Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits.

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels.

Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations.

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations.

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized methods that leverage shared regulatory effects across different conditions, in this case, across different populations may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWAS) using different methods (Elastic Net, Joint-Tissue Imputation (JTI), Matrix eQTL, Multivariate Adaptive Shrinkage in R (MASHR), and Transcriptome-Integrated Genetic Association Resource (TIGAR)) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios.

Protein prediction for trait mapping in diverse populations.

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.

On the cross-population generalizability of gene expression prediction models.

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.

Macroeconomic dataset for comparative studies on coastal and inland regions in innovation space of Russia.

This article presents regional-level data that can be used for comparative territorial studies on innovation dynamics. The dataset covers a series of 50 indicators grouped into a matrix of 5 elements of regional innovation system (human resources - HR, infrastructure, research & development sector - R&D, innovative milieu, framework conditions) and 5 components of innovation security (economic, scientific and technological - S&T, social, political, geo-ecological). This complex set of interrelated data enables to grasp the catalyst and inhibitor factors that have a significant impact on the sustainable development of a particular regional innovation system.

Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations.

Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent.

Geospatial dataset for analyzing socio-economic regional divergence of European regions.

This data article presents macroeconomic data that can be used for comparative territorial studies. The data cover a sample of 413 regions (national administrative-territorial units corresponding to second level of a common classification of territorial units for statistics of the European Commission - NUTS 2 level region of the European Union, and comparable administrative-territorial units outside the EU) of 48 European countries, including Cyprus, Turkey, the European part of Russia, and two partially recognized states - the Republic of Kosovo and the Pridnestrovian Moldavian Republic. The statistical database covers a five-year period of 2010-2014.

Herpes Simplex Virus Type 1 Shedding in Tears and Nasal and Oral Mucosa of Healthy Adults.

Background: Herpes simplex virus type 1 (HSV-1) is prevalent worldwide and causes mucocutaneous infections of the oral area. We aimed to define the frequency and anatomic distribution of HSV-1 reactivation in the facial area in persons with a history of oral herpes.

Methods: Eight immunocompetent HSV-1 seropositive adults were evaluated for shedding of HSV-1 from 12 separate orofacial sites (8 from oral mucosa, 2 from nose, and 2 from conjunctiva) 5 days a week and from the oral cavity 7 days a week for approximately 5 consecutive weeks by a HSV DNA PCR assay.