Copy number variants (CNVs) play important roles in a number of human diseases and in pharmacogenetics. Powerful methods exist for CNV detection in whole genome sequencing (WGS) data, but such data are costly to obtain. Many disease causal CNVs span or are found in genome coding regions (exons), which makes CNV detection using whole exome sequencing (WES) data attractive. If reliably validated against WGS-based CNVs, exome-derived CNVs have potential applications in a clinical setting. Several algorithms have been developed to exploit exome data for CNV detection and comparisons made to find the most suitable methods for particular data samples. The results are not consistent across studies. Here, we review some of the exome CNV detection methods based on depth of coverage profiles and examine their performance to identify problems contributing to discrepancies in published results. We also present a streamlined strategy that uses a single metric, the likelihood ratio, to compare exome methods, and we demonstrated its utility using the VarScan 2 and eXome Hidden Markov Model (XHMM) programs using paired normal and tumour exome data from chronic lymphocytic leukaemia patients. We use array-based somatic CNV (SCNV) calls as a reference standard to compute prevalence-independent statistics, such as sensitivity, specificity and likelihood ratio, for validation of the exome-derived SCNVs. We also account for factors known to influence the performance of exome read depth methods, such as CNV size and frequency, while comparing our findings with published results.

Several small studies have shown associations between breastfeeding and genome-wide DNA methylation (DNAm). We performed a comprehensive Epigenome-Wide Association Study (EWAS) to identify associations between breastfeeding and DNAm patterns in childhood. We analysed DNAm data from the Isle of Wight Birth Cohort at birth, 10, 18 and 26 years. The feeding method was categorized as breastfeeding duration >3 months and >6 months, and exclusive breastfeeding duration >3 months. EWASs using robust linear regression were performed to identify differentially methylated positions (DMPs) in breastfed and non-breastfed children at age 10 (false discovery rate of 5%). Differentially methylated regions (DMRs) were identified using comb-p. The persistence of significant associations was evaluated in neonates and individuals at 18 and 26 years. Two DMPs, in genes SNX25 and LINC00840, were significantly associated with breastfeeding duration >6 months at 10 years and was replicated for >3 months of exclusive breastfeeding. Additionally, a significant DMR spanning the gene FDFT1 was identified in 10-year-old children who were exposed to a breastfeeding duration >3 months. None of these signals persisted to 18 or 26 years. This study lends further support for a suggestive role of DNAm in the known benefits of breastfeeding on a child’s future health.