Re: Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk.
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ABSTRACT:
SUBMITTER: Spitz MR
PROVIDER: S-EPMC2794300 | biostudies-literature | 2009 Dec
REPOSITORIES: biostudies-literature
Publications
Re: Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk.
Spitz Margaret R MR Amos C I CI D'Amelio A A Dong Q Q Etzel C C
Journal of the National Cancer Institute 20091201 24
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Project description:One purpose for seeking common alleles that are associated with disease is to use them to improve models for projecting individualized disease risk. Two genome-wide association studies and a study of candidate genes recently identified seven common single-nucleotide polymorphisms (SNPs) that were associated with breast cancer risk in independent samples. These seven SNPs were located in FGFR2, TNRC9 (now known as TOX3), MAP3K1, LSP1, CASP8, chromosomal region 8q, and chromosomal region 2q35. I used estimates of relative risks and allele frequencies from these studies to estimate how much these SNPs could improve discriminatory accuracy measured as the area under the receiver operating characteristic curve (AUC). A model with these seven SNPs (AUC = 0.574) and a hypothetical model with 14 such SNPs (AUC = 0.604) have less discriminatory accuracy than a model, the National Cancer Institute's Breast Cancer Risk Assessment Tool (BCRAT), that is based on ages at menarche and at first live birth, family history of breast cancer, and history of breast biopsy examinations (AUC = 0.607). Adding the seven SNPs to BCRAT improved discriminatory accuracy to an AUC of 0.632, which was, however, less than the improvement from adding mammographic density. Thus, these seven common alleles provide less discriminatory accuracy than BCRAT but have the potential to improve the discriminatory accuracy of BCRAT modestly. Experience to date and quantitative arguments indicate that a huge increase in the numbers of case patients with breast cancer and control subjects would be required in genome-wide association studies to find enough SNPs to achieve high discriminatory accuracy.
| S-EPMC2528005 | biostudies-literature
Project description:Three lung cancer (LC) models have recently been constructed to predict an individual's absolute risk of LC within a defined period. Given their potential application in prevention strategies, a comparison of their accuracy in an independent population is important.We used data for 3197 patients with LC and 1703 cancer-free controls recruited to an ongoing case-control study at the Harvard School of Public Health and Massachusetts General Hospital. We estimated the 5-year LC risk for each risk model and compared the discriminatory power, accuracy, and clinical utility of these models.Overall, the Liverpool Lung Project (LLP) and Spitz models had comparable discriminatory power (0.69), whereas the Bach model had significantly lower power (0.66; P=0.02). Positive predictive values were highest with the Spitz models, whereas negative predictive values were highest with the LLP model. The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model.We observed modest differences in discriminatory power among the three LC risk models, but discriminatory powers were moderate at best, highlighting the difficulty in developing effective risk models.
| S-EPMC2920015 | biostudies-other
Project description:Estrogen and progesterone hormones are key regulators of a wide variety of biological processes. In addition to their influence on reproduction, cell differentiation and apoptosis, they affect inflammatory response, cell metabolism and most importantly, they regulate physiological breast tissue proliferation and differentiation as well as the development and progression of breast cancer. In order to assess whether genetic variants in the steroid hormone receptor gene ESR1 (estrogen receptor alpha) had an effect on sporadic breast cancer susceptibility, we assessed 7 ESR1 single nucleotide polymorphisms (SNPs) for associations with breast cancer susceptibility and clinical parameters in 221 breast cancer patients and 221 controls, respectively. We identified ESR1 intron SNP +2464 C/T (rs3020314) and ESR1 intron SNP -4576 A/C (rs1514348) to correlate with breast cancer susceptibility and progesterone receptor expression status. Patients genotyped CT for ESR1 intron SNP +2464 (rs3020314) (p ≤ 0.045) or genotyped AC for ESR1 intron SNP -4576 (rs1514348) (p ≤ 0.000026) were identified to carry a significant risk as to the development of breast cancer in the Central European Caucasian population (both together: p ≤ 0.000488). Our study could confirm previous associations and revealed new associations of SNP rs1514348 with susceptibility to breast cancer and clinical outcome, which might be used as new additional SNP markers.
| S-EPMC3631553 | biostudies-literature
Project description:BackgroundAfrican American women more often present with more aggressive types of breast cancer than Caucasian women, but little is known whether genetic polymorphisms specific to or disproportionate in African Americans are associated with their risk of breast cancer.MethodsA population-based case-control study was conducted including 194 cases identified through the Metropolitan Detroit Cancer Surveillance System and 189 controls recruited through random digit dialing to examine polymorphisms in genes involved in estrogen metabolism and action.ResultsThe African American-specific CYP1A1 5639C allele was associated with an increased risk of breast cancer (odds ratio (OR)=2.34, 95% confidence interval (CI) 1.23-4.44) and this association with the CYP1A1 5639 locus was dependent on another polymorphism in the CYP3A4 gene (P=0.043 for the interaction). In addition, African American-predominant CYP1B1 432 Val allele was significantly more often found in the cases than in the controls overall and the HSD17B1 312 Gly allele was specifically associated with premenopausal breast cancer risk (OR=3.00, 95%CI 1.29-6.99).ConclusionThese observations need to be confirmed in larger studies due to the limited statistical power of the study based on a small number of cases.
| S-EPMC2761149 | biostudies-literature
Project description:Many investigators are now using haplotype-tagging single-nucleotide polymorphism (htSNPs) as a way of screening regions of the genome for association with disease. A common approach is to genotype htSNPs in a study population and to use this information to draw inferences about each individual's haplotypic makeup, including SNPs that were not directly genotyped. To test the validity of this approach, we simulated the exercise of typing htSNPs in a large sample of individuals and compared the true and inferred haplotypes. The accuracy of haplotype inference varied, depending on the method of selecting htSNPs, the linkage-disequilibrium structure of the region, and the amount of missing data. At the stage of selection of htSNPs, haplotype-block-based methods required a larger number of htSNPs than did unstructured methods but gave lower levels of error in haplotype inference, particularly when there was a significant amount of missing data. We present a Web-based utility that allows investigators to compare the likely error rates of different sets of htSNPs and to arrive at an economical set of htSNPs that provides acceptable levels of accuracy in haplotype inference.
| S-EPMC1196396 | biostudies-literature
Project description:IGF2R has been proposed to be a tumor suppressor gene given its antagonist role on cellular growth and evidence of loss of heterozygosity in several cancers, including breast cancer. To investigate whether inherited differences in potentially functional IGF2R variants influence the risk of breast cancer, we sequenced 46 exons of IGF2R to identify novel missense single-nucleotide polymorphisms (SNP) and tested 12 missense SNPs for their associations with breast cancer risk among 1,614 breast cancer cases and 1,960 controls from the Multiethnic Cohort. None of these missense SNPs were significantly associated with breast cancer risk. Our findings provide no evidence that missense SNPs in IGF2R influence breast cancer susceptibility
| S-EPMC2878205 | biostudies-literature
Project description:BackgroundVenous thrombosis (VT) is multifactorial trait that contributes to the global burden of cardiovascular diseases. Although abundant single nucleotide polymorphisms (SNPs) provoke the susceptibility of an individual to VT, research has found that the five most strongly associated SNPs, namely, rs6025 (F5 Leiden), rs2066865 (FGG), rs2036914 (F11), rs8176719 (ABO), and rs1799963 (F2), play the greatest role. Association and risk prediction models are rarely established by using merely the five strongly associated SNPs. This study aims to explore the combined VT risk predictability of the five SNPs and well-known non-genetic VT risk factors such as aging and obesity in the Hungarian population.MethodsSNPs were genotyped in the VT group (n = 298) and control group (n = 400). Associations were established using standard genetic models. Genetic risk scores (GRS) [unweighted GRS (unGRS), weighted GRS (wGRS)] were also computed. Correspondingly, the areas under the receiver operating characteristic curves (AUCs) for genetic and non-genetic risk factors were estimated to explore their VT risk predictability in the study population.Resultsrs6025 was the most prevalent VT risk allele in the Hungarian population. Its risk allele frequency was 3.52-fold higher in the VT group than that in the control group [adjusted odds ratio (AOR) = 3.52, 95% CI: 2.50-4.95]. Using all genetic models, we found that rs6025 and rs2036914 remained significantly associated with VT risk after multiple correction testing was performed. However, rs8176719 remained statistically significant only in the multiplicative (AOR = 1.33, 95% CI: 1.07-1.64) and genotypic models (AOR = 1.77, 95% CI: 1.14-2.73). In addition, rs2066865 lost its significant association with VT risk after multiple correction testing was performed. Conversely, the prothrombin mutation (rs1799963) did not show any significant association. The AUC of Leiden mutation (rs6025) showed better discriminative accuracy than that of other SNPs (AUC = 0.62, 95% CI: 0.57-0.66). The wGRS was a better predictor for VT than the unGRS (AUC = 0.67 vs. 0.65). Furthermore, combining genetic and non-genetic VT risk factors significantly increased the AUC to 0.89 with statistically significant differences (Z = 3.924, p < 0.0001).ConclusionsOur study revealed that the five strongly associated SNPs combined with non-genetic factors could efficiently predict individual VT risk susceptibility. The combined model was the best predictor of VT risk, so stratifying high-risk individuals based on their genetic profiling and well-known non-modifiable VT risk factors was important for the effective and efficient utilization of VT risk preventive and control measures. Furthermore, we urged further study that compares the VT risk predictability in the Hungarian population using the formerly discovered VT SNPs with the novel strongly associated VT SNPs.
| S-EPMC10511882 | biostudies-literature
Project description:Many populations of early-stage cancer patients have non-negligible latent cure fractions that can be modeled using transformation cure models. However, there is a lack of statistical metrics to evaluate prognostic utility of biomarkers in this context due to the challenges associated with unknown cure status and heavy censorship. In this article, we develop general concordance measures as evaluation metrics for the discriminatory accuracy of transformation cure models including the so-called promotion time cure models and mixture cure models. We introduce explicit formulas for the consistent estimates of the concordance measures, and show that their asymptotically normal distributions do not depend on the unknown censoring distribution. The estimates work for both parametric and semiparametric transformation models as well as transformation cure models. Numerical feasibility of the estimates and their robustness to the censoring distributions are illustrated via simulation studies and demonstrated using a melanoma data set.
| S-EPMC6075574 | biostudies-literature
Project description:Autism is widely believed to be a heterogeneous disorder; diagnosis is currently based solely on clinical criteria, although genetic, as well as environmental, influences are thought to be prominent factors in the etiology of most forms of autism. Our goal is to determine whether a predictive model based on single-nucleotide polymorphisms (SNPs) can predict symptom severity of autism spectrum disorder (ASD). We divided 118 ASD children into a mild/moderate autism group (n = 65) and a severe autism group (n = 53), based on the Childhood Autism Rating Scale (CARS). For each child, we obtained 29 SNPs of 9 ASD-related genes. To generate predictive models, we employed three machine-learning techniques: decision stumps (DSs), alternating decision trees (ADTrees), and FlexTrees. DS and FlexTree generated modestly better classifiers, with accuracy = 67%, sensitivity = 0.88 and specificity = 0.42. The SNP rs878960 in GABRB3 was selected by all models, and was related associated with CARS assessment. Our results suggest that SNPs have the potential to offer accurate classification of ASD symptom severity.
| S-EPMC3244507 | biostudies-literature
Project description:Breast cancer (BC), a heterogeneous, aggressive illness with high mortality, is essentially a genomic disease. While the high-penetrance genes BRCA1 and BRCA2 play important roles in tumorigenesis, moderate- and low-penetrance genes are also involved. Single-nucleotide polymorphisms (SNPs) in microRNA (miRNA) genes have recently been identified as BC risk factors. miRNA genes are currently classified as low-penetrance. SNPs are the most common variations in the human genome. While the role of miRNA SNPs in BC susceptibility has been studied extensively, results have been inconsistent. This review analyzes the results of association studies between miRNA SNPs and BC risk from countries around the world. We conclude that: (a) By continent, the largest proportion of studies to date were conducted in Asia (65.0 %) and the smallest proportion in Africa (1.8 %); (b) Association studies have been completed for 67 different SNPs; (c) 146a, 196a2, 499, 27a, and 423 are the most-studied miRNAs; (d) The SNPs rs2910164 (miRNA-146a), rs11614913 (miRNA-196a2), rs3746444 (miRNA-499) and rs6505162 (miRNA-423) were the most widely associated with increased BC risk; (e) The majority of studies had small samples, which may affect the precision and power of the results; and (f) The effect of an SNP on BC risk depends on the ethnicity of the population. This review also discusses potential explanations for controversial findings.
| S-EPMC8401249 | biostudies-literature