Distinct Modes of Regulation by Chromatin Encoded through Nucleosome Positioning Signals

The detailed positions of nucleosomes profoundly impact gene regulation and are partly encoded by the genomic DNA sequence. However, less is known about the functional consequences of this encoding. Here, we address this question using a genome-wide map of ∼380,000 yeast nucleosomes that we sequenced in their entirety. Utilizing the high resolution of our map, we refine our understanding of how nucleosome organizations are encoded by the DNA sequence and demonstrate that the genomic sequence is highly predictive of the in vivo nucleosome organization, even across new nucleosome-bound sequences that we isolated from fly and human. We find that Poly(dA:dT) tracts are an important component of these nucleosome positioning signals and that their nucleosome-disfavoring action results in large nucleosome depletion over them and over their flanking regions and enhances the accessibility of transcription factors to their cognate sites. Our results suggest that the yeast genome may utilize these nucleosome positioning signals to regulate gene expression with different transcriptional noise and activation kinetics and DNA replication with different origin efficiency. These distinct functions may be achieved by encoding both relatively closed (nucleosome-covered) chromatin organizations over some factor binding sites, where factors must compete with nucleosomes for DNA access, and relatively open (nucleosome-depleted) organizations over other factor sites, where factors bind without competition

The cognitive and emotional effects of cognitive bias modification in interpretations in behaviorally inhibited youth

Cognitive bias modification (CBM) procedures follow from the view that interpretive biases play an important role in the development and maintenance of anxiety. As such, understanding the link between interpretive biases and anxiety in youth at risk for anxiety (e.g., behaviorally inhibited children) could elucidate the mechanisms involved in the development of pediatric anxiety. However, to date, the majority of CBM-I work only studies adult populations. The present article presents the results of a CBM study examining effects of positive interpretive bias modification on mood, stress vulnerability, and threat-related attention bias in a group of behaviorally inhibited children (n = 45). Despite successful modification of interpretive bias in the at-risk youth, minimal effects on stress vulnerability or threat-related attention bias were found. The current findings highlight the need for continued research on cognitive biases in anxiety

Reduced signal for polygenic adaptation of height in UK Biobank

Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect estimates are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification. Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.Editorial noteThis article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter)

High Nucleosome Occupancy Is Encoded at Human Regulatory Sequences

Active eukaryotic regulatory sites are characterized by open chromatin, and yeast promoters and transcription factor binding sites (TFBSs) typically have low intrinsic nucleosome occupancy. Here, we show that in contrast to yeast, DNA at human promoters, enhancers, and TFBSs generally encodes high intrinsic nucleosome occupancy. In most cases we examined, these elements also have high experimentally measured nucleosome occupancy in vivo. These regions typically have high G+C content, which correlates positively with intrinsic nucleosome occupancy, and are depleted for nucleosome-excluding poly-A sequences. We propose that high nucleosome preference is directly encoded at regulatory sequences in the human genome to restrict access to regulatory information that will ultimately be utilized in only a subset of differentiated cells

Gene expression divergence in yeast is coupled to evolution of DNA-encoded nucleosome organization

Eukaryotic transcription occurs within a chromatin environment, whose organization plays an important regulatory role and is partly encoded in cis by the DNA sequence itself1-6. Here, we examine whether evolutionary changes in gene expression are linked to changes in the DNA-encoded nucleosome organization of promoters. We find that in aerobic yeast species, where cellular respiration genes are active under typical growth conditions, the promoter sequences of these genes encode a relatively open (nucleosome-depleted) chromatin organization. This nucleosome-depleted organization requires only DNA sequence information, is independent of any co-factors and of transcription, and is a general property of growth-related genes. In contrast, in anaerobic yeast species, where cellular respiration genes are inactive under typical growth conditions, respiration gene promoters encode relatively closed (nucleosome-occupied) chromatin organizations. Thus, our results suggest a previously unidentified genetic mechanism underlying phenotypic diversity, consisting of DNA sequence changes that directly alter the DNA-encoded nucleosome organization of promoters

The DNA-encoded nucleosome organization of a eukaryotic genome

Nucleosome organization is critical for gene regulation1. In living cells this organization is determined by multiple factors, including the action of chromatin remodellers2, competition with site-specific DNA-binding proteins3, and the DNA sequence preferences of the nucleosomes themselves4-8. However, it has been difficult to estimate the relative importance of each of these mechanisms in vivo7,9-11, because in vivo nucleosome maps reflect the combined action of all influencing factors. Here we determine the importance of nucleosome DNA sequence preferences experimentally by measuring the genome-wide occupancy of nucleosomes assembled on purified yeast genomic DNA. The resulting map, in which nucleosome occupancy is governed only by the intrinsic sequence preferences of nucleosomes, is similar to in vivo nucleosome maps generated in three different growth conditions. In vitro, nucleosome depletion is evident at many transcription factor binding sites and around gene start and end sites, indicating that nucleosome depletion at these sites in vivo is partly encoded in the genome. We confirm these results with a micrococcal nuclease-independent experiment that measures the relative affinity of nucleosomes for ∼40,000 double-stranded 150-base-pair oligonucleotides. Using our in vitro data, we devise a computational model of nucleosome sequence preferences that is significantly correlated with in vivo nucleosome occupancy in Caenorhabditis elegans. Our results indicate that the intrinsic DNA sequence preferences of nucleosomes have a central role in determining the organization of nucleosomes in vivo

Rare penetrant mutations confer severe risk of common diseases

[INTRODUCTION] Genome-wide association studies (GWASs) have identified thousands of common genetic variants that are predictive of common disease susceptibility, but these variants individually have mild effects on disease owing to the effects of natural selection. By contrast, rare genetic variants can have large effects on common disease risk, but their use in genetic risk prediction has been limited to date owing to the difficulty of distinguishing pathogenic from benign variants and estimating the magnitude of their effects.[RATIONALE] PrimateAI-3D is a three-dimensional convolutional neural network for missense variant–effect prediction, which was trained with common genetic variants from the population sequencing of 233 primate species. By applying this method to estimate the pathogenicity of rare coding variants in 454,712 UK Biobank individuals, we aimed to improve rare-variant association tests and genetic risk prediction for common diseases and complex traits.[RESULTS] We performed rare-variant burden tests for 90 well-powered, clinically relevant phenotypes in the UK Biobank exome dataset. Stratifying missense variants with PrimateAI-3D greatly improved gene discovery, revealing 73% more significant gene-phenotype associations (false discovery rate <0.05) compared with not using PrimateAI-3D. When benchmarked against prior studies, gene-phenotype pairs identified with our method were better supported by orthogonal genetic evidence from GWAS and genes from related Mendelian disorders. In addition, PrimateAI-3D scores showed the strongest correlation among existing variant interpretation algorithms for predicting the quantitative effects of rare variants on continuous clinical phenotypes. Having validated our method for finding gene-phenotype relationships, we next constructed a rare-variant polygenic risk score (PRS) model by combining the rare-variant genes for each phenotype, weighting variants by their PrimateAI-3D prediction score and the direction and effect size of each associated gene. For comparison, we constructed common-variant PRS models and evaluated the performance of the two models for genetic risk prediction in a withheld-test subset of the cohort. Although common variants better explained overall population variance, rare-variant PRSs had more power at the ends of the distribution to identify individuals at the greatest risk for disease, and thus may be more relevant for population genetic screening and risk management. By contrast to common-variant PRS models derived from European populations that show poor generalization to non-Europeans, rare-variant PRSs were substantially more portable to different cohorts and ancestry groups that were not seen during model training. Moreover, because they incorporate orthogonal information from nonoverlapping sets of variants, we combined rare- and common-variant PRS models into a unified model and observed further improvement in genetic risk prediction for common diseases. To understand the extent by which rare-variant PRSs can be expected to improve with increases in discovery cohort size, we repeated our analyses in down-sampled subsets of the UK Biobank cohort. We found that the number of genes contributing to the rare-variant PRS increased linearly, with no signs of plateauing at a half-million exomes. Newly discovered rare-variant genes were strongly enriched at GWAS loci, forming allelic series with effect sizes that were ~10-fold larger on average than the respective common GWAS variant. Among well-powered GWAS loci that could be unambiguously assigned to a single gene, the majority showed subthreshold signal on the rare-variant burden test, indicating that rare penetrant variants exist at a large fraction of GWAS loci and can be incorporated into the rare-variant PRS with further advances in cohort size and variant effect prediction.[CONCLUSION] Understanding the impact of rare variants in common diseases is of prime interest for both precision medicine and the discovery of drug targets. By leveraging advances in variant effect prediction, we have demonstrated major improvements in rare-variant burden testing and genetic risk prediction. Notably, we observed that nearly all individuals carried at least one rare penetrant variant for the phenotypes we examined, demonstrating the utility of personal genome sequencing for otherwise healthy individuals in the general population.T.M.B. is supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 864203), PID2021-126004NB-100 (MICIIN/FEDER, UE) and Secretaria d’Universitats i Recerca, and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2021 SGR 00177).Peer reviewe

The Youth Anxiety Measure for DSM-5 (YAM-5):Development and First Psychometric Evidence of a New Scale for Assessing Anxiety Disorders Symptoms of Children and Adolescents

The Youth Anxiety Measure for DSM-5 (YAM-5) is a new self- and parent-report questionnaire to assess anxiety disorder symptoms in children and adolescents in terms of the contemporary classification system. International panels of childhood anxiety researchers and clinicians were used to construct a scale consisting of two parts: part one consists of 28 items and measures the major anxiety disorders including separation anxiety disorder, selective mutism, social anxiety disorder, panic disorder, and generalized anxiety disorder, whereas part two contains 22 items that focus on specific phobias and (given its overlap with situational phobias) agoraphobia. In general, the face validity of the new scale was good; most of its items were successfully linked to the intended anxiety disorders. Notable exceptions were the selective mutism items, which were frequently considered as symptoms of social anxiety disorder, and some specific phobia items especially of the natural environment, situational and other type, that were regularly assigned to an incorrect category. A preliminary investigation of the YAM-5 in non-clinical (N = 132) and clinically referred (N = 64) children and adolescents indicated that the measure was easy to complete by youngsters. In addition, support was found for the psychometric qualities of the measure: that is, the internal consistency was good for both parts, as well as for most of the subscales, the parent-child agreement appeared satisfactory, and there was also evidence for the validity of the scale. The YAM-5 holds promise as a tool for assessing anxiety disorder symptoms in children and adolescents

The landscape of tolerated genetic variation in humans and primates.

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases