The human gene damage index as a gene-level approach to prioritizing exome variants

The protein-coding exome of a patient with a monogenic disease contains about 20,000 variants, only one or two of which are disease causing. We found that 58% of rare variants in the protein-coding exome of the general population are located in only 2% of the genes. Prompted by this observation, we aimed to develop a gene-level approach for predicting whether a given human protein-coding gene is likely to harbor disease-causing mutations. To this end, we derived the gene damage index (GDI): a genome-wide, gene-level metric of the mutational damage that has accumulated in the general population. We found that the GDI was correlated with selective evolutionary pressure, protein complexity, coding sequence length, and the number of paralogs. We compared GDI with the leading gene-level approaches, genic intolerance, and de novo excess, and demonstrated that GDI performed best for the detection of false positives (i.e., removing exome variants in genes irrelevant to disease), whereas genic intolerance and de novo excess performed better for the detection of true positives (i.e., assessing de novo mutations in genes likely to be disease causing). The GDI server, data, and software are freely available to noncommercial users from lab.rockefeller.edu/casanova/GDI.Germ-line mutations can contribute to the long-term adaptation of humans, but at the expense of causing a large number of genetic diseases (1). The advent of next-generation sequencing (NGS)-based approaches, including whole-exome sequencing (WES), whole-genome sequencing (WGS), and RNA-Seq, has facilitated the large-scale detection of gene variants at both the individual and population levels (2–6). In patients suffering from a monogenic disease, at most two variants are disease causing [true positives (TP)], and the other 20,000 or so protein-coding exome variants are false positives (FP; type I error). Several variant-level metrics predicting the biochemical impact of DNA mutations (7–9) can be used to prioritize candidate variants for a phenotype of interest (10, 11). Gene-level metrics aim to prioritize the genes themselves, providing information that can be used for the further prioritization of variants. There are currently fewer gene-level than variant-level computational methods. They provide complementary information, as it is best to predict the impact of a variant by also taking into account population genetics data for its locus. Current gene-level methods include genic intolerance, as measured by the residual variation intolerance score (RVIS) (12) and de novo excess (DNE) (13). These metrics are particularly useful for determining whether a given gene (and, by inference, its variants) is a plausible candidate for involvement in a particular genetic disease (i.e., for the selection of a short list of candidate genes and variants, which include the TPs). However, owing to the large number and diversity of variants, the selection of a single candidate gene from the NGS data for a given patient with a specific disease remains challenging.We reasoned that genes frequently mutated in healthy populations would be unlikely to cause inherited and rare diseases, but would probably make a disproportionate contribution to the variant calls observed in any given patient. Conversely, mutations in genes that are never or only rarely mutated under normal circumstances are more likely to be disease-causing. Leading gene-level strategies are based on selective pressure (12) and de novo mutation rate estimates (13). These methods are tailored to detect genes likely to harbor TPs. However, these methods do not directly calculate quantitatively the mutational load for human genes in the general (i.e., “healthy”) population or the frequencies of mutant alleles. These methods may, therefore, not be optimal for filtering out highly mutated genes, which are likely to harbor many FPs. Moreover, there has been no formal comparison of the power of these gene-level methods and their combinations for maximizing the discovery of FPs and TPs by NGS. We therefore aimed to generate a robust metric of the cumulative mutational damage to each human protein-coding gene, to make it easier to distinguish the FP variants harbored by highly damaged genes (e.g., under relaxed constraint or positive selection) from potential candidate genes and variants, including the TPs. By damaged genes, we refer to genes displaying many nonsynonymous mutations, which are not necessarily damaging biochemically or evolutionarily. We developed the gene damage index (GDI), which defines, in silico, the mutational damage accumulated by each protein-coding human gene in the general population, and reflecting the combined influences of drifts and selections. We then tested this approach with the WES data for 84 patients in our in-house database, each of these patients having a known primary immunodeficiency (PID). Finally, we used receiver operating characteristic (ROC) curves for formal comparisons of performance between GDI and the existing gene-level RVIS and DNE approaches, and to assess the power of the gene-level methods for detecting enrichment in de novo mutations in cases versus controls. We also tested whether these methods could act in synergy to filter out FPs and select TPs.     

Toward discovery science of human brain function

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual''s “functional connectome.” Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual''s functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain–behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.     

A network-based meta-analysis for characterizing the genetic landscape of human aging

Great amounts of omics data are generated in aging research, but their diverse and partly complementary nature requires integrative analysis approaches for investigating aging processes and connections to age-related diseases. To establish a broader picture of the genetic and epigenetic landscape of human aging we performed a large-scale meta-analysis of 6600 human genes by combining 35 datasets that cover aging hallmarks, longevity, changes in DNA methylation and gene expression, and different age-related diseases. To identify biological relationships between aging-associated genes we incorporated them into a protein interaction network and characterized their network neighborhoods. In particular, we computed a comprehensive landscape of more than 1000 human aging clusters, network regions where genes are highly connected and where gene products commonly participate in similar processes. In addition to clusters that capture known aging processes such as nutrient-sensing and mTOR signaling, we present a number of clusters with a putative functional role in linking different aging processes as promising candidates for follow-up studies. To enable their detailed exploration, all datasets and aging clusters are made freely available via an interactive website (https://gemex.eurac.edu/bioinf/age/).     

Telomere length is associated with disease severity and declines with age in dyskeratosis congenita

Background

Dyskeratosis congenita is a cancer-prone bone marrow failure syndrome caused by aberrations in telomere biology.

Design and Methods

We studied 65 patients with dyskeratosis congenita and 127 unaffected relatives. Telomere length was measured by automated multicolor flow fluorescence in situ hybridization in peripheral blood leukocyte subsets. We age-adjusted telomere length using Z-scores (standard deviations from the mean for age).

Results

We confirmed that telomere lengths below the first percentile for age are very sensitive and specific for the diagnosis of dyskeratosis congenita. We provide evidence that lymphocytes alone and not granulocytes may suffice for clinical screening, while lymphocyte subsets may be required for challenging cases, including identification of silent carriers. We show for the first time using flow fluorescence in situ hybridization that the shortest telomeres are associated with severe variants (Hoyeraal-Hreidarsson and Revesz syndromes), mutations in DKC1, TINF2, or unknown genes, and moderate or severe aplastic anemia. In the first longitudinal follow up of dyskeratosis congenita patients, we demonstrate that telomere lengths decline with age, in contrast to the apparent stable telomere length observed in cross-sectional data.

Conclusions

Telomere length by flow fluorescence in situ hybridization is an important diagnostic test for dyskeratosis congenita; age-adjusted values provide a quantitative measure of disease severity (clinical subset, mutated gene, and degree of bone marrow failure). Patients with dyskeratosis congenita have accelerated telomere shortening. This study is registered at www.clinicaltrials.gov (identifier: {"type":"clinical-trial","attrs":{"text":"NCT00027274","term_id":"NCT00027274"}}NCT00027274).     

Wiring cost and topological participation of the mouse brain connectome

Brain connectomes are topologically complex systems, anatomically embedded in 3D space. Anatomical conservation of “wiring cost” explains many but not all aspects of these networks. Here, we examined the relationship between topology and wiring cost in the mouse connectome by using data from 461 systematically acquired anterograde-tracer injections into the right cortical and subcortical regions of the mouse brain. We estimated brain-wide weights, distances, and wiring costs of axonal projections and performed a multiscale topological and spatial analysis of the resulting weighted and directed mouse brain connectome. Our analysis showed that the mouse connectome has small-world properties, a hierarchical modular structure, and greater-than-minimal wiring costs. High-participation hubs of this connectome mediated communication between functionally specialized and anatomically localized modules, had especially high wiring costs, and closely corresponded to regions of the default mode network. Analyses of independently acquired histological and gene-expression data showed that nodal participation colocalized with low neuronal density and high expression of genes enriched for cognition, learning and memory, and behavior. The mouse connectome contains high-participation hubs, which are not explained by wiring-cost minimization but instead reflect competitive selection pressures for integrated network topology as a basis for higher cognitive and behavioral functions.Network organization of the brain is fundamental to the emergence of complex neuronal dynamics, cognition, learning, and behavior. Modern concepts of anatomical network connectivity originated in the 19th and early 20th century with the ascendancy of the neuron theory: the concept of discrete nerve cells contiguously connected via axonal projections and synaptic junctions (1, 2). In the last decade, the connectome has emerged as a new word to define the complete structural “wiring diagram” of a nervous system or brain (3). At the small scale of synaptically connected neurons, the connectome has only been completely mapped for the 302-neuron nervous system of the roundworm Caenorhabditis elegans, using serial electron microscopy and painstaking visual synaptic reconstruction (4). At the large scale of axonally connected brain regions, draft connectomes have been mapped for the cat and macaque, by collation of primary tract-tracing studies (5–7), and for the human, using in vivo diffusion-weighted magnetic resonance imaging measures of white matter tract organization (8), or interregional covariation measures of cortical thickness or volume (9).Topological analyses of these connectomes have consistently demonstrated a repertoire of complex network properties, including the simultaneous presence of modules and hubs (10). The seemingly ubiquitous appearance of these topological features, e.g., both at the cellular scale of the worm brain and at the areal scale of the human brain, supports scale- and species- invariant organizational principles of nervous systems, consistent with Ramón y Cajal’s seminal “laws of conservation for time, space and material” (1, 11–13). Anatomically localized and functionally specialized modules conserve space and (biological) material by reducing the average length of axonal projections, or wiring cost; anatomically distributed and functionally integrative hubs conserve (conduction) time by reducing the average axonal delay, or speed of interneuronal communication. The simultaneous presence of modules and hubs supports a contemporary reformulation of Ramón y Cajal’s laws as a trade-off between minimization of wiring cost and maximization of topological integration.Magnetic resonance imaging (MRI) allowed for testing such organizational principles in large-scale mammalian connectomes with high throughput whole-brain imaging. However, MRI methods measure anatomical connectivity indirectly and at low (millimeter scale) spatial resolution (14). In contrast, tract tracing methods measure anatomical connectivity directly, by detecting axonally mediated propagation of injected tracer, and at higher (micrometer scale) spatial resolution. Tract-tracing methods represent the current “gold standard” for mapping mammalian connectomes. However, most tract-tracing connectome studies to date have been limited to metaanalyses of primary datasets with limited brain coverage and variable definitions of brain regions and interregional connections (6, 7). Tract-tracing methods for comprehensive and systematic mapping of the connectome did not exist until recently (15–18).The recent step change in the quality and quantity of available tract-tracing measurements in mammalian species, such as the macaque and the mouse, provides a crucial opportunity to test theories of connectome organization more rigorously. Some of the first systematic high-quality tract tracing studies in the macaque have revealed many previously unreported weak and long-range axonal projections (19, 20). These studies have also shown that spatial constraints on wiring cost, modeled by an exponential decay weight–distance relationship, can account for many important aspects of the macaque connectome (21, 22).We therefore considered it important to comprehensively evaluate the design principles of the mouse connectome in a systematically acquired dataset of axonal tract-tracing experiments (17). We measured the topological and spatial properties of this connectome and compared these properties to equivalent properties of reference lattice and random graphs. We hypothesized that the connectome would have a complex topology and include integrative hubs inexplicable by minimization of wiring cost. We also explored the neurobiological substrates of the mouse connectome by correlating topological properties with histological and gene-expression properties quantified from independently acquired datasets.     

An Internet Service Supporting Quality Assessment of Inpatient Glycemic Control

Background

Several studies have linked the maintenance of normoglycemia in acutely ill inpatients with improved clinical outcomes. We previously proposed a few standard definitions for monitoring inpatient glycemic control, or “glucometrics.” In clinical practice, limited data management resources for developing and refining measurement protocols can slow quality improvement efforts. With regard to glucometrics, there are few baseline data regarding the quality of hospital glycemic management. Furthermore, there are no reliable methods for hospitals to gauge the progress of their quality improvement efforts.

Methods

We built a novel Web application that calculates glucometrics on anonymized blood glucose data files uploaded by registered users. This Web site also collects many key characteristics of the users and institutions utilizing the service. This application will allow us to pool data from several institutions to calculate aggregate glucometrics, providing baseline data for quality improvement efforts and ongoing metrics for institutions to gauge their progress.

Results

The application, accessible at http://metrics.med.yale.edu, has already drawn visitors from several countries. A number of users have registered formally, and some have begun to upload institutional glucose data. The application delivers detailed glucometrics reports to registered users, complete with visual displays. Quality improvement staff from large health systems have been the predominant users.

Conclusions

We have created an open access Web application to facilitate quality monitoring and improvement efforts—as well as clinical research—regarding inpatient glycemic management. If employed widely, this application could help establish national performance standards for glycemic control.     

Large-scale topology and the default mode network in the mouse connectome

Noninvasive functional imaging holds great promise for serving as a translational bridge between human and animal models of various neurological and psychiatric disorders. However, despite a depth of knowledge of the cellular and molecular underpinnings of atypical processes in mouse models, little is known about the large-scale functional architecture measured by functional brain imaging, limiting translation to human conditions. Here, we provide a robust processing pipeline to generate high-resolution, whole-brain resting-state functional connectivity MRI (rs-fcMRI) images in the mouse. Using a mesoscale structural connectome (i.e., an anterograde tracer mapping of axonal projections across the mouse CNS), we show that rs-fcMRI in the mouse has strong structural underpinnings, validating our procedures. We next directly show that large-scale network properties previously identified in primates are present in rodents, although they differ in several ways. Last, we examine the existence of the so-called default mode network (DMN)—a distributed functional brain system identified in primates as being highly important for social cognition and overall brain function and atypically functionally connected across a multitude of disorders. We show the presence of a potential DMN in the mouse brain both structurally and functionally. Together, these studies confirm the presence of basic network properties and functional networks of high translational importance in structural and functional systems in the mouse brain. This work clears the way for an important bridge measurement between human and rodent models, enabling us to make stronger conclusions about how regionally specific cellular and molecular manipulations in mice relate back to humans.Understanding the functional architecture of brain systems in both typical and atypical populations has the potential to improve diagnosis, prevention, and treatment of various neurologic and mental illnesses. Human functional neuroimaging, because of its ease of use, noninvasive nature, and wide availability, has significantly advanced this goal. However, because functional brain imaging is an indirect measure of the underlying neuronal dynamics (1), a number of basic questions about the molecular and structural underpinnings of these functional signals needs to be answered before the full clinical promise of the technique can be realized. Insight into these underpinnings would be vastly enhanced by translation to rodent models, where rich methodology for studying high-throughput genetic, histological, and therapeutic conditions in a tightly controlled environment exists. Mouse models, in particular, are likely to contribute significantly to this end.Efforts aimed at using mouse models to enrich findings obtained in humans with noninvasive imaging would benefit greatly from bridge measurements—measurements that can be obtained and compared directly between species, such as resting-state functional connectivity MRI (rs-fcMRI). Importantly, rs-fcMRI has provided invaluable insight into the large-scale topological organization of the human brain (2–4), how it relates to complex behaviors, and how it can be disrupted in disordered populations (5–8). In addition, rs-fcMRI is comparable across species, persists under light anesthesia, and allows for a broad view of intricate regional functional interactions without task inputs (9, 10). The capacity to image the murine brain with rs-fcMRI would effectively bridge clinical studies of human subjects with a vast array of techniques used to understand brain function with mouse models.Although functional brain networks have been well-characterized in humans and to an increasing extent, macaques, a remaining question is whether there is conservation between species in large-scale topological features, such as the “Rich Club”—a system where highly connected brain regions (or hubs) also connect strongly with each other (11–13). Advances in rs-fcMRI and its computational evaluation have begun to shed some light on homology between brain networks in primates (14); however, there is a paucity of studies comparing primates with rodents. Despite evidence for intrinsic functional connectivity in rats (15–19) and to a lesser extent, mice (20–24), comparing large-scale network organization between mice and primates has proven difficult.Of particular interest are prototypical functional networks, such as the default mode network (DMN). The DMN is a set of interconnected brain regions that were originally shown to decrease their level of activity in humans during goal-directed tasks (25, 26). These regions have subsequently been shown to be highly functionally connected in the human (27) and the macaque (9, 28). In addition, strength of functional connectivity in this system has been tied to several neurologic and psychiatric conditions, including Alzheimer’s disease, Autism Spectrum Disorders, and Attention Deficit Hyperactivity Disorder (ADHD) among others (29). In rats, functional connectivity work has now revealed a potential default system surrogate (16); however, this network has yet to be revealed in the mouse, where rich genetic models, behavioral methodology, and the complete structural connectome exist. In addition, it is unclear whether this surrogate default system corresponds to direct connections of the underlying structural connectome.This report fills this void by developing a high-resolution rs-fcMRI approach in mice, which combines with a brain-wide axonal projection mapping matrix [Allen Institute for Brain Science; connectivity.brain-map.org (30)] to (i) examine the structure–function relationships of rs-fcMRI in the mouse, (ii) directly test how well basic functional connectional topology is conserved between primates and the mouse, and (iii) considering this topology, identify whether a default mode-like network in mice exists.     

From the Cover: Self-organization of axial polarity,inside-out layer pattern,and species-specific progenitor dynamics in human ES cell–derived neocortex

Here, using further optimized 3D culture that allows highly selective induction and long-term growth of human ES cell (hESC)-derived cortical neuroepithelium, we demonstrate unique aspects of self-organization in human neocorticogenesis. Self-organized cortical tissue spontaneously forms a polarity along the dorsocaudal-ventrorostral axis and undergoes region-specific rolling morphogenesis that generates a semispherical structure. The neuroepithelium self-forms a multilayered structure including three neuronal zones (subplate, cortical plate, and Cajal-Retzius cell zones) and three progenitor zones (ventricular, subventricular, and intermediate zones) in the same apical-basal order as seen in the human fetal cortex in the early second trimester. In the cortical plate, late-born neurons tend to localize more basally to early-born neurons, consistent with the inside-out pattern seen in vivo. Furthermore, the outer subventricular zone contains basal progenitors that share characteristics with outer radial glia abundantly found in the human, but not mouse, fetal brain. Thus, human neocorticogenesis involves intrinsic programs that enable the emergence of complex neocortical features.The mammalian neocortex has a multilayered structure (layers I–VI) (1). The neocortex arises from the neuroepithelium (NE) of the dorsal telencephalon, which evaginates to form a semispherical brain vesicle (Fig. S1A) (2). The dorsocaudal side of the neocortex is flanked by the cortical hem, whereas its ventrorostral side is neighbored by the lateral ganglionic eminence (LGE; striatum anlage) and septum. Layer I [its fetal primordium is called the marginal zone (MZ); Fig. S1B] is qualitatively different from other layers, as this superficial-most layer is mainly composed of Reelin⁺ Cajal-Retzius (CR) cells, which are largely derived from neighboring tissues such as the cortical hem and septum (3) (in the case of human cortex, some Reelin⁺ cells also arise directly from neocortical NE) (4). The rest of the cortical layers are generated with the “inside-out” pattern: the deeper the layer, the earlier the neurons are born from cortical progenitors (Fig. S1B) (5, 6).A detailed understanding of early human corticogenesis remains elusive because of the limited access to fetal brain tissues. We previously established a 3D culture of mouse and human ES cell (hESC) aggregates that recapitulates early steps of corticogenesis [or serum-free floating culture of embryoid body-like aggregates with quick reaggregation (SFEBq)] (7–9). This method has been also applied to human induced pluripotent stem (iPS) cell culture (10). In this self-organization culture, large domains of cortical NE self-form within a floating hESC aggregate and spontaneously develop ventricular zone (VZ), cortical plate (CP) (mostly deep-layer neurons), and MZ by culture day 40–45. This cortical NE was still immature, mimicking human corticogenesis during the first trimester (Fig. S1C) (7).Here, using an optimized culture, we revealed unique self-organizing aspects of human corticogenesis. Moreover, the optimized culture generates species-specific progenitors in the outer subventricular zone (oSVZ), called outer radial glia (oRG), which are abundantly present in the human neocortex (11, 12) but rare in the mouse cortex (13, 14). Thus, an unexpectedly wide range of self-organizing events is internally programmed within the cortical NE.     

Whole genome sequencing in patients with retinitis pigmentosa reveals pathogenic DNA structural changes and NEK2 as a new disease gene

We performed whole genome sequencing in 16 unrelated patients with autosomal recessive retinitis pigmentosa (ARRP), a disease characterized by progressive retinal degeneration and caused by mutations in over 50 genes, in search of pathogenic DNA variants. Eight patients were from North America, whereas eight were Japanese, a population for which ARRP seems to have different genetic drivers. Using a specific workflow, we assessed both the coding and noncoding regions of the human genome, including the evaluation of highly polymorphic SNPs, structural and copy number variations, as well as 69 control genomes sequenced by the same procedures. We detected homozygous or compound heterozygous mutations in 7 genes associated with ARRP (USH2A, RDH12, CNGB1, EYS, PDE6B, DFNB31, and CERKL) in eight patients, three Japanese and five Americans. Fourteen of the 16 mutant alleles identified were previously unknown. Among these, there was a 2.3-kb deletion in USH2A and an inverted duplication of ∼446 kb in EYS, which would have likely escaped conventional screening techniques or exome sequencing. Moreover, in another Japanese patient, we identified a homozygous frameshift (p.L206fs), absent in more than 2,500 chromosomes from ethnically matched controls, in the ciliary gene NEK2, encoding a serine/threonine-protein kinase. Inactivation of this gene in zebrafish induced retinal photoreceptor defects that were rescued by human NEK2 mRNA. In addition to identifying a previously undescribed ARRP gene, our study highlights the importance of rare structural DNA variations in Mendelian diseases and advocates the need for screening approaches that transcend the analysis of the coding sequences of the human genome.The identification of the genetic causes of rare Mendelian diseases is becoming increasingly important following some success with gene-based therapy, as recently reported for patients with a form of Leber congenital amaurosis (LCA), a severe autosomal recessive hereditary retinal dystrophy (1–3). The evidence that restoring a gene in the diseased retina could yield therapeutic effects has stimulated the pursuit of the genetic causes of other retinal dystrophies, including retinitis pigmentosa (RP).RP is the name given to a group of hereditary retinal conditions in which degeneration of rod photoreceptors, responsible for vision under starlight or moonlight conditions, is more pronounced than that of cone photoreceptors, which mediate daylight vision. Individuals with RP typically experience night blindness at first, followed by progressive and unstoppable visual impairment in daytime conditions as well (4). Their visual fields become reduced gradually and sight is lost from the midperiphery to the periphery and then from the midperiphery to the center, resulting eventually in complete or near-complete blindness if left untreated. Most patients show intraretinal pigment in a bone spicule configuration around the fundus periphery, for which this condition was named. In addition, they typically show retinal arteriolar attenuation, elevated final dark adapted thresholds, and reduced and delayed electroretinograms (ERGs) (4). Vitamin A supplementation in combination with an omega-3 rich diet can slow the course of retinal degeneration and preserve visual acuity among adults with this condition (5, 6). Autosomal, recessively inherited RP (ARRP) is the most common form of hereditary retinal degeneration in humans. To date, over 50 genes have been associated with ARRP and allied disorders, among patients who are predominantly of European ancestry (RetNet; www.sph.uth.tmc.edu/retnet/home.htm). However, despite this high number of identified disease genes, ∼40–50% of all diagnosed cases have no mutations in recognized loci (7). Furthermore, genetic defects in RP are also population specific. For example, a screening of 193 unrelated Japanese patients with isolate or autosomal recessive RP for 30 disease genes identified commonly within North American or European patients revealed candidate pathogenic mutations in only 14% of the cohort (8).Recent advances in massively parallel sequencing have enabled the analysis of large amounts of sequences (genes) at reasonable costs, revolutionizing the traditional approach of exon-by-exon Sanger sequencing (9). The two major forms of sequencing strategies allowing large-scale analyses are whole genome sequencing (WGS) and whole exome sequencing (WES). The former reads the entire genome with no distinction between exons and nonexonic regions. It allows the detection of intergenic variants, copy number variations (CNVs), and other structural rearrangements, as well as unrecognized exonic sequences. The latter technique relies on targeted DNA capture and focuses on the analysis of the known exonic content of the genome, performed according to the genomic annotation available at a given point in time.In this study, we performed WGS as a method for mutation discovery in a highly genetically heterogeneous Mendelian disease; to this end, we evaluated 16 unrelated RP patients from diverse ethnic backgrounds.     

Expression profiling of mouse subplate reveals a dynamic gene network and disease association with autism and schizophrenia

The subplate zone is a highly dynamic transient sector of the developing cerebral cortex that contains some of the earliest generated neurons and the first functional synapses of the cerebral cortex. Subplate cells have important functions in early establishment and maturation of thalamocortical connections, as well as in the development of inhibitory cortical circuits in sensory areas. So far no role has been identified for cells in the subplate in the mature brain and disease association of the subplate-specific genes has not been analyzed systematically. Here we present gene expression evidence for distinct roles of the mouse subplate across development as well as unique molecular markers to extend the repertoire of subplate labels. Performing systematic comparisons between different ages (embryonic days 15 and 18, postnatal day 8, and adult), we reveal the dynamic and constant features of the markers labeling subplate cells during embryonic and early postnatal development and in the adult. This can be visualized using the online database of subplate gene expression at https://molnar.dpag.ox.ac.uk/subplate/. We also identify embryonic similarities in gene expression between the ventricular zones, intermediate zone, and subplate, and distinct postnatal similarities between subplate, layer 5, and layers 2/3. The genes expressed in a subplate-specific manner at some point during development show a statistically significant enrichment for association with autism spectrum disorders and schizophrenia. Our report emphasizes the importance of the study of transient features of the developing brain to better understand neurodevelopmental disorders.     

Pharmacokinetic and tolerability profile of twice-daily saquinavir hard gelatin capsules and saquinavir soft gelatin capsules boosted with ritonavir in healthy volunteers

Objective

To evaluate the pharmacokinetics and safety of a boosted saquinavir (SQV)/ritonavir (RTV) combination, administered as either the hard gelatin capsule (HGC) or soft gelatin capsule (SGC) formulation of SQV, in 24 healthy volunteers.

Methods

This was a single‐centre, open‐label, randomized, 2 × 2 crossover study. Twelve subjects were randomized to receive SQV/RTV 1000 mg/100 mg twice daily (BID) orally for 10 days, as either the HGC or SGC formulation. The pharmacokinetic profile of SQV was determined on day 10. Subjects then crossed over to the opposite SQV formulation, and the pharmacokinetic profile was determined again on day 20. The primary analysis was the assessment of bioequivalence based on logarithmically transformed values for AUC_{(0?24 h)} and C_max for the two formulations.

Results

There was a statistically significant increase in the geometric means of all the pharmacokinetic variables evaluated for SQV‐HGC/RTV compared with SQV‐SGC/RTV. A mean AUC_{0?24 h}‐value of 15.798 µg/mL/h was reported for the HGC formulation compared with 11.655 µg/mL/h for the SGC formulation (P = 0.0043). The SQV‐HGC/RTV combination was better tolerated in terms of gastrointestinal system disorders. Furthermore, no elevations in triglycerides or total cholesterol were reported with SQV/RTV during the entire study period.

Conclusion

In healthy volunteers, RTV boosting of SQV‐HGC produces plasma exposures at least comparable to SQV‐SGC, which is accompanied by an improvement in gastrointestinal system disorders.

     

Extramedullary disease portends poor prognosis in multiple myeloma and is over-represented in high-risk disease even in the era of novel agents

Background

Extramedullary disease is an uncommon manifestation in multiple myeloma and can either accompany newly diagnosed disease or develop with disease progression or relapse. We evaluated the impact of this disease feature on patients'' outcome in the context of novel agents.

Design and Methods

We analyzed clinical and biological features of extramedullary disease in 936 patients with multiple myeloma enrolled in Total Therapy protocols, 240 patients in non-Total Therapy protocols, and 789 non-protocol patients, all of whom had baseline positron emission tomography scans to document extramedullary disease at diagnosis and its subsequent development at the time of disease progression or relapse.

Results

The most common sites for extramedullary disease at diagnosis were skin and soft tissue whereas liver involvement was the striking feature in extramedullary disease at disease relapse or progression. Regardless of therapy, extramedullary disease was associated with shorter progression-free and overall survival, as well as the presence of anemia, thrombocytopenia, elevated serum lactate dehydrogenase, cytogenetic abnormalities, and high-risk features in 70-and 80-gene risk models in univariate analysis. Multivariate analysis with logistic regression revealed that this disease feature was more prevalent in patients with an elevated centrosome index, as determined by gene expression profiling, as well as in myeloma molecular subtypes that are more prone to relapse. These include the MF subtype (also called the “MAF” subtype, associated with over-expression of the MAF gene seen with chromosome translocation 14;16 or 14;20) and the PR subtype (also called the “Proliferation” subtype, associated with overexpression of pro-proliferative genes).

Conclusions

These data show that extramedullary disease is more prevalent in genomically defined high-risk multiple myeloma and is associated with shorter progression-free survival and overall survival, even in the era of novel agents. All clinical trials included in the analyses were registered with www.clinicaltrials.gov ({"type":"clinical-trial","attrs":{"text":"NCT00083551","term_id":"NCT00083551"}}NCT00083551, {"type":"clinical-trial","attrs":{"text":"NCT00083876","term_id":"NCT00083876"}}NCT00083876, {"type":"clinical-trial","attrs":{"text":"NCT00081939","term_id":"NCT00081939"}}NCT00081939, {"type":"clinical-trial","attrs":{"text":"NCT00572169","term_id":"NCT00572169"}}NCT00572169, {"type":"clinical-trial","attrs":{"text":"NCT00644228","term_id":"NCT00644228"}}NCT00644228,{"type":"clinical-trial","attrs":{"text":"NCT00002548","term_id":"NCT00002548"}}NCT00002548,{"type":"clinical-trial","attrs":{"text":"NCT00734877","term_id":"NCT00734877"}}NCT00734877).Key words: extramedullary disease, transplant, myeloma, survival     

Association of familial and sporadic rheumatoid arthritis with a single corticotropin‐releasing hormone genomic region (8q12.3) haplotype

Objective

Rheumatoid arthritis (RA) is a common disabling autoimmune disease with a complex genetic component. We have previously described linkage of a region of chromosome 8q12.3 with RA and association of the microsatellite marker CRHRA1 with RA in 295 affected sibling‐pair families. In the current study we aimed to physically link the RA‐associated marker with the corticotropin‐releasing hormone (CRH) candidate gene, and to examine the genomic region for additional short tandem repeat (STR) genetic markers in order to clarify the association with RA.

Methods

We examined the association of 2 STR markers with disease in the original 295 multicase families and in a cohort of 131 simplex families to refine our understanding of this genetic region in disease susceptibility in sporadic and familial RA. Genomic library screening and sequencing were used to generate physical sequences in the CRH genomic region. Bioinformatic analysis of the sequence flanking the CRH structural gene was used to screen for additional STRs and other genetic features. Genotyping was carried out using a standard fluorescence approach. Estimations of haplotype frequencies were performed to assess linkage disequilibrium. The transmission disequilibrium test was performed using TRANSMIT.

Results

Physical cloning and sequencing analyses identified the genomic region linking the CRHRA1 marker and the CRH structural locus. Moreover, we identified a further STR, CRHRA2, which was in strong linkage disequilibrium with CRHRA1 (P = 4.0 × 10⁻¹⁴). A haplotype, CRHRA1*10;CRHRA2*14, was preferentially carried by unaffected parents at a frequency of 8.6% compared with the expected frequency of 3.1%. This haplotype was overtransmitted in the multiply affected families (P = 0.0077) and, similarly, in the simplex families (P = 0.024). Combined analysis of both family cohorts confirmed significant evidence for linkage (P = 4.9 × 10⁻⁴) and association (P = 5.5 × 10⁻³) for this haplotype with RA.

Conclusion

In demonstrating significant linkage disequilibrium between these 2 markers, we have refined the disease‐associated region to a single haplotype and confirmed the significance of this region in our understanding of the genetics of RA.

     

Programmed Allee effect in bacteria causes a tradeoff between population spread and survival

Dispersal is necessary for spread into new habitats, but it has also been shown to inhibit spread. Theoretical studies have suggested that the presence of a strong Allee effect may account for these counterintuitive observations. Experimental demonstration of this notion is lacking due to the difficulty in quantitative analysis of such phenomena in a natural setting. We engineered Escherichia coli to exhibit a strong Allee effect and examined how the Allee effect would affect the spread of the engineered bacteria. We showed that the Allee effect led to a biphasic dependence of bacterial spread on the dispersal rate: spread is promoted for intermediate dispersal rates but inhibited at low or high dispersal rates. The shape of this dependence is contingent upon the initial density of the source population. Moreover, the Allee effect led to a tradeoff between effectiveness of population spread and survival: increasing the number of target patches during dispersal allows more effective spread, but it simultaneously increases the risk of failing to invade or of going extinct. We also observed that total population growth is transiently maximized at an intermediate number of target patches. Finally, we demonstrate that fluctuations in cell growth may contribute to the paradoxical relationship between dispersal and spread. Our results provide direct experimental evidence that the Allee effect can explain the apparently paradoxical effects of dispersal on spread and have implications for guiding the spread of cooperative organisms.A fundamental question in biology is how the spread and survival of an organism is influenced by various factors (1), including population density (2), dispersal rate (3), and habitat configuration (4). Addressing this question has implications for understanding and controlling biological invasions caused by the introduction of a new species into an established ecosystem (1), the spread of infectious diseases, or the emergence of new pathogens (5).Dispersal has been recognized as being particularly critical in promoting successful spread (e.g., ref. 1; additional examples in SI Text). However, dispersal has also been shown to reduce spread (e.g., ref. 6; additional examples in SI Text). Theoretical studies have proposed that this paradoxical observation can be explained by the Allee effect, which is defined as a positive relationship between individual fitness and the total density of the population (7, 8). In the extreme case, called a strong Allee effect, the population will display a negative fitness, which can be manifested as a negative growth rate, when its initial density is below a critical threshold. Often, a strong Allee effect can be due to the inability to initiate a cooperative behavior at low density (7). This dynamic is observed in several contexts of biology including invasive species, reintroduction biology, epidemiology, the infection of an individual host by microbial pathogens, and quorum sensing (SI Text).By assuming a strong Allee effect, theoretical studies have predicted that dispersal can have a dual effect on population survival and spread. Slow dispersal can prevent the colonization of new territories because the number of individuals arriving in a new area is insufficient to establish a new population (e.g., ref. 9; additional examples in SI Text). Fast dispersal can act as a drain on a source population, which can become too small to be maintained (e.g., ref. 10; additional examples in SI Text). These predictions have been invoked previously to explain the failure of organisms to expand their ranges or to become established (SI Text and Table S1).Although this theoretical explanation is plausible, its experimental demonstration is lacking. This is particularly difficult to verify experimentally in a natural setting because such settings are subject to numerous confounding factors that can obscure the contribution of individual components to the outcome of successful spread. Along this line, it has been suggested that environmental and demographic stochasticity may contribute to population extinction, even in species without an Allee effect (SI Text). The role of a strong Allee effect is further complicated by the limited number of empirical studies that demonstrate the existence of an Allee effect (11), in part due to difficulty in quantifying and studying small populations.To overcome these difficulties, we engineered a gene circuit to confer a strong Allee effect in Escherichia coli and examined its impact on spread and survival. Synthetic biology involves creating novel behaviors in biological systems using gene circuits. These synthetic systems have resulted in numerous novel behaviors including spatial patterning (12) and modulation of fitness (13). Synthetic systems have several advantages over both field and theoretical studies (14). These systems provide a well-defined system to focus on the key, fundamental parameters in a more definitive manner, and they allow direct mapping between modeling and experiments. Although modeling is often used as a driving force in such studies, the ability to confirm the model predictions in a living system serves as a critical proof-of-principle for the plausibility of these predictions. The use of synthetic gene circuits can be thought of as an extension to the use of microbes as model systems to examine questions in evolution and ecology (e.g., ref. 15).     

From the Cover: Predicting multilingual effects on executive function and individual connectomes in children: An ABCD study

While there is a substantial amount of work studying multilingualism’s effect on cognitive functions, little is known about how the multilingual experience modulates the brain as a whole. In this study, we analyzed data of over 1,000 children from the Adolescent Brain Cognitive Development (ABCD) Study to examine whether monolinguals and multilinguals differ in executive function, functional brain connectivity, and brain–behavior associations. We observed significantly better performance from multilingual children than monolinguals in working-memory tasks. In one finding, we were able to classify multilinguals from monolinguals using only their whole-brain functional connectome at rest and during an emotional n-back task. Compared to monolinguals, the multilingual group had different functional connectivity mainly in the occipital lobe and subcortical areas during the emotional n-back task and in the occipital lobe and prefrontal cortex at rest. In contrast, we did not find any differences in behavioral performance and functional connectivity when performing a stop-signal task. As a second finding, we investigated the degree to which behavior is reflected in the brain by implementing a connectome-based behavior prediction approach. The multilingual group showed a significant correlation between observed and connectome-predicted individual working-memory performance scores, while the monolingual group did not show any correlations. Overall, our observations suggest that multilingualism enhances executive function and reliably modulates the corresponding brain functional connectome, distinguishing multilinguals from monolinguals even at the developmental stage.

Learning and managing more than two languages is never easy, but the ability to use multiple languages is drawing more and more attention, because it allows multilinguals to understand different cultures and gives social and economic benefits (1, 2). In addition to these benefits, using multiple languages has been suggested to enhance executive functions (3–6), and the executive function advantages of multilinguals can be explained by how our brain juggles multiple languages. Whenever multilinguals engage in linguistic situations (i.e., when they are in a conversation with others or writing a letter), their known languages activate simultaneously even though they choose to use one language over others (7–9). Due to this phenomenon, proper manipulation of cognitive functions, such as focusing on the target language and suppressing other languages at the same time, is crucial for successful communication, and constant usage of these functions lead to better cognitive functioning.Attention and working memory play a key role in learning and are intimately related. Attention control, the ability to focus on specific stimuli in the environment, is engaged in multilinguals’ language use due to the fact that multilinguals need to select and maintain focus on the target language over the other languages. Working-memory domain, the cognitive capacity for holding information to process a given task that is generally involved in managing interference or conflict (10), is another cognitive domain that utilizes language processing of multilinguals. Multilinguals'' better attention-control and working-memory capacities have been reported across different age groups, including children (attention: refs. 11 and 12; working memory: refs. 13 and 14), young adults (attention: refs. 15 and 16; working memory: ref. 17), and elders (attention: refs. 18 and 19; working memory: ref. 17).However, the multilinguals’ executive advantages, which have been thought to be robust and well-established byproducts of being multilingual, have recently started to face challenges. A growing body of recent studies has claimed that such advantages are not replicable, reporting no differences in executive functions between monolinguals and multilinguals (20–23). Paap et al. (24), for example, questioned the multilingual benefit on cognitive control skills and discussed that previously reported studies are likely to be biased by uncontrolled confounding factors such as unmatched demographic factors, socioeconomic status (SES), or small sample size. Replication failures brought about a debate on the existence of such multilingual benefits across the human lifespan, including children (21, 22, 25), adults (20, 26, 27), and elders (28, 29). However, the debate regarding multilingualism and its association with executive function has arisen from studies with greater variability in multilingual factors, such as age of the second-language acquisition, proficiency of the second language, and the extent of using an additional language on a daily basis (30).Throughout the human lifespan, the early stage of life plays a pivotal role in language learning and cognitive development (31, 32). The importance of experience and development in this period is not limited to the dynamic phase but has lifelong impacts on health and behavior (33, 34). Therefore, evidence on multilingualism and its relationship with executive functions in children should be accumulated to better understand the lifespan trajectory of multilingualism and executive control.Moreover, to comprehend the multilingual impact on cognitive functions, we need to understand the brain mechanisms that underlie the behavioral and cognitive advantages reported for multilinguals. Previous studies have reported multilingualism’s effect on brain structure, mainly in the dorsolateral prefrontal cortex, left caudate nucleus, and anterior cingulate cortex, the brain regions that play a role in both linguistic and nonlinguistic cognitive control (refer to refs. 35 and 36 for review). Compared to monolinguals, multilinguals show different functional connectivity patterns between these regions (37). However, examining the whole brain rather than limited regions would give a better understanding on multilingualism’s effect on executive functions considering that using languages encompasses a variety of cognitive functions. It has been suggested in general that studying the whole brain could lead to a better understanding of our brain and behavior (38).A whole-brain functional connectivity, the degree to which brain activities in distinct neural regions are correlated with each other over time, is a reliable measurement that allows researchers to observe how brain regions are engaged in certain cognitive processes (39). The whole-brain functional connectivity pattern, or functional connectome, can be obtained either during task performance (task connectome) or at rest without any explicit tasks (rest connectome). While the task connectome can highlight brain networks engaging in a specific task, the rest connectome shows the brain’s intrinsic networks, including the default mode network. The functional connectome is unique to each person and can predict people’s personal traits, including fluid intelligence (40, 41), attention (42–44), memory (45, 46), language (47), and personality (48, 49).Is whole-brain functional connectivity shaped by multilingual experience in children? Can we tell whether a child is monolingual or multilingual by only looking at the brain’s functional connectome? Here, we ask whether the whole-brain connectome reflects multilingualism in children and to what extent their cognitive abilities are embedded in the brain. To this end, we start by comparing the behavioral performances of the two cognitive domains, attention and working memory, between multilingual and monolingual groups of children. We analyze a large sample of more than 1,000 children aged between 9 and 10 from the Adolescent Brain Cognitive Development (ABCD) Study, which provides a large and representative sample of the adolescent population with more than 10,000 children from 21 different study sites (50). A result obtained from this large-scale data could add statistically reliable evidence on whether speaking more than one language affects attention and working-memory functions. Next, we investigate whether the two groups have distinct whole-brain functional connectomes that distinguish multilinguals from monolinguals during task performance and at rest. We attempt to classify the two groups using only functional connectomes by means of a support vector machine (SVM). This can inform us whether each group has its own representative connectivity patterns that robustly distinguish one from the other, implying that multilingualism significantly alters the brain’s functional connectome. Furthermore, we directly compare the two groups’ connectomes to investigate whether different edges are engaged in each group during task performance and at rest. Lastly, as another feature of this study, we attempt to predict the behavioral performance of individual participants in the multilingual and monolingual groups using connectome-based predictive modeling (CPM). This modeling can predict an unseen individual''s behavior from their functional brain connectivity based on the model-defined associations between behavior and brain connectivity. In sum, we provide a comprehensive study of multilingualism’s effects on behavior, brain, and their associations in the adolescent stage.     

Association between single nucleotide polymorphism-genotype and outcome of patients with chronic lymphocytic leukemia in a randomized chemotherapy trial

Background

There is variability in the outcome of patients with chronic lymphocytic leukemia with apparently the same stage of disease. Identifying genetic variants that influence patients’ outcome and response to treatment may provide important insights into the biology of the disease.

Design and Methods

We investigated the possibility that genetic variation influences outcome by conducting a genome-wide analysis of 346,831 single nucleotide polymorphisms in 356 patients entered into a phase III trial comparing the efficacy of fludarabine, chlorambucil, and fludarabine with cyclophosphamide as first-line treatment. Genotypes were linked to individual patients’ outcome data and response to chemotherapy. The association between genotype and progression-free survival was assessed by Cox regression analysis adjusting for treatment and clinicopathology.

Results

The strongest associations were shown for rs1949733 (ACOX3; P=8.22x10-7), rs1342899 (P=7.72x10⁻⁷) and rs11158493 (PPP2R5E; P=8.50×10⁻⁷). In addition, the 52 single nucleotide polymorphisms associated at P<10⁻⁴ included rs438034 (CENPF; P=4.86×10⁻⁶), previously correlated with cancer progression, and rs2255235 (B2M; P=3.10×10⁻⁵) and rs2064501 (IL22RA2; P=4.81×10⁻⁵) which map to B-cell genes.

Conclusions

Our findings provide evidence that genetic variation is a determinant of progression-free survival of patients with chronic lymphocytic leukemia. Specific associations warrant further analyses. (Clinicaltrials.gov identifier: {"type":"clinical-trial","attrs":{"text":"NCT00004218","term_id":"NCT00004218"}}NCT00004218)     

From the Cover: INAUGURAL ARTICLE by a Recently Elected Academy Member:A comparison of worldwide phonemic and genetic variation in human populations

Worldwide patterns of genetic variation are driven by human demographic history. Here, we test whether this demographic history has left similar signatures on phonemes—sound units that distinguish meaning between words in languages—to those it has left on genes. We analyze, jointly and in parallel, phoneme inventories from 2,082 worldwide languages and microsatellite polymorphisms from 246 worldwide populations. On a global scale, both genetic distance and phonemic distance between populations are significantly correlated with geographic distance. Geographically close language pairs share significantly more phonemes than distant language pairs, whether or not the languages are closely related. The regional geographic axes of greatest phonemic differentiation correspond to axes of genetic differentiation, suggesting that there is a relationship between human dispersal and linguistic variation. However, the geographic distribution of phoneme inventory sizes does not follow the predictions of a serial founder effect during human expansion out of Africa. Furthermore, although geographically isolated populations lose genetic diversity via genetic drift, phonemes are not subject to drift in the same way: within a given geographic radius, languages that are relatively isolated exhibit more variance in number of phonemes than languages with many neighbors. This finding suggests that relatively isolated languages are more susceptible to phonemic change than languages with many neighbors. Within a language family, phoneme evolution along genetic, geographic, or cognate-based linguistic trees predicts similar ancestral phoneme states to those predicted from ancient sources. More genetic sampling could further elucidate the relative roles of vertical and horizontal transmission in phoneme evolution.Both languages and genes experience descent with modification, and both are affected by evolutionary processes such as migration, population divergence, and drift. Thus, although languages and genes are transmitted differently, combining linguistic and genetic analyses is a natural approach to studying human evolution (1, 2). Cavalli-Sforza et al. (3) juxtaposed a genetic phylogeny with linguistic phyla proposed by Greenberg (described in ref. 4) and observed qualitative concordance; however, their comparison of linguistic and genetic variation was not quantitative. A later analysis of genetic polymorphisms and language boundaries suggested a causal role for language in restricting gene flow in Europe (5). More recently, population-level genetic data have been compared with patterns expected from language family classifications (2, 6–12). Other studies addressed whether the serial founder effect model from genetics—human expansion from an origin in Africa, followed by serial contractions in effective population size during the peopling of the world (13, 14)—explains various linguistic patterns (15–19).Past studies are generally asymmetrical in their approaches to the comparison of genes and languages: some focus on genetic analysis and use linguistics to interpret results, and others analyze linguistic data in light of genetic models. Our study directly compares the signatures of human demographic history in microsatellite polymorphisms from 246 worldwide populations (20) and complete sets of phonemes (phoneme inventories) for 2,082 languages; these are the largest available datasets of both genotyped populations and phonemes, the smallest units of sound that can distinguish meaning between words. Languages do not hold information about deep ancestry as genes do, and phoneme evolution is complex: phonemes can be transmitted vertically from parents to offspring or horizontally between speakers of different languages, and phonemes can change over time within a language (21–23). We compare the geographic and historical patterns evident in phonemes and genes to determine the traces of human history in each data type.Phonemic data were compiled by M.R. (the Ruhlen database); for 2,082 languages with complete phoneme inventories and referenced sources in this database, we annotated each language with geographic coordinates (Fig. 1A) and the number of speakers reported (24). We also analyzed PHOIBLE (PHOnetics Information Base and Lexicon) (25), a linguistic database with phoneme inventories for 968 languages. For 139 globally distributed populations in the Ruhlen database (114 in PHOIBLE), we matched each population’s genetic data to the phoneme inventory of its native language (20), producing novel “phoneme–genome datasets” that allow joint analysis of genes and languages.Open in a separate windowFig. 1.Procrustes-transformed PCs for all phonemes and regional axes of phonemic and genetic differentiation. (A) Locations of 2,082 languages in the Ruhlen database. Phoneme inventory size of each language is indicated by the color bar. We performed Procrustes analyses to compare the first two PCs of phonemic data (B and C) and genetic data (D) to the geographic locations of languages/populations (P < 10⁻⁵ for all three comparisons after 100,000 permutations). The mean Procrustes-transformed PC values (B) for phonemes in the Ruhlen database (t₀ = 0.57), (C) for phonemes in PHOIBLE (t₀ = 0.52), and (D) for allele frequencies (t₀ = 0.69) are displayed in each geographic region. Circle size corresponds to number of languages (B and C) or populations (D). (E) For the Ruhlen phoneme–genome dataset, pairwise geographic distance matrices were projected along different axes (calculated at 1° intervals); within each region, the rotated axis of geographic distance that was most strongly associated (greatest Mantel r) with phonemic distance (black arrows) and genetic distance (gray dashed arrows) is shown. Thinner arrows (Europe, East Asia, South America) indicate nonsignificant associations. Black dots indicate population locations for the Ruhlen phoneme–genome dataset. With the exception of North America, axes of phonemic differentiation and genetic differentiation are similar in most regions (North America: 78° difference; other regions: mean difference 16°).To compare the signatures of human demographic history on genetic variation and phoneme inventories, we used Procrustes analyses to compare principal components (PCs) for both data types with sample geographic locations and determined whether phonemic and genetic distance are more correlated than expected from geographic distance alone. We also developed a new method for identifying regional axes of linguistic and genetic differentiation and tested whether the origin of the human expansion out of Africa can be detected from the geographic distribution of the numbers of phonemes in languages (phoneme inventory sizes). Conflicting predictions exist for the effects of geographic isolation and population contact on language evolution (e.g., refs. 26–29); we tested these by comparing phoneme inventories according to language density at varying radii. We also quantified the extent to which phoneme evolution can be modeled along genetic, geographic, and cognate-based phylogenies. With these joint analyses, we tested whether phonemes and alleles carry signatures of ancient population divergence and recent human migrations, and we identified demographic processes that have different effects on phonemes and alleles.