Entropy-based selection for maternal-fetal genotype incompatibility with application to preterm prelabor rupture of membranes

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• 作者：Shaoyu Li (1)
  Yuehua Cui (2) (3)
  Roberto Romero (4) (5) (6)
  
  1. Department of Biostatistics ; St Jude Children鈥檚 Research Hospital ; 262 Danny Thomas Place ; Memphis ; USA
  2. Department of Statistics and Probability ; Michigan State University ; Wells Hall ; East Lansing ; USA
  3. Division of Medical Statistics ; School of Public Health ; Shanxi Medical Universiy ; 030001 ; Taiyuan ; Shanxi ; China
  4. Perinatology Research Branch ; NICHD/NIH/DHHS ; Bethesda and Detroit ; USA
  5. Department of Obstetrics and Gynecology ; University of Michigan ; Ann Arbor ; USA
  6. Department of Epidemiology and Biostatistics ; Michigan State University ; East Lansing ; USA
  
• 关键词：Complex disease ; Pregnancy complications ; Association study ; Maternal ; fetal genotype incompatibility
• 刊名：BMC Genetics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：634 KB
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• 刊物主题：Life Sciences, general; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics; Genetics and Population Dynamics;
• 出版者：BioMed Central
• ISSN：1471-2156

文摘

Background Maternal-fetal genotype incompatibility (MFGI) is increasingly reported to influence human diseases, especially pregnancy-related complications. In practice, it is challenging to identify the ideal incompatibility model for analysis, since the true MFGI mechanism is generally unknown. The underlying MFGI mechanism for different genetic variants can vary, and to use a single incompatibility model for all circumstances would cause power loss in testing MFGI. Results In this article, we propose a practical 2-step procedure that incorporates a model selection strategy based on an entropy measurement to select the most appropriate MFGI model represented by data and test the significance of the MFGI effect using the chosen model within the generalized linear regression framework. Conclusions Our simulation studies show that the proposed two-step procedure controls the type I error rate and increase the testing power under various scenarios. In a real data application, our analysis reveals genes having an MFGI effect, which may not be detected with a non-model selection counterpart.