Performance of the log-linear approach to case-parent triad data for assessing maternal genetic associations with offspring disease: type I error, power, and bias.
Maternal genetic variation may serve as a biomarker in studies aimed at clarifying fetal determinants of infant or adult disease. The log-linear approach to case-parent triad data (LCPT) can be used to investigate maternal genetic polymorphisms in relation to offspring disease risk, but LCPT operating characteristics have been reported for only a limited range of situations. The authors performed a simulation study to investigate the performance of the LCPT for assessing maternal associations with offspring disease risk over a wide range of scenarios with varying sample sizes (n), high-risk allele frequencies (f ), and modes of inheritance, all of which greatly affect the expected number of triads in informative categories. For most f values less than 0.5, the LCPT approach with 200 triads allowed for approximately 80% power to detect valid, unbiased maternal relative risks of 2 when inheritance was log-additive or dominant. When inheritance was recessive, this was true for most f 's greater than 0.35. Outside of this range, however, power and bias depended greatly on the mode of inheritance, f, and n. On the basis of these findings, epidemiologists may consider the LCPT a useful approach for assessing maternal relative risks unless one expects a very rare or fairly common maternal allele to increase offspring disease risk.
Starr
Jacqueline R
JR
Department of Pediatrics, School of Medicine, University of Washington, Box 359300 (M2-8), Seattle, WA 98105-0371, USA. jrstarr@u.washingtn.edu
Hsu
Li
L
Schwartz
Stephen M
SM
D015982:Bias; D003198:Computer Simulation; D005189:False Positive Reactions; D005260:Female; D005787:Gene Frequency; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D018392:Genomic Imprinting; D005838:Genotype; D006801:Humans; D040582:Inheritance Patterns; D016014:Linear Models; D008297:Male; D008957:Models, Genetic; D015203:Reproducibility of Results; D012306:Risk; D018401:Sample Size