Search

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.

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.

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.