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Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets. We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown. Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms. Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes., Version of Record

Prochlorococcus is the numerically dominant photosynthetic organism throughout much of the world's oceans, yet little is known about the ecology and genetic diversity of populations inhabiting tropical waters. To help close this gap, we examined natural Prochlorococcus communities in the tropical Pacific Ocean using a single-cell whole-genome amplification and sequencing. Analysis of the gene content of just 10 single cells from these waters added 394 new genes to the Prochlorococcus pan-genome-that is, genes never before seen in a Prochlorococcus cell. Analysis of marker genes, including the ribosomal internal transcribed sequence, from dozens of individual cells revealed several representatives from two uncultivated clades of Prochlorococcus previously identified as HNLC1 and HNLC2. While the HNLC clades can dominate Prochlorococcus communities under certain conditions, their overall geographic distribution was highly restricted compared with other clades of Prochlorococcus. In the Atlantic and Pacific oceans, these clades were only found in warm waters with low Fe and high inorganic P levels. Genomic analysis suggests that at least one of these clades thrives in low Fe environments by scavenging organic-bound Fe, a process previously unknown in Prochlorococcus. Furthermore, the capacity to utilize organic-bound Fe appears to have been acquired horizontally and may be exchanged among other clades of Prochlorococcus. Finally, one of the single Prochlorococcus cells sequenced contained a partial genome of what appears to be a prophage integrated into the genome. [ABSTRACT FROM AUTHOR]

Wastewater-based disease surveillance is a promising approach for monitoring community outbreaks. Here we describe a nationwide campaign to monitor SARS-CoV-2 in the wastewater of 159 counties in 40 U.S. states, covering 13% of the U.S. population from February 18 to June 2, 2020. Out of 1,751 total samples analyzed, 846 samples were positive for SARS-CoV-2 RNA, with overall viral concentrations declining from April to May. Wastewater viral titers were consistent with, and appeared to precede, clinical COVID-19 surveillance indicators, including daily new cases. Wastewater surveillance had a high detection rate (>80%) of SARS-CoV-2 when the daily incidence exceeded 13 per 100,000 people. Detection rates were positively associated with wastewater treatment plant catchment size. To our knowledge, this work represents the largest-scale wastewater-based SARS-CoV-2 monitoring campaign to date, encompassing a wide diversity of wastewater treatment facilities and geographic locations. Our findings demonstrate that a national wastewater-based approach to disease surveillance may be feasible and effective., Competing Interests: Competing Interests MM and NG are cofounders of Biobot Analytics. EJA is advisor to Biobot Analytics. CD, KAM, KF, and NE are employees at Biobot Analytics, and all these authors hold shares in the company.

Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we use longitudinal wastewater analysis to track SARS-CoV-2 dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. Viral titers in wastewater increased exponentially from mid-March to mid-April, after which they began to decline. Viral titers in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4-10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral titers as a convolution of back-dated new clinical cases with the viral shedding function of an individual. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. Finally, we found that wastewater viral titers at the neighborhood level correlate better with demographic variables than with population size. This work suggests that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and may shed light on infection characteristics that are difficult to capture in clinical investigations, such as early viral shedding dynamics.