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1.  The COPD genetic association compendium: a comprehensive online database of COPD genetic associations 
Human Molecular Genetics  2009;19(3):526-534.
Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide. COPD is thought to arise from the interaction of environmental exposures and genetic susceptibility, and major research efforts are underway to identify genetic determinants of COPD susceptibility. With the exception of SERPINA1, genetic associations with COPD identified by candidate gene studies have been inconsistently replicated, and this literature is difficult to interpret. We conducted a systematic review and meta-analysis of all population-based, case–control candidate gene COPD studies indexed in PubMed before 16 July 2008. We stored our findings in an online database, which serves as an up-to-date compendium of COPD genetic associations and cumulative meta-analysis estimates. On the basis of our systematic review, the vast majority of COPD candidate gene era studies are underpowered to detect genetic effect odds ratios of 1.2–1.5. We identified 27 genetic variants with adequate data for quantitative meta-analysis. Of these variants, four were significantly associated with COPD susceptibility in random effects meta-analysis, the GSTM1 null variant (OR 1.45, CI 1.09–1.92), rs1800470 in TGFB1 (0.73, CI 0.64–0.83), rs1800629 in TNF (OR 1.19, CI 1.01–1.40) and rs1799896 in SOD3 (OR 1.97, CI 1.24–3.13). In summary, most COPD candidate gene era studies are underpowered to detect moderate-sized genetic effects. Quantitative meta-analysis identified four variants in GSTM1, TGFB1, TNF and SOD3 that show statistically significant evidence of association with COPD susceptibility.
PMCID: PMC2798725  PMID: 19933216
2.  Ultra High Resolution 1H-13C HSQC Spectra of Metabolite Mixtures using non-linear sampling and Forward Maximum Entropy (FM) Reconstruction 
To obtain a comprehensive assessment of metabolite levels from extracts of leukocytes, we have recorded ultra-high-resolution 1H-13C HSQC NMR spectra of cell extracts, which exhibit spectral signatures of numerous small molecules. However, conventional acquisition of such spectra is time consuming and hampers measurements on multiple samples, which would be needed for statistical analysis of metabolite concentrations. Here we show that the measurement time can be dramatically reduced without loss of spectral quality when using non-linear sampling (NLS) and a new high-fidelity Forward Maximum-entropy (FM) reconstruction algorithm. This FM reconstruction conserves all measured time domain data points and guesses the missing data points by an iterative process. This consists of discrete Fourier transformation of the sparse time-domain data set, computation of the spectral entropy, determination of a multidimensional entropy gradient, and calculation of new values for the missing time domain data points with a conjugate gradient approach. Since this procedure does not alter measured data points it reproduces signal intensities with high fidelity and does not suffer from a dynamic-range problem. As an example we measured a natural abundance 1H-13C HSQC spectrum of metabolites from granulocyte cell extracts. We show that a high-resolution 1H-13C HSQC spectrum with 4k complex increments recorded linearly within 3.7 days can be reconstructed from 1/7th of the increments with nearly identical spectral appearance, indistinguishable signal intensities and comparable or even lower root mean square (rms) and peak noise patterns measured in signal-free areas. Thus, this approach allows recording of ultra-high resolution 1H-13C HSQC spectra in a fraction of the time needed for recording linearly sampled spectra.
PMCID: PMC2631400  PMID: 17388596
NMR; non-linear sampling; maximum entropy reconstruction; data processing; metabolomics

Results 1-2 (2)