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- Smith, Jennifer A, et al.
(författare)
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Genome-wide association study identifies 74 loci associated with educational attainment
- 2016
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Ingår i: Nature (London). - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 533:7604, s. 539-542
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Tidskriftsartikel (refereegranskat)abstract
- Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.
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- Artursson, Tom, et al.
(författare)
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Study of Preprocessing Methods for the Determination of Crystalline Phases in Binary Mixtures of Drug Substances by X-ray Powder Diffraction and Multivariate Calibration
- 2000
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Ingår i: Applied Spectroscopy. - : SAGE Publications. - 0003-7028 .- 1943-3530. ; 54:8, s. 272A-301A
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, various preprocessing methods were tested on data generated by X-ray powder diffraction (XRPD) in order to enhance the partial least-squares (PLS) regression modeling performance. The preprocessing methods examined were 22 different discrete wavelet transforms, Fourier transform, Savitzky-Golay, orthogonal signal correction (OSC), and combinations of wavelet transform and OSC, and Fourier transform and OSC. Root mean square error of prediction (RMSEP) of an independent test set was used to measure the performance of the various preprocessing methods. The best PLS model was obtained with a wavelet transform (Symmlet 8), which at the same time compressed the data set by a factor of 9.5. With the use of wavelet and X-ray powder diffraction, concentrations of less than 10% of one crystal from could be detected in a binary mixture. The linear range was found to be in the range 10-70% of the crystalline form of phenacetin, although semiquantitative work could be carried out down to a level of approximately 2%. Furthermore, the wavelet-pretreated models were able to handle admixtures and deliberately added noise.
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- Forshed, Jenny, et al.
(författare)
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Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H-NMR data
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier B.V. - 0169-7439 .- 1873-3239. ; 85:2, s. 179-185
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Tidskriftsartikel (refereegranskat)abstract
- A method to enhance the multivariate data interpretation of, for instance, metabolic profiles is presented. This was done by correlation scaling of 1H NMR data by the time pattern of drug metabolite peaks identified by LC/MS, followed by parallel factor analysis (PARAFAC). The variables responsible for the discrimination between the dosed and control rats in this model were then eliminated in both data sets. Next, an additional PARAFAC analysis was performed with both LC/MS and 1H NMR data, fused by outer product analysis (OPA), to obtain sufficient class separation. The loadings from this second PARAFAC analysis showed new peaks discriminating between the classes. The time trajectories of these peaks did not agree with the drug metabolites and were detected as possible candidates for markers. These data analyses were also compared with the PARAFAC analysis of raw data, which showed very much the same loading peaks as for the correlation-scaled data, although the intensities differed. Elimination of the variables correlated with the drug metabolites was therefore necessary to be able to select the peaks which were not drug metabolites and which discriminated between the classes.1
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| 8. |
- Forshed, Jenny, et al.
(författare)
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Evaluation of different techniques for fusion of LC/MS and 1HNMR data
- 2007
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - 0169-7439 .- 1873-3239. ; 85:1, s. 102-109
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Tidskriftsartikel (refereegranskat)abstract
- In the analyses of highly complex samples (for example, metabolic fingerprinting), the data might not suffice for classification when using only a single analytical technique. Hence, the use of two complementary techniques, e.g., LUMS and H-1-NMR, might be advantageous. Another possible advantage from using two different techniques is the ability to verify the results (for instance, by verifying a time trend of a metabolic pattern). In this work, both LC/MS and H-1-NMR data from analysis of rat urine have been used to obtain metabolic fingerprints. A comparison of three different methods for data fusion of the two data sets was performed and the possibilities and difficulties associated with data fusion were discussed. When comparing concatenated data, full hierarchical modeling, and batch modeling, the first two approaches were found to be the most successful. Different types of block scaling and variable scaling were evaluated and the optimal scaling for each case was found by cross validation. Validations of the final models were performed by means of an external test set.(2)
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