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MotivationGenome-wide association studies (GWAS) have identified genetic variants, usually single-nucleotide polymorphisms (SNPs), associated with human traits, including disease and disease risk. These variants (or causal variants in linkage disequilibrium with them) usually affect the regulation or function of a nearby gene. A GWAS locus can span many genes, however, and prioritizing which gene or genes in a locus are most likely to be causal remains a challenge. Better prioritization and prediction of causal genes could reveal disease mechanisms and suggest interventions.ResultsWe describe a new Bayesian method, termed SigNet for significance networks, that combines information both within and across loci to identify the most likely causal gene at each locus. The SigNet method builds on existing methods that focus on individual loci with evidence from gene distance and expression quantitative trait loci (eQTL) by sharing information across loci using protein-protein and gene regulatory interaction network data. In an application to cardiac electrophysiology with 226 GWAS loci, only 46 (20%) have within-locus evidence from Mendelian genes, protein-coding changes, or colocalization with eQTL signals. At the remaining 180 loci lacking functional information, SigNet selects 56 genes other than the minimum distance gene, equal to 31% of the information-poor loci and 25% of the GWAS loci overall. Assessment by pathway enrichment demonstrates improved performance by SigNet. Review of individual loci shows literature evidence for genes selected by SigNet, including PMP22 as a novel causal gene candidate.

Abstract The Sc2.0 global consortium to design and construct a synthetic genome based on the Saccharomyces cerevisiae genome commenced in 2006, comprising 16 synthetic chromosomes and a new-to-nature tRNA neochromosome. In this paper we describe assembly and debugging of the 902,994-bp synthetic Saccharomyces cerevisiae chromosome synXVI of the Sc2.0 project. Application of the CRISPR D-BUGS protocol identified defective loci, which were modified to improve sporulation and recover wild-type like growth when grown on glycerol as a sole carbon source when grown at 37˚C. LoxPsym sites inserted downstream of dubious open reading frames impacted the 5’ UTR of genes required for optimal growth and were identified as a systematic cause of defective growth. Based on lessons learned from analysis of Sc2.0 defects and synXVI, an in-silico redesign of the synXVI chromosome was performed, which can be used as a blueprint for future synthetic yeast genome designs. The in-silico redesign of synXVI includes reduced PCR tag frequency, modified chunk and megachunk termini, and adjustments to allocation of loxPsym sites and TAA stop codons to dubious ORFs. This redesign provides a roadmap into applications of Sc2.0 strategies in non-yeast organisms.

Abstract People living with HIV are at higher risk of heart failure and associated left atrial remodeling compared to people without HIV. Mechanisms are unclear but have been linked to inflammation and premature aging. Here we obtain plasma proteomics concurrently with cardiac magnetic resonance imaging in two independent study populations to identify parallels between HIV-related and aging-related immune dysfunction that could contribute to atrial remodeling and clinical heart failure. We discover a plasma proteomic signature that may in part reflect or contribute to HIV-associated atrial remodeling, many features of which are associated with older age and time to incident heart failure among an independent community-based cohort without HIV. This proteomic profile was statistically enriched for immune checkpoint proteins, tumor necrosis factor signaling, ephrin signaling, and extracellular matrix organization, identifying possible shared pathways in HIV and aging that may contribute to risk of heart failure.