Melbourne School of Population and Global Health - Research Publications
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ItemNo Preview AvailableHeterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics(PUBLIC LIBRARY SCIENCE, 2008-04)A three-stage genome-wide association study recently identified single nucleotide polymorphisms (SNPs) in five loci (fibroblast growth receptor 2 (FGFR2), trinucleotide repeat containing 9 (TNRC9), mitogen-activated protein kinase 3 K1 (MAP3K1), 8q24, and lymphocyte-specific protein 1 (LSP1)) associated with breast cancer risk. We investigated whether the associations between these SNPs and breast cancer risk varied by clinically important tumor characteristics in up to 23,039 invasive breast cancer cases and 26,273 controls from 20 studies. We also evaluated their influence on overall survival in 13,527 cases from 13 studies. All participants were of European or Asian origin. rs2981582 in FGFR2 was more strongly related to ER-positive (per-allele OR (95%CI) = 1.31 (1.27-1.36)) than ER-negative (1.08 (1.03-1.14)) disease (P for heterogeneity = 10(-13)). This SNP was also more strongly related to PR-positive, low grade and node positive tumors (P = 10(-5), 10(-8), 0.013, respectively). The association for rs13281615 in 8q24 was stronger for ER-positive, PR-positive, and low grade tumors (P = 0.001, 0.011 and 10(-4), respectively). The differences in the associations between SNPs in FGFR2 and 8q24 and risk by ER and grade remained significant after permutation adjustment for multiple comparisons and after adjustment for other tumor characteristics. Three SNPs (rs2981582, rs3803662, and rs889312) showed weak but significant associations with ER-negative disease, the strongest association being for rs3803662 in TNRC9 (1.14 (1.09-1.21)). rs13281615 in 8q24 was associated with an improvement in survival after diagnosis (per-allele HR = 0.90 (0.83-0.97). The association was attenuated and non-significant after adjusting for known prognostic factors. Our findings show that common genetic variants influence the pathological subtype of breast cancer and provide further support for the hypothesis that ER-positive and ER-negative disease are biologically distinct. Understanding the etiologic heterogeneity of breast cancer may ultimately result in improvements in prevention, early detection, and treatment.
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ItemNo Preview AvailableAre the so-called low penetrance breast cancer genes, ATM, BRIP1, PALB2 and CHEK2, high risk for women with strong family histories?(BMC, 2008)A woman typically presents for genetic counselling because she has a strong family history and is interested in knowing the probability she will develop disease in the future; that is, her absolute risk. Relative risk for a given factor refers to risk compared with either population average risk (sense a), or risk when not having the factor, with all other factors held constant (sense b). Not understanding that these are three distinct concepts can result in failure to correctly appreciate the consequences of studies on clinical genetic testing. Several studies found that the frequencies of mutations in ATM, BRIP1, PALB2 and CHEK2 were many times greater for cases with a strong family history than for controls. To account for the selected case sampling (ascertainment), a statistical model that assumes that the effect of any measured variant multiplies the effect of unmeasured variants was applied. This multiplicative polygenic model in effect estimated the relative risk in the sense b, not sense a, and found it was in the range of 1.7 to 2.4. The authors concluded that the variants are "low penetrance". They failed to note that their model fits predicted that, for some women, absolute risk may be as high as for BRCA2 mutation carriers. This is because the relative risk multiplies polygenic risk, and the latter is predicted by family history. Therefore, mutation testing of these genes for women with a strong family history, especially if it is of early onset, may be as clinically relevant as it is for BRCA1 and BRCA2.
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ItemThe BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions(NATURE PUBLISHING GROUP, 2008-04-22)Multiple genetic loci confer susceptibility to breast and ovarian cancers. We have previously developed a model (BOADICEA) under which susceptibility to breast cancer is explained by mutations in BRCA1 and BRCA2, as well as by the joint multiplicative effects of many genes (polygenic component). We have now updated BOADICEA using additional family data from two UK population-based studies of breast cancer and family data from BRCA1 and BRCA2 carriers identified by 22 population-based studies of breast or ovarian cancer. The combined data set includes 2785 families (301 BRCA1 positive and 236 BRCA2 positive). Incidences were smoothed using locally weighted regression techniques to avoid large variations between adjacent intervals. A birth cohort effect on the cancer risks was implemented, whereby each individual was assumed to develop cancer according to calendar period-specific incidences. The fitted model predicts that the average breast cancer risks in carriers increase in more recent birth cohorts. For example, the average cumulative breast cancer risk to age 70 years among BRCA1 carriers is 50% for women born in 1920-1929 and 58% among women born after 1950. The model was further extended to take into account the risks of male breast, prostate and pancreatic cancer, and to allow for the risk of multiple cancers. BOADICEA can be used to predict carrier probabilities and cancer risks to individuals with any family history, and has been implemented in a user-friendly Web-based program (http://www.srl.cam.ac.uk/genepi/boadicea/boadicea_home.html).
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ItemSNP selection for genes of iron metabolism in a study of genetic modifiers of hemochromatosis(BMC, 2008-03-20)BACKGROUND: We report our experience of selecting tag SNPs in 35 genes involved in iron metabolism in a cohort study seeking to discover genetic modifiers of hereditary hemochromatosis. METHODS: We combined our own and publicly available resequencing data with HapMap to maximise our coverage to select 384 SNPs in candidate genes suitable for typing on the Illumina platform. RESULTS: Validation/design scores above 0.6 were not strongly correlated with SNP performance as estimated by Gentrain score. We contrasted results from two tag SNP selection algorithms, LDselect and Tagger. Varying r2 from 0.5 to 1.0 produced a near linear correlation with the number of tag SNPs required. We examined the pattern of linkage disequilibrium of three levels of resequencing coverage for the transferrin gene and found HapMap phase 1 tag SNPs capture 45% of the > or = 3% MAF SNPs found in SeattleSNPs where there is nearly complete resequencing. Resequencing can reveal adjacent SNPs (within 60 bp) which may affect assay performance. We report the number of SNPs present within the region of six of our larger candidate genes, for different versions of stock genotyping assays. CONCLUSION: A candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data. Tag SNP software must be fast and flexible to data changes, since tag SNP selection involves iteration as investigators seek to satisfy the competing demands of coverage within and between populations, and typability on the technology platform chosen.