Seoighe, C. | bioRxiv Science

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↗