Rare penetrant mutations confer severe risk of common diseases.

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ~10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared with common-variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.

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.

Rare penetrant mutations confer severe risk of common diseases.

We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction. One sentence summary: Rare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.

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 human. We show that these variants can be inferred to have non-deleterious effects in human 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. One Sentence Summary: Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

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 note: This 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).

Detection of human adaptation during the past 2000 years.

Detection of recent natural selection is a challenging problem in population genetics. Here we introduce the singleton density score (SDS), a method to infer very recent changes in allele frequencies from contemporary genome sequences. Applied to data from the UK10K Project, SDS reflects allele frequency changes in the ancestors of modern Britons during the past ~2000 to 3000 years. We see strong signals of selection at lactase and the major histocompatibility complex, and in favor of blond hair and blue eyes. For polygenic adaptation, we find that recent selection for increased height has driven allele frequency shifts across most of the genome. Moreover, we identify shifts associated with other complex traits, suggesting that polygenic adaptation has played a pervasive role in shaping genotypic and phenotypic variation in modern humans.

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise.

Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

How transcription factors identify regulatory sites in genomic sequence.

Binding of transcription factors to functional sites is a fundamental step in transcriptional regulation. In this chapter, we discuss how transcription factors are thought to achieve specificity to their functional targets, despite their typically low concentrations and degenerate binding specificities, and the fact that in large genomes their functional binding sites must compete with their widespread alternative binding sites. We highlight the importance of the chromatin structure context of the binding sites in this process, and its dependency on the genomic DNA sequence.

p53 binds preferentially to genomic regions with high DNA-encoded nucleosome occupancy.

The human transcription factor TP53 is a pivotal roadblock against cancer. A key unresolved question is how the p53 protein selects its genomic binding sites in vivo out of a large pool of potential consensus sites. We hypothesized that chromatin may play a significant role in this site-selection process. To test this, we used a custom DNA microarray to measure p53 binding at approximately 2000 sites predicted to possess high-sequence specificity, and identified both strongly bound and weakly bound sites. When placed within a plasmid, weakly bound sites become p53 responsive and regain p53 binding when stably integrated into random genomic locations. Notably, strongly bound sites reside preferentially within genomic regions whose DNA sequence is predicted to encode relatively high intrinsic nucleosome occupancy. Using in vivo nucleosome occupancy measurements under conditions where p53 is inactive, we experimentally confirmed this prediction. Furthermore, upon p53 activation, nucleosomes are partially displaced from a relatively broad region surrounding the bound p53 sites, and this displacement is rapidly reversed upon inactivation of p53. Thus, in contrast to the general assumption that transcription-factor binding is preferred in sites that have low nucleosome occupancy prior to factor activation, we find that p53 binding occurs preferentially within a chromatin context of high intrinsic nucleosome occupancy.

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 has an important regulatory function and is partly encoded in cis by the DNA sequence itself. 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 cofactors and of transcription, and is a general property of growth-related genes. In contrast, in anaerobic yeast species, where cellular respiration genes are relatively inactive under typical growth conditions, respiration gene promoters encode relatively closed (nucleosome-occupied) chromatin organizations. 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 regulation. In living cells this organization is determined by multiple factors, including the action of chromatin remodellers, competition with site-specific DNA-binding proteins, and the DNA sequence preferences of the nucleosomes themselves. However, it has been difficult to estimate the relative importance of each of these mechanisms in vivo, 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 approximately 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.

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 approximately 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.

Monoubiquitinated H2B is associated with the transcribed region of highly expressed genes in human cells.

Histone modifications have emerged as important regulators of transcription. Histone H2B monoubiquitination has also been implicated in transcription; however, better understanding of the biological significance of this modification in mammalian cells has been hindered by the lack of suitable reagents, particularly antibodies capable of specifically recognizing ubiquitinated H2B (ubH2B). Here, we report the generation of anti-ubH2B monoclonal antibodies using a branched peptide as immunogen. These antibodies provide a powerful tool for exploring the biochemical functions of H2B monoubiquitination at both a genome-wide and gene-specific level. Application of these antibodies in high resolution chromatin immunoprecipitation (ChIP)-chip experiments in human cells, using tiling arrays, revealed preferential association of ubiquitinated H2B with the transcribed regions of highly expressed genes. Unlike dimethylated H3K4, ubH2B was not associated with distal promoter regions. Furthermore, experimental modulation of the transcriptional activity of the tumour suppressor p53 was accompanied by rapid changes in the H2B ubiquitination status of its p21 target gene, attesting to the dynamic nature of this process. It has recently been demonstrated that the apparent extent of gene expression often reflects elongation rather than initiation rates; thus, our findings suggest that H2B ubiquitination is intimately linked with global transcriptional elongation in mammalian cells.

Systematic functional characterization of cis-regulatory motifs in human core promoters.

A large number of cis-regulatory motifs involved in transcriptional control have been identified, but the regulatory context and biological processes in which many of them function are unknown. Here, we computationally identify the sets of human core promoters targeted by motifs, and systematically characterize their function by using a robust gene-set-based approach and diverse sources of biological data. We find that the target sets of most motifs contain both genes with similar function and genes that are coregulated in vivo, thereby suggesting both the biological process regulated by the motifs and the conditions in which this regulation may occur. Our analysis also identifies many motifs whose target sets are predicted to be regulated by a common microRNA, suggesting a connection between transcriptional and post-transcriptional control processes. Finally, we predict novel roles for uncharacterized motifs in the regulation of specific biological processes and certain types of human cancer, and experimentally validate four such predictions, suggesting regulatory roles for four uncharacterized motifs in cell cycle progression. Our analysis thus provides a concrete framework for uncovering the biological function of cis-regulatory motifs genome wide.

A genomic code for nucleosome positioning.

Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome-DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain approximately 50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.