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ObjectiveTo determine the molecular basis of a new monogenetic recessive disorder that results in familial autonomic ganglionopathy with diffuse autonomic failure.MethodsTwo adult siblings from one family (I-4 and I-5) and another participant from a second family (II-3) presented with severe neurogenic orthostatic hypotension (nOH), small nonreactive pupils, and constipation. All 3 affected members had low norepinephrine levels and diffuse panautonomic failure.ResultsWhole exome sequencing of DNA from I-4 and I-5 showed compound heterozygosity for c.907_908delCT (p.L303Dfs*115)/c.688 G>A (p.D230N) pathologic variants in the acetylcholine receptor, neuronal nicotinic, α3 subunit gene (CHRNA3). II-3 from the second family was homozygous for the same frameshift (fs) variant (p.L303Dfs*115//p.L303Dfs*115). CHRNA3 encodes a critical subunit of the nicotinic acetylcholine receptors (nAChRs) responsible for fast synaptic transmission in the autonomic ganglia. The fs variant is clearly pathogenic and the p.D230N variant is predicted to be damaging (SIFT)/probably damaging (PolyPhen2). The p.D230N variant lies on the interface between CHRNA3 and other nAChR subunits based on structural modeling and is predicted to destabilize the nAChR pentameric complex.ConclusionsWe report a novel genetic disease that affected 3 individuals from 2 unrelated families who presented with severe nOH, miosis, and constipation. These patients had rare pathologic variants in the CHRNA3 gene that cosegregate with and are predicted to be the likely cause of their diffuse panautonomic failure.

Despite the importance of gene regulatory enhancers in human biology and evolution, we lack a comprehensive model of enhancer evolution and function. This substantially limits our understanding of the genetic basis of species divergence and our ability to interpret the effects of noncoding variants on human traits. To explore enhancer sequence evolution and its relationship to regulatory function, we traced the evolutionary origins of transcribed human enhancer sequences with activity across diverse tissues and cellular contexts from the FANTOM5 consortium. The transcribed enhancers are enriched for sequences of a single evolutionary age (“simple” evolutionary architectures) compared with enhancers that are composites of sequences of multiple evolutionary ages (“complex” evolutionary architectures), likely indicating constraint against genomic rearrangements. Complex enhancers are older, more pleiotropic, and more active across species than simple enhancers. Genetic variants within complex enhancers are also less likely to associate with human traits and biochemical activity. Transposable-element-derived sequences (TEDS) have made diverse contributions to enhancers of both architectures; the majority of TEDS are found in enhancers with simple architectures, while a minority have remodeled older sequences to create complex architectures. Finally, we compare the evolutionary architectures of transcribed enhancers with histone-mark-defined enhancers. Our results reveal that most human transcribed enhancers are ancient sequences of a single age, and thus the evolution of most human enhancers was not driven by increases in evolutionary complexity over time. Our analyses further suggest that considering enhancer evolutionary histories provides context that can aid interpretation of the effects of variants on enhancer function. Based on these results, we propose a framework for analyzing enhancer evolutionary architecture.

Recent revolutionary advances at the intersection of medicine, omics, data sciences, computing, epidemiology, and related technologies inspire us to ponder their impact on health. Their potential impact is particularly germane to the biology of pregnancy and perinatal medicine, where limited improvement in health outcomes for women and children has remained a global challenge. We assembled a group of experts to establish a Pregnancy Think Tank to discuss a broad spectrum of major gestational disorders and adverse pregnancy outcomes that affect maternal-infant lifelong health and should serve as targets for leveraging the many recent advances. This report reflects avenues for future effects that hold great potential in 3 major areas: developmental genomics, including the application of methodologies designed to bridge genotypes, physiology, and diseases, addressing vexing questions in early human development; gestational physiology, from immune tolerance to growth and the timing of parturition; and personalized and population medicine, focusing on amalgamating health record data and deep phenotypes to create broad knowledge that can be integrated into healthcare systems and drive discovery to address pregnancy-related disease and promote general health. We propose a series of questions reflecting development, systems biology, diseases, clinical approaches and tools, and population health, and a call for scientific action. Clearly, transdisciplinary science must advance and accelerate to address adverse pregnancy outcomes. Disciplines not traditionally involved in the reproductive sciences, such as computer science, engineering, mathematics, and pharmacology, should be engaged at the study design phase to optimize the information gathered and to identify and further evaluate potentially actionable therapeutic targets. Information sources should include noninvasive personalized sensors and monitors, alongside instructive “liquid biopsies” for noninvasive pregnancy assessment. Future research should also address the diversity of human cohorts in terms of geography, racial and ethnic distributions, and social and health disparities. Modern technologies, for both data-gathering and data-analyzing, make this possible at a scale that was previously unachievable. Finally, the psychosocial and economic environment in which pregnancy takes place must be considered to promote the health and wellness of communities worldwide.

Neanderthal ancestry remains across modern Eurasian genomes, and introgressed sequences influence diverse phenotypes. Here we demonstrate that introgressed sequences reintroduced thousands of ancestral alleles that were lost in Eurasian populations prior to introgression. Our simulations and variant effect predictions argue that these reintroduced alleles (RAs) are more likely to be tolerated by modern humans than introgressed Neanderthal-derived alleles (NDAs) due to their distinct evolutionary histories. Consistent with this, we show enrichment for RAs and depletion for NDAs on introgressed haplotypes with expression quantitative trait loci (eQTL) and phenotype associations. Analysis of available cross-population eQTLs and massively parallel reporter assay (MPRA) data show that RAs commonly influence gene expression independent of linked NDAs. We further validate these independent effects for one RA in vitro. Finally, we demonstrate that NDAs are depleted for regulatory activity compared to RAs, while RAs have activity levels similar to non-introgressed variants. In summary, our study reveals that Neanderthal introgression reintroduced thousands of lost ancestral variants with gene regulatory activity and that these RAs were more tolerated than NDAs. Thus, RAs and their distinct evolutionary histories must be considered when evaluating the effects of introgression. ONE SENTENCE SUMMARY Neanderthal interbreeding with anatomically modern humans restored thousands of ancient alleles that were previously lost in Eurasian populations.

Currently, there is no comprehensive framework to evaluate the evolutionary forces acting on genomic regions associated with human complex traits and contextualize the relationship between evolution and molecular function. Here, we develop an approach to test for signatures of diverse evolutionary forces on trait-associated genomic regions. We apply our method to regions associated with spontaneous preterm birth (sPTB), a complex disorder of global health concern. We find that sPTB-associated regions harbor diverse evolutionary signatures including conservation, excess population differentiation, accelerated evolution, and balanced polymorphism. Furthermore, we integrate evolutionary context with molecular evidence to hypothesize how these regions contribute to sPTB risk. Finally, we observe enrichment in signatures of diverse evolutionary forces in sPTB-associated regions compared to genomic background. By quantifying multiple evolutionary forces acting on sPTB-associated regions, our approach improves understanding of both functional roles and the mosaic of evolutionary forces acting on loci. Our work provides a blueprint for investigating evolutionary pressures on complex traits., There is a need for analytical frameworks to investigate the evolution of complex genetic traits. Here, the authors develop an approach to simultaneously evaluate different evolutionary signatures on trait-associated genetic variants for complex traits and apply it to study spontaneous preterm birth.

Structural variants (SVs) contribute to many disorders, yet, functionally annotating them remains a major challenge. Here, we integrate SVs with RNA-sequencing from human post-mortem brains to quantify their dosage and regulatory effects. We show that genic and regulatory SVs exist at significantly lower frequencies than intergenic SVs. Functional impact of copy number variants (CNVs) stems from both the proportion of genic and regulatory content altered and loss-of-function intolerance of the gene. We train a linear model to predict expression effects of rare CNVs and use it to annotate regulatory disruption of CNVs from 14,891 independent genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders show significantly more extreme regulatory disruption scores and if rank ordered would be prioritized higher than using frequency or length alone. This work shows the deleteriousness of regulatory SVs, particularly those altering CTCF sites and provides a simple approach for functionally annotating the regulatory consequences of CNVs., Structural variants (SVs) contribute to the genetic architecture of many brain-related disorders. Here, the authors integrate SV calls from genome sequencing (n = 755) with RNA-seq data (n = 629) from post-mortem dorsal lateral prefrontal cortex to annotate the gene regulatory effects of SVs in the human brain and their potential to contribute to disease.

Eurasians have ~2% Neanderthal ancestry, but we lack a comprehensive understanding of the genome-wide influence of Neanderthal introgression on modern human diseases and traits. Here, we quantify the contribution of introgressed alleles to the heritability of more than 400 diverse traits. We show that genomic regions in which detectable Neanderthal ancestry remains are depleted of heritability for all traits considered, except those related to skin and hair. Introgressed variants themselves are also depleted for contributions to the heritability of most traits. However, introgressed variants shared across multiple Neanderthal populations are enriched for heritability and have consistent directions of effect on several traits with potential relevance to human adaptation to non-African environments, including hair and skin traits, autoimmunity, chronotype, bone density, lung capacity, and menopause age. Integrating our results, we propose a model in which selection against introgressed functional variation was the dominant trend (especially for cognitive traits); however, for a few traits, introgressed variants provided beneficial variation via uni-directional (e.g., lightening skin color) or bi-directional (e.g., modulating immune response) effects., We lack a comprehensive understanding of how Neanderthal ancestry influences human traits. This study finds that regions with Neanderthal ancestry are broadly depleted of trait-associated variation; yet, introgressed variants likely contributed to human adaptation in a few traits, like skin color and immune response modulation.

Evolutionary changes in enhancers are widely associated with variation in human traits and diseases. However, studies comprehensively quantifying levels of selection on enhancers at multiple evolutionary time points during recent human evolution and how enhancer evolution varies across human tissues are lacking. To address these questions, we integrated a dataset of 41,561 transcribed enhancers active in 41 different human tissues (FANTOM Consortium) with whole genome sequences of 1,668 individuals from the African, Asian, and European populations (1000 Genomes Project). Our analyses based on four different metrics (Tajima’s D, FST, H12, nSL) showed that ~5.90% of enhancers considered showed evidence of recent positive selection and that genes associated with enhancers under positive selection are enriched for diverse immune-related functions. The distributions of these metrics for brain and testis enhancers were often statistically significantly different compared to those of other tissues; the same was true for brain and testis enhancers that are tissue-specific compared to those that are tissue-broad and for testis enhancers associated with tissue-enriched and non-tissue-enriched genes. These differences varied considerably across metrics and tissues and were generally due to changes in distributions’ shapes rather than shifts in their values. These results suggest that many human enhancers experienced recent positive selection throughout multiple time periods in human evolutionary history, that this selection occurred in a tissue-dependent and immune-related functional context, and that much like the evolution of their coding counterparts, the evolution of brain and testis enhancers has been markedly different from that of enhancers in other tissues.

Rare variants in the cardiac potassium channel KV7.1 (KCNQ1) and sodium channel NaV1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of NaV1.5 (n = 356) and KV7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported NaV1.5 and KV7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting NaV1.5 peak current (adj. R2 = 0.27) and KV7.1 + KCNE1 half-maximal voltage of activation (adj. R2 = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers., Graphical abstract Unlabelled Image

Background and objectives The diversity of eutherian reproductive strategies has led to variation in many traits, such as number of offspring, age of reproductive maturity and gestation length. While reproductive trait variation has been extensively investigated and is well established in mammals, the genetic loci contributing to this variation remain largely unknown. The domestic dog, Canis lupus familiaris is a powerful model for studies of the genetics of inherited disease due to its unique history of domestication. To gain insight into the genetic basis of reproductive traits across domestic dog breeds, we collected phenotypic data for four traits, cesarean section rate, litter size, stillbirth rate and gestation length, from primary literature and breeders' handbooks. Methodology By matching our phenotypic data to genomic data from the Cornell Veterinary Biobank, we performed genome-wide association analyses for these four reproductive traits, using body mass and kinship among breeds as covariates. Results We identified 12 genome-wide significant associations between these traits and genetic loci, including variants near CACNA2D3 with gestation length, MSRB3 and MSANTD1 with litter size, SMOC2 with cesarean section rate and UFM1 with stillbirth rate. A few of these loci, such as CACNA2D3 and MSRB3, have been previously implicated in human reproductive pathologies, whereas others have been associated with domestication-related traits, including brachycephaly (SMOC2) and coat curl (KRT71). Conclusions and implications We hypothesize that the artificial selection that gave rise to dog breeds also influenced the observed variation in their reproductive traits. Overall, our work establishes the domestic dog as a system for studying the genetics of reproductive biology and disease. LAY SUMMARY The genetic contributors to variation in mammalian reproductive traits remain largely unknown. We took advantage of the domestic dog, a powerful model system, to test for associations between genome-wide variants and four reproductive traits (cesarean section rate, litter size, stillbirth rate and gestation length) that vary extensively across breeds. We identified associations at a dozen loci, including ones previously associated with domestication-related traits, suggesting that selection on dog breeds also influenced their reproductive traits.

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual’s DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential., Our evolutionary history has resulted in highly complex and sophisticated human physiology. Yet evolutionary footprints have also left us prone to diseases. In this Review, the authors discuss how events from the earliest history of life on Earth through to modern human evolution influence many disease traits and outcomes. They describe how an understanding and application of evolutionary frameworks can inform precision medicine initiatives.

Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs-TAD boundaries-contribute to the regulation of gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD-boundary disruption have been implicated in rare-disease pathogenesis; however, we have a limited framework for integrating TADs and their variation across cell types into the interpretation of common-trait-associated variants. Here, we investigate an attribute of 3D genome architecture-the stability of TAD boundaries across cell types-and demonstrate its relevance to understanding how genetic variation in TADs contributes to complex disease. By synthesizing TAD maps across 37 diverse cell types with 41 genome-wide association studies (GWASs), we investigate the differences in disease association and evolutionary pressure on variation in TADs versus TAD boundaries. We demonstrate that genetic variation in TAD boundaries contributes more to complex-trait heritability, especially for immunologic, hematologic, and metabolic traits. We also show that TAD boundaries are more evolutionarily constrained than TADs. Next, stratifying boundaries by their stability across cell types, we find substantial variation. Compared to boundaries unique to a specific cell type, boundaries stable across cell types are further enriched for complex-trait heritability, evolutionary constraint, CTCF binding, and housekeeping genes. Thus, considering TAD boundary stability across cell types provides valuable context for understanding the genome's functional landscape and enabling variant interpretation that takes 3D structure into account.

Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features they learn. Several models of how combinatorial binding of transcription factors, i.e. the regulatory grammar, drives enhancer activity have been proposed, ranging from the flexible TF billboard model to the stringent enhanceosome model. However, there is limited knowledge of the prevalence of these (or other) sequence architectures across enhancers. Here we perform several hypothesis-driven analyses to explore the ability of DNNs to learn the regulatory grammar of enhancers. We created synthetic datasets based on existing hypotheses about combinatorial transcription factor binding site (TFBS) patterns, including homotypic clusters, heterotypic clusters, and enhanceosomes, from real TF binding motifs from diverse TF families. We then trained deep residual neural networks (ResNets) to model the sequences under a range of scenarios that reflect real-world multi-label regulatory sequence prediction tasks. We developed a gradient-based unsupervised clustering method to extract the patterns learned by the ResNet models. We demonstrated that simulated regulatory grammars are best learned in the penultimate layer of the ResNets, and the proposed method can accurately retrieve the regulatory grammar even when there is heterogeneity in the enhancer categories and a large fraction of TFBS outside of the regulatory grammar. However, we also identify common scenarios where ResNets fail to learn simulated regulatory grammars. Finally, we applied the proposed method to mouse developmental enhancers and were able to identify the components of a known heterotypic TF cluster. Our results provide a framework for interpreting the regulatory rules learned by ResNets, and they demonstrate that the ability and efficiency of ResNets in learning the regulatory grammar depends on the nature of the prediction task., Author summary Gene regulatory sequences function through the combinatorial binding of transcription factors (TFs). However, the specific binding combinations and patterns that specify regulatory activity in different cellular contexts (“regulatory grammars”) are poorly understood. Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying regulatory DNA sequences active in different contexts, but they have provided only limited biological insight due to the complexity of the statistical patterns they learn. In this study, we explore the power and limitations of DNNs in learning regulatory grammars through biologically motivated simulations. We simulated regulatory sequences based on existing hypotheses about the structure of possible regulatory grammars and trained DNNs to model these sequences under a range of scenarios that reflect real-world regulatory sequence prediction tasks. We developed an unsupervised clustering method to extract learned patterns from the trained DNNs and compare them to the simulated grammars. We found that our method can successfully extract regulatory grammars when they are learned by the DNN, but the ability of DNNs to learn regulatory grammars highly depends on the nature of the prediction task. Finally, we show that our DNN approach highlights a known heart regulatory grammar when applied to real mouse enhancer sequences.

Germline disease-causing variants are generally more spatially clustered in protein 3-dimensional structures than benign variants. Motivated by this tendency, we develop a fast and powerful protein-structure-based scan (PSCAN) approach for evaluating gene-level associations with complex disease and detecting signal variants. We validate PSCAN’s performance on synthetic data and two real data sets for lipid traits and Alzheimer’s disease. Our results demonstrate that PSCAN performs competitively with existing gene-level tests while increasing power and identifying more specific signal variant sets. Furthermore, PSCAN enables generation of hypotheses about the molecular basis for the associations in the context of protein structures and functional domains.

Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is the cause of the global pandemic of coronavirus disease‐2019 (COVID‐19). SARS‐CoV‐2 is a zoonotic disease, but little is known about variations in species susceptibility that could identify potential reservoir species, animal models, and the risk to pets, wildlife, and livestock. Certain species, such as domestic cats and tigers, are susceptible to SARS‐CoV‐2 infection, while other species such as mice and chickens are not. Most animal species, including those in close contact with humans, have unknown susceptibility. Hence, methods to predict the infection risk of animal species are urgently needed. SARS‐CoV‐2 spike protein binding to angiotensin‐converting enzyme 2 (ACE2) is critical for viral cell entry and infection. Here we integrate species differences in susceptibility with multiple in‐depth structural analyses to identify key ACE2 amino acid positions including 30, 83, 90, 322, and 354 that distinguish susceptible from resistant species. Using differences in these residues across species, we developed a susceptibility score that predicts an elevated risk of SARS‐CoV‐2 infection for multiple species including horses and camels. We also demonstrate that SARS‐CoV‐2 is nearly optimal for binding ACE2 of humans compared to other animals, which may underlie the highly contagious transmissibility of this virus among humans. Taken together, our findings define potential ACE2 and SARS‐CoV‐2 residues for therapeutic targeting and identification of animal species on which to focus research and protection measures for environmental and public health.

Context Hypophosphatasia (HPP) is a syndrome marked by low serum alkaline phosphatase (AlkP) activity as well as musculoskeletal and/or dental disease. While the majority of subjects with HPP carry a pathogenic variant in the ALPL gene or its regulatory regions, individual pathogenic variants are often not tightly correlated with clinical symptomatology. We sought to better understand the genotype/phenotype correlation in HPP by examining the clinical and biochemical data of 37 subjects with 2 rare variants in ALPL. Methods Through BioVU, a DNA biobank that pairs individuals’ genetic information with their de-identified medical records, we identified subjects with 2 rare variants with distinct reported clinical phenotypes (p.D294A and p.T273M). We then performed a manual review of these subjects’ de-identified medical records along with computational modeling of protein structure to construct a genetic, biochemical and clinical phenotype for each subject and variant. Results Twenty subjects with the p.D294A variant and 17 with the p.T273M variant had sufficient data for analysis. Among subjects in our cohort with the p.D294A variant, 6 (30.0%) had both clinical bone disease and serum AlkP activity below 40 IU/L while 4 subjects (23.5%) with the p.T273M variant met the same criteria despite the distinct clinical phenotypes of these variants. Conclusions Given the loose genotype/phenotype correlation in HPP seen in our cohort, clinical context is crucial for the interpretation of genetic test results to guide clinical care in this population. Otherwise, over- or under-diagnosis may occur, resulting in misidentification of those who may benefit from additional screening and perhaps pharmacologic intervention.

Background Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs—TAD boundaries— regulate gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD boundary disruption have been implicated in rare disease pathogenesis; however, we have a limited framework for integrating cross-cell-type TAD maps into the study of genome evolution and interpretation of common trait-associated variants. Here, we investigate an unexplored attribute of 3D genome architecture—the stability of TAD boundaries across cell types—and demonstrate its relevance to understanding how genetic variation in TADs contributes to complex disease. Results By synthesizing TAD maps across 37 diverse cell types with multiple annotations and 41 genome-wide association studies (GWAS), we investigate the differences in function and evolutionary constraint between TADs versus TAD boundaries. We demonstrate that TAD boundaries are more evolutionarily constrained than TADs. We find that boundaries are enriched for disease heritability, especially for immunologic, hematologic, and metabolic traits. Next, we stratify boundaries by their stability across cell types and show that more than 30% of boundaries are unique to a specific tissue. Compared to these unique boundaries, cell-type-stable boundaries are enriched for CTCF binding, evolutionary constraint, housekeeping genes, and trait heritability, suggesting greater functional importance. Conclusions Cell-type-stable TAD boundaries have greater sequence-level constraint and complex trait heritability enrichment than cell-type-unique boundaries and TADs overall. Thus, considering TAD and boundary stability across cell types provides valuable context for understanding the genome’s functional landscape and enabling 3D-structural aware variant interpretation.

Background Next-generation sequencing of individuals with genetic diseases often detects candidate rare variants in numerous genes, but determining which are causal remains challenging. We hypothesized that the spatial distribution of missense variants in protein structures contains information about function and pathogenicity that can help prioritize variants of unknown significance (VUS) and elucidate the structural mechanisms leading to disease. Results To illustrate this approach in a clinical application, we analyzed 13 candidate missense variants in regulator of telomere elongation helicase 1 (RTEL1) identified in patients with Familial Interstitial Pneumonia (FIP). We curated pathogenic and neutral RTEL1 variants from the literature and public databases. We then used homology modeling to construct a 3D structural model of RTEL1 and mapped known variants into this structure. We next developed a pathogenicity prediction algorithm based on proximity to known disease causing and neutral variants and evaluated its performance with leave-one-out cross-validation. We further validated our predictions with segregation analyses, telomere lengths, and mutagenesis data from the homologous XPD protein. Our algorithm for classifying RTEL1 VUS based on spatial proximity to pathogenic and neutral variation accurately distinguished 7 known pathogenic from 29 neutral variants (ROC AUC = 0.85) in the N-terminal domains of RTEL1. Pathogenic proximity scores were also significantly correlated with effects on ATPase activity (Pearson r = −0.65, p = 0.0004) in XPD, a related helicase. Applying the algorithm to 13 VUS identified from sequencing of RTEL1 from patients predicted five out of six disease-segregating VUS to be pathogenic. We provide structural hypotheses regarding how these mutations may disrupt RTEL1 ATPase and helicase function. Conclusions Spatial analysis of missense variation accurately classified candidate VUS in RTEL1 and suggests how such variants cause disease. Incorporating spatial proximity analyses into other pathogenicity prediction tools may improve accuracy for other genes and genetic diseases. Electronic supplementary material The online version of this article (doi: 10.1186/s12859-018-2010-z) contains supplementary material, which is available to authorized users.

Studies of regulatory activity and gene expression have revealed an intriguing dichotomy: There is substantial turnover in the regulatory activity of orthologous sequences between species; however, the expression level of orthologous genes is largely conserved. Understanding how distal regulatory elements, for example, enhancers, evolve and function is critical, as alterations in gene expression levels can drive the development of both complex disease and functional divergence between species. In this study, we investigated determinants of the conservation of regulatory enhancer activity for orthologous sequences across mammalian evolution. Using liver enhancers identified from genome-wide histone modification profiles in ten diverse mammalian species, we compared orthologous sequences that exhibited regulatory activity in all species (conserved-activity enhancers) to shared sequences active only in a single species (species-specific-activity enhancers). Conserved-activity enhancers have greater regulatory potential than species-specific-activity enhancers, as quantified by both the density and diversity of transcription factor binding motifs. Consistent with their greater regulatory potential, conserved-activity enhancers have greater regulatory activity in humans than species-specific-activity enhancers: They are active across more cellular contexts, and they regulate more genes than species-specific-activity enhancers. Furthermore, the genes regulated by conserved-activity enhancers are expressed in more tissues and are less tolerant of loss-of-function mutations than those targeted by species-specific-activity enhancers. These consistent results across various stages of gene regulation demonstrate that conserved-activity enhancers are more pleiotropic than their species-specific-activity counterparts. This suggests that pleiotropy is associated with the conservation of regulatory across mammalian evolution.

Background microRNAs (miRNAs) are essential to the regulation of gene expression in eukaryotes, and improper expression of miRNAs contributes to hundreds of diseases. Despite the essential functions of miRNAs, the evolutionary dynamics of how they are integrated into existing gene regulatory and functional networks is not well understood. Knowledge of the origin and evolutionary history a gene has proven informative about its functions and disease associations; we hypothesize that incorporating the evolutionary origins of miRNAs into analyses will help resolve differences in their functional dynamics and how they influence disease. Results We computed the phylogenetic age of miRNAs across 146 species and quantified the relationship between human miRNA age and several functional attributes. Older miRNAs are significantly more likely to be associated with disease than younger miRNAs, and the number of associated diseases increases with age. As has been observed for genes, the miRNAs associated with different diseases have different age profiles. For example, human miRNAs implicated in cancer are enriched for origins near the dawn of animal multicellularity. Consistent with the increasing contribution of miRNAs to disease with age, older miRNAs target more genes than younger miRNAs, and older miRNAs are expressed in significantly more tissues. Furthermore, miRNAs of all ages exhibit a strong preference to target older genes; 93% of validated miRNA gene targets were in existence at the origin of the targeting miRNA. Finally, we find that human miRNAs in evolutionarily related families are more similar in their targets and expression profiles than unrelated miRNAs. Conclusions Considering the evolutionary origin and history of a miRNA provides useful context for the analysis of its function. Consistent with recent work in Drosophila, our results support a model in which miRNAs increase their expression and functional regulatory interactions over evolutionary time, and thus older miRNAs have increased potential to cause disease. We anticipate that these patterns hold across mammalian species; however, comprehensively evaluating them will require refining miRNA annotations across species and collecting functional data in non-human systems. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-4073-z) contains supplementary material, which is available to authorized users.

RationalePartial or complete loss of function variants inSCN5Aare the most common genetic cause of the arrhythmia disorder Brugada Syndrome (BrS1). However, the pathogenicity ofSCN5Avariants is often unknown or disputed; 80% of the 1,390SCN5Amissense variants observed in at least one individual to date are variants of uncertain significance (VUS). The designation of VUS is a barrier to the use of sequence data in clinical care.ObjectiveWe selected 83 variants for study: 10 previously studied control variants, 10 suspected benign variants, and 63 suspected Brugada Syndrome-associated variants, selected on the basis of their frequency in the general population and in patients with Brugada Syndrome. We used high-throughput automated patch clamping to study the function of the 83 variants, with the goal of reclassifying variants with functional data.Methods and ResultsTen previously studied variants had functional properties concordant with published manual patch clamp data. All 10 suspected benign variants had wildtype-like function. 22 suspected BrS variants had loss of channel function (ConclusionsHigh-throughput automated patch clamping enabled the reclassification of the majority of tested VUS’s inSCN5A.

Moving in synchrony to the beat is a fundamental component of musicality. Here, we conducted a genome-wide association study (GWAS) to identify common genetic variants associated with beat synchronization in 606,825 individuals. Beat synchronization exhibited a highly polygenic architecture, with sixty-nine loci reaching genome-wide significance (p−8) and SNP-based heritability (on the liability scale) of 13%-16%. Heritability was enriched for genes expressed in brain tissues, and for fetal and adult brain-specific gene regulatory elements, underscoring the role of central nervous system-expressed genes linked to the genetic basis of the trait. We performed validations of the self-report phenotype (through internet-based experiments) and of the GWAS (polygenic scores for beat synchronization were associated with patients algorithmically classified as musicians in medical records of a separate biobank). Genetic correlations with breathing function, motor function, processing speed, and chronotype suggest shared genetic architecture with beat synchronization and provide avenues for new phenotypic and genetic explorations.

Human pregnancy requires the coordinated function of multiple tissues in both mother and fetus and has evolved in concert with major human adaptations. As a result, pregnancy-associated phenotypes and related disorders are genetically complex and have likely been sculpted by diverse evolutionary forces. However, there is no framework to comprehensively evaluate how these traits evolved or to explore the relationship of evolutionary signatures on trait-associated genetic variants to molecular function. Here we develop an approach to test for signatures of diverse evolutionary forces, including multiple types of selection, and apply it to genomic regions associated with spontaneous preterm birth (sPTB), a complex disorder of global health concern. We find that sPTB-associated regions harbor diverse evolutionary signatures including evolutionary sequence conservation (consistent with the action of negative selection), excess population differentiation (local adaptation), accelerated evolution (positive selection), and balanced polymorphism (balancing selection). Furthermore, these genomic regions show diverse functional characteristics which enables us to use evolutionary and molecular lines of evidence to develop hypotheses about how these genomic regions contribute to sPTB risk. In summary, we introduce an approach for inferring the spectrum of evolutionary forces acting on genomic regions associated with complex disorders. When applied to sPTB-associated genomic regions, this approach both improves our understanding of the potential roles of these regions in pathology and illuminates the mosaic nature of evolutionary forces acting on genomic regions associated with sPTB.

Immunity genes have repeatedly experienced natural selection during mammalian evolution. Galectins are carbohydrate-binding proteins that regulate diverse immune responses, including maternal–fetal immune tolerance in placental pregnancy. Seven human galectins, four conserved across vertebrates and three specific to primates, are involved in placental development. To comprehensively study the molecular evolution of these galectins, both across mammals and within humans, we conducted a series of between- and within-species evolutionary analyses. By examining patterns of sequence evolution between species, we found that primate-specific galectins showed uniformly high substitution rates, whereas two of the four other galectins experienced accelerated evolution in primates. By examining human population genomic variation, we found that galectin genes and variants, including variants previously linked to immune diseases, showed signatures of recent positive selection in specific human populations. By examining one nonsynonymous variant in Galectin-8 previously associated with autoimmune diseases, we further discovered that it is tightly linked to three other nonsynonymous variants; surprisingly, the global frequency of this four-variant haplotype is ∼50%. To begin understanding the impact of this major haplotype on Galectin-8 protein structure, we modeled its 3D protein structure and found that it differed substantially from the reference protein structure. These results suggest that placentally expressed galectins experienced both ancient and more recent selection in a lineage- and population-specific manner. Furthermore, our discovery that the major Galectin-8 haplotype is structurally distinct from and more commonly found than the reference haplotype illustrates the significance of understanding the evolutionary processes that sculpted variants associated with human genetic disease.

Structural variants (SVs) contribute substantially to risk of many brain related disorders including autism and schizophrenia. However, annotating the potential contribution of SVs to disease remains a major challenge. Here, we integrated high resolution SV calling from genome-sequencing in 755 human post-mortem brains with dorsal lateral prefrontal cortex RNA-sequencing from a subset of 629 samples to quantify the dosage and regulatory effects of SVs. We show that genic (p = 5.44×10−9) and regulatory SVs (enhancer p = 3.22×10−23, CTCF p = 3.86×10−18) are present at significantly lower frequencies than intergenic SVs after correcting for SV length. Copy number variants (CNVs)—deletions and duplications—exhibit a significant quantitative and directional relationship between the proportion of genic and regulatory content altered and gene expression, and the size of the effect is inversely correlated with the loss-of-function intolerance of the gene. We trained a joint linear model that leverages genic and regulatory annotations to predict expression effects of rare CNVs in independent samples (R2= 0.21-0.41). We further developed a regulatory disruption score for each CNV that aggregates the predicted expression across all affected genes weighted by the genes’ intolerance score and applied it to an independent set of SVs from 14,891 genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders by ClinGen had significantly more extreme regulatory disruption scores than the rest of the SVs. Rank ordering based on the most extreme regulatory disruption scores prioritized pathogenic deletions that would not have been prioritized by frequency or length alone. This work points to the deleteriousness of regulatory SVs, particularly those altering CTCF sites. We further provide a simple approach for functionally annotating the regulatory effects of SVs in the human brain that has potential to be useful in larger SV studies and should improve as more regulatory annotation data is generated.

Genomic databases are substantially biased towards European ancestry populations, and this bias contributes to health disparities. Here, we quantify how well 66,971 experimentally characterized human protein 3D structures represent the diversity of protein sequences observed across the 1000 Genomes Project. More than 85% of available structures do not match a sequence observed in at least one individual, and on average structures match the sequence of 74% of individuals. Nearly 23% of human structures do not matchanyobserved sequences; however, after masking engineered/known mutations, this decreases to ~4%. African ancestry sequences are modestly, but significantly, less likely to be represented by structures (73.5% vs. 74.0%). These differences are mainly driven by the greater genetic diversity of African populations. We identify thousands of variants unrepresented in available structures that influence protein structure and function. Thus, the use of a single structure as representative of “the wild type” protein will often bias results against many individuals. The diversity of protein sequence and structure must be considered to enable accurate, reproducible, and generalizable conclusions from structural analyses.

Novel regulatory elements that enabled expression of pre-existing immune genes in reproductive tissues and novel immune genes with pregnancy-specific roles in eutherians have shaped the evolution of mammalian pregnancy by facilitating the emergence of novel mechanisms for immune regulation over its course. Trade-offs arising from conflicting fitness effects on reproduction and host defenses have further influenced the patterns of genetic variation of these genes. These three mechanisms (novel regulatory elements, novel immune genes, and trade-offs) played a pivotal role in refining the regulation of maternal immune systems during pregnancy in eutherians, likely facilitating the establishment of prolonged direct maternal-fetal contact in eutherians without causing immunological rejection of the genetically distinct fetus.

Neanderthal ancestry remains across modern Eurasian genomes, and introgressed sequences influence diverse phenotypes, including immune, skin, and neuropsychiatric diseases. Interpretation of introgressed sequences has focused on alleles derived in the Neanderthal lineage. Here, we demonstrate that Neanderthal introgression also reintroduced thousands of ancestral hominin alleles lost in the Eurasian out of Africa bottleneck. Combining evolutionary simulations, expression quantitative trait loci (eQTL), massively parallel reporter assay (MPRA) data, and in vitro validation, we show that reintroduced alleles (RAs) have different fitness effects than Neanderthal-derived alleles (NDAs) and that some RAs regulate gene expression independent of NDAs. Illustrating the broad potential influence of RAs, we find that over 70% of known phenotype associations with NDAs are equally associated with RAs. Finally, we discover enrichment for RA eQTL activity in several tissues, with strongest enrichment in the brain. In summary, our study reveals that Neanderthal introgression supplied Eurasians with many lost functional variants and demonstrates that RAs must be considered when evaluating the effects of introgression.ONE SENTENCE SUMMARYNeanderthal interbreeding with modern humans restored to Eurasians, hundreds of thousands of ancient alleles that were lost in the out of Africa bottleneck.

Enhancers and promoters both regulate gene expression by recruiting transcription factors (TFs); however, the degree to which enhancer vs. promoter activity is due to differences in their sequences or to genomic context is the subject of ongoing debate. We examined this question by analyzing the sequences of thousands of transcribed enhancers and promoters from hundreds of cellular contexts previously identified by cap analysis of gene expression. Support vector machine classifiers trained on counts of all possible 6-bp-long sequences (6-mers) were able to accurately distinguish promoters from enhancers and distinguish their breadth of activity across tissues. Classifiers trained to predict enhancer activity also performed well when applied to promoter prediction tasks, but promoter-trained classifiers performed poorly on enhancers. This suggests that the learned sequence patterns predictive of enhancer activity generalize to promoters, but not vice versa. Our classifiers also indicate that there are functionally relevant differences in enhancer and promoter GC content beyond the influence of CpG islands. Furthermore, sequences characteristic of broad promoter or broad enhancer activity matched different TFs, with predicted ETS- and RFX-binding sites indicative of promoters, and AP-1 sites indicative of enhancers. Finally, we evaluated the ability of our models to distinguish enhancers and promoters defined by histone modifications. Separating these classes was substantially more difficult, and this difference may contribute to ongoing debates about the similarity of enhancers and promoters. In summary, our results suggest that high-confidence transcribed enhancers and promoters can largely be distinguished based on biologically relevant sequence properties.

Greater genetic variability in an individual is protective against recessive disease. However, existing quantifications of autozygosity, such as runs of homozygosity (ROH), have proved highly sensitive to genotyping density and have yielded inconclusive results about the relationship of diversity and disease risk. Using genotyping data from three data sets with >43,000 subjects, we demonstrated that an alternative approach to quantifying genetic variability, the heterozygosity ratio, is a robust measure of diversity and is positively associated with the nondisease trait height and several disease phenotypes in subjects of European ancestry. The heterozygosity ratio is the number of heterozygous sites in an individual divided by the number of nonreference homozygous sites and is strongly affected by the degree of genetic admixture of the population and varies across human populations. Unlike quantifications of ROH, the heterozygosity ratio is not sensitive to the density of genotyping performed. Our results establish the heterozygosity ratio as a powerful new statistic for exploring the patterns and phenotypic effects of different levels of genetic variation in populations.

The importance of epistasis—or statistical interactions between genetic variants—to the development of complex disease in humans has been controversial. Genome-wide association studies of statistical interactions influencing human traits have recently become computationally feasible and have identified many putative interactions. However, statistical models used to detect interactions can be confounded, which makes it difficult to be certain that observed statistical interactions are evidence for true molecular epistasis. In this study, we investigate whether there is evidence for epistatic interactions between genetic variants within the cis -regulatory region that influence gene expression after accounting for technical, statistical, and biological confounding factors. We identified 1,119 (FDR = 5%) interactions that appear to regulate gene expression in human lymphoblastoid cell lines, a tightly controlled, largely genetically determined phenotype. Many of these interactions replicated in an independent dataset (90 of 803 tested, Bonferroni threshold). We then performed an exhaustive analysis of both known and novel confounders, including ceiling/floor effects, missing genotype combinations, haplotype effects, single variants tagged through linkage disequilibrium, and population stratification. Every interaction could be explained by at least one of these confounders, and replication in independent datasets did not protect against some confounders. Assuming that the confounding factors provide a more parsimonious explanation for each interaction, we find it unlikely that cis -regulatory interactions contribute strongly to human gene expression, which calls into question the relevance of cis -regulatory interactions for other human phenotypes. We additionally propose several best practices for epistasis testing to protect future studies from confounding.

BackgroundThe integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.MethodsTo better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).ResultsWe developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub (https://github.com/haleyeidem/integRATE).ConclusionsDesirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

Sequencing DNA derived from archaic bones has enabled genetic comparison of Neanderthals and anatomically modern humans (AMHs), and revealed that they interbred. However, interpreting what genetic differences imply about their phenotypic differences remains challenging. Here, we introduce an approach for identifying divergent gene regulation between archaic hominins, such as Neanderthals, and AMH sequences, and find 766 genes that are likely to have been divergently regulated (DR) by Neanderthal haplotypes that do not remain in AMHs. DR genes include many involved in phenotypes known to differ between Neanderthals and AMHs, such as the structure of the rib cage and supraorbital ridge development. They are also enriched for genes associated with spontaneous abortion, polycystic ovary syndrome, myocardial infarction and melanoma. Phenotypes associated with modern human variation in these genes’ regulation in ~23,000 biobank patients further support their involvement in immune and cardiovascular phenotypes. Comparing DR genes between two Neanderthals and a Denisovan revealed divergence in the immune system and in genes associated with skeletal and dental morphology that are consistent with the archaeological record. These results establish differences in gene regulatory architecture between AMHs and archaic hominins, and provide an avenue for exploring phenotypic differences between archaic groups from genomic information alone. This study presents a method to identify divergent gene regulation between archaic hominin and anatomically modern human sequences, and shows differences in gene regulatory architecture between the two groups.

Placental dysfunction is implicated in many pregnancy complications, including preeclampsia and preterm birth (PTB). While both these syndromes are influenced by environmental risk factors, they also have a substantial genetic component that is not well understood. Precisely controlled gene expression during development is crucial to proper placental function and often mediated through gene regulatory enhancers. However, we lack accurate maps of placental enhancer activity due to the challenges of assaying the placenta and the difficulty of comprehensively identifying enhancers. To address the gap in our knowledge of gene regulatory elements in the placenta, we used a two-step machine learning pipeline to synthesize existing functional genomics studies, transcription factor (TF) binding patterns, and evolutionary information to predict placental enhancers. The trained classifiers accurately distinguish enhancers from the genomic background and placental enhancers from enhancers active in other tissues. Genomic features collected from tissues and cell lines involved in pregnancy are the most predictive of placental regulatory activity. Applying the classifiers genome-wide enabled us to create a map of 33,010 predicted placental enhancers, including 4,562 high-confidence enhancer predictions. The genome-wide placental enhancers are significantly enriched nearby genes associated with placental development and birth disorders and for SNPs associated with gestational age. These genome-wide predicted placental enhancers provide candidate regions for further testing in vitro, will assist in guiding future studies of genetic associations with pregnancy phenotypes, and aid interpretation of potential mechanisms of action for variants found through genetic studies.

The diversity of eutherian reproductive strategies has led to variation in many traits, such as number of offspring, age of reproductive maturity, and gestation length. While reproductive trait variation has been extensively investigated and is well established in mammals, the genetic loci contributing to this variation remain largely unknown. The domestic dog, Canis lupus familiaris is a powerful model for studies of the genetics of inherited disease due to its unique history of domestication. To gain insight into the genetic basis of reproductive traits across domestic dog breeds, we collected phenotypic data for four traits, cesarean section rate, litter size, stillbirth rate, and gestation length, from primary literature and breeders’ handbooks. By matching our phenotypic data to genomic data from the Cornell Veterinary Biobank, we performed genome wide association analyses for these four reproductive traits, using body mass and kinship among breeds as co-variates. We identified 13 genome-wide significant associations between these traits and genetic loci, including variants near CACNA2D3 with gestation length, MSRB3 and KRT71 with litter size, SM0C2 with cesarean section rate, and HTR2C with stillbirth rate. Some of these loci, such as CACNA2D3 and MSRB3, have been previously implicated in human reproductive pathologies, whereas others have been previously associated with domestication-related traits, including brachycephaly (SM0C2), coat curl (KRT71), and tameness (HTR2C). These results raise the hypothesis that the artificial selection that gave rise to dog breeds also shaped the observed variation in their reproductive traits. Overall, our work establishes the domestic dog as a system for studying the genetics of reproductive biology and disease. Lay Summary Variation in reproductive traits across mammals has been extensively investigated and is well established, but the genetic contributors to this variation remain largely unknown. Here, we take advantage of the domestic dog, a powerful model for mammalian genetics, to gain insight into the genetic basis of reproductive traits. By examining the association between more than a hundred thousand genetic variants and four reproductive traits that vary extensively across dog breeds, we identified more than a dozen significant associations for cesarean section rate, litter size, stillbirth rate, and gestation length. Some of the variants that we identify are nearby genes previously implicated in human reproductive pathologies, whereas several others have been previously associated with domestication-related traits. Our results establish the domestic dog as a tractable system for studying the genetics of reproductive traits and underscore the potential for cryptic interactions between reproductive and other traits favored over the course of adaptation.

Seemingly paradoxical characteristics of psychiatric disorders, including moderate to high prevalence, reduced fecundity, and high heritability have motivated explanations for the persistence of common risk alleles for severe psychiatric phenotypes throughout human evolution. Proposed mechanisms include balancing selection, drift, and weak polygenic adaptation acting either directly, or indirectly through selection on correlated traits. While many mechanisms have been proposed, few have been empirically tested. Leveraging publicly available data of unprecedented sample size, we studied twenty-five traits (i.e., ten neuropsychiatric disorders, three personality traits, total intracranial volume, seven subcortical brain structure volume traits, and four complex traits without neuropsychiatric associations) for evidence of several different signatures of selection over a range of evolutionary time scales. Consistent with the largely polygenic architecture of neuropsychiatric traits, we found no enrichment of trait-associated single-nucleotide polymorphisms (SNPs) in regions of the genome that underwent classical selective sweeps (i.e., events which would have driven selected alleles to near fixation). However, we discovered that SNPs associated with some, but not all, behaviors and brain structure volumes are enriched in genomic regions under selection since divergence from Neanderthals ~600,000 years ago, and show further evidence for signatures of ancient and recent polygenic adaptation. Individual subcortical brain structure volumes demonstrate genome-wide evidence in support of a mosaic theory of brain evolution while total intracranial volume and height appear to share evolutionary constraints consistent with concerted evolution. We further characterized the biological processes potentially targeted by selection, through expression Quantitative Trait Locus (eQTL) and Gene Ontology (GO) enrichment analyses and found evidence for the role of regulatory functions among selected SNPs in immune and brain tissues. Taken together, our results suggest that alleles associated with neuropsychiatric, behavioral, and brain volume phenotypes have experienced both ancient and recent polygenic adaptation in human evolution, acting through neurodevelopmental and immune-mediated pathways.

Background Enhancers are DNA regulatory elements that influence gene expression. There is substantial diversity in enhancers’ activity patterns: some enhancers drive expression in a single cellular context, while others are active across many. Sequence characteristics, such as transcription factor (TF) binding motifs, influence the activity patterns of regulatory sequences; however, the regulatory logic through which specific sequences drive enhancer activity patterns is poorly understood. Recent analysis of Drosophila enhancers suggested that short dinucleotide repeat motifs (DRMs) are general enhancer sequence features that drive broad regulatory activity. However, it is not known whether the regulatory role of DRMs is conserved across species. Results We performed a comprehensive analysis of the relationship between short DNA sequence patterns, including DRMs, and human enhancer activity in 38,538 enhancers across 411 different contexts. In a machine-learning framework, the occurrence patterns of short sequence motifs accurately predicted broadly active human enhancers. However, DRMs alone were weakly predictive of broad enhancer activity in humans and showed different enrichment patterns than in Drosophila. In general, GC-rich sequence motifs were significantly associated with broad enhancer activity, and consistent with this enrichment, broadly active human TFs recognize GC-rich motifs. Conclusions Our results reveal the importance of specific sequence motifs in broadly active human enhancers, demonstrate the lack of evolutionary conservation of the role of DRMs, and provide a computational framework for investigating the logic of enhancer sequences. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3934-9) contains supplementary material, which is available to authorized users.

In mammals, genomic regions with enhancer activity turnover rapidly; in contrast, gene expression patterns and transcription factor binding preferences are largely conserved. Based on this conservation, we hypothesized that enhancers active in different mammals would exhibit conserved sequence patterns in spite of their different genomic locations. We tested this hypothesis by quantifying the conservation of sequence patterns underlying histone-mark defined enhancers across six diverse mammals in two machine learning frameworks. We first trained support vector machine (SVM) classifiers based on the frequency spectrum of short DNA sequence patterns. These classifiers accurately identified many adult liver, developing limb, and developing brain enhancers in each species. Then, we applied these classifiers across species and found that classifiers trained in one species and tested in another performed nearly as well as classifiers trained and tested on the same species. This indicates that the short sequence patterns predictive of enhancers are largely conserved. We also observed similar cross-species conservation when applying the models to human and mouse enhancers validated in transgenic assays. The sequence patterns most predictive of enhancers in each species matched the binding motifs for a common set of TFs enriched for expression in relevant tissues, supporting the biological relevance of the learned features. To test the conservation of more complex sequences patterns, we trained convolutional neural networks (CNNs) on enhancer sequences in each species. The CNNs demonstrated better performance overall, but worse cross-species generalization than SVMs, suggesting the importance of combinatorial interactions between motifs, but less conservation of these more complex sequence patterns. Thus, despite the rapid change of active enhancer locations between mammals, cross-species enhancer prediction is often possible. Furthermore, short sequence patterns encoding enhancer activity have been maintained across more than 180 million years of mammalian evolution, with evolutionary change in more complex sequence patterns.Author summaryAlterations in gene expression levels are a driving force of both speciation and complex disease; therefore, it is of great importance to understand the mechanisms underlying the evolution and function gene regulatory DNA sequences. Recent studies have revealed that while gene expression patterns and transcription factor binding preferences are broadly conserved across diverse animals, there is extensive turnover in distal gene regulatory regions, called enhancers, between closely related species. We investigate this seeming incongruence by analyzing genome-wide enhancer datasets from six diverse mammalian species. We trained two machine-learning classifiers—a k-mer spectrum support vector machine (SVM) and convolutional neural network (CNN)—to distinguish enhancers from the genomic background. The k-mer spectrum SVM models the occurrences of short sequence patterns while the CNN models both the short sequences patterns and their combinatorial patterns. Both the SVM and CNN enhancer prediction models trained in one species are able to predict enhancers in the same cellular context in other species. However, CNNs performed better at predicting enhancers in each species, but they generalize less well across species than the SVMs. This argues that the short sequence properties encoding regulatory activity are remarkably conserved across more than 180 million years of mammalian evolution with more evolutionary turnover in the more complex combinations of the conserved short sequence motifs.

Motivation: Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular function-al specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. Results: We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolution-ary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. Availability: Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/ Contact: msingh@cs.princeton.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Background G-quadruplexes (G4s) are stable non-canonical DNA secondary structures consisting of stacked arrays of four guanines, each held together by Hoogsteen hydrogen bonds. Sequences with the ability to form these structures in vitro, G4 motifs, are found throughout bacterial and eukaryotic genomes. The budding yeast Pif1 DNA helicase, as well as several bacterial Pif1 family helicases, unwind G4 structures robustly in vitro and suppress G4-induced DNA damage in S. cerevisiae in vivo. Results We determined the genomic distribution and evolutionary conservation of G4 motifs in four fission yeast species and investigated the relationship between G4 motifs and Pfh1, the sole S. pombe Pif1 family helicase. Using chromatin immunoprecipitation combined with deep sequencing, we found that many G4 motifs in the S. pombe genome were associated with Pfh1. Cells depleted of Pfh1 had increased fork pausing and DNA damage near G4 motifs, as indicated by high DNA polymerase occupancy and phosphorylated histone H2A, respectively. In general, G4 motifs were underrepresented in genes. However, Pfh1-associated G4 motifs were located on the transcribed strand of highly transcribed genes significantly more often than expected, suggesting that Pfh1 has a function in replication or transcription at these sites. Conclusions In the absence of functional Pfh1, unresolved G4 structures cause fork pausing and DNA damage of the sort associated with human tumors. Electronic supplementary material The online version of this article (doi:10.1186/s12915-014-0101-5) contains supplementary material, which is available to authorized users.

The genetic changes underlying the dramatic differences in form and function between humans and other primates are largely unknown, although it is clear that gene regulatory changes play an important role. To identify regulatory sequences with potentially human-specific functions, we and others used comparative genomics to find non-coding regions conserved across mammals that have acquired many sequence changes in humans since divergence from chimpanzees. These regions are good candidates for performing human-specific regulatory functions. Here, we analysed the DNA sequence, evolutionary history, histone modifications, chromatin state and transcription factor (TF) binding sites of a combined set of 2649 non-coding human accelerated regions (ncHARs) and predicted that at least 30% of them function as developmental enhancers. We prioritized the predicted ncHAR enhancers using analysis of TF binding site gain and loss, along with the functional annotations and expression patterns of nearby genes. We then tested both the human and chimpanzee sequence for 29 ncHARs in transgenic mice, and found 24 novel developmental enhancers active in both species, 17 of which had very consistent patterns of activity in specific embryonic tissues. Of these ncHAR enhancers, five drove expression patterns suggestive of different activity for the human and chimpanzee sequence at embryonic day 11.5. The changes to human non-coding DNA in these ncHAR enhancers may modify the complex patterns of gene expression necessary for proper development in a human-specific manner and are thus promising candidates for understanding the genetic basis of human-specific biology.

Lysine acetylation regulates transcription by targeting histones and nonhistone proteins. Here we report that the central regulator of transcription, RNA polymerase II, is subject to acetylation in mammalian cells. Acetylation occurs at eight lysines within the C-terminal domain (CTD) of the largest polymerase subunit and is mediated by p300/KAT3B. CTD acetylation is specifically enriched downstream of the transcription start sites of polymerase-occupied genes genome-wide, indicating a role in early stages of transcription initiation or elongation. Mutation of lysines or p300 inhibitor treatment causes the loss of epidermal growth-factor-induced expression of c-Fos and Egr2, immediate-early genes with promoter-proximally paused polymerases, but does not affect expression or polymerase occupancy at housekeeping genes. Our studies identify acetylation as a new modification of the mammalian RNA polymerase II required for the induction of growth factor response genes.

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available., Author Summary Interpreting patterns of DNA sequence variation in the genomes of closely related species is critically important for understanding the causes and functional effects of nucleotide substitutions. Classical models describe patterns of substitution in terms of the fundamental forces of mutation, recombination, neutral drift, and natural selection. However, an entirely separate force, called GC-biased gene conversion (gBGC), also appears to have an important influence on substitution patterns in many species. gBGC is a recombination-associated evolutionary process that favors the fixation of strong (G/C) over weak (A/T) alleles. In mammals, gBGC is thought to promote variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations. However, its genome-wide influence remains poorly understood, in part because, it is difficult to incorporate gBGC into statistical models of evolution. In this paper, we describe a new evolutionary model that jointly describes the effects of selection and gBGC and apply it to the human and chimpanzee genomes. Our genome-wide predictions of gBGC tracts indicate that gBGC has been an important force in recent human evolution. Our publicly available computer program, called phastBias, and our genome-wide predictions will enable other researchers to consider gBGC in their analyses.

Heart development is exquisitely sensitive to the precise temporal regulation of thousands of genes that govern developmental decisions during differentiation. However, we currently lack a detailed understanding of how chromatin and gene expression patterns are coordinated during developmental transitions in the cardiac lineage. Here, we interrogated the transcriptome and several histone modifications across the genome during defined stages of cardiac differentiation. We find distinct chromatin patterns that are coordinated with stage-specific expression of functionally related genes, including many human disease-associated genes. Moreover, we discover a novel preactivation chromatin pattern at the promoters of genes associated with heart development and cardiac function. We further identify stage-specific distal enhancer elements and find enriched DNA binding motifs within these regions that predict sets of transcription factors that orchestrate cardiac differentiation. Together, these findings form a basis for understanding developmentally regulated chromatin transitions during lineage commitment and the molecular etiology of congenital heart disease. © 2012 Elsevier Inc.

SIRT1 and SIRT3 are NAD+-dependent protein deacetylases that are evolutionarily conserved across mammals. These proteins are located in the cytoplasm/nucleus and mitochondria, respectively. Previous reports demonstrated that human SIRT1 deacetylates Acetyl-CoA Synthase 1 (AceCS1) in the cytoplasm, whereas SIRT3 deacetylates the homologous Acetyl-CoA Synthase 2 (AceCS2) in the mitochondria. We recently showed that 3-hydroxy-3-methylglutaryl CoA synthase 2 (HMGCS2) is deacetylated by SIRT3 in mitochondria, and we demonstrate here that SIRT1 deacetylates the homologous 3-hydroxy-3-methylglutaryl CoA synthase 1 (HMGCS1) in the cytoplasm. This novel pattern of substrate homology between cytoplasmic SIRT1 and mitochondrial SIRT3 suggests that considering evolutionary relationships between the sirtuins and their substrates may help to identify and understand the functions and interactions of this gene family. In this perspective, we take a first step by characterizing the evolutionary history of the sirtuins and these substrate families.

SummaryG-quadruplex (G4) DNA structures are extremely stable four-stranded secondary structures held together by noncanonical G-G base pairs. Genome-wide chromatin immunoprecipitation was used to determine the in vivo binding sites of the multifunctional Saccharomyces cerevisiae Pif1 DNA helicase, a potent unwinder of G4 structures in vitro. G4 motifs were a significant subset of the high-confidence Pif1-binding sites. Replication slowed in the vicinity of these motifs, and they were prone to breakage in Pif1-deficient cells, whereas non-G4 Pif1-binding sites did not show this behavior. Introducing many copies of G4 motifs caused slow growth in replication-stressed Pif1-deficient cells, which was relieved by spontaneous mutations that eliminated their ability to form G4 structures, bind Pif1, slow DNA replication, and stimulate DNA breakage. These data suggest that G4 structures form in vivo and that they are resolved by Pif1 to prevent replication fork stalling and DNA breakage.

BackgroundThe DBA/2J mouse is not only the oldest inbred strain,but also one of the most widely used strains. DBA/2Jexhibits many unique anatomical, physiological, andbehavior traits. In addition, DBA/2J is one parent of thelarge BXD family of recombinant inbred strains [1]. Thegenome of the other parent of this BXD family—C57BL/6J—has been sequenced and serves as themouse reference genome [2]. We sequenced the gen-ome of DBA/2J using SOLiD and Illumina highthroughput short read protocols to generate a compre-hensive set of ~5 million sequence variants segregatingin the BXD family that ultimately cause developmental,anatomical, functional and behavioral differences amongthese 80+ strains.ResultsWe generated approximately 13.2 and 38.9× whole-gen-ome short reads of DBA/2J females using Illumina GA2and ABI SOLiD massively parallel DNA sequencingplatforms. Comparing to the C57BL/6J reference gen-ome sequence, we identified over 4.5 million singlenucleotide polymorphisms (SNPs), including 84 non-sense and ~11,000 missense mutations, 78% of whichare novel. We also detected ~568,000 insertions anddeletions (indels) within single short reads and ~9,400between mate-paired reads. Approximately 300 inver-sions were detected by SOLiD mate-pair reads, 46 ofwhich span at least one exon. In addition, we identified~22,000 copy number variants (CNVs) in the range of1 Kb to 100 Kb (Figure 1).ConclusionOur study generates the first consensus sequence for theDBA/2J and creates a compendium of sequence andstructural variations that will be used by the communityof researchers who study complex traits in mouse mod-els.Thesequencedataprovideanovelresourcewithwhich to initiate reverse genetic analysis of complex

A report on the 23rd annual meeting on 'The Biology of Genomes', 11-15 May 2010, Cold Spring Harbor, USA. Meeting report Recent advances in high-throughput sequencing technologies have greatly increased the scale and scope of genomics research, and this was evident throughout the recent Biology of Genomes meeting at the Cold Spring Harbor Laboratory. Here we describe some highlights of the meeting.

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology., Author Summary The human genome contains an immense amount of non-protein-coding DNA with unknown function. Some of this DNA regulates when, where, and at what levels genes are active during development. Enhancers, one type of regulatory element, are short stretches of DNA that can act as “switches” to turn a gene on or off at specific times in specific cells or tissues. Understanding where in the genome enhancers are located can provide insight into the genetic basis of development and disease. Enhancers are hard to identify, but clues about their locations are found in different types of data including DNA sequence, evolutionary history, and where proteins bind to DNA. Here, we introduce a new tool, called EnhancerFinder, which combines these data to predict the location and activity of enhancers during embryonic development. We trained EnhancerFinder on a large set of functionally validated human enhancers, and it proved to be very accurate. We used EnhancerFinder to predict tens of thousands of enhancers in the human genome and validated several of the predictions near three important developmental genes in mouse or zebrafish. EnhancerFinder's predictions will be useful in understanding functional regions hidden in the vast amounts of human non-coding DNA.

The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed ProteinHistorian to make tools for performing analyses of protein origins widely available. Given a list of proteins of interest, ProteinHistorian estimates the phylogenetic age of each protein, quantifies enrichment for proteins of specific ages, and compares variation in protein age with other protein attributes. ProteinHistorian allows flexibility in the definition of protein age by including several algorithms for estimating ages from different databases of evolutionary relationships. We illustrate the use of ProteinHistorian with three example analyses. First, we demonstrate that proteins with high expression in human, compared to chimpanzee and rhesus macaque, are significantly younger than those with human-specific low expression. Next, we show that human proteins with annotated regulatory functions are significantly younger than proteins with catalytic functions. Finally, we compare protein length and age in many eukaryotic species and, as expected from previous studies, find a positive, though often weak, correlation between protein age and length. ProteinHistorian is available through a web server with an intuitive interface and as a set of command line tools; this allows biologists and bioinformaticians alike to integrate these approaches into their analysis pipelines. ProteinHistorian's modular, extensible design facilitates the integration of new datasets and algorithms. The ProteinHistorian web server, source code, and pre-computed ages for 32 eukaryotic genomes are freely available under the GNU public license at http://lighthouse.ucsf.edu/ProteinHistorian/.