Statistical Applications in Genetics and Molecular Biology A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy

Human diseases developed during pregnancy could be caused by the direct effects of both maternal and fetal genes, and/or by the indirect effects caused by genetic conflicts. Genetic conflicts exist when the effects of fetal genes are opposed by the effects of maternal genes, or when there is a conflict between the maternal and paternal genes within the fetal genome. The two types of genetic conflicts involve the functions of different genes in different genomes and are genetically distinct. Differentiating and further dissecting the two sets of genetic conflict effects that increase disease risk during pregnancy present statistical challenges, and have been traditionally pursued as two separate endeavors. In this article, we develop a unified framework to model and test the two sets of genetic conflicts via a regularized regression approach. Our model is developed considering real situations in which the paternal information is often completely missing; an assumption that fails most of the current family-based studies. A mixture model-based penalized logistic regression is proposed for data sampled from a natural population. We develop a variable selection procedure to select significant genetic features. Simulation studies show that the model has high power and good false positive control under reasonable sample sizes and disease allele frequency. A case study of small for gestational age (SGA) is provided to show the utility of the proposed approach. Our model provides a powerful tool for dissecting genetic conflicts that increase disease risk during pregnancy, and offers a testable framework for the genetic conflict hypothesis previously proposed.

[1]  Cees G. M. Snoek,et al.  Variable Selection , 2019, Model-Based Clustering and Classification for Data Science.

[2]  K. Goddard,et al.  Analytical approaches to detect maternal/fetal genotype incompatibilities that increase risk of pre-eclampsia , 2008, BMC Medical Genetics.

[3]  Steven Buyske,et al.  Maternal genotype effects can alias case genotype effects in case–control studies , 2008, European Journal of Human Genetics.

[4]  Qing Lu,et al.  Using the optimal receiver operating characteristic curve to design a predictive genetic test, exemplified with type 2 diabetes. , 2008, American journal of human genetics.

[5]  Hongzhe Li,et al.  Group SCAD regression analysis for microarray time course gene expression data , 2007, Bioinform..

[6]  P. Czernichow,et al.  Small for gestational age: short stature and beyond. , 2007, Endocrine reviews.

[7]  W. Fung,et al.  An Extension of the Transmission Disequilibrium Test Incorporating Imprinting , 2007, Genetics.

[8]  H. Zou.  The Adaptive Lasso and Its Oracle Properties , 2006 .

[9]  J. Sinsheimer,et al.  Allowing for Missing Data at Highly Polymorphic Genes when Testing for Maternal, Offspring and Maternal-Fetal Genotype Incompatibility Effects , 2006, Human Heredity.

[10]  Paul Wordsworth,et al.  The v‐MFG test: investigating maternal, offspring and maternal‐fetal genetic incompatibility effects on disease and viability , 2006, Genetic epidemiology.

[11]  J. Gardosi,et al.  New Definition of Small for Gestational Age Based on Fetal Growth Potential , 2006, Hormone Research in Paediatrics.

[12]  D. Dunger,et al.  Genetic Variations and Normal Fetal Growth , 2006, Hormone Research in Paediatrics.

[13]  I. Cetin,et al.  Placental LPL Gene Expression Is Increased in Severe Intrauterine Growth-Restricted Pregnancies , 2006, Pediatric Research.

[14]  H. Spencer,et al.  A census of mammalian imprinting. , 2005, Trends in genetics : TIG.

[15]  Wenjiang J. Fu.  Nonlinear GCV and quasi-GCV for shrinkage models , 2005 .

[16]  S. Shete,et al.  Parametric Approach to Genomic Imprinting Analysis with Applications to Angelman’s Syndrome , 2005, Human Heredity.

[17]  J. Sinsheimer,et al.  An exact maternal‐fetal genotype incompatibility (MFG) test , 2005, Genetic epidemiology.

[18]  T. Hastie,et al.  Classification of gene microarrays by penalized logistic regression. , 2004, Biostatistics.

[19]  D. Haig,et al.  Evolutionary conflicts in pregnancy and calcium metabolism--a review. , 2004, Placenta.

[20]  Tianhua Niu,et al.  A candidate gene association study on preterm delivery: application of high-throughput genotyping technology and advanced statistical methods. , 2004, Human molecular genetics.

[21]  Janet S Sinsheimer,et al.  Detecting genotype combinations that increase risk for disease: Maternal‐Fetal genotype incompatibility test , 2003, Genetic epidemiology.

[22]  B. Tycko,et al.  Physiological functions of imprinted genes , 2002, Journal of cellular physiology.

[23]  Sanjay Shete,et al.  Testing for genetic linkage in families by a variance-components approach in the presence of genomic imprinting. , 2002, American journal of human genetics.

[24]  M. Odent.  Hypothesis: preeclampsia as a maternal-fetal conflict. , 2001, MedGenMed : Medscape general medicine.

[25]  R. Hanson,et al.  Assessment of parent-of-origin effects in linkage analysis of quantitative traits. , 2001, American journal of human genetics.

[26]  K. Pfeifer,et al.  Mechanisms of genomic imprinting. , 2000, American journal of human genetics.

[27]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[28]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[29]  A. Rizzino,et al.  Effects of differentiation on the transcriptional regulation of the FGF‐4 gene: Critical roles played by a distal enhancer , 1998, Molecular reproduction and development.

[30]  C R Weinberg,et al.  A log-linear approach to case-parent-triad data: assessing effects of disease genes that act either directly or through maternal effects and that may be subject to parental imprinting. , 1998, American journal of human genetics.

[31]  H. Wollmann.  Intrauterine Growth Restriction: Definition and Etiology , 1998, Hormone Research in Paediatrics.

[32]  D. Stevenson,et al.  The Cognitive Outcome of Full‐Term Small for Gestational Age Infants at Late Adolescence , 1995, Obstetrics and gynecology.

[33]  R. Tibshirani,et al.  An Introduction to the Bootstrap , 1995 .

[34]  D. Haig,et al.  Genetic Conflicts in Human Pregnancy , 1993, The Quarterly Review of Biology.

[35]  Robert Gray,et al.  Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis , 1992 .

[36]  S. Cessie,et al.  Ridge Estimators in Logistic Regression , 1992 .

[37]  M. Onis,et al.  The differential neonatal morbidity of the intrauterine growth retardation syndrome. , 1990, American Journal of Obstetrics and Gynecology.

[38]  D. Taylor,et al.  Fetal growth achievement and neurodevelopmental disability , 1989, British journal of obstetrics and gynaecology.

[39]  R. Rochat,et al.  Causes of Maternal Mortality in the United States , 1985, Obstetrics and gynecology.

[40]  Mee Young Park,et al.  Penalized logistic regression for detecting gene interactions. , 2008, Biostatistics.

[41]  Anthony R Isles,et al.  Imprinted genes and mother-offspring interactions. , 2005, Early human development.

[42]  Gavin Kelsey,et al.  Resourceful imprinting : Fertility , 2004 .

[43]  G. Badger,et al.  Morbidity and mortality among very-low-birth-weight neonates with intrauterine growth restriction , 2000 .

[44]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[45]  M. Lynch,et al.  Genetics and Analysis of Quantitative Traits , 1996 .

[46]  M. Silvapulle,et al.  Ridge estimation in logistic regression , 1988 .