BackgroundNeurodevelopmental disorders (NDDs) such as autism spectrum disorder, intellectual disability, developmental disability, and epilepsy are characterized by abnormal brain development that may affect cognition, learning, behavior, and motor skills. High co-occurrence (comorbidity) of NDDs indicates a shared, underlying biological mechanism. The genetic heterogeneity and overlap observed in NDDs make it difficult to identify the genetic causes of specific clinical symptoms, such as seizures.MethodsWe present a computational method, MAGI-S, to discover modules or groups of highly connected genes that together potentially perform a similar biological function. MAGI-S integrates protein-protein interaction and co-expression networks to form modules centered around the selection of a single “seed” gene, yielding modules consisting of genes that are highly co-expressed with the seed gene. We aim to dissect the epilepsy phenotype from a general NDD phenotype by providing MAGI-S with high confidence NDD seed genes with varying degrees of association with epilepsy, and we assess the enrichment of de novo mutation, NDD-associated genes, and relevant biological function of constructed modules.ResultsThe newly identified modules account for the increased rate of de novo non-synonymous mutations in autism, intellectual disability, developmental disability, and epilepsy, and enrichment of copy number variations (CNVs) in developmental disability. We also observed that modules seeded with genes strongly associated with epilepsy tend to have a higher association with epilepsy phenotypes than modules seeded at other neurodevelopmental disorder genes. Modules seeded with genes strongly associated with epilepsy (e.g., SCN1A, GABRA1, and KCNB1) are significantly associated with synaptic transmission, long-term potentiation, and calcium signaling pathways. On the other hand, modules found with seed genes that are not associated or weakly associated with epilepsy are mostly involved with RNA regulation and chromatin remodeling.ConclusionsIn summary, our method identifies modules enriched with de novo non-synonymous mutations and can capture specific networks that underlie the epilepsy phenotype and display distinct enrichment in relevant biological processes. MAGI-S is available at https://github.com/jchow32/magi-s.

In the era of genomics, single-nucleotide polymorphisms (SNPs) have become a preferred molecular marker to study signatures of selection and population structure and to enable improved population monitoring and conservation of vulnerable populations. We apply a SNP calling pipeline to assess population differentiation, visualize linkage disequilibrium, and identify loci with sex-specific genotypes of 45 loggerhead sea turtles (Caretta caretta) sampled from the southeastern coast of the United States, including 42 individuals experimentally confirmed for gonadal sex. By performing reference-based SNP calling in independent runs of Stacks, 3,901–6,998 SNPs and up to 30 potentially sex-specific genotypes were identified. Up to 68 pairs of loci were found to be in complete linkage disequilibrium, potentially indicating regions of natural selection and adaptive evolution. This study provides a valuable SNP diagnostic workflow and a large body of new biomarkers for guiding targeted studies of sea turtle genome evolution and for managing legally protected nonmodel iconic species that have high economic and ecological importance but limited genomic resources.

The early detection of neurodevelopmental disorders (NDDs) can significantly improve patient outcomes. The differential burden of non-synonymous de novo mutation among NDD cases and controls indicates that de novo coding variation can be used to identify a subset of samples that will likely display an NDD phenotype. Thus, we have developed an approach for the accurate prediction of NDDs with very low false positive rate (FPR) using de novo coding variation for a small subset of cases. We use a shallow neural network that integrates de novo likely gene-disruptive and missense variants, measures of gene constraint, and conservation information to predict a small subset of NDD cases at very low FPR and prioritizes NDD risk genes for future clinical study.

Background: Digital transcriptomics is rapidly emerging as a powerful new technology for modelling the environmental dynamics of the adaptive landscape in diverse lineages. This is particularly valuable in taxa such as turtles and tortoises (order Testudines) which contain a large fraction of endangered species at risk due to anthropogenic impacts on the environment, including pollution, overharvest, habitat degradation, and climate change. Sea turtles (family Cheloniidae) in particular invite a genomics-enabled approach to investigating their remarkable portfolio of adaptive evolution. The sex of the endangered loggerhead sea turtle (Caretta caretta) is subject to temperature-dependent sex determination (TSD), a mechanism by which exposure to temperatures during embryonic development irreversibly determines sex. Higher temperatures produce mainly female turtles and lower temperatures produce mainly male turtles. Incubation temperature can have long term effects on the immunity, migratory ability, and ultimately longevity of hatchlings. We perform RNA-seq differential expression analysis to investigate tissue- and temperature-specific gene expression within brain (n = 7) and gonadal (n = 4) tissue of male and female loggerhead hatchlings. Results: We assemble tissue- and temperature-specific transcriptomes and identify differentially expressed genes relevant to sexual development and life history traits of broad adaptive interest to turtles and other amniotic species. We summarize interactions among differentially expressed genes by producing network visualizations, and highlight shared biological pathways related to migration, immunity, and longevity reported in the avian and reptile literature. Conclusions: The measurement of tissue- and temperature-specific global gene expression of an endangered, flagship species such as the loggerhead sea turtle (Caretta caretta) reveals the genomic basis for potential resiliency and is crucial to future management and conservation strategies with attention to changing climates. Brain and gonadal tissue collected from experimentally reared loggerhead male and female hatchlings comprise an exceedingly rare dataset that permits the identification of genes enriched in functions related to sexual development, immunity, longevity, and migratory behavior and will serve as a large, new genomic resource for the investigation of genotype–phenotype relationships in amniotes.

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.