| 1. |
- Garcia-Closas, Montserrat, et al.
(författare)
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Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics
- 2008
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Ingår i: PLoS genetics. - : Public Library of Science (PLoS). - 1553-7404. ; 4:4, s. e1000054-
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 2. |
- Leu, Monica, 1977, et al.
(författare)
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Bias and bias-correction of estimates of familial risk in population-based register
- 2009
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Ingår i: International Journal of Epidemiology. - 0300-5771. ; 39:1, s. 80-8
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Tidskriftsartikel (refereegranskat)abstract
- Background In addition to guiding molecular epidemiology investigations, estimates of the increased risk of disease in relatives of affected persons are also important for screening and counselling decisions. Since precise estimation of such familial risks (FRs) requires large sample sizes, many of the estimates in common use have been obtained from historical electronic records of disease in entire populations, where the relatives of affected and unaffected persons are compared. These estimates may be biased due to failure to identify relatives as affected if they are diagnosed before the start-up date of disease registration. Methods This article presents a method for correcting the bias in FR estimates from such misclassification of family history, using a simple formula that depends on the prevalence and sensitivity of the observed family history. The sensitivity is estimated by using the R package poplab to create realistic populations of related individuals and then imposing the start-up effect of disease registration. Results For a range of FRs, the truncation of family history is demonstrated to result in non-differential misclassification, and sensitivity that has little or no dependence on the FR. The bias is most pronounced for high FRs and for registers with a short life span, and increases with the age of the study cohort. In all the situations studied, the bias-corrected estimates are in excellent agreement with the true values. Conclusions In summary, our method can correct the inevitable bias in FRs induced by using electronic population data, and is a feasible alternative to the use of validation samples.
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| 3. |
- Leu, Monica, 1977, et al.
(författare)
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Evaluation of bias in familial risk estimates: a study of common cancers using Swedish population-based registers
- 2008
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Ingår i: Journal of the National Cancer Institute. - 0027-8874. ; 100:18, s. 1318-25
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Tidskriftsartikel (refereegranskat)abstract
- Background Bias in estimates of familial cancer may result if population-based registers fail to identify relatives as affected when disease occurs before the start-up of registration (ie, “left-truncation” of family history). Methods Apparent familial relative risks (among offspring of parents with cancer) of colorectal, lung, breast, and prostate cancers and melanoma in a Swedish cohort were compared with relative risks in a simulated population. The study cohort (approximately 7 million individuals) was based on the Swedish MultiGenerational Register linked to the Swedish Cancer Register for the period 1961–2002. A similar population of related individuals (approximately 7 million) with complete family information was simulated by using the R-package PopLab and used to estimate the sensitivity of the observed family history. This sensitivity was then used to calculate corrected age group–specific and overall risks, which were compared with the apparent familial risks of cancer in the cohort. Result The apparent familial risks for colorectal, lung, breast, and prostate cancers and melanoma were 1.99 (95% confidence interval [CI]=1.85 to 2.14), 2.05 (95% CI=1.86 to 2.26), 1.84 (95% CI=1.76 to 1.92), 2.33 (95% CI=2.19 to 2.48), and 2.68 (95% CI=2.35 to 3.07), with corresponding absolute rates of 3.69, 2.59, 16.05, 10.38, and 2.96 per 10000 person-years, among offspring of parents diagnosed with the same cancer. Corrected age group–specific and overall estimates of the familial risks were close to these apparent risks for all studied cancers (all approximately 2.0), except for melanoma. For melanoma, the corrected estimate of 3.18 (95% CI=2.73 to 3.64) was somewhat larger than the apparent estimate and was not included in the confidence interval for the apparent estimate. When the exposure of interest was a parent affected at a younger age, this bias was more pronounced; the apparent estimate for melanoma changed from 4.07 (95% CI=3.21 to 5.16) to 5.67 (95% CI=4.51 to 6.83) after correction. Conclusions For common cancers, risk estimates from the Swedish MultiGenerational cohort do not generally appear to be biased by left-truncation.
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| 4. |
- Leu, Monica, 1977, et al.
(författare)
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“Population Lab”: The creation of virtual populations in Genetic Epidemiology Research
- 2007
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Ingår i: Epidemiology. - 1044-3983. ; 18:4, s. 433-40
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Studies of familial aggregation of disease routinely use linked population registers to construct retrospective cohorts. Although such resources have provided numerous estimates of familial risk, little is known regarding the sensitivity of the estimates to assumed disease models, changing demographics and incidence, and incompleteness of the data. Furthermore, there are no standard tools for testing the validity of estimates from standard epidemiologic designs and from new analytic strategies using register data. METHODS: We present a method and a software package for simulating realistic populations of related individuals, using easily available vital statistics (population counts and fertility and mortality rates). The virtual population is stored in a pedigree file, allowing for easy retrieval of relatives and family structures. We simulate breast cancer in our population using age-specific incidence rates. RESULTS: The Swedish population is simulated as dynamically evolving over the calendar period 1955-2002. The simulated and real population agree well on important features such as age profile, sibship size distribution, and average age at first birth. Using breast cancer as an example, we present several models of familial disease aggregation and show that the parameters used in the simulations are faithfully estimated. In addition, we illustrate how our simulated population provides insight into how incomplete family history in real register data can affect estimates of familial risk. CONCLUSIONS: This simulation method can be used to investigate various underlying models of disease aggregation in families and enhance the development of optimal approaches for family studies. The software package, Population Lab, is available for free download (http://www.meb.ki.se/ approximately marrei/software/poplab/ and http://cran.at.r-project.org/).
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| 5. |
- Leu, Monica, 1977, et al.
(författare)
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The impact of truncation and missing family links in population-based registers on familial risk estimates
- 2007
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Ingår i: American Journal of Epidemiology. - 0002-9262 .- 1476-6256. ; 166:12, s. 1461-7
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Tidskriftsartikel (refereegranskat)abstract
- Family history information is often incomplete in population-based disease registers because of truncation and/or missing family links. In this study, the authors simulated complete populations of related individuals with realistic age, family structure, and incidence rates. After mimicking the realities of register-based data, such as left truncation of family history and missing family links due to death, the authors explored recovery of familial association parameters from standard epidemiologic models. Truncation of family history produced almost no bias for a familial risk of 2 and 50 years of follow-up, but it had a dramatic impact when the familial risk was 10. The age distribution of disease and the magnitude of background incidence rates also affected family history loss and thus the magnitude of bias. One can safeguard against bias by starting follow-up later, with the number of registration years to be ignored in the analysis depending on the value of familial risk. The missing familial links due to death had no effect, except when there was differential mortality for cases with and without a family history of disease. In summary, truncation, and to a lesser extent missing family links, induces bias in familial risk estimates from population-based registers.
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