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Biology subjects

Warrington, N.

Publications and source records attributed to Warrington, N..

4 recordsLinked to original sources

p53 mutations exhibit sex specific gain-of-function activity in gliomagenesis

The tumor suppressor TP53 is the most frequently mutated gene in cancer. Most TP53 mutations are missense mutations in the DNA-binding domain, which in addition to loss of canonical p53 activity, frequently confer gain-of-function (GOF) aberrant transcriptional activity through mutant p53 localization to non-canonical genes. GOF phenotypes differ by mutation and cell identity and are reported to include increased proliferation, migration, metabolic reprogramming, and therapy resistance. We found that several recurring p53 mutations exhibit a sex-bias in patients with glioblastoma (GBM). In vitro and in vivo analysis of three mutations, p53R172H, p53Y202C, and p53Y217C revealed sex differences in each mutations ability to transform primary mouse astrocytes. p53R172H exhibited a far greater ability to transform female astrocytes than males, p53Y202C transformed both male and female astrocytes with a small male bias, and p53Y217C only exhibited GOF transformation effects in male astrocytes. These phenotypic differences reflect an interaction between sex and GOF mutation to drive unique gene expression patterns in cancer pathways. We found that mutant p53 exhibits sex and mutation specific aberrant genomic localization to the transcriptional start sites of upregulated genes, whose promoter regions were enriched for different sets of transcription factor DNA-binding motifs. Together, our data establish a novel paradigm for sex specific mutant p53 GOF activity in GBM with implications for all cancer.

cancer biology↗

The effect of plasma lipids and lipid lowering interventions on bone mineral density: a Mendelian randomization study

Statin treatment increases bone mineral density (BMD) and reduces fracture risk, but the underlying mechanism is unclear. We used Mendelian randomization (MR) to assess whether this relation is explained by a specific effect in response to statin use, or by a general effect of lipid-lowering. We utilized 400 single nucleotide polymorphisms (SNPs) robustly associated with plasma lipid levels and results from a heel BMD GWAS (derived from quantitative ultrasound) in 426,824 individuals from the UK Biobank. We performed univariate and multivariable MR analyses of low-density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and triglyceride levels on BMD. To test whether the effect of statins on BMD was mediated by lowering lipid levels, MR was repeated with and without SNPs in the HMGCR region, the gene targeted by statins. Univariate MR analyses provided evidence for a causal effect of LDL-C on BMD ({beta} = -0.060; -0.084 to -0.036; P = 4x10-6; standard deviation change in BMD per standard deviation change in LDL-C, with 95% CI), but not HDL or triglycerides. Multivariable MR analysis suggested that the effect of LDL-C on BMD was independent of HDL-C and triglycerides, and sensitivity analyses involving MR Egger and weighted median MR approaches suggested that the LDL-C results were robust to pleiotropy. MR analyses of LDL-C restricted to SNPs in the HMGCR region showed similar effects on BMD({beta} = -0.083; -0.132 to -0.034; P = 0.001) to those excluding these SNPs ({beta} = -0.063; -0.090 to -0.036; P = 8x10-6). Bidirectional MR analyses provided some evidence for a causal effect of BMD on plasma LDL-C levels. Our results suggest that effects of statins on BMD are at least partly due to their LDL-C lowering effect. Further studies are required to examine the potential role of modifying plasma lipid levels in treating osteoporosis.

epidemiology↗

Using Structural Equation Modeling to Jointly Estimate Maternal and Foetal Effects on Birthweight in the UK Biobank

BackgroundTo date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individuals own genotype on their birthweight, their mothers genotype, or both.\n\nMethodsWe demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and foetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study.\n\nResultsUnlike simple regression models, our approach is unbiased when there is both a maternal and foetal effect. The method can be used when either the individuals own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype, and reasonable power to detect whether it is a foetal and/or maternal effect. We also identify a subset of birth weight associated SNPs that have opposing maternal and foetal effects in the UK Biobank.\n\nConclusionsOur results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.\n\nKey MessagesO_LIWe describe a structural equation model to estimate both maternal and foetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual.\nC_LIO_LIUsing simulation, we show that our approach is unbiased when there is both a maternal and foetal effect, unlike simple linear regression models. Additionally, we illustrate that the structural equation model is largely robust to measurement error and missing data for either the individuals own phenotype or the phenotype of their offspring.\nC_LIO_LIWe describe how the flexibility of the structural equation modelling framework will allow the inclusion of summary statistics from studies that are unable to share raw data.\nC_LIO_LIUsing the structural equation model to estimate the maternal and foetal effects of known birthweight associated loci in the UK Biobank, we identify three loci that have primary effects through the maternal genome and six loci that have opposite effects in the maternal and foetal genomes.\nC_LI

genetics↗

Maternal and fetal genetic contribution to gestational weight gain

BackgroundClinical recommendations to limit gestational weight gain (GWG) imply high GWG is causally related to adverse outcomes in mother or offspring, but GWG is the sum of several inter-related complex phenotypes (maternal fat deposition and vascular expansion, placenta, amniotic fluid and fetal growth). Understanding the genetic contribution to GWG could help clarify the potential effect of its different components on maternal and offspring health. Here we explore the genetic contribution to total, early and late GWG.\n\nParticipants and MethodsA genome-wide association study was used to identify maternal and fetal variants contributing to GWG in up to 10,543 mothers and up to 16,317 offspring of European origin, with replication in 10,660 mothers and 7,561 offspring. Additional analyses determined the proportion of variability in GWG from maternal and fetal common genetic variants and the overlap of established genome-wide significant variants for phenotypes relevant to GWG (e.g. maternal BMI and glucose, birthweight).\n\nResultsWe found that approximately 20% of the variability in GWG was tagged by common maternal genetic variants, and that the fetal genome made a surprisingly minor contribution to explaining variation in GWG. We were unable to identify any genetic variants that reached genome-wide levels of significance (P<5x10-8) and replicated. Some established maternal variants associated with increased BMI, fasting glucose and type 2 diabetes were associated with lower early, and higher later GWG. Maternal variants related to higher systolic blood pressure were related to lower late GWG. Established maternal and fetal birthweight variants were largely unrelated to GWG.\n\nConclusionWe found a modest contribution of maternal common variants to GWG and some overlap of maternal BMI, glucose and type 2 diabetes variants with GWG. These findings suggest that associations between GWG and later offspring/maternal outcomes may be due to the relationship of maternal BMI and diabetes with GWG.

genetics↗