1. |
- Aad, G., et al.
(författare)
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- 2013
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Tidskriftsartikel (refereegranskat)
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2. |
- Aad, G., et al.
(författare)
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- 2011
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Tidskriftsartikel (refereegranskat)
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3. |
- Aad, G., et al.
(författare)
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- 2013
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swepub:Mat__t (refereegranskat)
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4. |
- Aad, G., et al.
(författare)
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- 2011
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swepub:Mat__t (refereegranskat)
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5. |
- Aad, G., et al.
(författare)
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- 2013
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Ingår i: Journal of High Energy Physics. - 1029-8479 .- 1126-6708. ; :4
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Tidskriftsartikel (refereegranskat)
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6. |
- Aad, G., et al.
(författare)
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Readiness of the ATLAS Tile Calorimeter for LHC collisions
- 2010
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Ingår i: European Physical Journal C. Particles and Fields. - : Springer Science and Business Media LLC. - 1434-6044 .- 1434-6052. ; 70:4, s. 1193-1236
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Tidskriftsartikel (refereegranskat)abstract
- The Tile hadronic calorimeter of the ATLAS detector has undergone extensive testing in the experimental hall since its installation in late 2005. The readout, control and calibration systems have been fully operational since 2007 and the detector has successfully collected data from the LHC single beams in 2008 and first collisions in 2009. This paper gives an overview of the Tile Calorimeter performance as measured using random triggers, calibration data, data from cosmic ray muons and single beam data. The detector operation status, noise characteristics and performance of the calibration systems are presented, as well as the validation of the timing and energy calibration carried out with minimum ionising cosmic ray muons data. The calibration systems' precision is well below the design value of 1%. The determination of the global energy scale was performed with an uncertainty of 4%.
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7. |
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8. |
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9. |
- Erzurumluoglu, A. Mesut, et al.
(författare)
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Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci
- 2020
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Ingår i: Molecular Psychiatry. - : Nature Publishing Group. - 1359-4184 .- 1476-5578. ; 25:10, s. 2392-2409
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Tidskriftsartikel (refereegranskat)abstract
- Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations with P < 5 × 10-8 in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P < 5 × 10-8) in the discovery samples. Ten novel SNVs, including rs12616219 near TMEM182, were followed-up and five of them (rs462779 in REV3L, rs12780116 in CNNM2, rs1190736 in GPR101, rs11539157 in PJA1, and rs12616219 near TMEM182) replicated at a Bonferroni significance threshold (P < 4.5 × 10-3) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, in CCDC141 and two low-frequency SNVs in CEP350 and HDGFRP2. Functional follow-up implied that decreased expression of REV3L may lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.
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10. |
- Thomas, Minta, et al.
(författare)
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Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.
- 2020
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Ingår i: American Journal of Human Genetics. - Cambridge : Elsevier BV. - 0002-9297 .- 1537-6605. ; 107:3, s. 432-444
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Tidskriftsartikel (refereegranskat)abstract
- Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
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