Advantages and diagnostic effectiveness of the two most widely used resequencing approaches, whole exome (WES) and whole genome (WGS) sequencing, are often debated. WES dominated large-scale resequencing projects because of lower cost and easier data storage and processing. Rapid development of 3^rd generation sequencing methods and novel exome sequencing kits predicate the need for a robust statistical framework allowing informative and easy performance comparison of the emerging methods. In our study we developed a set of statistical tools to systematically assess coverage of coding regions provided by several modern WES platforms, as well as PCR-free WGS. We identified a substantial problem in most previously published comparisons which did not account for mappability limitations of short reads. Using regression analysis and simple machine learning, as well as several novel metrics of coverage evenness, we analyzed the contribution from the major determinants of CDS coverage. Contrary to a common view, most of the observed bias in modern WES stems from mappability limitations of short reads and exome probe design rather than sequence composition. We also identified the ~ 500 kb region of human exome that could not be effectively characterized using short read technology and should receive special attention during variant analysis. Using our novel metrics of sequencing coverage, we identified main determinants of WES and WGS performance. Overall, our study points out avenues for improvement of enrichment-based methods and development of novel approaches that would maximize variant discovery at optimal cost.

Pre-eclampsia (PE) is a complication of pregnancy that affects 5‑8% of women after 20 weeks of gestation. It is usually diagnosed based on the de novo onset of hypertension and proteinuria. Preexisting hypertension in women developing PE, also known as superimposed PE on chronic hypertension (SPE), leads to elevated risk of maternal and fetal mortality. PE is associated with an altered microRNA (miRNA) expression pattern in the placenta, suggesting that miRNA deregulation is involved in the pathogenesis of PE. Whether and how the miRNA expression pattern is changed in the SPE placenta remains unclear. The present study analyzed the placental miRNA expression profile in pregnancies complicated by SPE. miRNA expression profiles in SPE and normal placentas were investigated using an Ion Torrent sequencing system. Sequencing data were processed using a comprehensive analysis pipeline for deep miRNA sequencing (CAP‑miRSeq). A total of 22 miRNAs were identified to be deregulated in placentas from patients with SPE. They included 16 miRNAs previously known to be associated with PE and 6 novel miRNAs. Among the 6 novel miRNAs, 4 were upregulated (miR‑518a, miR‑527, miR‑518e and miR‑4532) and 2 downregulated (miR‑98 and miR‑135b) in SPE placentas compared with controls. The present results suggest that SPE is associated with specific alterations in the placental miRNA expression pattern, which differ from alterations detected in PE placentas, and therefore, provide novel targets for further investigation of the molecular mechanisms underlying SPE pathogenesis.

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease resulting from insulin resistance and progressively reduced insulin secretion, which leads to impaired glucose utilization, dyslipidemia and hyperinsulinemia and progressive pancreatic beta cell dysfunction. The incidence of type 2 diabetes mellitus is increasing worldwide and nowadays T2D already became a global epidemic. The well-known interindividual variability of T2D drug actions such as biguanides, sulfonylureas/meglitinides, DPP-4 inhibitors/GLP1R agonists and SGLT-2 inhibitors may be caused, among other things, by genetic factors. Pharmacogenetic findings may aid in identifying new drug targets and obtaining in-depth knowledge of the causes of disease and its physiological processes, thereby, providing an opportunity to elaborate an algorithm for tailor or precision treatment. The aim of this article is to summarize recent progress and discoveries for T2D pharmacogenetics and to discuss the factors which limit the furthering accumulation of genetic variability knowledge in patient response to therapy that will allow improvement the personalized treatment of T2D.

Polymorphisms in genes coding xenobiotic‐metabolizing enzymes are considered as risk factors modifying susceptibility to cancer. We developed a biochip for the analysis of 18 mutations in 10 genes of metabolizing system: CYP1A1, CYP2D6, GSTT1, GSTM1, MTHFR, MTRR, NQO1, CYP2C9, CYP2C19, and NAT2. Using allele‐specific hybridization on the biochip 76 T‐cell non‐Hodgkin's lymphoma (NHL) patients, 83 B‐cell chronic lymphocytic leukemia (B‐CLL) patients, and 177 healthy donors were tested. Polymorphic CYP1A1 alleles were more frequent in B‐CLL patients relative to normal controls, for example, a combination of polymorphic variants 4887C > A, 4889A > G, and 6235T > C (OR = 1.76, 95% CI = 1.0–3.1). The GSTM1 null genotype was more frequent in NHL patients relative to controls (OR = 1.82, 95% CI = 1.1–3.1). The combination of unfavorable polymorphic CYP1A1 variants and GSTM1 null genotype was found more frequently in B‐CLL patients relative to controls (OR = 2.52, 95% CI = 1.3–4.9). In addition, male B‐CLL patients demonstrated a significantly increased occurrence of heterozygous and homozygous allele *2 of CYP2C9 gene (OR = 2.38, 95% CI = 1.1–5.2) as well as a combination of alleles *2 and *3 of the gene (OR = 2.09, 95% CI = 1.1–3.9). Thus, our findings show the association between polymorphic alleles of CYP1A1, GSTM1, and CYP2C9 genes and the risk to develop NHL or B‐CLL. The developed biochip can be considered as a convenient analytical tool for research studies and predictive analysis in oncohematology. Am. J. Hematol., 2008.