A haplotype-based normalization technique for the analysis and detection of allele specific expression.

BACKGROUND: Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects. RESULTS: Here, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking. CONCLUSIONS: The correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes.

Comparative Analysis of Iron Homeostasis in Sub-Saharan African Children with Sickle Cell Disease and Their Unaffected Siblings.

Iron is an essential trace element subject to tight regulation to ensure adequate running of biological processes. In sub-Saharan Africa where hemoglobinopathies are common, iron homeostasis is likely to be impaired by these conditions. Here, we assessed and compared key serum proteins associated with iron metabolism between sub-Saharan African children with sickle cell disease (SCD) and their unaffected siblings. Complete blood counts and serum concentrations of four key proteins involved in iron regulation (ferritin, transferrin, sTfR, and hepcidin) were measured for 73 children with SCD and 68 healthy siblings in Benin, West Africa. We found significant differences in concentration of transferrin, sTfR, and ferritin between the two groups. Hepcidin concentrations were found at unusually high concentrations but did not differ among the two groups. We found a significant negative correlation between hepcidin levels and both MCH and MCV in the SCD group and report that sTfR concentrations show a correlation with MCV and MHC in opposite directions in the two groups. These results highlight the unusually high levels of hepcidin in the Beninese population and the patterns of differential iron homeostasis taking place under SCD status. These results lay the foundation for a systematic evaluation of the underlying mechanisms deregulating iron homeostasis in populations with SCD or high prevalence of iron deficiency.