Variations in Inflammatory and Cell Cycle Genes and Preterm Birth, Small for Gestational Age and Hypertensive Disorders of Pregnancy

The maternal outcome of preeclampsia and the fetal outcomes of preterm birth and poor intrauterine growth often occur together, share placental pathology and are marked by changes in inflammatory biomarkers. Genetic polymorphisms in inflammatory genes have been investigated with respect to all of these outcomes with conflicting results. In previous studies case groups have been small or non-representative of US populations, and coverage of candidate genes has been sparse. We sought to expand coverage of inflammatory genes related to natural killer cells and T cells and in addition included candidate genes related to cell cycle function. In a sample of 1646 women from a bi-racial prospective pregnancy cohort, we examined the relationship between 503 tagSNPs in 40 genes and the outcomes of preterm birth, small for gestational age, gestational hypertension and preeclampsia. Six genes involved in natural killer cell function (IL12A, CSF2, IFNGR2 and KIR3DL2) and Th2 immunity (IL13 and IL4) were associated with preterm birth among European Americans with some evidence of an association for African Americans as well (IL12A and CSF2). IL6 and KLRD1 were associated with term small for gestational age births among African Americans with similar results for IL6 alone among European Americans. LTA, TNF and TBKBP1 were associated with preeclampsia among European Americans only. There were no associations with any cell cycle genes or with the outcome of gestational hypertension. In summary, this study found novel associations with a number of genes related to natural killer cells, Th2 immunity and TNF signaling pathways and the outcomes of preterm birth, poor fetal growth and preeclampsia among both European and African Americans.Doctor of Philosoph

Kernel Machine SNP-Set Testing Under Multiple Candidate Kernels

Joint testing for the cumulative effect of multiple single nucleotide polymorphisms grouped on the basis of prior biological knowledge has become a popular and powerful strategy for the analysis of large scale genetic association studies. The kernel machine (KM) testing framework is a useful approach that has been proposed for testing associations between multiple genetic variants and many different types of complex traits by comparing pairwise similarity in phenotype between subjects to pairwise similarity in genotype, with similarity in genotype defined via a kernel function. An advantage of the KM framework is its flexibility: choosing different kernel functions allows for different assumptions concerning the underlying model and can allow for improved power. In practice, it is difficult to know which kernel to use a priori since this depends on the unknown underlying trait architecture and selecting the kernel which gives the lowest p-value can lead to inflated type I error. Therefore, we propose practical strategies for KM testing when multiple candidate kernels are present based on constructing composite kernels and based on efficient perturbation procedures. We demonstrate through simulations and real data applications that the procedures protect the type I error rate and can lead to substantially improved power over poor choices of kernels and only modest differences in power versus using the best candidate kernel