| 1. |
- Aad, G., et al.
(author)
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- 2012
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In: Journal of High Energy Physics. - 1029-8479 .- 1126-6708. ; :9
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Journal article (peer-reviewed)
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- Antoniou, A. C., et al.
(author)
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Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers : Implications for risk prediction
- 2010
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In: Cancer Research. - : American Association for Cancer Research. - 0008-5472 .- 1538-7445. ; 70:23, s. 9742-9754
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Journal article (peer-reviewed)abstract
- The known breast cancer susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1, LSP1, and 2q35 confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of 3 additional single nucleotide polymorphisms (SNPs), rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11, and rs10941679 at 5p12, and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased breast cancer risk for BRCA2 carriers (per-allele HR = 1.10, 95% CI: 1.03-1.18, P = 0.006 and HR = 1.09, 95% CI: 1.01-1.19, P = 0.03, respectively). Neither SNP was associated with breast cancer risk for BRCA1 carriers, and rs6504950 was not associated with breast cancer for either BRCA1 or BRCA2 carriers. Of the 9 polymorphisms investigated, 7 were associated with breast cancer for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, P = 7 × 10-11 - 0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (P = 0.0049, 0.03, respectively). All risk-associated polymorphisms appear to interact multiplicatively on breast cancer risk for mutation carriers. Based on the joint genotype distribution of the 7 risk-associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e., between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing breast cancer by age 80, compared with 42% to 50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences might be sufficient to influence the clinical management of mutation carriers.
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- Hamdi, Yosr, et al.
(author)
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Association of breast cancer risk in BRCA1 and BRCA2 mutation carriers with genetic variants showing differential allelic expression : identification of a modifier of breast cancer risk at locus 11q22.3
- 2017
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In: Breast Cancer Research and Treatment. - : Springer Science and Business Media LLC. - 0167-6806 .- 1573-7217. ; 161:1, s. 117-134
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Journal article (peer-reviewed)abstract
- Purpose: Cis-acting regulatory SNPs resulting in differential allelic expression (DAE) may, in part, explain the underlying phenotypic variation associated with many complex diseases. To investigate whether common variants associated with DAE were involved in breast cancer susceptibility among BRCA1 and BRCA2 mutation carriers, a list of 175 genes was developed based of their involvement in cancer-related pathways. Methods: Using data from a genome-wide map of SNPs associated with allelic expression, we assessed the association of ~320 SNPs located in the vicinity of these genes with breast and ovarian cancer risks in 15,252 BRCA1 and 8211 BRCA2 mutation carriers ascertained from 54 studies participating in the Consortium of Investigators of Modifiers of BRCA1/2. Results: We identified a region on 11q22.3 that is significantly associated with breast cancer risk in BRCA1 mutation carriers (most significant SNP rs228595 p = 7 × 10−6). This association was absent in BRCA2 carriers (p = 0.57). The 11q22.3 region notably encompasses genes such as ACAT1, NPAT, and ATM. Expression quantitative trait loci associations were observed in both normal breast and tumors across this region, namely for ACAT1, ATM, and other genes. In silico analysis revealed some overlap between top risk-associated SNPs and relevant biological features in mammary cell data, which suggests potential functional significance. Conclusion: We identified 11q22.3 as a new modifier locus in BRCA1 carriers. Replication in larger studies using estrogen receptor (ER)-negative or triple-negative (i.e., ER-, progesterone receptor-, and HER2-negative) cases could therefore be helpful to confirm the association of this locus with breast cancer risk.
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- Darabi, H, et al.
(author)
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Fine scale mapping of the 17q22 breast cancer locus using dense SNPs, genotyped within the Collaborative Oncological Gene-Environment Study (COGs)
- 2016
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6, s. 32512-
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Journal article (peer-reviewed)abstract
- Genome-wide association studies have found SNPs at 17q22 to be associated with breast cancer risk. To identify potential causal variants related to breast cancer risk, we performed a high resolution fine-mapping analysis that involved genotyping 517 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of genotypes for 3,134 SNPs in more than 89,000 participants of European ancestry from the Breast Cancer Association Consortium (BCAC). We identified 28 highly correlated common variants, in a 53 Kb region spanning two introns of the STXBP4 gene, that are strong candidates for driving breast cancer risk (lead SNP rs2787486 (OR = 0.92; CI 0.90–0.94; P = 8.96 × 10−15)) and are correlated with two previously reported risk-associated variants at this locus, SNPs rs6504950 (OR = 0.94, P = 2.04 × 10−09, r2 = 0.73 with lead SNP) and rs1156287 (OR = 0.93, P = 3.41 × 10−11, r2 = 0.83 with lead SNP). Analyses indicate only one causal SNP in the region and several enhancer elements targeting STXBP4 are located within the 53 kb association signal. Expression studies in breast tumor tissues found SNP rs2787486 to be associated with increased STXBP4 expression, suggesting this may be a target gene of this locus.
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- Spurdle, Amanda B., et al.
(author)
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Common Genetic Variation at BARD1 Is Not Associated with Breast Cancer Risk in BRCA1 or BRCA2 Mutation Carriers
- 2011
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In: Cancer Epidemiology Biomarkers & Prevention. - 1538-7755 .- 1055-9965. ; 20:5, s. 1032-1038
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Journal article (peer-reviewed)abstract
- Background: Inherited BRCA1 and BRCA2 (BRCA1/2) mutations confer elevated breast cancer risk. Knowledge of factors that can improve breast cancer risk assessment in BRCA1/2 mutation carriers may improve personalized cancer prevention strategies. Methods: A cohort of 5,546 BRCA1 and 2,865 BRCA2 mutation carriers was used to evaluate risk of breast cancer associated with BARD1 Cys557Ser. In a second nonindependent cohort of 1,537 of BRCA1 and 839 BRCA2 mutation carriers, BARD1 haplotypes were also evaluated. Results: The BARD1 Cys557Ser variant was not significantly associated with risk of breast cancer from single SNP analysis, with a pooled effect estimate of 0.90 (95% CI: 0.71-1.15) in BRCA1 carriers and 0.87 (95% CI: 0.59-1.29) in BRCA2 carriers. Further analysis of haplotypes at BARD1 also revealed no evidence that additional common genetic variation not captured by Cys557Ser was associated with breast cancer risk. Conclusion: Evidence to date does not support a role for BARD1 variation, including the Cy557Ser variant, as a modifier of risk in BRCA1/2 mutation carriers. Impact: Interactors of BRCA1/2 have been implicated as modifiers of BRCA1/2-associated cancer risk. Our finding that BARD1 does not contribute to this risk modification may focus research on other genes that do modify BRCA1/2-associated cancer risk. Cancer Epidemiol Biomarkers Prev; 20(5); 1032-38. (C) 2011 AACR.
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| 23. |
- Kriege, Nils M., et al.
(author)
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A survey on graph kernels
- 2020
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In: Applied Network Science. - : Springer Science and Business Media LLC. - 2364-8228. ; 5:1
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Research review (peer-reviewed)abstract
- Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years. We describe and categorize graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice. In an extensive experimental evaluation, we study the classification accuracy of a large suite of graph kernels on established benchmarks as well as new datasets. We compare the performance of popular kernels with several baseline methods and study the effect of applying a Gaussian RBF kernel to the metric induced by a graph kernel. In doing so, we find that simple baselines become competitive after this transformation on some datasets. Moreover, we study the extent to which existing graph kernels agree in their predictions (and prediction errors) and obtain a data-driven categorization of kernels as result. Finally, based on our experimental results, we derive a practitioner’s guide to kernel-based graph classification.
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| 24. |
- Oettershagen, Lutz, et al.
(author)
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A Higher-Order Temporal H-Index for Evolving Networks
- 2023
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In: KDD 2023. - : Association for Computing Machinery (ACM). ; , s. 1770-1782
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Conference paper (peer-reviewed)abstract
- The H-index of a node in a static network is the maximum value h such that at least h of its neighbors have a degree of at least h. Recently, a generalized version, the n-th order H-index, was introduced, allowing to relate degree centrality, H-index, and the k-core of a node. We extend the n-th order H-index to temporal networks and define corresponding temporal centrality measures and temporal core decompositions. Our n-th order temporal H-index respects the reachability in temporal networks leading to node rankings, which reflect the importance of nodes in spreading processes. We derive natural decompositions of temporal networks into subgraphs with strong temporal coherence. We analyze a recursive computation scheme and develop a highly scalable streaming algorithm. Our experimental evaluation demonstrates the efficiency of our algorithms and the conceptional validity of our approach. Specifically, we show that the n-th order temporal H-index is a strong heuristic for identifying possible super-spreaders in evolving social networks and detects temporally well-connected components.
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