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Application of transmission disequilibrium tests to nonsyndromic oral clefts: Including candidate genes and environmental exposures in the models

✍ Scribed by Maestri, Nancy E.; Beaty, Terri H.; Hetmanski, Jacqueline; Smith, E. Anne; McIntosh, Iain; Wyszynski, Diego F.; Liang, Kung-Yee; Duffy, David L.; VanderKolk, Craig


Publisher
John Wiley and Sons
Year
1997
Tongue
English
Weight
42 KB
Volume
73
Category
Article
ISSN
0148-7299
DOI
10.1002/(sici)1096-8628(19971219)73:3<337::aid-ajmg21>3.0.co;2-j

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✦ Synopsis


Extensive epidemiological and genetic studies of the cause of oral clefts have demonstrated strong familial aggregation but have failed to yield definitive evidence of any single genetic mechanism. We used the transmission/disequilibrium test (TDT) to investigate the relationship between oral clefts and markers associated with five candidate genes by utilizing 160 parentoffspring trios. Conditional logistic regression models extended the TDT to include covariates as effect modifiers, thus permitting tests for gene-environment interactions. For four of these candidates [transforming growth factor alpha (TGFA), transforming growth factor beta 3 (TGFB3), retinoic acid receptor (RARA), and the proto-oncogene BCL3], we detected modestly elevated odds ratios for the transmission of one marker allele to cleft probands when all the trios were analyzed together. These odds ratios increased when information on type of cleft, race, family history, or maternal smoking were incorporated as effect modifiers. We detected significant interaction between maternal smoking and the transmission of alleles for markers near TGFA and TGFB3; excess transmission of allele 3 at BCL3 was most significant among cleft lip probands; and the odds ratios for transmission of alleles at D19S178 and THRA1 were significant when ethnic group was included in the model. We suggest that utilizing an analytical strategy that allows for stratification of data and incorporating environmental effects into a single analysis may be more effective for detecting genes of small effect.


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