Martin Oti

Genomes can be compared at different levels of divergence, either between species or within species. Within species genomes can be compared between different subpopulations, such as human subpopulations from different continents. Investigating the genomic differences between different human subpopulations is important when studying complex diseases that are affected by many genetic variants, as the variants involved can differ between populations. The 1000 Genomes Project collected genome-scale variation data for 2504 human individuals from 26 different populations, enabling a systematic comparison of variation between human subpopulations. In this chapter, we present step-by-step a basic protocol for the identification of population-specific variants employing the 1000 Genomes data. These variants are subsequently further investigated for those that affect the proteome or RNA splice sites, to investigate potentially biologically relevant differences between the populations.

Since the pioneering studies of Thomas Hunt Morgan and coworkers at the dawn of the twentieth century, Drosophila melanogaster and its sister species have tremendously contributed to unveil the rules underlying animal genetics, development, behavior, evolution, and human disease. Recent advances in DNA sequencing technologies launched Drosophila into the post-genomic era and paved the way for unprecedented comparative genomics investigations. The complete sequencing and systematic comparison of the genomes from 12 Drosophila species represents a milestone achievement in modern biology, which allowed a plethora of different studies ranging from the annotation of known and novel genomic features to the evolution of chromosomes and, ultimately, of entire genomes. Despite the efforts of countless laboratories worldwide, the vast amount of data that were produced over the past 15 years is far from being fully explored. In this chapter, we will review some of the bioinformatic approaches that were developed to interrogate the genomes of the 12 Drosophila species. Setting off from alignments of the entire genomic sequences, the degree of conservation can be separately evaluated for every region of the genome, providing already first hints about elements that are under purifying selection and therefore likely functional. Furthermore, the careful analysis of repeated sequences sheds light on the evolutionary dynamics of transposons, an enigmatic and fascinating class of mobile elements housed in the genomes of animals and plants. Comparative genomics also aids in the computational identification of the transcriptionally active part of the genome, first and foremost of protein-coding loci, but also of transcribed nevertheless apparently noncoding regions, which were once considered “junk” DNA. Eventually, the synergy between functional and comparative genomics also facilitates in silico and in vivo studies on cis-acting regulatory elements, like transcription factor binding sites, that due to the high degree of sequence variability usually impose increased challenges for bioinformatics approaches.

Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA- and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing—alternative splice sites, introns and cleavage sites—which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.

The CCTC-binding factor (CTCF) protein is involved in genome organization, including mediating three-dimensional chromatin interactions. Human patient lymphocytes with mutations in a single copy of the CTCF gene have reduced expression of enhancer-associated genes involved in response to stimuli. We hypothesize that CTCF interactions stabilize enhancer-promoter chromatin interaction domains, facilitating increased expression of genes in response to stimuli. Here we systematically investigate this model using computational analyses. We use CTCF ChIA-PET data from the ENCODE project to show that CTCF-associated chromatin loops have a tendency to enclose regions of enhancer-regulated stimulus responsive genes, insulating them from neighboring regions of constitutively expressed housekeeping genes. To facilitate cell type-specific CTCF loop identification, we develop an algorithm to predict CTCF loops from ChIP-seq data alone by exploiting the CTCF motif directionality in loop anchors. We apply this algorithm to a hundred ENCODE cell line datasets, confirming the universality of our observations as well as identifying a general distinction between primary and immortal cells in loop-enclosed gene content. Finally, we combine the existing evidence to propose a model for the formation of CTCF loops in which partner sites are brought together by chromatin template reeling through stationary RNA polymerases, consistent with the transcription factory hypothesis. We provide computational evidence that CTCF-mediated chromatin interactions enclose domains of stimulus responsive enhancer-regulated genes, insulating them from nearby housekeeping genes.

Orofacial clefts (OFCs) represent a large fraction of human birth defects and are one of the most common phenotypes affected by large copy number variants (CNVs). Due to the limited number of CNV patients in individual centers, CNV analyses of a large number of OFC patients are challenging. The present study analyzed 249 genomic deletions and 226 duplications from a cohort of 312 OFC patients reported in two publicly accessible databases of chromosome imbalance and phenotype in humans, DECIPHER and ECARUCA. Genomic regions deleted or duplicated in multiple patients were identified, and genes in these overlapping CNVs were prioritized based on the number of genes encompassed by the region and gene expression in embryonic mouse palate. Our analyses of these overlapping CNVs identified two genes known to be causative for human OFCs, SATB2 and MEIS2, and 12 genes (DGCR6, FGF2, FRZB, LETM1, MAPK3, SPRY1, THBS1, TSHZ1, TTC28, TULP4, WHSC1, WHSC2) that are associated with OFC or orofacial development. Additionally, we report 34 deleted and 24 duplicated genes that have not previously been associated with OFCs but are associated with the BMP, MAPK and RAC1 pathways. Statistical analyses show that the high number of overlapping CNVs is not due to random occurrence. The identified genes are not located in highly variable genomic regions in healthy populations and are significantly enriched for genes that are involved in orofacial development. In summary, we report a CNV analysis pipeline of a large cohort of OFC patients and identify novel candidate OFC genes.

The transcription factor p63 plays a pivotal role in keratinocyte proliferation and differentiation in the epidermis. However, how p63 regulates epidermal genes during differentiation is not yet clear. Using epigenome profiling of differentiating human primary epidermal keratinocytes, we characterized a catalog of dynamically regulated genes and p63‐bound regulatory elements that are relevant for epithelial development and related diseases. p63‐bound regulatory elements occur as single or clustered enhancers, and remarkably, only a subset is active as defined by the co‐presence of the active enhancer mark histone modification H3K27ac in epidermal keratinocytes. We show that the dynamics of gene expression correlates with the activity of p63‐bound enhancers rather than with p63 binding itself. The activity of p63‐bound enhancers is likely determined by other transcription factors that cooperate with p63. Our data show that inactive p63‐bound enhancers in epidermal keratinocytes may be active during the development of other epithelial‐related structures such as limbs and suggest that p63 bookmarks genomic loci during the commitment of the epithelial lineage and regulates genes through temporal‐ and spatial‐specific active enhancers. Synopsis The key epithelial transcription factor p63 functions as a placeholder to bookmark genomic loci for gene regulation in epithelial development. The data suggest that different sets of co‐regulators are required to activate proliferation and differentiation genes during epidermal differentiation and developmental genes in other epithelial tissues. We report p63‐regulated gene expression dynamics during epidermal differentiation. Gene expression correlates with the activity of p63‐dependent enhancers rather than p63 binding itself. Co‐regulators of p63 are required during epidermal differentiation and the development of other epithelial tissues.

Current single‐locus‐based analyses and candidate disease gene prediction methodologies used in genome‐wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple‐locus‐based approach, drawing on protein pathway‐ or domain‐based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC‐implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.

Intellectual Disability (ID) disorders, defined by an IQ below 70, are genetically and phenotypically highly heterogeneous. Identification of common molecular pathways underlying these disorders is crucial for understanding the molecular basis of cognition and for the development of therapeutic intervention strategies. To systematically establish their functional connectivity, we used transgenic RNAi to target 270 ID gene orthologs in the Drosophila eye. Assessment of neuronal function in behavioral and electrophysiological assays and multiparametric morphological analysis identified phenotypes associated with knockdown of 180 ID gene orthologs. Most of these genotype-phenotype associations were novel. For example, we uncovered 16 genes that are required for basal neurotransmission and have not previously been implicated in this process in any system or organism. ID gene orthologs with morphological eye phenotypes, in contrast to genes without phenotypes, are relatively highly expressed in the human nervous system and are enriched for neuronal functions, suggesting that eye phenotyping can distinguish different classes of ID genes. Indeed, grouping genes by Drosophila phenotype uncovered 26 connected functional modules. Novel links between ID genes successfully predicted that MYCN, PIGV and UPF3B regulate synapse development. Drosophila phenotype groups show, in addition to ID, significant phenotypic similarity also in humans, indicating that functional modules are conserved. The combined data indicate that ID disorders, despite their extreme genetic diversity, are caused by disruption of a limited number of highly connected functional modules. Intellectual Disability (ID) affects 2% of our population and is associated with many different disorders. Although more than 400 causative genes (‘ID genes’) have been identified, their function remains poorly understood and the degree to which these disorders share a common molecular basis is unknown. Here, we systematically characterized behavioral and morphological phenotypes associated with 270 conserved ID genes, using the Drosophila eye and photoreceptor neurons as a model. These and follow up approaches generated previously undescribed genotype-phenotype associations for the majority (180) of ID gene orthologs, and identified, among others, 16 novel regulators of basal neurotransmission. Importantly, groups of genes that show the same phenotype in Drosophila are highly enriched in known connectivity, also share increased phenotypic similarity in humans and successfully predicted novel gene functions. In total, we mapped 26 conserved functional modules that together comprise 100 ID gene orthologs. Our findings provide unbiased evidence for the long suspected but never experimentally demonstrated functional coherence among ID disorders. The identified conserved functional modules may aid to develop therapeutic strategies that target genetically heterogeneous ID patients with a common treatment.

Background: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. Description: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. Conclusions: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/.