Genetic Epidemiology Laboratory

Laboratory, Australia

Genetic Epidemiology Laboratory

Laboratory, Australia
SEARCH FILTERS
Time filter
Source Type

PubMed | Karolinska Institutet, University of Cologne, Genome Institute of Singapore, Ministry of Public Health and 93 more.
Type: Journal Article | Journal: Human molecular genetics | Year: 2014

Previous studies have suggested that polymorphisms in CASP8 on chromosome 2 are associated with breast cancer risk. To clarify the role of CASP8 in breast cancer susceptibility, we carried out dense genotyping of this region in the Breast Cancer Association Consortium (BCAC). Single-nucleotide polymorphisms (SNPs) spanning a 1 Mb region around CASP8 were genotyped in 46 450 breast cancer cases and 42 600 controls of European origin from 41 studies participating in the BCAC as part of a custom genotyping array experiment (iCOGS). Missing genotypes and SNPs were imputed and, after quality exclusions, 501 typed and 1232 imputed SNPs were included in logistic regression models adjusting for study and ancestry principal components. The SNPs retained in the final model were investigated further in data from nine genome-wide association studies (GWAS) comprising in total 10 052 case and 12 575 control subjects. The most significant association signal observed in European subjects was for the imputed intronic SNP rs1830298 in ALS2CR12 (telomeric to CASP8), with per allele odds ratio and 95% confidence interval [OR (95% confidence interval, CI)] for the minor allele of 1.05 (1.03-1.07), P = 1 10(-5). Three additional independent signals from intronic SNPs were identified, in CASP8 (rs36043647), ALS2CR11 (rs59278883) and CFLAR (rs7558475). The association with rs1830298 was replicated in the imputed results from the combined GWAS (P = 3 10(-6)), yielding a combined OR (95% CI) of 1.06 (1.04-1.08), P = 1 10(-9). Analyses of gene expression associations in peripheral blood and normal breast tissue indicate that CASP8 might be the target gene, suggesting a mechanism involving apoptosis.


Royce S.G.,Genetic Epidemiology Laboratory | Alsop K.,Genetic Epidemiology Laboratory | Haydon A.,Monash University | Mead L.,Genetic Epidemiology Laboratory | And 6 more authors.
Colorectal Disease | Year: 2010

Objective: Chromosomal loss within the region of 18q and loss of SMAD4 expression have been reported to be frequent somatic events during colorectal cancer tumour progression; however, their associations with age at onset have not been widely studied. Method: We analysed 109 tumours from a population-based case-family study based on colorectal cancers diagnosed before the age of 45 years. These patients with early-onset colorectal cancer had been previously screened for germ-line mismatch repair gene mutations, microsatellite instability (that included the mononucleotide repeat in TGFβRII) and somatic k-ras mutations. We measured SMAD4 protein expression using immunohistochemistry and SMAD4 copy number using quantitative real-time PCR. Results: Loss of SMAD4 protein expression was observed in 27/109 (25%) of cancers tested and was more commonly observed in rectal tumours (15/41, 36%) when compared with tumours arising in the colon (11/66, 17%) (P = 0.04). There was no association between SMAD4 protein expression and TGFβR11 mutation status, SMAD4 copy number, family history, MSI status, tumour stage or grade. Conclusion: Loss of SMAD4 expression is a common feature of early-onset colorectal tumours as it is in colorectal cancers diagnosed in other age-groups. Taken together, the molecular pathways (genetic and epigenetic) now known to be involved in early-onset colorectal cancer only explain a small proportion of the disease and require further exploration. © 2010 The Authors. Journal Compilation © 2010 The Association of Coloproctology of Great Britain and Ireland.


Brun C.C.,University of California at Los Angeles | Brun C.C.,University of Pennsylvania | Lepore N.,University of California at Los Angeles | Pennec X.,French Institute for Research in Computer Science and Automation | And 7 more authors.
IEEE Transactions on Medical Imaging | Year: 2011

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies. © 2010 IEEE.


Southey M.C.,Genetic Epidemiology Laboratory | Ramus S.J.,University College London | Dowty J.G.,University of Melbourne | Smith L.D.,Genetic Epidemiology Laboratory | And 11 more authors.
British Journal of Cancer | Year: 2011

Background:Knowing a young woman with newly diagnosed breast cancer has a germline BRCA1 mutation informs her clinical management and that of her relatives. We sought an optimal strategy for identifying carriers using family history, breast cancer morphology and hormone receptor status data.Methods:We studied a population-based sample of 452 Australian women with invasive breast cancer diagnosed before age 40 years for whom we conducted extensive germline mutation testing (29 carried a BRCA1 mutation) and a systematic pathology review, and collected three-generational family history and tumour ER and PR status. Predictors of mutation status were identified using multiple logistic regression. Areas under receiver operator characteristic (ROC) curves were estimated using five-fold stratified cross-validation.Results:The probability of being a BRCA1 mutation carrier increased with number of selected histology features even after adjusting for family history and ER and PR status (P<0.0001). From the most parsimonious multivariate model, the odds ratio for being a carrier were: 9.7 (95% confidence interval: 2.6-47.0) for trabecular growth pattern (P0.001); 7.8 (2.7-25.7) for mitotic index over 50 mitoses per 10 high-powered field (P0.0003); and 2.7 (1.3-5.9) for each first-degree relative with breast cancer diagnosed before age 60 years (P<0.01).The area under the ROC curve was 0.87 (0.83-0.90).Conclusion:Pathology review, with attention to a few specific morphological features of invasive breast cancers, can identify almost all BRCA1 germline mutation carriers among women with early-onset breast cancer without taking into account family history. © 2011 Cancer Research UK All rights reserved.

Loading Genetic Epidemiology Laboratory collaborators
Loading Genetic Epidemiology Laboratory collaborators