Biology subjects

Fischer, K.

Publications and source records attributed to Fischer, K..

7 recordsLinked to original sources

bioRxiv2018

Polygenic prediction of breast cancer: comparison of genetic predictors and implications for screening

BackgroundPublished genetic risk scores for breast cancer (BC) so far have been based on a relatively small number of markers and are not necessarily using the full potential of large-scale Genome-Wide Association Studies. This study aims to identify an efficient polygenic predictor for BC based on best available evidence and to assess its potential for personalized risk prediction and screening strategies.\n\nMethodsFour different genetic risk scores (two already published and two newly developed) and their combinations (metaGRS) are compared in the subsets of two population-based biobank cohorts: the UK Biobank (UKBB, 3157 BC cases, 43,827 controls) and Estonian Biobank (EstBB, 317 prevalent and 308 incident BC cases in 32,557 women). In addition, correlations between different genetic risk scores and their associations with BC risk factors are studied in both cohorts.\n\nResultsThe metaGRS that combines two genetic risk scores (metaGRS2 - based on 75 and 898 Single Nucleotide Polymorphisms, respectively) has the strongest association with prevalent BC status in both cohorts. One standard deviation difference in the metaGRS2 corresponds to an Odds Ratio = 1.6 (95% CI 1.54 to 1.66, p = 9.7*10-135) in the UK Biobank and accounting for family history marginally attenuates the effect (Odds Ratio = 1.58, 95% CI 1.53 to 1.64, p = 9.1*10-129). In the EstBB cohort, the hazard ratio of incident BC for the women in the top 5% of the metaGRS2 compared to women in the lowest 50% is 4.2 (95% CI 2.8 to 6.2, p = 8.1*10-13). The different GRSs are only moderately correlated with each other and are associated with different known predictors of BC. The classification of genetic risk for the same individual may vary considerably depending on the chosen GRS.\n\nConclusionsWe have shown that metaGRS2 that combines on the effects of more than 900 SNPs provides best predictive ability for breast cancer in two different population-based cohorts. The strength of the effect of metaGRS2 indicates that the GRS could potentially be used to develop more efficient strategies for breast cancer screening for genotyped women.

genetics↗

bioRxiv2018

Genomic underpinnings of lifespan allow prediction and reveal basis in modern risks

We use a multi-stage genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near GADD45G, KCNK3, LDLR, POM121C, ZC3HC1, and ABO. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and tissue-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer -but not other cancers-explain the most variance, possibly reflecting modern susceptibilities, whilst cancer may act through many rare variants, or the environment. Resultant polygenic scores predict a mean lifespan difference of around five years of life across the deciles.

genomics↗

bioRxiv2018

Kunitz type protease inhibitor EgKI-1 from the canine tapeworm Echinococcus granulosus as a promising anti-cancer therapeutic

EgKI-1, a member of the Kunitz type protease inhibitor family, is highly expressed by the oncosphere of the canine tapeworm Echinococcus granulosus, the stage that is infectious to humans and ungulates, giving rise to a hydatid cyst localized to the liver and other organs. Larval protoscoleces, which develop within the hydatid cyst, have been shown to possess anti-cancer properties, although the precise molecules involved have not been identified. We show that recombinant EgKI-1 inhibits the growth and migration of a range of human cancers including breast, melanoma and cervical cancer cell lines in a dose-dependent manner in vitro without affecting normal cell growth. Furthermore, EgKI-1 treatment arrested the cancer cell growth by disrupting the cell cycle and induced apoptosis of cancer cells in vitro. An in vivo model of triple negative breast cancer (MDA-MB-231) in BALB/c nude mice showed significant tumor growth reduction in EgKI-1-treated mice compared with controls. These findings indicate that EgKI-1 shows promise for future development as an anti-cancer therapeutic.

cancer biology↗

bioRxiv2018

Filamentation and restoration of normal growth in E.coli using a combined CRISPRi sgRNA/antisense RNA approach

CRISPR interference (CRISPRi) using dCas9/sgRNA is a powerful tool for the exploration and manipulation of gene functions. Here we quantify the reversible switching of crucial cellular processes by CRISPRi and an antisense RNA mechanism. Reversible induction of filamentous growth in E. coli has been recently demonstrated by controlling the expression levels of the bacterial cell division proteins FtsZ/FtsA via CRISPRi. If FtsZ falls below a critical level, cells cannot divide. However, the cells remain metabolically active and continue with DNA replication. We surmised that this makes them amenable to an inducible antisense RNA strategy to counteract FtsZ inhibition. We show that both static and inducible thresholds can adjust the characteristics of the switching process. Combining bulk data with single cell measurements, we clarify the role of bacterial heterogeneity and population dynamics for gene circuits affecting cell division. Filamentation is shown to strongly increase gene expression variability in the bacteria. Furthermore, we find reversible switching only in a small subpopulation of the bacteria, which takes over the population upon continued cell division. Successful restoration of division occurs faster in the presence of antisense sgRNAs than upon simple termination of CRISPRi induction.

synthetic biology↗

bioRxiv2018

Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of {small tilde}27M variants ({small tilde}21M with minor allele frequency [MAF]<5%), identified 243 genome-wide significant loci (p<5x10-8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10-5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%[≤]MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

genomics↗

bioRxiv2017

PAIRUP-MS: Pathway Analysis and Imputation to Relate Unknowns in Profiles from Mass Spectrometry-based metabolite data

Metabolomics is a powerful approach for discovering biomarkers and metabolic quantitative trait loci. While untargeted profiling methods can measure up to thousands of metabolite signals in a single experiment, many signals cannot be readily identified as known metabolites or compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To deal with these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets using an imputation-based approach and to generate pathway annotations for these signals. We performed meta and pathway analyses for both known and unknown signals in multiple datasets and then validated the results using genetic associations. Finally, we applied the methods to detect metabolite signals and pathways associated with body mass index, demonstrating that our framework is useful for analyzing unknown signals in a robust and biologically meaningful manner and for improving the power of untargeted metabolomics studies.

bioinformatics↗

bioRxiv2017

Education and coronary heart disease: a Mendelian randomization study

ObjectivesTo determine whether educational attainment is a causal risk factor in the development of coronary heart disease.\n\nDesignMendelian randomization study, where genetic data are used as proxies for education, in order to minimize confounding. A two-sample design was applied, where summary level genetic data was analysed from two publically available consortia.\n\nSettingIn the main analysis, we analysed genetic data from two large consortia (CARDIoGRAM and SSGAC), comprising of 112 cohorts from predominantly high-income countries. In addition, we also analysed genetic data from 7 additional large consortia, in order to identify putative causal mediators.\n\nParticipantsThe main analysis was of 589 377 men and women, predominantly of European origin.\n\nExposureA one standard deviation increase in the genetic predisposition towards higher education (i.e. 3.6 years of additional schooling). This was measured by 162 genetic variants that have been previously associated with education.\n\nMain outcomeCombined fatal and nonfatal coronary heart disease (63 746 events).\n\nResults3.6 years of additional education lowered the risk of coronary heart disease by a third (odds ratio = 0.67, 95% confidence interval [CI], 0.59 to 0.77, p=0.01). Equivalent increases in education were also causally associated with reductions in smoking, BMI and improvements in blood lipid profiles.\n\nConclusionsMore time spent in education is causally associated with a large reduction in the risk of coronary heart disease. This may be partly explained by changes to smoking, BMI and a blood lipids. These findings offer support for policy interventions that increase education, in order to also reduce the burden of cardiovascular disease.

epidemiology↗