title: Analytical approaches to detect maternal/fetal genotype incompatibilities that increase risk of pre-eclampsia.
abstract: BACKGROUND
  In utero interactions between incompatible maternal and fetal genotypes are a potential mechanism for the onset or progression of pregnancy related diseases such as pre-eclampsia (PE). However, the optimal analytical approach and study design for evaluating incompatible maternal/offspring genotype combinations is unclear.
  
  
  METHODS
  Using simulation, we estimated the type I error and power of incompatible maternal/offspring genotype models for two analytical approaches: logistic regression used with case-control mother/offspring pairs and the log-linear regression used with case-parent triads. We evaluated a real dataset consisting of maternal/offspring pairs with and without PE for incompatibility effects using the optimal analysis based on the results of the simulation study.
  
  
  RESULTS
  We identified a single coding scheme for the incompatibility effect that was equally or more powerful than all of the alternative analysis models evaluated, regardless of the true underlying model for the incompatibility effect. In addition, the log-linear regression was more powerful than the logistic regression when the heritability was low, and more robust to adjustment for maternal or fetal effects. For the PE data, this analysis revealed three genes, lymphotoxin alpha (LTA), von Willebrand factor (VWF), and alpha 2 chain of type IV collagen (COL4A2) with possible incompatibility effects.
  
  
  CONCLUSIONS
  The incompatibility model should be evaluated for complications of pregnancy, such as PE, where the genotypes of two individuals may contribute to the presence of disease.
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA. NParimi@sfcc-cpmc.net
authors.forename: Neeta
authors.initials: N
authors.lastname: Parimi
authors.forename: Gerard
authors.initials: G
authors.lastname: Tromp
authors.forename: Helena
authors.initials: H
authors.lastname: Kuivaniemi
authors.forename: Jyh Kae
authors.initials: JK
authors.lastname: Nien
authors.forename: Ricardo
authors.initials: R
authors.lastname: Gomez
authors.forename: Roberto
authors.initials: R
authors.lastname: Romero
authors.forename: Katrina Ab
authors.initials: KA
authors.lastname: Goddard
mesh_terms: D003198:Computer Simulation; D005260:Female; D005333:Fetus; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008431:Maternal-Fetal Exchange / Q000235:genetics*; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D011225:Pre-Eclampsia / Q000235:genetics*; D011247:Pregnancy; D012307:Risk Factors
chemical_list: D005819:Genetic Markers