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PNAS Papers Describe Genetic Effects of Fishing-Associated Selection, Rare Variants in Schizophrenia, Single-Cell RNA Analysis

Editor's Note: Some of the articles described below are not yet available at the PNAS site but are scheduled to be posted this week.

Population density influences the genomic basis of fishing-associated selection, highlighting the need to consider both direct human-induced selection and other environmental effects when managing wild populations subject to harvest by humans, according to a new report in this week's Proceedings of the National Academy of Sciences. Human fishing is a major pressure on natural fish populations, but the genetic effects of this practice are unclear. To investigate, scientists from the University of Glasgow evaluated experimental fish reared at two densities and repeatedly harvested by simulated trawling. They found that simulated commercial fishing techniques consistently remove fish with traits associated with growth, metabolism, and social behavior, but the genes under fishing selection differ depending on the density of the targeted population. "Harvest-associated reduction in population density could thus fundamentally shift the evolutionary trajectory of targeted populations at the genomic level," the authors write. Instead of a one-size-fits-all evolutionary approach to managing wild populations harvested by humans, they suggest a more integrative approach that considers both direct human-induced selection and other environmental effects such as population density.

By focusing on individuals with extreme forms of schizophrenia, a team led by scientists from Columbia University has identified rare variants associated with the disease more effectively than studies using broader patient populations. In the report, which appears in PNAS this week, the researchers sequenced 112 individuals with severe, extremely treatment-resistant schizophrenia as well as 218 individuals with typical schizophrenia and 4,929 controls. They then compared the burden of rare, damaging missense, and loss-of-function variants between the groups. They find that the extreme schizophrenia patients had a high burden of rare loss-of-function and damaging missense variants in intolerant genes, with about 48 percent of these individuals carrying at least one such variant versus roughly 30 percent in typical schizophrenia patients. "Restricting to genes previously associated with schizophrenia risk further strengthened the enrichment with 8.9 percent of individuals with severe, extremely treatment-resistant schizophrenia carrying a damaging missense or loss-of-function variant compared to 2.3 percent of typical schizophrenia and 1.6 percent of controls," the investigators write. The findings not only provide a proof-of-principle for using extreme phenotype case identification and sequencing in psychiatric genetics, but also "provide a more complete understanding of the genetic architecture of the schizophrenia spectrum, which now seems to resemble that of autism spectrum disorder and developmental delay," with severely affects individuals having a larger burden of rare variants in intolerant genes.

A method for assaying a broad spectrum of coding and noncoding RNA from single cells is reported by Stanford University researchers in PNAS this week. While the ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states, current single-cell RNA sequencing methods are unable to simultaneously monitor all forms of RNA transcripts at the single-cell level. To address this limitation, the investigators developed Smart-seq-total, a scalable one-pot method designed to capture both coding and noncoding transcripts, regardless of their length. They show that their method outperforms current poly(A)-independent total RNA-seq protocols by capturing transcripts of a broad size range, enabling simultaneous analysis of protein-coding, long-noncoding, microRNA, and other noncoding RNA transcripts from single cells, and use it to analyze the content of hundreds of human and mouse cells.