Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs ...has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.
Transcriptome-wide association studies using predicted expression have identified thousands of genes whose locally regulated expression is associated with complex traits and diseases. In this work, ...we show that linkage disequilibrium induces significant gene-trait associations at non-causal genes as a function of the expression quantitative trait loci weights used in expression prediction. We introduce a probabilistic framework that models correlation among transcriptome-wide association study signals to assign a probability for every gene in the risk region to explain the observed association signal. Importantly, our approach remains accurate when expression data for causal genes are not available in the causal tissue by leveraging expression prediction from other tissues. Our approach yields credible sets of genes containing the causal gene at a nominal confidence level (for example, 90%) that can be used to prioritize genes for functional assays. We illustrate our approach by using an integrative analysis of lipid traits, where our approach prioritizes genes with strong evidence for causality.
Hierarchical structures and the size distribution of star formation regions in the nearby spiral galaxy NGC 628 are studied over a range of scales from 50 to 1000 pc using optical images obtained ...with the 1.5-m telescope of the Maidanak Observatory. We have found hierarchically structured concentrations of star formation regions in the galaxy, and smaller regions with a higher surface brightness are located inside larger complexes that have a lower surface brightness. We illustrate this hierarchy using a dendrogram, or structure tree, of the detected star formation regions, which demonstrates that most of these regions are combined into larger structures over several levels. We have found three characteristic sizes of young star groups: ≈65 pc (OB associations), ≈240 pc (stellar aggregates) and ≈600 pc (star complexes). The cumulative size distribution function of star formation regions is found to be a power law with a slope of approximately −1.5 on scales appropriate to diameters of associations, aggregates and complexes. This slope is close to the slope found earlier by B. Elmegreen et al. for star formation regions in the galaxy on scales from 2 to 100 pc.
Here, we propose DeCAF (DEconvoluted cell type Allele specific Function), a new method to identify cell-fraction (cf) QTLs in tumors by leveraging both allelic and total expression information. ...Applying DeCAF to RNA-seq data from TCGA, we identify 3664 genes with cfQTLs (at 10% FDR) in 14 cell types, a 5.63× increase in discovery over conventional interaction-eQTL mapping. cfQTLs replicated in external cell-type-specific eQTL data are more enriched for cancer risk than conventional eQTLs. Our new method, DeCAF, empowers the discovery of biologically meaningful cfQTLs from bulk RNA-seq data in moderately sized studies.
We introduce an approach to identify disease-relevant tissues and cell types by analyzing gene expression data together with genome-wide association study (GWAS) summary statistics. Our approach uses ...stratified linkage disequilibrium (LD) score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We applied our approach to gene expression data from several sources together with GWAS summary statistics for 48 diseases and traits (average N = 169,331) and found significant tissue-specific enrichments (false discovery rate (FDR) < 5%) for 34 traits. In our analysis of multiple tissues, we detected a broad range of enrichments that recapitulated known biology. In our brain-specific analysis, significant enrichments included an enrichment of inhibitory over excitatory neurons for bipolar disorder, and excitatory over inhibitory neurons for schizophrenia and body mass index. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signals.
Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study ...(TWAS) integrating a schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium with expression data from brain, blood, and adipose tissues across 3,693 primarily control individuals. We identified 157 TWAS-significant genes, of which 35 did not overlap a known GWAS locus. Of these 157 genes, 42 were associated with specific chromatin features measured in independent samples, thus highlighting potential regulatory targets for follow-up. Suppression of one identified susceptibility gene, mapk3, in zebrafish showed a significant effect on neurodevelopmental phenotypes. Expression and splicing from the brain captured most of the TWAS effect across all genes. This large-scale connection of associations to target genes, tissues, and regulatory features is an essential step in moving toward a mechanistic understanding of GWAS.
Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the ...correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan, UTMOST, or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, 5% and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate.
Frontiers in Nanotoxicology Gusev, Alexander A
Nanomaterials (Basel, Switzerland),
09/2022, Letnik:
12, Številka:
18
Journal Article
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The Special Issue of Nanomaterials “Frontiers in Nanotoxicology” highlights the modern problems of nanotoxicology and nanobiomedicine, including the toxicity of metal-based, silicon-based, ...carbon-based, and other types of nanoparticles, occupational safety of nanoproduction workers, comprehensive assessment on new biomedical nanomaterials, improvement of nanotoxicology methods, as well as the current state and prospects of research in the fields of theoretical, experimental, and toxicological aspects of the prospective biomedical application of functionalized magnetic nanoparticles activated by a low-frequency non-heating alternating magnetic field, biomedical applications and the toxicity of graphene nanoribbons, and fetotoxicity of nanoparticles ...
The relative contribution of various physical processes to the spatial and temporal distribution of molecular clouds and star-forming regions in the disks of galaxies has not yet been the subject of ...extensive study. Investigating the spatial regularity in the distribution of the young stellar population in spiral and ring structures is a good test for studying this contribution. In this paper, we look at the photometric properties of the ring and spiral arms in the barred spiral galaxy NGC 6217 based on an analysis using GALEX ultraviolet, optical
UBVRI
, and H
α
surface photometry data. The ring in the galaxy is located near the corotation area. We found evidence of spatial regularity in the distribution of the young stellar population along the galaxy ring. The characteristic scale of spacing is about 700 pc. At the same time, we did not find a similar regularity in the distribution of the young stellar population along the spiral arms of NGC 6217. The spatial regularity in the concentration of young stellar groupings along spiral arms is a quite rare phenomenon and it has never previously been seen in galactic rings.
Heritability analyses of genome-wide association study (GWAS) cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further ...discoveries. Here we analyze the genetic architectures of schizophrenia in 49,806 samples from the PGC and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1-Mb genomic regions harbor ≥1 variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.
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