Selection and mutation shape the genetic variation underlying human traits, but the specific evolutionary mechanisms driving complex trait variation are largely unknown. We developed a statistical method that uses polarized genome‐wide association study (GWAS) summary statistics from a single population to detect signals of mutational bias and selection. We found evidence for nonneutral signals on variation underlying several traits (body mass index [BMI], schizophrenia, Crohn's disease, educational attainment, and height). We then used simulations that incorporate simultaneous negative and positive selection to show that these signals are consistent with mutational bias and shifts in the fitness‐phenotype relationship, but not stabilizing selection or mutational bias alone. We additionally replicate two of our top three signals (BMI and educational attainment) in an external cohort, and show that population stratification may have confounded GWAS summary statistics for height in the GIANT cohort. Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other species, and offer insights into the evolutionary mechanisms driving variation in human polygenic traits.

Transcriptome engineering requires flexible RNA-targeting technologies that can perturb mammalian transcripts in a robust and scalable manner. CRISPR systems that natively target RNA molecules, such as Cas13 enzymes, are enabling rapid progress in the investigation of RNA biology and advancement of RNA therapeutics. Here, we sought to develop a Cas13 platform for high-throughput phenotypic screening and elucidate the design principles underpinning its RNA targeting efficiency. We employed the RfxCas13d (CasRx) system in a positive selection screen by tiling 55 known essential genes with single nucleotide resolution. Leveraging this dataset of over 127,000 guide RNAs, we systematically compared a series of linear regression and machine learning algorithms to train a convolutional neural network (CNN) model that is able to robustly predict guide RNA performance based on guide sequence alone. We further incorporated secondary features including secondary structure, free energy, target site position, and target isoform percent. To evaluate model performance, we conducted orthogonal screens via cell surface protein knockdown. The final CNN model is able to predict highly effective guide RNAs (gRNAs) within each transcript with >90% accuracy in this independent test set. To provide user interpretability, we evaluate feature contributions using both integrated gradients and SHapley Additive exPlanations (SHAP). We identify a specific sequence motif at guide position 15-24 along with selected secondary features to be predictive of highly efficient guides. Taken together, we derive Cas13d guide design rules from large-scale screen data, release a guide design tool (http://rnatargeting.org) to advance the RNA targeting toolbox, and describe a path for systematic development of deep learning models to predict CRISPR activity.

Selection alters human genetic variation, but the evolutionary mechanisms shaping complex traits and the extent of selection's impact on polygenic trait evolution remain largely unknown. Here, we develop a novel polygenic selection inference method (Polygenic Ancestral Selection Test Encompassing Linkage, or PASTEL) relying on GWAS summary data from a single population. We use model-based simulations of complex traits that incorporate human demography, stabilizing selection, and polygenic adaptation to show how shifts in the fitness landscape generate distinct signals in GWAS summary data. Our test retains power for relatively ancient selection events and controls for potential confounding from linkage disequilibrium. We apply PASTEL to nine complex traits, and find evidence for selection acting on five of them (height, BMI, schizophrenia, Crohn's disease, and educational attainment). This study provides evidence that selection modulates the relationship between frequency and effect size of trait-altering alleles for a wide range of traits, and provides a flexible framework for future investigations of selection on complex traits using GWAS data. IntroductionNatural selection shapes patterns of genetic variation within and between human populations, but the phenotypic targets of selection and the evolutionary mechanisms shaping causal variation for selected traits remain largely unknown. Most studies of selection in humans have focused on classic selective sweeps [1][2][3][4][5], but other selection mechanisms such as stabilizing selection [6], polygenic adaptation [7,8], and soft sweeps [9] may also play an important role in shaping human diversity. Methods to detect 1 . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/173815 doi: bioRxiv preprint first posted online Aug. 8, 2017; selection under these more complex models are needed if we are to fulfill the promise of genomics to explain the evolutionary mechanisms driving the distribution of heritable traits in human populations [10].With the recent proliferation of paired genotype and phenotype data from large human cohorts, it is now feasible to develop and implement statistical tests for polygenic selection in humans. Recently, studies have proposed methods to detect polygenic selection that capitalize on these rich datasets, and have begun to uncover evidence that selection acts on complex traits. Two studies proposed empirical methods that test for an excess of allele frequency differentiation at trait-associated loci [7,11], and showed that selection may have driven increases in the height of northern Europeans. This approach was later extended to a model-based framework that also incorporated environmental variables and was applied to several phenotypes in diverse human populations, providing additional evidence for selection on height and identifying a strong selection signal for skin pigmentation [8]. Recently, a nove...

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Cell cycle (CC) is a fundamental biological process with robust, cyclical gene expression programs to facilitate cell division. In the immune system, a productive immune response requires the expansion of pathogen-responsive cell types, but whether CC also confers unique gene expression programs that inform the subsequent immunological response remains unclear. Here we demonstrate that single macrophages adopt different plasticity states in CC, which is a major source of heterogeneity in response to polarizing cytokines. Specifically, macrophage plasticity to interferon gamma (IFNG) is substantially reduced, while interleukin 4 (IL-4) can induce S-G2/M-biased gene expression. Additionally, IL-4 polarization shifts the CC-phase distribution of the population towards G2/M phase, providing a mechanism for reduced IFNG-induced repolarization. Finally, we show that macrophages express tissue remodeling genes in the S-G2/M-phases of CC, that can be also detected in vivo during muscle regeneration. Therefore, macrophage inflammatory and regenerative responses are gated by CC in a cyclical phase-dependent manner.