Development and validation of correction formulas for self-reported height and weight to estimate BMI in adolescents. Results from the KiGGS study.

OBJECTIVE: The use of reported instead of measured height and weight induces a bias in prevalence rates for overweight and obesity. Therefore, correction formulas are necessary. METHODS: Self-reported and measured height and weight were available from the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) baseline study (2003-2006) from 3,468 adolescents aged 11-17 years. With regression analyses, correction formulas for height and weight were developed. Cross-validation was conducted in order to validate and compare the formulas. Corrected BMI was calculated, and corrected prevalence rates were estimated. Sensitivity, specificity, and predictive values for overweight and obesity were calculated. RESULTS: Through the correction procedure, the mean differences between reported and measured height and weight become remarkably smaller and thus the estimated prevalence rates more accurate. The corrected proportions for overweight and obesity are less under-reported, while the corrected proportions for underweight are less over-reported. Sensitivity for overweight and obesity increased after correction. Specificity remained high. CONCLUSION: The validation process showed that the correction formulas are an appropriate tool to correct self-reports on an individual level in order to estimate corrected prevalence rates of overweight and obesity in adolescents for studies which have collected self-reports only.

Analyzing genome coverage profiles with applications to quality control in metagenomics.

MOTIVATION: Genome coverage, the number of sequencing reads mapped to a position in a genome, is an insightful indicator of irregularities within sequencing experiments. While the average genome coverage is frequently used within algorithms in computational genomics, the complete information available in coverage profiles (i.e. histograms over all coverages) is currently not exploited to its full extent. Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for. Making this information accessible can improve the quality of sequencing experiments and quantitative analyses. RESULTS: We introduce a framework for fitting mixtures of probability distributions to genome coverage profiles. Besides commonly used distributions, we introduce distributions tailored to account for common artifacts. The mixture models are iteratively fitted based on the Expectation-Maximization algorithm. We introduce use cases with focus on metagenomics and develop new analysis strategies to assess the validity of a reference genome with respect to (meta-) genomic read data. The framework is evaluated on simulated data as well as applied to a large-scale metagenomic study, for which we compute the validity of 75 microbial genomes. The results indicate that the choice and quality of reference genomes is vital for metagenomic analyses and that validation of coverage profiles is crucial to avoid incorrect conclusions. AVAILABILITY: The code is freely available and can be downloaded from http://sourceforge.net/projects/fitgcp/. CONTACT: RenardB@rki.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.