Biology subjects

Seoighe, C.

Publications and source records attributed to Seoighe, C..

11 recordsLinked to original sources

bioRxiv2026

A gene-centric analysis of copy number variation reveals tumour suppressor modulation of cancer risk and novel associations with common diseases

Copy number variants (CNVs) are a significant source of genomic variation in humans. They can span large genomic regions that encompass whole genes and their flanking regions, having the potential to alter the number of functional copies in an individual. Typically, CNVs are analysed individually or combined into regions without regard to the genes they span. Here, we employ a gene-centric approach to link changes in whole-gene copy number to phenotype. We first examined the relationship between CNVs spanning whole genes and gene expression using data from GTEx and found that predicted effects on gene expression are often realised. Applying this method, we identified 34 gene deletions associated with cancer incidence, including several novel candidate associations. Most notably, we discovered that gene deletions in the 13q14 region are linked to increased cancer risk, a novel extension of a known somatic association to the germline, which we did not find using region-based methods. Statistical associations were found between whole-gene loss of tumour suppressor genes and overall cancer risk, as hypothesized. It was additionally found that the total number of whole-gene losses was strongly associated with increased cancer risk, even when known cancer-related genes are not included in the count. Finally, we extended these methods to non-cancer phenotypes, uncovering significant associations with common diseases, including asthma and heart attack.

genomics↗

bioRxiv2025

Intratumoral heterogeneity in microsatellite instability status at single cell resolution

Subclonal diversity within a tumor is highly relevant for tumor evolution and treatment. This diversity is often referred to as intratumoral heterogeneity and is known to complicate the interpretation of single-test biomarkers. Microsatellite instability (MSI) is one such biomarker, which is used to guide immune check-point inhibitor treatment through the classification of samples as either having high microsatellite instability (MSI-H) or as being microsatellite stable (MSS). Although established as a therapeutic biomarker, it remains unclear whether MSI itself is a heterogeneous phenomenon. To investigate heterogeneity in MSI status, we integrated single-cell sequencing data from 134 samples across 49 individuals and developed a computational pipeline to infer MSI-H cells and quantify heterogeneity in MSI status. We found evidence of intratumoral heterogeneity in MSI both in individuals originally classified as MSI-H and MSS. Approximately a third of individuals showed evidence of divergence in MSI status between distinct clusters of cancer cells and most individuals had distinct MSI-H and MSS subclones. These results challenge the assumption that MSI should be treated as a binary biomarker and suggest the single-biopsy tests in current use could overlook a salient feature of this important molecular phenotype. Accounting for heterogeneity may lead to improved biomarker performance and, potentially, help explain reports of intrinsic treatment resistance and low overall response rate in MSI-H cancers. Further studies are warranted to determine the frequency of heterogeneity in MSI at the population level, and whether the presence of both MSI-H and MSS subclones can have clinical implications.

bioinformatics↗

bioRxiv2024

Probing the limits of cis-acting gene regulation using a model of allelic imbalance quantitative trait loci

Imbalance in gene expression between alleles is a hallmark of cis-acting expression quantitative trait loci (eQTLs) and several methods have been developed to exploit allelic imbalance to support the identification of eQTLs. Allelic imbalance is also of scientific and, potentially, clinical interest as it can erode the degree to which the effects of deleterious variants are buffered in a diploid organism and has been reported to be associated with the penetrance of pathological genomic variants. Here, we develop and apply a statistical model that is designed to evaluate whether the genotype of a locus is associated with the degree of allelic imbalance of a gene and refer to such loci as allelic imbalance quantitative trait loci (aiQTLs). An advantage of our approach is that it does not depend on link-age disequilibrium between the aiQTL and the associated gene and is, therefore, suited to the identification of eQTLs that act in cis over very large distances. We applied our model to data from the GTEx consortium and examined the relationship between the distance of an eQTL from the TSS of the associated gene and the evidence that the eQTL acts in cis. Previous studies have used a distance of 1Mb from the target gene as an indication that an eQTL acts in cis; however, our results suggest that the majority of eQTLs at distances more than 500 kb from the TSS of the target gene are likely to act in trans (and thus to affect both gene copies). The model used here is also well suited to comparing the overall extent of allelic imbalance between samples. We show that in some tissues allelic imbalance is correlated with age; however, this correlation may be due to changes in the abundance of immune cell populations with age, as we found strong correlations between sample-level allelic imbalance and the inferred abundance of multiple immune cell types across whole blood samples.

genomics↗

bioRxiv2023

Regulatory Landscape Enrichment Analysis (RLEA) using gaiaAssociation

MotivationTo understand whether sets of genomic loci are enriched at the regulatory loci of one or more cell types, we developed the gaiaAssociation package to perform Regulatory Landscape Enrichment Analysis (RLEA). RLEA is a novel analytical process that tests for enrichment of sets of loci in cell type-specific open chromatin regions (OCRs) in the genome. ResultsWe demonstrate that the application of RLEA to genome-wide association study (GWAS) data reveals cell types likely to be mediating the phenotype studied, and clusters OCRs based on their shared regulatory profiles. GaiaAssociation is Python code that is freely available for use in functional genomics studies. Availability and ImplementationGaia Association is available on PyPi (https://pypi.org/project/gaiaAssociation/0.6.0/#description) for pip download and use on the command line or as an inline Python package. Gaia Association can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation. Contactjohn.greally@einsteinmed.edu

bioinformatics↗

bioRxiv2022

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates to reduce the influence of genes with greater uncertainty in effect size estimate on distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes. We also show that the approach naturally suggests alternative test types that are not usually considered in GSA; it can, for example, be applied to identify differences in effect size distributions between sample subgroups in a gene set of interest. Applying this approach to an analysis of gene expression changes between matched colon tumour and normal samples, we found several gene sets that showed distinct behaviour in patient subgroups with different prognoses. These may help to explain the clinical differences that have been reported between these patient groups. Author summaryThe role of gene set analysis is to identify groups of genes that are perturbed in a genomics experiment. There are many tools available for this task and they do not all test for the same types of changes. Here we propose a new way to carry out gene set analysis that involves first working out the distribution of the group effect in the gene set and then comparing this distribution to the equivalent distribution in other genes. Tests performed by existing tools for gene set analysis can be related to different comparisons in these distributions of group effects. A unified framework for gene set analysis provides for more explicit null hypotheses against which to test sets of genes for different types of responses to the experimental conditions. These results are more interpretable, because the group effect distributions can be compared visually, providing an indication of how the experimental effect differs between the gene sets. We can also apply this method to identify sets of genes that behave atypically in subgroups of samples. This enabled us to identify differences in the expression of several gene sets in colon cancer samples between individuals with reduced mortality and those without this benefit.

bioinformatics↗

bioRxiv2021

No evidence that HLA genotype influences the driver mutations that occur in cancer patients

The major histocompatibility (MHC) molecules are capable of presenting neoantigens resulting from somatic mutations on cell surfaces, potentially directing immune responses against cancer. This led to the hypothesis that cancer driver mutations may occur in gaps in the capacity to present neoantigens that are dependent on MHC genotype. If this is correct, it has important implications for understanding oncogenesis and may help to predict driver mutations based on genotype data. In support of this hypothesis, it has been reported that driver mutations that occur frequently tend to be poorly presented by common MHC alleles and that the capacity of a patients MHC alleles to present the resulting neoantigens is predictive of the driver mutations that are observed in their tumour. Here we show that these reports of a strong relationship between driver mutation occurrence and patient MHC alleles are a consequence of unjustified statistical assumptions. Our reanalysis of the data provides no evidence of an effect of MHC genotype on the oncogenic mutation landscape.

cancer biology↗

bioRxiv2020

Controlling for off-target genetic effects using polygenic scores improves the power of genome-wide association studies

Ongoing increases in the size of human genotype and phenotype collections offer the promise of improved understanding of the genetics of complex diseases. In addition to the biological insights that can be gained from the nature of the variants that contribute to the genetic component of complex trait variability, these data bring forward the prospect of predicting complex traits and the risk of complex genetic diseases from genotype data. Here we show that advances in phenotype prediction can be applied to improve the power of genome-wide association studies. We demonstrate a simple and efficient method to model genetic background effects using polygenic scores derived from SNPs that are not on the same chromosome as the target SNP. Using simulated and real data we found that this can result in a substantial increase in the number of variants passing genome-wide significance thresholds. This increase in power to detect trait-associated variants also translates into an increase in the accuracy with which the resulting polygenic score predicts the phenotype from genotype data. Our results suggest that advances in methods for phenotype prediction can be exploited to improve the control of background genetic effects, leading to more accurate GWAS results and further improvements in phenotype prediction.

genomics↗

bioRxiv2019

Selection shapes synonymous stop codon use in mammals

Phylogenetic models of the evolution of protein-coding sequences can provide insights into the selection pressures that have shaped them. In the application of these models synonymous nucleotide substitutions, which do not alter the encoded amino acid, are often assumed to have limited functional consequences and used as a proxy for the neutral rate of evolution. The ratio of nonsynonymous to synonymous substitution rates is then used to categorize the selective regime that applies to the protein (e.g. purifying selection, neutral evolution, diversifying selection). Here, we extend these models to explore the extent of purifying selection acting on substitutions between synonymous stop codons. Using a large collection of coding sequence alignments we estimate that a high proportion (approximately 57%) of mammalian genes are affected by selection acting on stop codon preference. This proportion varies substantially by codon, with UGA stop codons far more likely to be conserved. Genes with evidence of selection acting on synonymous stop codons have distinctive characteristics, compared to unconserved genes with the same stop codon, including enrichment for specific functional properties. Notably, genes with conserved stop codons have longer 3 UTRs and are associated with shorter mRNA half-life than other genes. The coding regions of these genes are also much more likely to be under strong purifying selection pressure. Our results suggest that the preference for UGA stop codons found in many multicellular eukaryotes is selective rather than mutational in origin.

evolutionary biology↗

bioRxiv2018

Cancer eQTLs can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity

Expression quantitative trait loci (eQTLs) identified using tumor gene expression data could affect gene expression in cancer cells, tumor-associated normal cells, or both. Here, we demonstrate a method to identify eQTLs affecting expression in cancer cells by modeling the statistical interaction between genotype and tumor purity. Only one-third of breast cancer risk variants, identified as eQTLs from a conventional analysis, could be confidently attributed to cancer cells. The remaining variants could affect cells of the tumor microenvironment, such as immune cells and fibroblasts. Deconvolution of tumor eQTLs will help determine how inherited polymorphisms influence cancer risk, development, and treatment response.

genomics↗

bioRxiv2018

Insights from deconvolution of cell subtype proportions enhance the interpretation of functional genomic data.

Cell subtype proportional differences between samples significantly contribute to variation of functional genomic properties such as gene expression or DNA methylation. Current analytical approaches typically deal with cell subtype proportion influences as a nuisance variable to be eliminated. Here we demonstrate how harvesting information about cell subtype proportions from functional genomics data provides insights into the cellular events in human phenotypes. We note a striking concordance between cell subtype proportions estimated from orthogonal genome-wide assays, and demonstrate the potential for single-cell RNA-seq data to be used in tissues for which reference cell subtype functional genomic datasets are not available. Taken together, our results confirm the importance of estimating cell subtype proportions when testing a model of cellular reprogramming in human phenotypic association studies, and the value of simultaneously testing for systematic cell subtype proportional alterations as a separate phenotypic association, gaining extra insights from functional genomic studies.

genomics↗

bioRxiv2016

Inference of candidate germline mutator loci in humans from genome-wide haplotype data

The rate of germline mutation varies widely between species but little is known about the extent of variation in the germline mutation rate between individuals of the same species. Here we demonstrate that an allele that increases the rate of germline mutation can result in a distinctive signature in the genomic region linked to the affected locus, characterized by a number of haplotypes with a locally high proportion of derived alleles, against a background of haplotypes carrying a typical proportion of derived alleles. We searched for this signature in human haplotype data from phase 3 of the 1000 Genomes Project and report a number of candidate mutator loci, several of which are located close to or within genes involved in DNA repair or the DNA damage response. To investigate whether mutator alleles remained active at any of these loci, we used de novo mutation counts from human parent-offspring trios in the 1000 Genomes and Genome of the Netherlands cohorts, looking for an elevated number of de novo mutations in the offspring of parents carrying a candidate mutator haplotype at each of these loci. We found some support for two of the candidate loci, including one locus just upstream of the BRSK2 gene, which is expressed in the testis and has been reported to be involved in the response to DNA damage.\n\nAuthor SummaryEach time a genome is replicated there is the possibility of error resulting in the incorporation of an incorrect base or bases in the genome sequence. When these errors occur in cells that lead to the production of gametes they can be incorporated into the germline. Such germline mutations are the basis of evolutionary change; however, to date there has been little attempt to quantify the extent of genetic variation in human populations in the rate at which they occur. This is particularly important because new spontaneous mutations are thought to make an important contribution to many human diseases. Here we present a new way to identify genetic loci that may be associated with an elevated rate of germline mutation and report the application of this method to data from a large number of human genomes, generated by the 1000 Genomes Project. Several of the candidate loci we report are in or near genes involved in DNA repair and some were supported by direct measurement of the mutation rate obtained from parent-offspring trios.

genetics↗