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.

Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.

Novel Alzheimer's disease genes and epistasis identified using machine learning GWAS platform.

Alzheimer's disease (AD) is a complex genetic disease, and variants identified through genome-wide association studies (GWAS) explain only part of its heritability. Epistasis has been proposed as a major contributor to this 'missing heritability', however, many current methods are limited to only modelling additive effects. We use VariantSpark, a machine learning approach to GWAS, and BitEpi, a tool for epistasis detection, to identify AD associated variants and interactions across two independent cohorts, ADNI and UK Biobank. By incorporating significant epistatic interactions, we captured 10.41% more phenotypic variance than logistic regression (LR). We validate the well-established AD loci, APOE, and identify two novel genome-wide significant AD associated loci in both cohorts, SH3BP4 and SASH1, which are also in significant epistatic interactions with APOE. We show that the SH3BP4 SNP has a modulating effect on the known pathogenic APOE SNP, demonstrating a possible protective mechanism against AD. SASH1 is involved in a triplet interaction with pathogenic APOE SNP and ACOT11, where the SASH1 SNP lowered the pathogenic interaction effect between ACOT11 and APOE. Finally, we demonstrate that VariantSpark detects disease associations with 80% fewer controls than LR, unlocking discoveries in well annotated but smaller cohorts.

Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach.

Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 106 datapoints, establishing its usability for large-scale datasets, like genomics. Furthermore, RFlocalfdr is compatible with any RF implementation that returns a VIM and counts, making it a versatile feature selection tool that reduces false discoveries.

Balancing the safeguarding of privacy and data sharing: perceptions of genomic professionals on patient genomic data ownership in Australia.

There are inherent complexities and tensions in achieving a responsible balance between safeguarding patients' privacy and sharing genomic data for advancing health and medical science. A growing body of literature suggests establishing patient genomic data ownership, enabled by blockchain technology, as one approach for managing these priorities. We conducted an online survey, applying a mixed methods approach to collect quantitative (using scale questions) and qualitative data (using open-ended questions). We explored the views of 117 genomic professionals (clinical geneticists, genetic counsellors, bioinformaticians, and researchers) towards patient data ownership in Australia. Data analysis revealed most professionals agreed that patients have rights to data ownership. However, there is a need for a clearer understanding of the nature and implications of data ownership in this context as genomic data often is subject to collective ownership (e.g., with family members and laboratories). This research finds that while the majority of genomic professionals acknowledge the desire for patient data ownership, bioinformaticians and researchers expressed more favourable views than clinical geneticists and genetic counsellors, suggesting that their views on this issue may be shaped by how closely they interact with patients as part of their professional duties. This research also confirms that stronger health system infrastructure is a prerequisite for enabling patient data ownership, which needs to be underpinned by appropriate digital infrastructure (e.g., central vs. decentralised data storage), patient identity ownership (e.g., limited vs. self-sovereign identity), and policy at both federal and state levels.

A metabolic associated fatty liver disease risk variant in MBOAT7 regulates toll like receptor induced outcomes.

The breakdown of toll-like receptor (TLR) tolerance results in tissue damage, and hyperactivation of the TLRs and subsequent inflammatory consequences have been implicated as risk factors for more severe forms of disease and poor outcomes from various diseases including COVID-19 and metabolic (dysfunction) associated fatty liver disease (MAFLD). Here we provide evidence that membrane bound O-acyltransferase domain containing 7 (MBOAT7) is a negative regulator of TLR signalling. MBOAT7 deficiency in macrophages as observed in patients with MAFLD and in COVID-19, alters membrane phospholipid composition. We demonstrate that this is associated with a redistribution of arachidonic acid toward proinflammatory eicosanoids, induction of endoplasmic reticulum stress, mitochondrial dysfunction, and remodelling of the accessible inflammatory-related chromatin landscape culminating in macrophage inflammatory responses to TLRs. Activation of MBOAT7 reverses these effects. These outcomes are further modulated by the MBOAT7 rs8736 (T) MAFLD risk variant. Our findings suggest that MBOAT7 can potentially be explored as a therapeutic target for diseases associated with dysregulation of the TLR signalling cascade.

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

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.

Genetic Analysis of Tryptophan Metabolism Genes in Sporadic Amyotrophic Lateral Sclerosis.

The essential amino acid tryptophan (TRP) is the initiating metabolite of the kynurenine pathway (KP), which can be upregulated by inflammatory conditions in cells. Neuroinflammation-triggered activation of the KP and excessive production of the KP metabolite quinolinic acid are common features of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). In addition to its role in the KP, genes involved in TRP metabolism, including its incorporation into proteins, and synthesis of the neurotransmitter serotonin, have also been genetically and functionally linked to these diseases. ALS is a late onset neurodegenerative disease that is classified as familial or sporadic, depending on the presence or absence of a family history of the disease. Heritability estimates support a genetic basis for all ALS, including the sporadic form of the disease. However, the genetic basis of sporadic ALS (SALS) is complex, with the presence of multiple gene variants acting to increase disease susceptibility and is further complicated by interaction with potential environmental factors. We aimed to determine the genetic contribution of 18 genes involved in TRP metabolism, including protein synthesis, serotonin synthesis and the KP, by interrogating whole-genome sequencing data from 614 Australian sporadic ALS cases. Five genes in the KP (AFMID, CCBL1, GOT2, KYNU, HAAO) were found to have either novel protein-altering variants, and/or a burden of rare protein-altering variants in SALS cases compared to controls. Four genes involved in TRP metabolism for protein synthesis (WARS) and serotonin synthesis (TPH1, TPH2, MAOA) were also found to carry novel variants and/or gene burden. These variants may represent ALS risk factors that act to alter the KP and lead to neuroinflammation. These findings provide further evidence for the role of TRP metabolism, the KP and neuroinflammation in ALS disease pathobiology.

Genetic analysis of GLT8D1 and ARPP21 in Australian familial and sporadic amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by the progressive degeneration of motor neurons. Recently, genetic variants in GLT8D1 and ARPP21 were associated with ALS in a cohort of European descent. A synergistic relationship was proposed between ALS associated variants in GLT8D1 and ARPP21. We aimed to determine the prevalence of genetic variation in GLT8D1 and ARPP21 in an Australian cohort of familial (n = 81) and sporadic ALS (n = 618) cases using whole-exome and whole-genome sequencing data. No novel mutations were identified in either gene, nor was there significant enrichment of protein-altering sequence variation among ALS cases. GLT8D1 and ARPP21 mutations are not a common cause of ALS in Australian familial and sporadic cohorts.

Identity by descent analysis identifies founder events and links SOD1 familial and sporadic ALS cases.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterised by the loss of upper and lower motor neurons resulting in paralysis and eventual death. Approximately 10% of ALS cases have a family history of disease, while the remainder present as apparently sporadic cases. Heritability studies suggest a significant genetic component to sporadic ALS, and although most sporadic cases have an unknown genetic aetiology, some familial ALS mutations have also been found in sporadic cases. This suggests that some sporadic cases may be unrecognised familial cases with reduced disease penetrance in their ancestors. A powerful strategy to uncover a familial link is identity-by-descent (IBD) analysis, which detects genomic regions that have been inherited from a common ancestor. IBD analysis was performed on 83 Australian familial ALS cases from 25 families and three sporadic ALS cases, each of whom carried one of three SOD1 mutations (p.I114T, p.V149G and p.E101G). We defined five unique 350-SNP haplotypes that carry these mutations in our cohort, indicative of five founder events. This included two founder haplotypes that carry SOD1 p.I114T; linking familial and sporadic cases. We found that SOD1 p.E101G arose independently in each family that carries this mutation and linked two families that carry SOD1 p.V149G. The age of disease onset varied between cases that carried each SOD1 p.I114T haplotype. Linking families with identical ALS mutations allows for larger sample sizes and increased statistical power to identify putative phenotypic modifiers.

Evidence for polygenic and oligogenic basis of Australian sporadic amyotrophic lateral sclerosis.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with phenotypic and genetic heterogeneity. Approximately 10% of cases are familial, while remaining cases are classified as sporadic. To date, >30 genes and several hundred genetic variants have been implicated in ALS. METHODS: Seven hundred and fifty-seven sporadic ALS cases were recruited from Australian neurology clinics. Detailed clinical data and whole genome sequencing (WGS) data were available from 567 and 616 cases, respectively, of which 426 cases had both datasets available. As part of a comprehensive genetic analysis, 853 genetic variants previously reported as ALS-linked mutations or disease-associated alleles were interrogated in sporadic ALS WGS data. Statistical analyses were performed to identify correlation between clinical variables, and between phenotype and the number of ALS-implicated variants carried by an individual. Relatedness between individuals carrying identical variants was assessed using identity-by-descent analysis. RESULTS: Forty-three ALS-implicated variants from 18 genes, including C9orf72, ATXN2, TARDBP, SOD1, SQSTM1 and SETX, were identified in Australian sporadic ALS cases. One-third of cases carried at least one variant and 6.82% carried two or more variants, implicating a potential oligogenic or polygenic basis of ALS. Relatedness was detected between two sporadic ALS cases carrying a SOD1 p.I114T mutation, and among three cases carrying a SQSTM1 p.K238E mutation. Oligogenic/polygenic sporadic ALS cases showed earlier age of onset than those with no reported variant. CONCLUSION: We confirm phenotypic associations among ALS cases, and highlight the contribution of genetic variation to all forms of ALS.

Genetic and immunopathological analysis of CHCHD10 in Australian amyotrophic lateral sclerosis and frontotemporal dementia and transgenic TDP-43 mice.

OBJECTIVE: Since the first report of CHCHD10 gene mutations in amyotrophiclateral sclerosis (ALS)/frontotemporaldementia (FTD) patients, genetic variation in CHCHD10 has been inconsistently linked to disease. A pathological assessment of the CHCHD10 protein in patient neuronal tissue also remains to be reported. We sought to characterise the genetic and pathological contribution of CHCHD10 to ALS/FTD in Australia. METHODS: Whole-exome and whole-genome sequencing data from 81 familial and 635 sporadic ALS, and 108 sporadic FTD cases, were assessed for genetic variation in CHCHD10. CHCHD10 protein expression was characterised by immunohistochemistry, immunofluorescence and western blotting in control, ALS and/or FTD postmortem tissues and further in a transgenic mouse model of TAR DNA-binding protein 43 (TDP-43) pathology. RESULTS: No causal, novel or disease-associated variants in CHCHD10 were identified in Australian ALS and/or FTD patients. In human brain and spinal cord tissues, CHCHD10 was specifically expressed in neurons. A significant decrease in CHCHD10 protein level was observed in ALS patient spinal cord and FTD patient frontal cortex. In a TDP-43 mouse model with a regulatable nuclear localisation signal (rNLS TDP-43 mouse), CHCHD10 protein levels were unaltered at disease onset and early in disease, but were significantly decreased in cortex in mid-stage disease. CONCLUSIONS: Genetic variation in CHCHD10 is not a common cause of ALS/FTD in Australia. However, we showed that in humans, CHCHD10 may play a neuron-specific role and a loss of CHCHD10 function may be linked to ALS and/or FTD. Our data from the rNLS TDP-43 transgenic mice suggest that a decrease in CHCHD10 levels is a late event in aberrant TDP-43-induced ALS/FTD pathogenesis.

Monozygotic twins and triplets discordant for amyotrophic lateral sclerosis display differential methylation and gene expression.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by the loss of upper and lower motor neurons. ALS exhibits high phenotypic variability including age and site of onset, and disease duration. To uncover epigenetic and transcriptomic factors that may modify an ALS phenotype, we used a cohort of Australian monozygotic twins (n = 3 pairs) and triplets (n = 1 set) that are discordant for ALS and represent sporadic ALS and the two most common types of familial ALS, linked to C9orf72 and SOD1. Illumina Infinium HumanMethylation450K BeadChip, EpiTYPER and RNA-Seq analyses in these ALS-discordant twins/triplets and control twins (n = 2 pairs), implicated genes with consistent longitudinal differential DNA methylation and/or gene expression. Two identified genes, RAD9B and C8orf46, showed significant differential methylation in an extended cohort of >1000 ALS cases and controls. Combined longitudinal methylation-transcription analysis within a single twin set implicated CCNF, DPP6, RAMP3, and CCS, which have been previously associated with ALS. Longitudinal transcriptome data showed an 8-fold enrichment of immune function genes and under-representation of transcription and protein modification genes in ALS. Examination of these changes in a large Australian sporadic ALS cohort suggest a broader role in ALS. Furthermore, we observe that increased methylation age is a signature of ALS in older patients.

Molecular Phenotyping of Telomerized Human Bone Marrow Skeletal Stem Cells Reveals a Genetic Program of Enhanced Proliferation and Maintenance of Differentiation Responses.

Long-term in vitro expansion of bone marrow stromal (skeletal) stem cells (also known as human mesenchymal stem cells [hMSC]) is associated with replicative senescence and impaired functions. We have previously reported that telomerization of hMSC through hTERT overexpression led to bypassing a replicative senescence phenotype and improved in vitro and in vivo functions. However, the molecular consequence of telomerization is poorly characterized. Thus, we compared the molecular phenotype of a well-studied telomerized hMSC (hMSC-TERT) cell line with primary hMSC. At a cellular level, both cell populations exhibited strong concordance for the known hMSC CD markers, similar responses to osteoblast (OB) differentiation induction, and formed heterotopic bone in vivo. Overall gene expression was highly correlated between both cell types with an average Pearson's correlation coefficient (R2) between the gene expression of all primary hMSC and all hMSC-TERT samples of 0.95 (range 0.93-0.96). Quantitative analysis of gene expression of CD markers, OB cell markers, and transcription factors (TF) showed a high degree of similarity between the two cell populations (72%, 77%, and 81%, respectively). The hMSC-TERT population was enriched mainly for genes associated with cell cycle and cell cycle signaling when compared with primary hMSC. Other enrichment was observed for genes involved in cell adhesion and skeletal system development and immune response pathways. Interestingly, hMSC-TERT shared a telomerization signature with upregulation of cancer/testis antigens, MAGE, and PAGE genes. Our data demonstrate that the enhanced biological characteristics of hMSC after telomerization are mainly due to enhanced expression of cell proliferation genes, whereas gene expression responses to differentiation are maintained. © 2018 The Authors. JBMR Plus Published by Wiley Periodicals, Inc. on behalf of the American Society for Bone and Mineral Research.

Transcription factor ZNF25 is associated with osteoblast differentiation of human skeletal stem cells.

BACKGROUND: The differentiation of human bone marrow derived skeletal stem cells (known as human bone marrow stromal or mesenchymal stem cells, hMSCs) into osteoblasts involves the activation of a small number of well-described transcription factors. To identify additional osteoblastic transcription factors, we studied gene expression of hMSCs during ex vivo osteoblast differentiation. RESULTS: Clustering of gene expression, and literature investigation, revealed three transcription factors of interest - ZNF25, ZNF608 and ZBTB38. siRNA knockdown of ZNF25 resulted in significant suppression of alkaline phosphatase (ALP) activity. This effect was not present for ZNF608 and ZBTB38. To identify possible target genes of ZNF25, we analyzed gene expression following ZNF25 siRNA knockdown. This revealed a 23-fold upregulation of matrix metallopeptidase 1 and an 18-fold upregulation of leucine-rich repeat containing G protein-coupled receptor 5 and RAN-binding protein 3-like. We also observed enrichment in extracellular matrix organization, skeletal system development and regulation of ossification in the entire upregulated set of genes. Consistent with its function as a transcription factor during osteoblast differentiation of hMSC, we showed that the ZNF25 protein exhibits nuclear localization and is expressed in osteoblastic and osteocytic cells in vivo. ZNF25 is conserved in tetrapod vertebrates and contains a KRAB (Krueppel-associated box) transcriptional repressor domain. CONCLUSIONS: This study shows that the uncharacterized transcription factor, ZNF25, is associated with differentiation of hMSC to osteoblasts.

Proteomic Validation of Transcript Isoforms, Including Those Assembled from RNA-Seq Data.

Human proteome analysis now requires an understanding of protein isoforms. We recently published the PG Nexus pipeline, which facilitates high confidence validation of exons and splice junctions by integrating genomics and proteomics data. Here we comprehensively explore how RNA-seq transcriptomics data, and proteomic analysis of the same sample, can identify protein isoforms. RNA-seq data from human mesenchymal (hMSC) stem cells were analyzed with our new TranscriptCoder tool to generate a database of protein isoform sequences. MS/MS data from matching hMSC samples were then matched against the TranscriptCoder-derived database, along with Ensembl and the neXtProt database. Querying the TranscriptCoder-derived or Ensembl database could unambiguously identify ∼450 protein isoforms, with isoform-specific proteotypic peptides, including candidate hMSC-specific isoforms for the genes DPYSL2 and FXR1. Where isoform-specific peptides did not exist, groups of nonisoform-specific proteotypic peptides could specifically identify many isoforms. In both the above cases, isoforms will be detectable with targeted MS/MS assays. Unfortunately, our analysis also revealed that some isoforms will be difficult to identify unambiguously as they do not have peptides that are sufficiently distinguishing. We covisualize mRNA isoforms and peptides in a genome browser to illustrate the above situations. Mass spectrometry data is available via ProteomeXchange (PXD001449).

Molecular characterisation of stromal populations derived from human embryonic stem cells: Similarities to immortalised bone marrow derived stromal stem cells.

Human bone marrow-derived stromal (skeletal) stem cells (BM-hMSC) are being employed in an increasing number of clinical trials for tissue regeneration. A limiting factor for their clinical use is the inability to obtain sufficient cell numbers. Human embryonic stem cells (hESC) can provide an unlimited source of clinical grade cells for therapy. We have generated MSC-like cells from hESC (called here hESC-stromal) that exhibit surface markers and differentiate to osteoblasts and adipocytes, similar to BM-hMSC. In the present study, we used microarray analysis to compare the molecular phenotype of hESC-stromal and immortalised BM-hMSC cells (hMSC-TERT). Of the 7379 genes expressed above baseline, only 9.3% of genes were differentially expressed between undifferentiated hESC-stromal and BM-hMSC. Following ex vivo osteoblast induction, 665 and 695 genes exhibited ≥ 2-fold change (FC) in hESC-stromal and BM-hMSC, respectively with 172 genes common to both cell types. Functional annotation of significantly changing genes revealed similarities in gene ontology between the two cell types. Interestingly, genes in categories of cell adhesion/motility and epithelial-mesenchymal transition (EMT) were highly enriched in hESC-stromal whereas genes associated with cell cycle processes were enriched in hMSC-TERT. This data suggests that while hESC-stromal cells exhibit a similar molecular phenotype to hMSC-TERT, differences exist that can be explained by ontological differences between these two cell types. hESC-stromal cells can thus be considered as a possible alternative candidate cells for hMSC, to be employed in regenerative medicine protocols.