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- Climaco Pinto, Rui, et al.
(författare)
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Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets.
- 2022
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Ingår i: Analytical chemistry. - : American Chemical Society (ACS). - 1520-6882 .- 0003-2700. ; 94:14, s. 5493-5503
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Tidskriftsartikel (refereegranskat)abstract
- Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
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| 2. |
- Figueroa, Jonine D., et al.
(författare)
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Genome-wide association study identifies multiple loci associated with bladder cancer risk
- 2014
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Ingår i: Human Molecular Genetics. - : Oxford University Press. - 0964-6906 .- 1460-2083. ; 23:5, s. 1387-1398
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Tidskriftsartikel (refereegranskat)abstract
- andidate gene and genome-wide association studies (GWAS) have identified 11 independent susceptibility loci associated with bladder cancer risk. To discover additional risk variants, we conducted a new GWAS of 2422 bladder cancer cases and 5751 controls, followed by a meta-analysis with two independently published bladder cancer GWAS, resulting in a combined analysis of 6911 cases and 11 814 controls of European descent. TaqMan genotyping of 13 promising single nucleotide polymorphisms with P < 1 × 10−5 was pursued in a follow-up set of 801 cases and 1307 controls. Two new loci achieved genome-wide statistical significance: rs10936599 on 3q26.2 (P = 4.53 × 10−9) and rs907611 on 11p15.5 (P = 4.11 × 10−8). Two notable loci were also identified that approached genome-wide statistical significance: rs6104690 on 20p12.2 (P = 7.13 × 10−7) and rs4510656 on 6p22.3 (P = 6.98 × 10−7); these require further studies for confirmation. In conclusion, our study has identified new susceptibility alleles for bladder cancer risk that require fine-mapping and laboratory investigation, which could further understanding into the biological underpinnings of bladder carcinogenesis.
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| 3. |
- Figueroa, Jonine D., et al.
(författare)
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Genome-wide interaction study of smoking and bladder cancer risk
- 2014
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Ingår i: Carcinogenesis. - : Oxford University Press. - 0143-3334 .- 1460-2180. ; 35:8, s. 1737-1744
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Tidskriftsartikel (refereegranskat)abstract
- Bladder cancer is a complex disease with known environmental and genetic risk factors. We performed a genome-wide interaction study (GWAS) of smoking and bladder cancer risk based on primary scan data from 3002 cases and 4411 controls from the National Cancer Institute Bladder Cancer GWAS. Alternative methods were used to evaluate both additive and multiplicative interactions between individual single nucleotide polymorphisms (SNPs) and smoking exposure. SNPs with interaction P values < 5 x 10(-5) were evaluated further in an independent dataset of 2422 bladder cancer cases and 5751 controls. We identified 10 SNPs that showed association in a consistent manner with the initial dataset and in the combined dataset, providing evidence of interaction with tobacco use. Further, two of these novel SNPs showed strong evidence of association with bladder cancer in tobacco use subgroups that approached genome-wide significance. Specifically, rs1711973 (FOXF2) on 6p25.3 was a susceptibility SNP for never smokers [combined odds ratio (OR) = 1.34, 95% confidence interval (CI) = 1.20-1.50, P value = 5.18 x 10(-7)]; and rs12216499 (RSPH3-TAGAP-EZR) on 6q25.3 was a susceptibility SNP for ever smokers (combined OR = 0.75, 95% CI = 0.67-0.84, P value = 6.35 x 10-7). In our analysis of smoking and bladder cancer, the tests for multiplicative interaction seemed to more commonly identify susceptibility loci with associations in never smokers, whereas the additive interaction analysis identified more loci with associations among smokers-including the known smoking and NAT2 acetylation interaction. Our findings provide additional evidence of gene-environment interactions for tobacco and bladder cancer.
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