PEPS: Polygenic Epistatic Phenotype Simulation

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity.

Data-driven platform for identifying variants of interest in COVID-19 virus

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We provide an automated way to identify emerging variants of concern using viral genome and patient-outcome data.

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We assembled 10,000 sample-strong case-control dataset and identified 117 single nucleotide variants (SNV) associated with adverse patient outcomes.

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We observe co-evolution of protective and pathogenic interactions between the spike, nsp14, and N region with either orf3a or nsp3.

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Structural modelling reveals mutation clusters in the Zn binding domain of nsp14 suggesting ongoing adaptation to the human host.

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Our approach identified Variants Being Monitored (VBM) a week before they were flagged by Health Organizations and offers a clade-independent function-orientated grouping.

New SARS-CoV-2 variants emerge as part of the virus’ adaptation to the human host. The Health Organizations are monitoring newly emerging variants with suspected impact on disease or vaccination efficacy as Variants Being Monitored (VBM), like Delta and Omicron. Genetic changes (SNVs) compared to the Wuhan variant characterize VBMs with current emphasis on the spike protein and lineage markers. However, monitoring VBMs in such a way might miss SNVs with functional effect on disease.

Here we introduce a lineage-agnostic genome-wide approach to identify SNVs associated with disease. We curated a case-control dataset of 10,520 samples and identified 117 SNVs significantly associated with adverse patient outcome. While 40% (47) SNV are already monitored and 36% (43) are in the spike protein, we also identified 70 new SNVs that are associated with disease outcome. 31 of these are disease-worsening and predominantly located in the 3′-5′ exonuclease (NSP14) with structural modelling revealing a concise cluster in the Zn binding domain that has known host-immune modulating function. Furthermore, we generate clade-independent VBM groupings by identifying interacting SNVs (epistasis). We find 37 sets of higher-order epistatic interactions joining 5 genomic regions (nsp3, nsp14, Spike S1, ORF3a, N). Structural modelling of these regions provides insights into potential mechanistic pathways of increased virulence as well as orthogonal methods of validation.

Clade-independent monitoring of functionally interacting (epistasis, co-evolution) SNVs detected emerging VBM a week before they were flagged by Health Organizations and in conjunction with structural modelling provides faster, mechanistic insight into emerging strains to guide public health interventions.

Fast and accurate exhaustive higher-order epistasis search with BitEpi

Complex genetic diseases may be modulated by a large number of epistatic interactions affecting a polygenic phenotype. Identifying these interactions is difficult due to computational complexity, especially in the case of higher-order interactions where more than two genomic variants are involved. In this paper, we present BitEpi, a fast and accurate method to test all possible combinations of up to four bi-allelic variants (i.e. Single Nucleotide Variant or SNV for short). BitEpi introduces a novel bitwise algorithm that is 1.7 and 56 times faster for 3-SNV and 4-SNV search, than established software. The novel entropy statistic used in BitEpi is 44% more accurate to identify interactive SNVs, incorporating a p-value-based significance testing. We demonstrate BitEpi on real world data of 4900 samples and 87,000 SNPs. We also present EpiExplorer to visualize the potentially large number of individual and interacting SNVs in an interactive Cytoscape graph. EpiExplorer uses various visual elements to facilitate the discovery of true biological events in a complex polygenic environment.

INSIDER: alignment-free detection of foreign DNA sequences

External DNA sequences can be inserted into an organism's genome either through natural processes such as gene transfer, or through targeted genome engineering strategies. Being able to robustly identify such foreign DNA is a crucial capability for health and biosecurity applications, such as anti-microbial resistance (AMR) detection or monitoring gene drives. This capability does not exist for poorly characterised host genomes or with limited information about the integrated sequence. To address this, we developed the INserted Sequence Information DEtectoR (INSIDER). INSIDER analyses whole genome sequencing data and identifies segments of potentially foreign origin by their significant shift in k-mer signatures. We demonstrate the power of INSIDER to separate integrated DNA sequences from normal genomic sequences on a synthetic dataset simulating the insertion of a CRISPR-Cas gene drive into wild-type yeast. As a proof-of-concept, we use INSIDER to detect the exact AMR plasmid in whole genome sequencing data from a Citrobacter freundii patient isolate. INSIDER streamlines the process of identifying integrated DNA in poorly characterised wild species or when the insert is of unknown origin, thus enhancing the monitoring of emerging biosecurity threats.

VariantSpark: Cloud-based machine learning for association study of complex phenotype and large-scale genomic data

BACKGROUND

Many traits and diseases are thought to be driven by >1 gene (polygenic). Polygenic risk scores (PRS) hence expand on genome-wide association studies by taking multiple genes into account when risk models are built. However, PRS only considers the additive effect of individual genes but not epistatic interactions or the combination of individual and interacting drivers. While evidence of epistatic interactions ais found in small datasets, large datasets have not been processed yet owing to the high computational complexity of the search for epistatic interactions.

FINDINGS

We have developed VariantSpark, a distributed machine learning framework able to perform association analysis for complex phenotypes that are polygenic and potentially involve a large number of epistatic interactions. Efficient multi-layer parallelization allows VariantSpark to scale to the whole genome of population-scale datasets with 100,000,000 genomic variants and 100,000 samples.

CONCLUSIONS

Compared with traditional monogenic genome-wide association studies, VariantSpark better identifies genomic variants associated with complex phenotypes. VariantSpark is 3.6 times faster than ReForeSt and the only method able to scale to ultra-high-dimensional genomic data in a manageable time.

Subcellular localization of the steroid receptor coactivators (SRCs) and MEF2 in muscle and rhabdomyosarcoma cells

Skeletal muscle differentiation and the activation of muscle-specific gene expression are dependent on the concerted action of the MyoD family and the MADS protein, MEF2, which function in a cooperative manner. The steroid receptor coactivator SRC-2/GRIP-1/TIF-2, is necessary for skeletal muscle differentiation, and functions as a cofactor for the transcription factor, MEF2. SRC-2 belongs to the SRC family of transcriptional coactivators/cofactors that also includes SRC-1 and SRC-3/RAC-3/ACTR/AIB-1. In this study we demonstrate that SRC-2 is essentially localized in the nucleus of proliferating myoblasts; however, weak (but notable) expression is observed in the cytoplasm. Differentiation induces a predominant localization of SRC-2 to the nucleus; furthermore, the nuclear staining is progressively more localized to dot-like structures or nuclear bodies. MEF2 is primarily expressed in the nucleus, although we observed a mosaic or variegated expression pattern in myoblasts; however, in myotubes all nuclei express MEF2. GRIP-1 and MEF2 are coexpressed in the nucleus during skeletal muscle differentiation, consistent with the direct interaction of these proteins. Rhabdomyosarcoma (RMS) cells derived from malignant skeletal muscle tumors have been proposed to be deficient in cofactors. Alveolar RMS cells very weakly express the steroid receptor coactivator, SRC-2, in a diffuse nucleocytoplasmic staining pattern. MEF2 and the cofactors, SRC-1 and SRC-3 are abundantly expressed in alveolar and embryonal RMS cells; however, the staining is not localized to the nucleus. Furthermore, the subcellular localization and transcriptional activity of MEF2C and a MEF2-dependent reporter are compromised in alveolar RMS cells. In contrast, embryonal RMS cells express SRC-2 in the nucleus, and MEF2 shuttles from the cytoplasm to the nucleus after serum withdrawal. In conclusion, this study suggests that the steroid receptor coactivator SRC-2 and MEF2 are localized to the nucleus during the differentiation process. In contrast, RMS cells display aberrant transcription factor SRC localization and expression, which may underlie certain features of the RMS phenotype.

Mutations in Sox18 underlie cardiovascular and hair follicle defects in ragged mice

Analysis of classical mouse mutations has been useful in the identification and study of many genes. We previously mapped Sox18, encoding an SRY-related transcription factor, to distal mouse chromosome 2. This region contains a known mouse mutation, ragged (Ra), that affects the coat and vasculature. Here we have directly evaluated Sox18 as a candidate for Ra. We found that Sox18 is expressed in the developing vascular endothelium and hair follicles in mouse embryos. Furthermore, we found no recombination between Sox18 and Ra in an interspecific backcross segregating for the Ra phenotype. We found point mutations in Sox18 in two different Ra alleles that result in missense translation and premature truncation of the encoded protein. Fusion proteins containing these mutations lack the ability to activate transcription relative to wild-type controls in an in vitro assay. Our observations implicate mutations in Sox18 as the underlying cause of the Ra phenotype, and identify Sox18 as a critical gene for cardiovascular and hair follicle formation.