Despite progress in defining genetic risk for psychiatric disorders, their molecular mechanisms remain elusive. Addressing this, the PsychENCODE Consortium has generated a comprehensive online resource for the adult brain across 1866 individuals. The PsychENCODE resource contains ~79,000 brain-active enhancers, sets of Hi-C linkages, and topologically associating domains; single-cell expression profiles for many cell types; expression quantitative-trait loci (QTLs); and further QTLs associated with chromatin, splicing, and cell-type proportions. Integration shows that varying cell-type proportions largely account for the cross-population variation in expression (with >88% reconstruction accuracy). It also allows building of a gene regulatory network, linking genome-wide association study variants to genes (e.g., 321 for schizophrenia). We embed this network into an interpretable deep-learning model, which improves disease prediction by ~6-fold versus polygenic risk scores and identifies key genes and pathways in psychiatric disorders.
Whole-genome sequencing (WGS) has facilitated the first genome-wide evaluations of the contribution of de novo noncoding mutations to complex disorders. Using WGS, we identified 255,106 de novo mutations among sample genomes from members of 1902 quartet families in which one child, but not a sibling or their parents, was affected by autism spectrum disorder (ASD). In contrast to coding mutations, no noncoding functional annotation category, analyzed in isolation, was significantly associated with ASD. Casting noncoding variation in the context of a de novo risk score across multiple annotation categories, however, did demonstrate association with mutations localized to promoter regions. We found that the strongest driver of this promoter signal emanates from evolutionarily conserved transcription factor binding sites distal to the transcription start site. These data suggest that de novo mutations in promoter regions, characterized by evolutionary and functional signatures, contribute to ASD.
Achieving high catalytic performance with the lowest possible amount of platinum is critical for fuel cell cost reduction. Here we describe a method of preparing highly active yet stable electrocatalysts containing ultralow-loading platinum content by using cobalt or bimetallic cobalt and zinc zeolitic imidazolate frameworks as precursors. Synergistic catalysis between strained platinum-cobalt core-shell nanoparticles over a platinum-group metal (PGM)–free catalytic substrate led to excellent fuel cell performance under 1 atmosphere of O2 or air at both high-voltage and high-current domains. Two catalysts achieved oxygen reduction reaction (ORR) mass activities of 1.08 amperes per milligram of platinum (A mgPt–1) and 1.77 A mgPt–1 and retained 64% and 15% of initial values after 30,000 voltage cycles in a fuel cell. Computational modeling reveals that the interaction between platinum-cobalt nanoparticles and PGM-free sites improves ORR activity and durability.
Paired measurements of 14C/12C and 230Th ages from two Hulu Cave stalagmites complete a precise record of atmospheric 14C covering the full range of the 14C dating method (~54,000 years). Over the last glacial period, atmospheric 14C/12C ranges from values similar to modern values to values 1.70 times higher (42,000 to 39,000 years ago). The latter correspond to 14C ages 5200 years less than calibrated ages and correlate with the Laschamp geomagnetic excursion followed by Heinrich Stadial 4. Millennial-scale variations are largely attributable to Earth’s magnetic field changes and in part to climate-related changes in the oceanic carbon cycle. A progressive shift to lower 14C/12C values between 25,000 and 11,000 years ago is likely related, in part, to progressively increasing ocean ventilation rates.