype
MH  - Quantitative Trait Loci/*genetics
EDAT- 2005/12/16 09:00
MHDA- 2006/04/04 09:00
CRDT- 2005/12/16 09:00
PHST- 2005/12/16 09:00 [pubmed]
PHST- 2006/04/04 09:00 [medline]
PHST- 2005/12/16 09:00 [entrez]
AID - 10.1002/gepi.20126 [doi]
PST - ppublish
SO  - Genet Epidemiol. 2006 Jan;30(1):77-93. doi: 10.1002/gepi.20126.