High-coverage sequencing of 79 (wild and captive) individuals representing all six non-human great ape species has identified over 88 million single nucleotide polymorphisms providing insight into ape genetic variation and evolutionary history and enabling comparison with human genetic diversity. In an effort to provide insights into great ape genetic variation, the authors sequence 79 wild- and captive-born individuals from across all six great ape species and seven subspecies. Their data and analyses shed light on population structure and gene flow, inbreeding, inferred dynamics of effective population sizes and the differences in the rate of gene loss among the great apes. This new catalogue of great ape genome diversity provides a valuable resource for evolutionary and conservation studies. Most great ape genetic variation remains uncharacterized1,2; however, its study is critical for understanding population history3,4,5,6, recombination7, selection8 and susceptibility to disease9,10. Here we sequence to high coverage a total of 79 wild- and captive-born individuals representing all six great ape species and seven subspecies and report 88.8 million single nucleotide polymorphisms. Our analysis provides support for genetically distinct populations within each species, signals of gene flow, and the split of common chimpanzees into two distinct groups: Nigeria–Cameroon/western and central/eastern populations. We find extensive inbreeding in almost all wild populations, with eastern gorillas being the most extreme. Inferred effective population sizes have varied radically over time in different lineages and this appears to have a profound effect on the genetic diversity at, or close to, genes in almost all species. We discover and assign 1,982 loss-of-function variants throughout the human and great ape lineages, determining that the rate of gene loss has not been different in the human branch compared to other internal branches in the great ape phylogeny. This comprehensive catalogue of great ape genome diversity provides a framework for understanding evolution and a resource for more effective management of wild and captive great ape populations.

Because pathogens are powerful selective agents, host-cell surface molecules used by pathogens as identification signals can reveal the signature of selection. Most of them are oligosaccharides, synthesized by glycosyltransferases. One known example is balancing selection shaping ABO evolution as a consequence of both, A and B antigens being recognized as receptors by some pathogens, and anti-A and/or anti-B natural antibodies produced by hosts conferring protection against the numerous infectious agents expressing A and B motifs. These antigens can also be found in tissues other than blood if there is activity of another enzyme, FUT2, a fucosyltransferase responsible for ABO biosynthesis in body fluids. Homozygotes for null variants at this locus present the nonsecretor phenotype (se), because they cannot express ABO antigens in secretions. Multiple independent mutations have been shown to be responsible for the nonsecretor phenotype, which is coexisting with the secretor phenotype in most populations. In this study, we have resequenced the coding region of FUT2 in 732 individuals from 39 worldwide human populations. We report a complex pattern of natural selection acting on the gene. Although frequencies of secretor and nonsecretor phenotypes are similar in different populations, the point mutations at the base of the phenotypes are different, with some variants showing a long history of balancing selection among Eurasian and African populations, and one recent variant showing a fast spread in East Asia, likely due to positive selection. Thus, a convergent phenotype composition has been achieved through different mutations with different evolutionary histories.

Common variable immunodeficiency (CVID) is the most frequent symptomatic primary immunodeficiency characterized by recurrent infections, hypogammaglobulinemia and poor response to vaccines. Its diagnosis is made based on clinical and immunological criteria, after exclusion of other diseases that can cause similar phenotypes. Currently, less than 20% of cases of CVID have a known underlying genetic cause. We have analyzed whole-exome sequencing and copy number variants data of 36 children and adolescents diagnosed with CVID and healthy relatives to estimate the proportion of monogenic cases. We have replicated an association of CVID to p.C104R in TNFRSF13B and reported the second case of homozygous patient to date. Our results also identify five causative genetic variants in LRBA, CTLA4, NFKB1, and PIK3R1, as well as other very likely causative variants in PRKCD, MAPK8, or DOCK8 among others. We experimentally validate the effect of the LRBA stop-gain mutation which abolishes protein production and downregulates the expression of CTLA4, and of the frameshift indel in CTLA4 producing expression downregulation of the protein. Our results indicate a monogenic origin of at least 15–24% of the CVID cases included in the study. The proportion of monogenic patients seems to be lower in CVID than in other PID that have also been analyzed by whole exome or targeted gene panels sequencing. Regardless of the exact proportion of CVID monogenic cases, other genetic models have to be considered for CVID. We propose that because of its prevalence and other features as intermediate penetrancies and phenotypic variation within families, CVID could fit with other more complex genetic scenarios. In particular, in this work, we explore the possibility of CVID being originated by an oligogenic model with the presence of heterozygous mutations in interacting proteins or by the accumulation of detrimental variants in particular immunological pathways, as well as perform association tests to detect association with rare genetic functional variation in the CVID cohort compared to healthy controls.

Searching for Darwinian selection in natural populations has been the focus of a multitude of studies over the last decades. Here we present the 1000 Genomes Selection Browser 1.0 (http://hsb.upf.edu) as a resource for signatures of recent natural selection in modern humans. We have implemented and applied a large number of neutrality tests as well as summary statistics informative for the action of selection such as Tajima’s D, CLR, Fay and Wu’s H, Fu and Li’s F* and D*, XPEHH, ΔiHH, iHS, F_ST, ΔDAF and XPCLR among others to low coverage sequencing data from the 1000 genomes project (Phase 1; release April 2012). We have implemented a publicly available genome-wide browser to communicate the results from three different populations of West African, Northern European and East Asian ancestry (YRI, CEU, CHB). Information is provided in UCSC-style format to facilitate the integration with the rich UCSC browser tracks and an access page is provided with instructions and for convenient visualization. We believe that this expandable resource will facilitate the interpretation of signals of selection on different temporal, geographical and genomic scales.

Motivation: Detecting positive selection in genomic regions is a recurrent topic in natural population genetic studies. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations. Furthermore, few methods address the challenge of classifying selective events according to specific features such as age, intensity or state (completeness). Results: We have developed a machine-learning classification framework that exploits the combined ability of some selection tests to uncover different polymorphism features expected under the hard sweep model, while controlling for population-specific demography. As a result, we achieve high sensitivity toward hard selective sweeps while adding insights about their completeness (whether a selected variant is fixed or not) and age of onset. Our method also determines the relevance of the individual methods implemented so far to detect positive selection under specific selective scenarios. We calibrated and applied the method to three reference human populations from The 1000 Genome Project to generate a genome-wide classification map of hard selective sweeps. This study improves detection of selective sweep by overcoming the classical selection versus no-selection classification strategy, and offers an explanation to the lack of consistency observed among selection tests when applied to real data. Very few signals were observed in the African population studied, while our method presents higher sensitivity in this population demography. Availability and implementation: The genome-wide results for three human populations from The 1000 Genomes Project and an R-package implementing the ‘Hierarchical Boosting’ framework are available at http://hsb.upf.edu/. Contact:jaume.bertranpetit@upf.edu Supplementary information: Supplementary data are available at Bioinformatics online.