Biology subjects

Bodmer, W. F.

Publications and source records attributed to Bodmer, W. F..

6 recordsLinked to original sources

bioRxiv2022

Newfoundland and Labrador: A mosaic founder population of an Irish and British diaspora from 300 years ago.

The founder population of Newfoundland and Labrador (NL) is a unique genetic resource, in part due to geographic and cultural isolation, where historical records describe a migration of European settlers primarily from Ireland and England to NL in the 18th and 19th centuries. Whilst its historical isolation, and increase prevalence of certain monogenic disorders, have been appreciated, the fine-scale genetic structure and ancestry of the population has not been well described. Understanding the genetic background on which functional, disease causing, genetic variation resides on would aid informed genetic mapping efforts in the Province. Here, we leverage dense genome-wide SNP data on 1,807 NL individuals to reveal fine-scale genetic structure in NL that is clustered around coastal communities and correlated with Christian denomination. We show that the majority of NL European ancestry can be traced back to the south-east and south-west of Ireland and England, respectively. We date a substantial population size bottleneck approximately 10-15 generations ago in NL, associated with increased haplotype sharing and autozygosity. Our results elucidate novel insights into the population history of NL and demonstrate evidence of a population conducive to further genetic studies and biomarker discovery. Significance StatementNewfoundland and Labrador (NL) has been identified as a founder population, though evidence of its magnitude and subsequent isolation is unclear. Here, analysis of 1,807 NL individuals demonstrates population structure associated with geographical isolation in coastal communities and religious denomination (Catholic or Protestant Christian). Further, NL European ancestry primarily descends from settlers from south-east Ireland and south-west England. This history is associated with increased sharing of longer haplotypes in NL, and NL-specific drift in some communities more than others, providing strong evidence of a founder event occurring about 10-15 generations ago. This study elucidates the detailed population structure of NL and shows enrichment for otherwise low frequency functional variants due to genetic drift useful for potential future biomarker discovery studies.

genetics↗

bioRxiv2022

Solute exchange through gap junctions lessens the adverse effects of inactivating mutations in metabolite-handling genes

Experimental inactivation of certain genes involved in metabolism attenuates cancer cell growth in vitro. However, loss-of-function mutations in metabolic pathways are not negatively selected in human cancers, indicating that these genes are not essential in vivo. We hypothesize that spontaneous mutations affecting metabolic pathways do not necessarily result in a functional defect because affected cells may be rescued by exchanging metabolites with neighboring wild-type cells via gap junctions. Using fluorescent substances to probe inter-cellular diffusion, we show that colorectal cancer (CRC) cells are coupled by gap junctions assembled from connexins, particularly the constitutively expressed Cx26. In co-cultures of wild-type cells with cells that had inactivated components of pH regulation (SLC9A1), glycolysis (ALDOA), or mitochondrial metabolism (NDUFS1), we show that diffusive coupling was able to rescue the functional defect associated with the inactivation of metabolite-handling genes. Function rescue was dependent on Cx26 channels and reduced phenotypic heterogeneity among cells. Since the phenotypic landscape did not map onto genotype, an individual cell should not be considered as the unit under selection, at least in the case of metabolite-handling processes. Our findings can explain why certain loss-of-function mutations in genes, previously ascribed as being essential, do not influence the growth of human cancers.

cancer biology↗

bioRxiv2022

GMMchi: Gene Expression Clustering Using Gaussian Mixture Modeling

MotivationCancer evolution consists of a stepwise acquisition of genetic and epigenetic changes, which alter the gene expression profiles of cells in a particular tissue and result in phenotypic alterations acted upon by natural selection. The recurrent appearance of specific genetic lesions across individual cancers and cancer types suggests the existence of certain "driver mutations," which likely make up the major contribution to tumors selective advantages over surrounding normal tissue and as such are responsible for the most consequential aspects of the cancer cells gene expression patterns and phenotypes. We hypothesize that such mutations are likely to cluster with specific dichotomous shifts in the expression of the genes they most closely control, and propose GMMchi, a Python package that leverages Gaussian Mixture Modeling to detect and characterize bimodal gene expression patterns across cancer samples, as a tool to analyze such correlations using 2x2 contingency table statistics. ResultsWe confirm that GMMchi robustly and reliably extracts bimodal patterns from both colorectal cancer (CRC) cell line-derived microarray and tumor-derived RNA-Seq data and verify previously reported gene expression correlates of some well-characterized CRC phenotypes. Using well-defined simulated data, we were able to confirm the robust performance of GMMchi, reaching 85% accuracy with a sample size of n = 90. We were also able to demonstrate a few examples of the application of GMMchi with respect to its capacity to characterize background florescent signal in microarray data, filter out uninformative background probe sets, as well as uncover novel genetic interrelationships and tumor characteristics. Our approach to analysing gene expression analysis in cancers provides an additional lens to supplement traditional continuous-valued statistical analysis by maximizing the information that can be gathered from bulk gene expression data. AvailabilityThe Python package GMMchi and our cell line microarray data used in this paper is available for downloading on GitHub at https://github.com/jeffliu6068/GMMchi Contactta-chun.liu@oncology.ox.ac.uk, peter.kalugin@hms.harvard.edu, walter.bodmer@hertford.ox.ac.uk

bioinformatics↗

bioRxiv2019

Somatic selection of poorly differentiating variant stem cell clones could be a key to human ageing

Any replicating system in which heritable variants with differing replicative potentials can arise is subject to a Darwinian evolutionary process. The continually replicating adult tissue stem cells that control the integrity of many tissues of long-lived, multicellular, complex vertebrate organisms, including humans, constitute such a replicating system. Our suggestion is that somatic selection for mutations (or stable epigenetic changes) that cause an increased rate of adult tissue stem cell proliferation, and their long-term persistence, at the expense of normal differentiation, is a major key to the ageing process. Once an organism has passed the reproductive age, there is no longer any significant counterselection at the organismal level to this inevitable cellular level Darwinian process. Author ContributionsWFB conceived the project. WFB and DJMC wrote the manuscript.

cell biology↗

bioRxiv2018

Genetic patterning in Central Eurasia: population history and pigmentation

Central-western Asia has often been underrepresented in population genetic studies, but it is important for the clarification of the peopling of Eurasia and the relationship between its western and eastern extremities. We genotyped individuals from over 40 population groups, mostly central Eurasian, for mitochondrial HVR1, CCR5del32 and five functional MC1R variants (p.Val60Leu, p.Val92Met, p.Arg151Cys, p.Arg160Trp, p.Arg163Gln), and collected published genotype data for comparison. Mitochondrial profiles confirm both the higher heterozygosity in Central Asia than in surrounding areas, and the broadly northern European distribution of CCR5del32. The MC1R variants profile alone is a good determinant of the longitudinal position of a population group, and combined FST values divide Eurasia into seven broad geographic divisions. We can conclude that Central Asia shares genetic features with both eastern and western Eurasia, compatible with both a scenario where the former acted as a source for the latter twos genetic diversity, or one where Central Asia is a hybrid zone where eastern and western peoples met. Furthermore, the overall high FST values for functional MC1R variants combined with presumed selection pressures on skin pigmentation in low-UV areas lead us to conclude that different variants were selected for in east and west Eurasia, an example of convergent evolution.

genetics↗

bioRxiv2017

The genetics of the human face: identification of large effect single gene variants

In order to discover specific variants with relatively large effects on the human face we have devised an approach to identifying facial features with high heritability. This is based on using twin data to estimate the additive genetic value of each point on a face, as provided by a 3D camera system. In addition, we have used the ethnic difference between East Asian and European faces as a further source of face genetic variation. We use principal components analysis to provide a fine definition of the surface features of human faces around the eyes and of the profile, and chose upper and lower 10% extremes of the most heritable PCs for looking for genetic associations. Using this strategy for the analysis of 3D images of 1832 unique volunteers from the well characterised People of the British Isles study [1, 2] and 1567 unique twin images from the TwinsUK cohort (www.twinsuk.ac.uk), together with genetic data for 500,000 SNPs, we have identified three specific genetic variants with notable effects on facial profiles and eyes.\n\nSignificance statementThe human face is extraordinarily variable and the extreme similarity of the faces of identical twins indicates that most of this variability is genetically determined. This level of genetic variability has probably arisen through natural selection, for example, for recognition of membership of a group or as a consequence of differential mate selection with respect to facial features. We have devised an approach to identifying specific genetic effects on particular facial features. This should enable the understanding, eventually at the molecular level, of the nature of this extraordinary genetic variability, which is such an important feature of our everyday human interactions.\n\nAuthor ContributionsWFB conceived the project. BW and TD organised collection of PoBI data and KH, DJMC, DM, AB and WFB assisted in data collection. TDS, PH and AN collected TwinsUK data. WPK and WJC conducted image registration analysis under supervision of JK. DJMC analysed registered image data and genetic data under supervision of WFB. WFB and DJMC wrote the manuscript, with WJC and WPK contributing additional technical material. WFB and BW supervised the project.

genetics↗