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Understanding variation in chromatin contact patterns across diverse humans is critical for interpreting noncoding variants and their effects on gene expression and phenotypes. However, experimental determination of chromatin contact patterns across large samples is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the variation in 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We apply this approach to thousands of human genomes from the 1000 Genomes Project and the inferred hominin ancestral genome. While patterns of 3D contact divergence genome wide are qualitatively similar to patterns of sequence divergence, we find substantial differences in 3D divergence and sequence divergence in local 1 megabase genomic windows. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, for 31% of genomic windows, a single individual has a rare divergent 3D contact map pattern. Using in silico mutagenesis, we find that most single nucleotide sequence changes do not result in changes to 3D chromatin contacts. However, in windows with substantial 3D divergence just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals variable contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations. [ABSTRACT FROM AUTHOR]

An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic–ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease. Integrating neuroimaging and molecular brain data across people and brain regions and states can help to understand human brain individuality. This study presents a resource that uses computational genomics to estimate genetically regulated components of gene expression and infer associations of this expression with neuroimaging and clinical phenotypes. [ABSTRACT FROM AUTHOR]

SLC22A10 is an orphan transporter with unknown substrates and function. The goal of this study is to elucidate its substrate specificity and functional characteristics. In contrast to orthologs from great apes, human SLC22A10, tagged with green fluorescent protein, is not expressed on the plasma membrane. Cells expressing great ape SLC22A10 orthologs exhibit significant accumulation of estradiol-17β-glucuronide, unlike those expressing human SLC22A10. Sequence alignments reveal a proline at position 220 in humans, which is a leucine in great apes. Replacing proline with leucine in SLC22A10-P220L restores plasma membrane localization and uptake function. Neanderthal and Denisovan genomes show proline at position 220, akin to modern humans, indicating functional loss during hominin evolution. Human SLC22A10 is a unitary pseudogene due to a fixed missense mutation, P220, while in great apes, its orthologs transport sex steroid conjugates. Characterizing SLC22A10 across species sheds light on its biological role, influencing organism development and steroid homeostasis. [ABSTRACT FROM AUTHOR]

When the ancestors of modern Eurasians migrated out of Africa and interbred with Eurasian archaic hominins, namely, Neanderthals and Denisovans, DNA of archaic ancestry integrated into the genomes of anatomically modern humans. This process potentially accelerated adaptation to Eurasian environmental factors, including reduced ultraviolet radiation and increased variation in seasonal dynamics. However, whether these groups differed substantially in circadian biology and whether archaic introgression adaptively contributed to human chronotypes remain unknown. Here, we traced the evolution of chronotype based on genomes from archaic hominins and present-day humans. First, we inferred differences in circadian gene sequences, splicing, and regulation between archaic hominins and modern humans. We identified 28 circadian genes containing variants with potential to alter splicing in archaics (e.g. CLOCK , PER2 , RORB , and RORC) and 16 circadian genes likely divergently regulated between present-day humans and archaic hominins, including RORA. These differences suggest the potential for introgression to modify circadian gene expression. Testing this hypothesis, we found that introgressed variants are enriched among expression quantitative trait loci for circadian genes. Supporting the functional relevance of these regulatory effects, we found that many introgressed alleles have associations with chronotype. Strikingly, the strongest introgressed effects on chronotype increase morningness, consistent with adaptations to high latitude in other species. Finally, we identified several circadian loci with evidence of adaptive introgression or latitudinal clines in allele frequency. These findings identify differences in circadian gene regulation between modern humans and archaic hominins and support the contribution of introgression via coordinated effects on variation in human chronotype. [ABSTRACT FROM AUTHOR]

Multiple distal cis- regulatory elements (CREs) often cooperate to regulate gene expression, and the presence of multiple CREs for a gene has been proposed to provide redundancy and robustness to variation. However, we do not understand how attributes of a gene's distal CRE landscape —the CREs that contribute to its regulation—relate to its expression and function. Here, we integrate three-dimensional chromatin conformation and functional genomics data to quantify the CRE landscape composition genome-wide across ten human tissues and relate their attributes to the function, constraint, and expression patterns of genes. Within each tissue, we find that expressed genes have larger CRE landscapes than nonexpressed genes and that genes with tissue-specific CREs are more likely to have tissue-specific expression. Controlling for the association between expression level and CRE landscape size, we also find that CRE landscapes around genes under strong constraint (e.g. loss-of-function intolerant and housekeeping genes) are not significantly smaller than other expressed genes as previously proposed; however, they do have more evolutionarily conserved sequences than CREs of expressed genes overall. We also show that CRE landscape size does not associate with expression variability across individuals; nonetheless, genes with larger CRE landscapes have a relative depletion for variants that influence expression levels (expression quantitative trait loci). Overall, this work illustrates how differences in gene function, expression, and evolutionary constraint are reflected in features of CRE landscapes. Thus, considering the CRE landscape of a gene is vital for understanding gene expression dynamics across biological contexts and interpreting the effects of noncoding genetic variants. [ABSTRACT FROM AUTHOR]

Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer's disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome. Author summary: Understanding how evolutionary forces shape patterns of human genomic variation is fundamental for evolutionary genomics and medicine. We developed a novel and very robust computational framework that measures enrichment for evolutionary forces acting on regions associated with variation in diverse complex traits. Application of this framework to more than 900 genome-wide association studies and 11 evolutionary measures, while accounting for potential confounders, generated a comprehensive evolutionary atlas that maps diverse signatures of selection on genomic regions associated with hundreds of complex human traits. Notably, genomic regions associated with human complex traits have been shaped by diverse modes of selection. Combined with the availability of a computational package that can perform these calculations for any set of genomic regions associated with any trait in any organism, this work is a major step forward toward understanding the relationship between genetic architecture and selection. [ABSTRACT FROM AUTHOR]

Thousands of human gene regulatory enhancers are composed of sequences with multiple evolutionary origins. These evolutionarily "complex" enhancers consist of older "core" sequences and younger "derived" sequences. However, the functional relationship between the sequences of different evolutionary origins within complex enhancers is poorly understood. We evaluated the function, selective pressures, and sequence variation across core and derived components of human complex enhancers. We find that both components are older than expected from the genomic background, and complex enhancers are enriched for core and derived sequences of similar evolutionary ages. Both components show strong evidence of biochemical activity in massively parallel report assays. However, core and derived sequences have distinct transcription factor (TF)-binding preferences that are largely similar across evolutionary origins. As expected, given these signatures of function, both core and derived sequences have substantial evidence of purifying selection. Nonetheless, derived sequences exhibit weaker purifying selection than adjacent cores. Derived sequences also tolerate more common genetic variation and are enriched compared with cores for expression quantitative trait loci associated with gene expression variability in human populations. In conclusion, both core and derived sequences have strong evidence of gene regulatory function, but derived sequences have distinct constraint profiles, TF-binding preferences, and tolerance to variation compared with cores. We propose that the step-wise integration of younger derived with older core sequences has generated regulatory substrates with robust activity and the potential for functional variation. Our analyses demonstrate that synthesizing study of enhancer evolution and function can aid interpretation of regulatory sequence activity and functional variation across human populations. [ABSTRACT FROM AUTHOR]

Background: Identifying pregnancies at risk for preterm birth, one of the leading causes of worldwide infant mortality, has the potential to improve prenatal care. However, we lack broadly applicable methods to accurately predict preterm birth risk. The dense longitudinal information present in electronic health records (EHRs) is enabling scalable and cost-efficient risk modeling of many diseases, but EHR resources have been largely untapped in the study of pregnancy.Methods: Here, we apply machine learning to diverse data from EHRs with 35,282 deliveries to predict singleton preterm birth.Results: We find that machine learning models based on billing codes alone can predict preterm birth risk at various gestational ages (e.g., ROC-AUC = 0.75, PR-AUC = 0.40 at 28 weeks of gestation) and outperform comparable models trained using known risk factors (e.g., ROC-AUC = 0.65, PR-AUC = 0.25 at 28 weeks). Examining the patterns learned by the model reveals it stratifies deliveries into interpretable groups, including high-risk preterm birth subtypes enriched for distinct comorbidities. Our machine learning approach also predicts preterm birth subtypes (spontaneous vs. indicated), mode of delivery, and recurrent preterm birth. Finally, we demonstrate the portability of our approach by showing that the prediction models maintain their accuracy on a large, independent cohort (5978 deliveries) from a different healthcare system.Conclusions: By leveraging rich phenotypic and genetic features derived from EHRs, we suggest that machine learning algorithms have great potential to improve medical care during pregnancy. However, further work is needed before these models can be applied in clinical settings. [ABSTRACT FROM AUTHOR]

Ancient DNA provides a powerful window into the biology of extant and extinct species, including humans' closest relatives: Denisovans and Neanderthals. Here, we review what is known about archaic hominin phenotypes from genomic data and how those inferences have been made. We contend that understanding the influence of variants on lower-level molecular phenotypes—such as gene expression and protein function—is a promising approach to using ancient DNA to learn about archaic hominin traits. Molecular phenotypes have simpler genetic architectures than organism-level complex phenotypes, and this approach enables moving beyond association studies by proposing hypotheses about the effects of archaic variants that are testable in model systems. The major challenge to understanding archaic hominin phenotypes is broadening our ability to accurately map genotypes to phenotypes, but ongoing advances ensure that there will be much more to learn about archaic hominin phenotypes from their genomes. [ABSTRACT FROM AUTHOR]

Human genetic variation associates with the composition of the gut microbiome, yet its influence on clinical traits remains largely unknown. We analyzed the consequences of nearly a thousand gut microbiome-associated variants (MAVs) on phenotypes reported in electronic health records from tens of thousands of individuals. We discovered and replicated associations of MAVs with neurological, metabolic, digestive, and circulatory diseases. Five significant MAVs in these categories correlate with the relative abundance of microbes down to the strain level. We also demonstrate that these relationships are independently observed and concordant with microbe by disease associations reported in case-control studies. Moreover, a selective sweep and population differentiation impacted some disease-linked MAVs. Combined, these findings establish triad relationships among the human genome, microbiome, and disease. Consequently, human genetic influences may offer opportunities for precision diagnostics of microbiomeassociated diseases but also highlight the relevance of genetic background for microbiome modulation and therapeutics. [ABSTRACT FROM AUTHOR]

Quantification of the tolerance of protein sites to genetic variation has become a cornerstone of variant interpretation. We hypothesize that the constraint on missense variation at individual amino acid sites is largely shaped by direct interactions with 3D neighboring sites. To quantify this constraint, we introduce a framework called COntact Set MISsense tolerance (or COSMIS) and comprehensively map the landscape of 3D mutational constraint on 6.1 million amino acid sites covering 16,533 human proteins. We show that 3D mutational constraint is pervasive and that the level of constraint is strongly associated with disease relevance both at the site and the protein level. We demonstrate that COSMIS performs significantly better at variant interpretation tasks than other population-based constraint metrics while also providing structural insight into the functional roles of constrained sites. We anticipate that COSMIS will facilitate the interpretation of protein-coding variation in evolution and prioritization of sites for mechanistic investigation. Here, Li et al. integrate population genetic and protein structural perspectives to map the landscape of 3D constraint on 80% of human proteins. They show that 3D mutational constraint is pervasive and strongly associated with functional relevance. [ABSTRACT FROM AUTHOR]

Objectives: To assess the accuracy of machine learning models in predicting kidney stone composition using variables extracted from the electronic health record (EHR). Materials and Methods: We identified kidney stone patients (n = 1296) with both stone composition and 24-hour (24H) urine testing. We trained machine learning models (XGBoost [XG] and logistic regression [LR]) to predict stone composition using 24H urine data and EHR-derived demographic and comorbidity data. Models predicted either binary (calcium vs noncalcium stone) or multiclass (calcium oxalate, uric acid, hydroxyapatite, or other) stone types. We evaluated performance using area under the receiver operating curve (ROC-AUC) and accuracy and identified predictors for each task. Results: For discriminating binary stone composition, XG outperformed LR with higher accuracy (91% vs 71%) with ROC-AUC of 0.80 for both models. Top predictors used by these models were supersaturations of uric acid and calcium phosphate, and urinary ammonium. For multiclass classification, LR outperformed XG with higher accuracy (0.64 vs 0.56) and ROC-AUC (0.79 vs 0.59), and urine pH had the highest predictive utility. Overall, 24H urine analyte data contributed more to the models' predictions of stone composition than EHR-derived variables. Conclusion: Machine learning models can predict calcium stone composition. LR outperforms XG in multiclass stone classification. Demographic and comorbidity data are predictive of stone composition; however, including 24H urine data improves performance. Further optimization of performance could lead to earlier directed medical therapy for kidney stone patients. [ABSTRACT FROM AUTHOR]

As humans populated the world, they adapted to many varying environmental factors, including climate, diet, and pathogens. Because many of these adaptations were mediated by multiple noncoding variants with small effects on gene regulation, it has been difficult to link genomic signals of selection to specific genes, and to describe the regulatory response to selection. To overcome this challenge, we adapted PrediXcan, a machine learning method for imputing gene regulation from genotype data, to analyze low-coverage ancient human DNA (aDNA). First, we used simulated genomes to benchmark strategies for adapting PrediXcan to increase robustness to incomplete data. Applying the resulting models to 490 ancient Eurasians, we found that genes with the strongest divergent regulation among ancient populations with hunter-gatherer, pastoralist, and agricultural lifestyles are enriched for metabolic and immune functions. Next, we explored the contribution of divergent gene regulation to two traits with strong evidence of recent adaptation: dietary metabolism and skin pigmentation. We found enrichment for divergent regulation among genes proposed to be involved in diet-related local adaptation, and the predicted effects on regulation often suggest explanations for known signals of selection, for example, at FADS1 , GPX1 , and LEPR. In contrast, skin pigmentation genes show little regulatory change over a 38,000-year time series of 2,999 ancient Europeans, suggesting that adaptation mainly involved large-effect coding variants. This work demonstrates that combining aDNA with present-day genomes is informative about the biological differences among ancient populations, the role of gene regulation in adaptation, and the relationship between genetic diversity and complex traits. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: Mayer–Rokitansky–Küster–Hauser (MRKH) syndrome consists of congenital absence of the uterus and vagina and is often associated with renal, skeletal, cardiac, and auditory defects. The genetic basis is largely unknown except for rare variants in several genes. Many candidate genes have been suggested by mouse models and human studies. The purpose of this study was to narrow down the number of candidate genes. Methods: Whole exome sequencing was performed on 111 unrelated individuals with MRKH; variant analysis focused on 72 genes suggested by mouse models, human studies of physiological candidates, or located near translocation breakpoints in t(3;16). Candidate variants (CV) predicted to be deleterious were confirmed by Sanger sequencing. Results: Sanger sequencing verified 54 heterozygous CV from genes identified through mouse (13 CV in 6 genes), human (22 CV in seven genes), and translocation breakpoint (19 CV in 11 genes) studies. Twelve patients had ≥ 2 CVs, including four patients with two variants in the same gene. One likely digenic combination of LAMC1 and MMP14 was identified. Conclusion: We narrowed 72 candidate genes to 10 genes that appear more likely implicated. These candidate genes will require further investigation to elucidate their role in the development of MRKH. [ABSTRACT FROM AUTHOR]

Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based prediction of ΔΔGs upon point mutation. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ΔΔG prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D convolutional neural networks can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms. Author summary: The thermodynamic stability of a protein, usually represented as the Gibbs free energy for the biophysical process of protein folding (ΔG), is a fundamental thermodynamic quantity. Predicting mutation-induced changes in protein thermodynamic stability (ΔΔG) is of great interest in protein engineering, variant interpretation, and protein biophysics. However, predicting ΔΔGs in an accurate and unbiased manner has been a long-standing challenge in the field of computational biology. In this work, we introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based ΔΔG prediction. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. ThermoNet demonstrates performance comparable to the best available methods. In addition, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We also demonstrate that the presence of homologous proteins in commonly used training and testing sets for ΔΔG prediction methods has likely influenced previous performance estimates. Finally, we highlight the practical utility of ThermoNet by applying it to predicting the ΔΔGs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Genetic variation in the membrane trafficking adapter protein complex 4 (AP‐4) can result in pathogenic neurological phenotypes including microencephaly, spastic paraplegias, epilepsy, and other developmental defects. We lack molecular mechanisms responsible for impaired AP‐4 function arising from genetic variation, because AP‐4 remains poorly understood structurally. Here, we analyze patterns of AP‐4 genetic evolution and conservation to identify regions that are likely important for function and thus more susceptible to pathogenic variation. We map known variants onto an AP‐4 homology model and predict the likelihood of pathogenic variation at a given location on the structure of AP‐4. We find significant clustering of likely pathogenic variants located at the interface between the β4 and N‐μ4 subunits, as well as throughout the C‐μ4 subunit. Our work offers an integrated perspective on how genetic and evolutionary forces affect AP‐4 structure and function. As more individuals with uncharacterized AP‐4 variants are identified, our work provides a foundation upon which their functional effects and disease relevance can be interpreted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Genomic regions with gene regulatory enhancer activity turnover rapidly across mammals. In contrast, gene expression patterns and transcription factor binding preferences are largely conserved between mammalian species. Based on this conservation, we hypothesized that enhancers active in different mammals would exhibit conserved sequence patterns in spite of their different genomic locations. To investigate this hypothesis, we evaluated the extent to which sequence patterns that are predictive of enhancers in one species are predictive of enhancers in other mammalian species by training and testing two types of machine learning models. We trained support vector machine (SVM) and convolutional neural network (CNN) classifiers to distinguish enhancers defined by histone marks from the genomic background based on DNA sequence patterns in human, macaque, mouse, dog, cow, and opossum. The classifiers accurately identified many adult liver, developing limb, and developing brain enhancers, and the CNNs outperformed the SVMs. Furthermore, classifiers trained in one species and tested in another performed nearly as well as classifiers trained and tested on the same species. We observed similar cross-species conservation when applying the models to human and mouse enhancers validated in transgenic assays. This indicates that many short sequence patterns predictive of enhancers are largely conserved. 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. Thus, despite the rapid change of active enhancer locations between mammals, cross-species enhancer prediction is often possible. Our results suggest that short sequence patterns encoding enhancer activity have been maintained across more than 180 million years of mammalian evolution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Transposable element (TE)-derived sequences make up approximately half of most mammalian genomes, and many TEs have been co-opted into gene regulatory elements. However, we lack a comprehensive tissue- and genome-wide understanding of how and when TEs gain regulatory activity in their hosts. We evaluated the prevalence of TE-derived DNA in enhancers and promoters across hundreds of human and mouse cell lines and primary tissues. Promoters are significantly depleted of TEs in all tissues compared with their overall prevalence in the genome (P<0.001); enhancers are also depleted of TEs, though not as strongly as promoters. The degree of enhancer depletion also varies across contexts (1.5-3x), with reproductive and immune cells showing the highest levels of TE regulatory activity in humans. Overall, in spite of the regulatory potential of many TE sequences, they are significantly less active in gene regulation than expected from their prevalence. TE age is predictive of the likelihood of enhancer activity; TEs originating before the divergence of amniotes are 9.2 times more likely to have enhancer activity than TEs that integrated in great apes. Context-specific enhancers are more likely to be TE-derived than enhancers active in multiple tissues, and young TEs are more likely to overlap context-specific enhancers than old TEs (86% vs. 47%). Once TEs obtain enhancer activity in the host, they have similar functional dynamics to one another and non-TE-derived enhancers, likely driven by pleiotropic constraints. However, a few TE families, most notably endogenous retroviruses, have greater regulatory potential. Our observations suggest a model of regulatory co-option in which TE-derived sequences are initially repressed, after which a small fraction obtains context-specific enhancer activity, with further gains subject to pleiotropic constraints. [ABSTRACT FROM AUTHOR]

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.Understandinghowdistal regulatory elements, for example, enhancers, evolve and function is critical, as alterations in gene expression levels can drive the development of bothcomplex 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 machinelearning 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. [ABSTRACT FROM AUTHOR]

Replicative DNA helicases expose the two strands of the double helix to the replication apparatus, but accessory helicases are often needed to help forks move past naturally occurring hard-to-replicate sites, such as tightly bound proteins, RNA/DNA hybrids, and DNA secondary structures. Although the Schizosaccharomyces pombe 5’-to-3’ DNA helicase Pfh1 is known to promote fork progression, its genomic targets, dynamics, and mechanisms of action are largely unknown. Here we address these questions by integrating genome-wide identification of Pfh1 binding sites, comprehensive analysis of the effects of Pfh1 depletion on replication and DNA damage, and proteomic analysis of Pfh1 interaction partners by immunoaffinity purification mass spectrometry. Of the 621 high confidence Pfh1-binding sites in wild type cells, about 40% were sites of fork slowing (as marked by high DNA polymerase occupancy) and/or DNA damage (as marked by high levels of phosphorylated H2A). The replication and integrity of tRNA and 5S rRNA genes, highly transcribed RNA polymerase II genes, and nucleosome depleted regions were particularly Pfh1-dependent. The association of Pfh1 with genomic integrity at highly transcribed genes was S phase dependent, and thus unlikely to be an artifact of high transcription rates. Although Pfh1 affected replication and suppressed DNA damage at discrete sites throughout the genome, Pfh1 and the replicative DNA polymerase bound to similar extents to both Pfh1-dependent and independent sites, suggesting that Pfh1 is proximal to the replication machinery during S phase. Consistent with this interpretation, Pfh1 co-purified with many key replisome components, including the hexameric MCM helicase, replicative DNA polymerases, RPA, and the processivity clamp PCNA in an S phase dependent manner. Thus, we conclude that Pfh1 is an accessory DNA helicase that interacts with the replisome and promotes replication and suppresses DNA damage at hard-to-replicate sites. These data provide insight into mechanisms by which this evolutionarily conserved helicase helps preserve genome integrity. [ABSTRACT FROM AUTHOR]

Background: RPB1, the largest subunit of RNA polymerase II, contains a highly modifiable C-terminal domain (CTD) that consists of variations of a consensus heptad repeat sequence (Y1S2P3T4S5P6S7). The consensus CTD repeat motif and tandem organization represent the ancestral state of eukaryotic RPB1, but across eukaryotes CTDs show considerable diversity in repeat organization and sequence content. These differences may reflect lineage-specific CTD functions mediated by protein interactions. Mammalian CTDs contain eight non-consensus repeats with a lysine in the seventh position (K1). Posttranslational acetylation of these sites was recently shown to be required for proper polymerase pausing and regulation of two growth factor-regulated genes. Results: To investigate the origins and function of RPB1 CTD acetylation (acRPB1), we computationally reconstructed the evolution of the CTD repeat sequence across eukaryotes and analyzed the evolution and function of genes dysregulated when acRPB1 is disrupted. Modeling the evolutionary dynamics of CTD repeat count and sequence content across diverse eukaryotes revealed an expansion of the CTD in the ancestors of Metazoa. The new CTD repeats introduced the potential for acRPB1 due to the appearance of distal repeats with lysine at position seven. This was followed by a further increase in the number of lysine-containing repeats in developmentally complex clades like Deuterostomia. Mouse genes enriched for acRPB1 occupancy at their promoters and genes with significant expression changes when acRPB1 is disrupted are enriched for several functions, such as growth factor response, gene regulation, cellular adhesion, and vascular development. Genes occupied and regulated by acRPB1 show significant enrichment for evolutionary origins in the early history of eukaryotes through early vertebrates. Conclusions: Our combined functional and evolutionary analyses show that RPB1 CTD acetylation was possible in the early history of animals, and that the K7 content of the CTD expanded in specific developmentally complex metazoan lineages. The functional analysis of genes regulated by acRPB1 highlight functions involved in the origin of and diversification of complex Metazoa. This suggests that acRPB1 may have played a role in the success of animals. [ABSTRACT FROM AUTHOR]

Motivation: DNA and protein patterns are usefully represented by sequence logos. However, the methods for logo generation in common use lack a proper statistical basis, and are non-optimal for recognizing functionally relevant alignment columns. Results: We redefine the information at a logo position as a perobservation multiple alignment log-odds score. Such scores are positive or negative, depending on whether a column's observations are better explained as arising from relatedness or chance. Within this framework, we propose distinct normalized maximum likelihood and Bayesian measures of column information. We illustrate these measures on High Mobility Group B (HMGB) box proteins and a dataset of enzyme alignments. Particularly in the context of protein alignments, our measures improve the discrimination of biologically relevant positions. Availability and implementation: Our new measures are implemented in an open-source Web-based logo generation program, which is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/logoddslogo/index.html. A stand-alone version of the program is also available from this site. [ABSTRACT FROM AUTHOR]

Background: Dynamic activation and inactivation of gene regulatory DNA produce the expression changes that drive the differentiation of cellular lineages. Identifying regulatory regions active during developmental transitions is necessary to understand how the genome specifies complex developmental programs and how these processes are disrupted in disease. Gene regulatory dynamics are mediated by many factors, including the binding of transcription factors (TFs) and the methylation and acetylation of DNA and histones. Genome-wide maps of TF binding and DNA and histone modifications have been generated for many cellular contexts; however, given the diversity and complexity of animal development, these data cover only a small fraction of the cellular and developmental contexts of interest. Thus, there is a need for methods that use existing epigenetic and functional genomics data to analyze the thousands of contexts that remain uncharacterized. Results: To investigate the utility of histone modification data in the analysis of cellular contexts without such data, I evaluated how well genome-wide H3K27ac and H3K4me1 data collected in different developmental stages, tissues, and species were able to predict experimentally validated heart enhancers active at embryonic day 11.5 (E11.5) in mouse. Using a machine-learning approach to integrate the data from different contexts, I found that E11.5 heart enhancers can often be predicted accurately from data from other contexts, and I quantified the contribution of each data source to the predictions. The utility of each dataset correlated with nearness in developmental time and tissue to the target context: data from late developmental stages and adult heart tissues were most informative for predicting E11.5 enhancers, while marks from stem cells and early developmental stages were less informative. Predictions based on data collected in non-heart tissues and in human hearts were better than random, but worse than using data from mouse hearts. Conclusions: The ability of these algorithms to accurately predict developmental enhancers based on data from related, but distinct, cellular contexts suggests that combining computational models with epigenetic data sampled from relevant contexts may be sufficient to enable functional characterization of many cellular contexts of interest. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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/. [ABSTRACT FROM AUTHOR]