Considerations when processing and interpreting genomics data of the placenta. (1st September 2019)

• Record Type: Journal Article
• Authors: Konwar, Chaini; Del Gobbo, Giulia; Yuan, Victor; Robinson, Wendy P.
• Is Part Of: Placenta. Volume 84(2019)
• Page Start: 57
• Page End: 62
• Publication Date: 2019-09-01
• Subjects: Placenta -- Genomics -- DNA methylation -- Microarray -- Gene expression; Placenta -- Periodicals; Reproduction -- Periodicals; Placenta -- Periodicals; Placenta -- Périodiques; Reproduction -- Périodiques; 612.63
• DOI: 10.1016/j.placenta.2019.01.006
• Languages: English
• ISSNs: 0143-4004

Abstract

Abstract: The application of genomic approaches to placental research has opened exciting new avenues to help us understand basic biological properties of the placenta, improve prenatal screening/diagnosis, and measure effects of in utero exposures on child health outcomes. In the last decade, such large-scale genomic data (including epigenomics and transcriptomics) have become more easily accessible to researchers from many disciplines due to the increasing ease of obtaining such data and the rapidly evolving computational tools available for analysis. While the potential of large-scale studies has been widely promoted, less attention has been given to some of the challenges associated with processing and interpreting such data. We hereby share some of our experiences in assessing data quality, reproducibility, and interpretation in the context of genome-wide studies of the placenta, with the aim to improve future studies. There is rarely a single "best" approach, as that can depend on the study question and sample cohort. However, being consistent, thoroughly assessing potential confounders in the data, and communicating key variables in the methods section of the manuscript are critically important to help researchers to collaborate and build on each other's work. Highlights: Placental genomics has great potential but technical challenges must be addressed. Use of biological replicates can help understand effects of placental variability. Sample quality and batch effects should be carefully considered in study design. Data visualization tools should be routinely employed in assessing data quality. Establishing rigorous practices will enhance data sharing opportunities.