The rich diversity of morphology and behavior displayed across primate species provides an informative context in which to study the impact of genomic diversity on fundamental biological processes. Analysis of that diversity provides insight into long-standing questions in evolutionary and conservation biology and is urgent given severe threats these species are facing. Here, we present high-coverage whole-genome data from 233 primate species representing 86% of genera and all 16 families. This dataset was used, together with fossil calibration, to create a nuclear DNA phylogeny and to reassess evolutionary divergence times among primate clades. We found within-species genetic diversity across families and geographic regions to be associated with climate and sociality, but not with extinction risk. Furthermore, mutation rates differ across species, potentially influenced by effective population sizes. Lastly, we identified extensive recurrence of missense mutations previously thought to be human specific. This study will open a wide range of research avenues for future primate genomic research.

No abstract

The accurate and complete assembly of both haplotype sequences of a diploid organism is essential to understanding the role of variation in genome functions, phenotypes, and diseases1. Here, using a trio-binning approach, we present a high-quality, diploid reference genome, with both haplotypes assembled independently at the chromosome level, for the common marmoset (Callithrix jacchus), an important primate model system widely used in biomedical research2,3. The full heterozygosity spectrum between the two haplotypes involves 1.36% of the genome, much higher than the 0.13% indicated by the standard single nucleotide heterozygosity estimation alone. The de novo mutation rate is 0.43 × 10-8 per site per generation, where the paternal inherited genome acquired twice as many mutations as the maternal. Our diploid assembly enabled us to discover a recent expansion of the sex differentiated region and unique evolutionary changes in the marmoset Y chromosome. Additionally, we identified many genes with signatures of positive selection that might have contributed to the evolution of Callithrix biological features. Brain related genes were highly conserved between marmosets and humans, though several genes experienced lineage-specific copy number variations or diversifying selection, providing important implications for the application of marmosets as a model system.

A central aim of population genetics is the inference of the evolutionary history of a population. To this end, the underlying process can be represented by a model of the evolution of allele frequencies parametrized by e.g., the population size, mutation rates and selection coefficients. A large class of models use forward-in-time models, such as the discrete Wright-Fisher and Moran models and the continuous forward diffusion, to obtain distributions of population allele frequencies, conditional on an ancestral initial allele frequency distribution. Backward-in-time diffusion processes have been rarely used in the context of parameter inference. Here, we demonstrate how forward and backward diffusion processes can be combined to efficiently calculate the exact joint probability distribution of sample and population allele frequencies at all times in the past, for both discrete and continuous population genetics models. This procedure is analogous to the forward-backward algorithm of hidden Markov models. While the efficiency of discrete models is limited by the population size, for continuous models it suffices to expand the transition density in orthogonal polynomials of the order of the sample size to infer marginal likelihoods of population genetic parameters. Additionally, conditional allele trajectories and marginal likelihoods of samples from single populations or from multiple populations that split in the past can be obtained. The described approaches allow for efficient maximum likelihood inference of population genetic parameters in a wide variety of demographic scenarios.

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.