Identification of Novel Candidate Markers of Type 2 Diabetes and Obesity in Russia by Exome Sequencing with a Limited Sample Size

Type 2 diabetes (T2D) and obesity are common chronic disorders with multifactorial etiology. In our study, we performed an exome sequencing analysis of 110 patients of Russian ethnicity together with a multi-perspective approach based on biologically meaningful filtering criteria to detect novel candidate variants and loci for T2D and obesity. We have identified several known single nucleotide polymorphisms (SNPs) as markers for obesity (rs11960429), T2D (rs9379084, rs1126930), and body mass index (BMI) (rs11553746, rs1956549 and rs7195386) (p < 0.05). We show that a method based on scoring of case-specific variants together with selection of protein-altering variants can allow for the interrogation of novel and known candidate markers of T2D and obesity in small samples. Using this method, we identified rs328 in LPL (p = 0.023), rs11863726 in HBQ1 (p = 8 × 10−5), rs112984085 in VAV3 (p = 4.8 × 10−4) for T2D and obesity, rs6271 in DBH (p = 0.043), rs62618693 in QSER1 (p = 0.021), rs61758785 in RAD51B (p = 1.7 × 10−4), rs34042554 in PCDHA1 (p = 1 × 10−4), and rs144183813 in PLEKHA5 (p = 1.7 × 10−4) for obesity; and rs9379084 in RREB1 (p = 0.042), rs2233984 in C6orf15 (p = 0.030), rs61737764 in ITGB6 (p = 0.035), rs17801742 in COL2A1 (p = 8.5 × 10−5), and rs685523 in ADAMTS13 (p = 1 × 10−6) for T2D as important susceptibility loci in Russian population. Our results demonstrate the effectiveness of whole exome sequencing (WES) technologies for searching for novel markers of multifactorial diseases in cohorts of limited size in poorly studied populations

Mosaicism in preimplantation human embryos

Since the very first publications on preimplantation genetic testing, researchers have faced a serious problem — a high mosaicism level in the preimplantation human embryos obtained by means of in vitro fertilization cycles. The nature of this mosaicism and its high impact on embryo development draws attention to this issue. In this research we studied the cells from different parts of preimplantation human embryos with mosaicism in the trophectoderm cells detected using Next-generation Sequencing (NGS). Six human blastocysts with mosaicism in their trophectoderm cells were each sectioned in three parts: two containing only trophectoderm cells and one predominantly inner cell mass. These parts were then analyzed individually. Our data indicate that the proportion of aneuploid cells in bioptate taken for preimplantation genetic testing does not necessarily reflect the true chromosomal status of the whole embryo and cannot be extrapolated to that in the embryoblast cells. The results of our study strongly suggest that mosaicism revealed in blastocyst reduces the likelihood of finding the euploid chromosome set in the other parts of the embryo. Karyotypes of cells from different parts of mosaic embryos show low concordance. Chromosomal abnormalities in mosaic embryos are unpredictably diverse, which may lead not only to loss of conception, but also to the development of genetic disease in the offspring. According to our data, the mosaic rate tends to increase in the samples containing trophectoderm adjacent to the embryoblast, which may have physiological significance for the implantation. Comparative studies focused on the concordance of mosaicism level of and the type of chromosomal abnormalities detected in different parts of preimplantation human embryos will improve clinical recommendations regarding the transfer of mosaic embryos

Identification of Genetic Risk Factors of Severe COVID-19 Using Extensive Phenotypic Data: A Proof-of-Concept Study in a Cohort of Russian Patients

The COVID-19 pandemic has drawn the attention of many researchers to the interaction between pathogen and host genomes. Over the last two years, numerous studies have been conducted to identify the genetic risk factors that predict COVID-19 severity and outcome. However, such an analysis might be complicated in cohorts of limited size and/or in case of limited breadth of genome coverage. In this work, we tried to circumvent these challenges by searching for candidate genes and genetic variants associated with a variety of quantitative and binary traits in a cohort of 840 COVID-19 patients from Russia. While we found no gene- or pathway-level associations with the disease severity and outcome, we discovered eleven independent candidate loci associated with quantitative traits in COVID-19 patients. Out of these, the most significant associations correspond to rs1651553 in MYH14p = 1.4 × 10−7), rs11243705 in SETX (p = 8.2 × 10−6), and rs16885 in ATXN1 (p = 1.3 × 10−5). One of the identified variants, rs33985936 in SCN11A, was successfully replicated in an independent study, and three of the variants were found to be associated with blood-related quantitative traits according to the UK Biobank data (rs33985936 in SCN11A, rs16885 in ATXN1, and rs4747194 in CDH23). Moreover, we show that a risk score based on these variants can predict the severity and outcome of hospitalization in our cohort of patients. Given these findings, we believe that our work may serve as proof-of-concept study demonstrating the utility of quantitative traits and extensive phenotyping for identification of genetic risk factors of severe COVID-19