Biology subjects

Capra, J. A.

Publications and source records attributed to Capra, J. A..

8 recordsLinked to original sources

bioRxiv2018

Genome-wide maps of distal gene regulatory regions active in the human placenta

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.

genomics↗

bioRxiv2018

integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

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.\n\nMethodsTo 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).\n\nResultsWe 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).\n\nConclusionsDesirability-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.

genomics↗

bioRxiv2018

Genome wide association analysis identifies genetic variants associated with reproductive variation across domestic dog breeds and uncovers links to domestication

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 (n = 97 breeds), litter size (n = 60), stillbirth rate (n = 57), and gestation length (n = 23) - 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 14 genome-wide significant associations between these traits and genetic loci, including variants near CACNA2D3 with gestation length, MSRB3 with litter size, SMOC2 with cesarean section rate, MITF with litter size and still birth rate, KRT71 with cesarean section rate, litter size, and stillbirth rate, and HTR2C with stillbirth rate. Some of these loci, such as CACNA2D3 and MSRB3, have been previously implicated in human reproductive pathologies. Many of the variants that we identified have been previously associated with domestication-related traits, including brachycephaly (SMOC2), coat color (MITF), 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.

evolutionary biology↗

bioRxiv2017

Polygenic selection underlies evolution of human brain structure and behavioral traits

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.

evolutionary biology↗

bioRxiv2017

Genome-wide Enhancer Maps Differ Significantly in Genomic Distribution, Evolution, and Function

Non-coding gene regulatory enhancers are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. In practice, most studies consider enhancers identified by only a single method, and the concordance of enhancers identified by different methods has not been comprehensively evaluated. Here, we assess the similarities of enhancer sets identified by ten representative strategies in four biological contexts and evaluate the robustness of downstream conclusions to the choice of identification strategy. All pairs of enhancer sets we evaluated overlap significantly more than expected by chance; however, we also found significant dissimilarity between enhancer sets in their genomic characteristics, evolutionary conservation, and association with functional loci within each context. We find most regions identified as enhancers are supported by only one method. The disagreement is sufficient to influence interpretation of GWAS SNPs and eQTL, and to lead to disparate conclusions about enhancer biology and disease mechanisms. We also find only limited evidence that regions identified by multiple enhancer identification methods are better candidates than those identified by a single method. Our results highlight the inherent complexity of enhancer biology and argue that current approaches have yet to adequately account for enhancer diversity. As a result, we cannot recommend the use of any single enhancer identification strategy in isolation. To facilitate assessment of enhancer diversity on studies conclusions, we developed creDB, a database of enhancer annotations designed to integrate into bioinformatics workflows. While our findings highlight a major challenge to mapping the genetic architecture of complex disease and interpreting regulatory variants found in patient genomes, a systematic understanding of similarities and differences in enhancer identification methodology will ultimately enable robust inferences about gene regulatory sequences.

genetics↗

bioRxiv2017

Short DNA sequence patterns accurately identify broadlyactive human enhancers

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. 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; consistent with this enrichment, broadly active human TFs recognize GC-rich motifs. 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.

genomics↗

bioRxiv2017

Sequence properties underlying gene regulatory enhancers are conserved across mammals

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.\n\nAuthor 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.

evolutionary biology↗

bioRxiv2017

Comprehensive Analysis of Constraint on the Spatial Distribution of Missense Variants in Human Protein Structures

The spatial distribution of genetic variation within proteins is shaped by evolutionary constraint and thus can provide insights into the functional importance of protein regions and the potential pathogenicity of protein alterations. Here, we comprehensively evaluate the 3D spatial patterns of constraint on human germline and somatic variation in 4,568 solved protein structures. Different classes of coding variants have significantly different spatial distributions. Neutral missense variants exhibit a range of 3D constraint patterns, with a general trend of spatial dispersion driven by constraint on core residues. In contrast, germline and somatic disease-causing variants are significantly more likely to be clustered in protein structure space. We demonstrate that this difference in the spatial distributions of disease-associated and benign germline variants provides a signature for accurately classifying variants of unknown significance (VUS) that is complementary to current approaches for VUS classification. We further illustrate the clinical utility of our approach by classifying new mutations identified from patients with familial idiopathic pneumonia (FIP) that segregate with disease.

genetics↗