• Inherited ciHHV-6 was detected in 1.4% of HCT recipients and 0.9% of their donors.• Acute GVHD grades 2-4 and cytomegalovirus viremia were more frequent when recipients or donors had inherited ciHHV-6.Human herpesvirus 6 (HHV-6) species have a unique ability to integrate into chromosomal telomeres. Mendelian inheritance via gametocyte integration results in HHV-6 in every nucleated cell. The epidemiology and clinical effect of inherited chromosomally integrated HHV-6 (iciHHV-6) in hematopoietic cell transplant (HCT) recipients is unclear.We identified 4319 HCT donor-recipient pairs (8638 subjects) who received an allogeneic HCT and had archived pre-HCT peripheral blood mononuclear cell samples. We screened these samples for iciHHV-6 and compared characteristics of HCT recipients and donors with iciHHV-6 with those of recipients and donors without iciHHV-6, respectively. We calculated Kaplan-Meier probability estimates and Cox proportional hazards models for post-HCT outcomes based on recipient and donor iciHHV-6 status. We identified 60 HCT recipients (1.4%) and 40 donors (0.9%) with iciHHV-6; both recipient and donor harbored iciHHV-6 in 13 HCTs. Thus, there were 87 HCTs (2%) in which the recipient, donor, or both harbored iciHHV-6. Acute graft-versus-host disease (GVHD) grades 2-4 was more frequent when recipients or donors had iciHHV-6 (adjusted hazard ratios, 1.7-1.9; P 5 .004-.001). Cytomegalovirus viremia (any and high-level) was more frequent among recipients with iciHHV-6 (adjusted HRs, 1.7-3.1; P 5 .001-.040). Inherited ciHHV-6 status did not significantly affect risk for chronic GVHD, hematopoietic cell engraftment, overall mortality, or nonrelapse mortality. Screening for iciHHV-6 could guide donor selection and post-HCT risk stratification and treatment. Further study is needed to replicate these findings and identify potential mechanisms. (Blood. 2017;130(8):1062-1069

No abstract

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multiethnic utility of gene expression prediction.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods have focused only on individuals with one primary continental ancestry, thus poorly accommodating recently-admixed individuals. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals by explicitly modeling ancestry-specific effects and jointly estimating ancestry-shared effects. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses.

No abstract

Among innovative e-learning approaches in the sphere of digital economy and logistics, there is a special focus on artificial intelligence technologies (AI), which, due to their capacity and efficiency in usage, have a significant potential for the development and to some extent are optimal IT tools. The objective of a study is to define an optimum IT software for the organization of massive open online courses (MOOC) in digital economy and digital logistics in the framework of training economics students. Authors have conducted a survey in terms of In-ternet use for education and self-education. The sampling volume makes up 1 600 respondents in at least 80 regions of the Russian Federation. The respondents are divided into four age groups: 18-24 years old, 25-39 years old, 40-54 years old, 55 years old and older. The study uses data from the survey conducted by KMDA.PRO related to digital transformation of 700 representatives from more than 300 Russian companies out of 15 industries and the results of in-depth in-terviews of four categories of employees: top managers, heads of units, mid-level managers and other employees. The study results testify to the need for trans-forming e-learning approaches, taking into account the new labor market re-quirements for training specialists in digital logistics and gaining respective skills such as an active training, coordination, negotiation skills, teaching others, infor-mation literacy, customer focus, oral communication, ability to solve complex is-sues, operational literacy, time management. The use of the research results in practice is possible in case of the organization of online training courses for eco-nomics students in the framework of the higher educational system.

Genome-wide association studies (GWAS) have identified thousands of single nucleotide variants and small indels that contribute to the genetic architecture of hematologic traits. While structural variants (SVs) are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of SVs to quantitative blood cell trait variation is unknown. Here we utilized SVs detected from whole genome sequencing (WGS) in ancestrally diverse participants of the NHLBI TOPMed program (N=50,675). Using single variant tests, we assessed the association of common and rare SVs with red cell-, white cell-, and platelet-related quantitative traits. The results show 33 independent SVs (23 common and 10 rare) reaching genome-wide significance. The majority of significant association signals (N=27) replicated in independent datasets from deCODE genetics and the UK BioBank. Moreover, most trait-associated SVs (N=24) are within 1Mb of previously-reported GWAS loci. SV analyses additionally discovered an association between a complex structural variant on 17p11.2 and white blood cell-related phenotypes. Based on functional annotation, the majority of significant SVs are located in non-coding regions (N=26) and predicted to impact regulatory elements and/or local chromatin domain boundaries in blood cells. We predict that several trait-associated SVs represent the causal variant. This is supported by genome-editing experiments which provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n=183), Chinese (n=71), European (n=416), and Hispanic/Latino (n=301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ≈50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837328.