Cervical microRNA expression and spontaneous preterm birth.

BACKGROUND: Preterm birth remains a major public health issue affecting 10% of all pregnancies and increases risks of neonatal morbidity and mortality. Approximately 50% to 60% of preterm births are spontaneous, resulting from preterm premature rupture of membranes or preterm labor. The pathogenesis of spontaneous preterm birth is incompletely understood, and prediction of preterm birth remains elusive. Accurate prediction of preterm birth would reduce infant morbidity and mortality through targeted patient referral to hospitals equipped to care for preterm infants. Two previous studies have analyzed cervical microRNAs in association with spontaneous preterm birth and the length of gestation, but the extent to which microRNAs serve as predictive biomarkers remains unknown. OBJECTIVE: This study aimed to examine associations between cervical microRNA expression and spontaneous preterm birth, with the specific goal of identifying a subset of microRNAs that predict spontaneous preterm birth. STUDY DESIGN: We performed a prospective, nested, case-control study of 25 cases with spontaneous preterm birth and 49 term controls. Controls were matched to cases in a 2:1 ratio on the basis of age, parity, and self-identified race. Cervical swabs were collected at a mean gestational age of 17.1 (4.8) weeks of gestation, and microRNAs were analyzed using a quantitative polymerase chain reaction array. Normalized microRNA expression was compared between cases and controls, and a false discovery rate of 0.2 was applied to account for multiple comparisons. Histopathologic analysis of slides of cervical swab samples was performed to quantify leukocyte burden for adjustment in conditional regression models. We explored the use of Relief-based unsupervised identification of top microRNAs and support vector machines to predict spontaneous preterm birth. We performed microRNA enrichment analysis to explore potential biologic targets and pathways in which up-regulated microRNAs might be involved. RESULTS: Of the 754 microRNAs on the polymerase chain reaction array, 346 were detected in ≥75% of participants' cervical swabs. Average cervical microRNA expression was significantly higher in cases of spontaneous preterm birth than in controls (P=.01). There were 95 significantly up-regulated individual microRNAs (>2-fold change) in cases of subsequent spontaneous preterm birth compared with term controls (P<.05; q<0.2). Notably, miR-143, miR-30e-3p, and miR-199b were all significantly up-regulated, which is consistent with the 1 previous study of cervical microRNA and spontaneous preterm birth. A Relief-based, novel variable (feature) selection machine learning approach had low-to-moderate prediction accuracy, with an area under the receiver operating curve of 0.71. Enrichment analysis revealed that identified microRNAs may modulate inflammatory cell signaling. CONCLUSION: In this prospective nested case-control study of cervical microRNA expression and spontaneous preterm birth, we identified a global increase in microRNA expression and up-regulation of 95 distinct microRNAs in association with subsequent spontaneous preterm birth. Larger and more diverse studies are required to determine the ability of microRNAs to accurately predict spontaneous preterm birth, and mechanistic work to facilitate development of novel therapeutic interventions to prevent spontaneous preterm birth is warranted.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Gene-Interaction-Sensitive enrichment analysis in congenital heart disease.

BACKGROUND: Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to incorporate SNP and gene-level interactions. To this end, gene scores are derived by Relief-based feature importance algorithms that efficiently detect both univariate and interaction effects (MultiSURF) or exclusively interaction effects (MultiSURF*). We compare these interaction-sensitive GSEA approaches to traditional χ2 rankings in simulated genome-wide array data, and in a target and replication cohort of congenital heart disease patients with conotruncal defects (CTDs). RESULTS: In the simulation study and for both CTD datasets, both Relief-based approaches to GSEA captured more relevant and significant gene ontology terms compared to the univariate GSEA. Key terms and themes of interest include cell adhesion, migration, and signaling. A leading edge analysis highlighted semaphorins and their receptors, the Slit-Robo pathway, and other genes with roles in the secondary heart field and outflow tract development. CONCLUSIONS: Our results indicate that interaction-sensitive approaches to enrichment analysis can improve upon traditional univariate GSEA. This approach replicated univariate findings and identified additional and more robust support for the role of the secondary heart field and cardiac neural crest cell migration in the development of CTDs.