Research article

Analytical approaches to detect maternal/fetal genotype incompatibilities that increase risk of pre-eclampsia

Neeta Parimi¹ , Gerard Tromp^2,3 , Helena Kuivaniemi^2,4 , Jyh Kae Nien⁵ , Ricardo Gomez^5,6 , Roberto Romero⁵ and Katrina AB Goddard^1,7

¹Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA

²Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA

³Department of Neurology, Wayne State University, Detroit, MI, USA

⁴Department of Surgery, Wayne State University, Detroit, MI, USA

⁵the Perinatology Research Branch, NICHD, NIH, Bethesda, MD, USA

⁶Center for Perinatal Diagnosis and Research, Sotero del Rio Hospital, Pontificia Universidad Catolica de Chile, Santiago, Chile

⁷Center for Health Research, Kaiser Permanente Northwest, 3800 N. Interstate Avenue, Portland, USA

author email corresponding author email

BMC Medical Genetics 2008, 9:60doi:10.1186/1471-2350-9-60

Published:	3 July 2008

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.

Conclusion

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.