| 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. |
- Cox, Angela, et al.
(författare)
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A common coding variant in CASP8 is associated with breast cancer risk
- 2007
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 39:3, s. 352-358
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Tidskriftsartikel (refereegranskat)abstract
- The Breast Cancer Association Consortium (BCAC) has been established to conduct combined case-control analyses with augmented statistical power to try to confirm putative genetic associations with breast cancer. We genotyped nine SNPs for which there was some prior evidence of an association with breast cancer: CASP8 D302H (rs1045485), IGFBP3 -202 C --> A (rs2854744), SOD2 V16A (rs1799725), TGFB1 L10P (rs1982073), ATM S49C (rs1800054), ADH1B 3' UTR A --> G (rs1042026), CDKN1A S31R (rs1801270), ICAM5 V301I (rs1056538) and NUMA1 A794G (rs3750913). We included data from 9-15 studies, comprising 11,391-18,290 cases and 14,753-22,670 controls. We found evidence of an association with breast cancer for CASP8 D302H (with odds ratios (OR) of 0.89 (95% confidence interval (c.i.): 0.85-0.94) and 0.74 (95% c.i.: 0.62-0.87) for heterozygotes and rare homozygotes, respectively, compared with common homozygotes; P(trend) = 1.1 x 10(-7)) and weaker evidence for TGFB1 L10P (OR = 1.07 (95% c.i.: 1.02-1.13) and 1.16 (95% c.i.: 1.08-1.25), respectively; P(trend) = 2.8 x 10(-5)). These results demonstrate that common breast cancer susceptibility alleles with small effects on risk can be identified, given sufficiently powerful studies.
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| 3. |
- Doherty, James M, et al.
(författare)
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Contributions of dopamine D1, D2, and D3 receptor subtypes to the disruptive effects of cocaine on prepulse inhibition in mice.
- 2008
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Ingår i: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. - : Springer Science and Business Media LLC. ; 33:11, s. 2648-56
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Tidskriftsartikel (refereegranskat)abstract
- Deficits in prepulse inhibition (PPI) of startle, an operational measure of sensorimotor gating, are characteristics of schizophrenia and related neuropsychiatric disorders. Previous studies in mice demonstrate a contribution of dopamine (DA) D(1)-family receptors in modulating PPI and DA D(2) receptors (D2R) in mediating the PPI-disruptive effects of amphetamine. To examine further the contributions of DA receptor subtypes in PPI, we used a combined pharmacological and genetic approach. In congenic C57BL/6 J wild-type mice, we tested whether the D1R antagonist SCH23390 or the D2/3R antagonist raclopride would attenuate the effects of the indirect DA agonist cocaine (40 mg/kg). Both the D1R and D2/3R antagonists attenuated the cocaine-induced PPI deficit. We also tested the effect of cocaine on PPI in wild-type and DA D1R, D2R, or D3R knockout mice. The cocaine-induced PPI deficit was influenced differently by the three DA receptor subtypes, being absent in D1R knockout mice, partially attenuated in D2R knockout mice, and exaggerated in D3R knockout mice. Thus, the D1R is necessary for the PPI-disruptive effects of cocaine, while the D2R partially contributes to these effects. Conversely, the D3R appears to inhibit the PPI-disruptive effects of cocaine. Uncovering neural mechanisms involved in PPI will further our understanding of substrates of sensorimotor gating and could lead to better therapeutics to treat complex cognitive disorders such as schizophrenia.
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| 4. |
- Aydt, Heiko, et al.
(författare)
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Toward an Evolutionary Computing Modeling Language
- 2011
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Ingår i: IEEE Transactions on Evolutionary Computation. - 1089-778X .- 1941-0026. ; 15:2, s. 230-247
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Tidskriftsartikel (refereegranskat)abstract
- The importance of domain knowledge in the design of effective evolutionary algorithms (EAs) is widely acknowledged in the meta-heuristics community. In the last few decades, a plethora of EAs has been manually designed by domain experts for solving domain-specific problems. Specialization has been achieved mainly by embedding available domain knowledge into the algorithms. Although programming libraries have been made available to construct EAs, a unifying framework for designing specialized EAs across different problem domains and branches of evolutionary computing does not exist yet. In this paper, we address this issue by introducing an evolutionary computing modeling language (ECML) which is based on the unified modeling language (UML). ECML incorporates basic UML elements and introduces new extensions that are specially needed for the evolutionary computation domain. Subsequently, the concept of meta evolutionary algorithms (MEAs) is introduced as a family of EAs that is capable of interpreting ECML. MEAs are solvers that are not restricted to a particular problem domain or branch of evolutionary computing through the use of ECML. By separating problem-specific domain knowledge from the EA implementation, we show that a unified framework for evolutionary computation can be attained. We demonstrate our approach by applying it to a number of examples.
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