Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

In a questionnaire survey the prevalence of back pain in 163 helicopter pilots was compared to that in a control group of 297 non-flying air force officers who underwent the same pre-employment medical examination. Since pilots document their hours of flight in a personal flight log, an accurate estimate of the duration of exposure could be made. In addition, vibration levels of the helicopters were measured and an accumulative vibration dose was calculated for each pilot. 'Transient' back pain of a short duration was more frequent amongst the pilots compared to the control group, and the prevalence of 'chronic' back pain of a persistent nature was also higher amongst the helicopter pilots. Transient back pain seemed to be most strongly related to the average hours of flight per day, whereas chronic back pain was more closely related to total hours of flight or the accumulative vibration dose. A significant higher prevalence of this chronic back pain was observed only after 2000 hours of flight or a vibration dose of 400 m2h/s4. The observed health effects may be due to vibration or constrained posture but are most likely due to concomitant exposure to both factors.

Purpose: Spontaneous reporting systems (SRSs) are used to discover previously unknown relationships between drugs and adverse drug reactions (ADRs). A plethora of statistical methods have been proposed over the years to identify these drug-ADR pairs. The objective of this study is to compare a wide variety of methods in their ability to detect these signals, especially when their detection is complicated by the presence of innocent bystanders (drugs that are mistaken to be associated with the ADR, since they are prescribed together with the drug that is the ADR's actual cause). Methods: Twelve methods, 24 measures in total, ranging from simple disproportionality measures (eg, the reporting odds ratio), hypothesis tests (eg, test of the Poisson mean), Bayesian shrinkage estimates (eg, the Bayesian confidence propagation neural network, BCPNN) to sparse regression (LASSO), are compared in their ability to detect drug-ADR pairs in a large number of simulated SRSs with varying numbers of innocent bystanders and effect sizes. The area under the precision-recall curve is used to assess the measures' performance.Results: Hypothesis tests (especially the test of the Poisson mean) perform best when the associations are weak and there is little to no confounding by other drugs.When the level of confounding increases and/or the effect sizes become larger, Bayesian shrinkage methods should be preferred. The LASSO proves to be the most robust against the innocent bystander effect. Conclusions:There is no absolute "winner". Which method to use for a particular SRS depends on the effect sizes and the level of confounding present in the data. K E Y W O R D S innocent bystander effect, pharmacoepidemiology, pharmacovigilance, side effect, surveillance

Accurate discovery of somatic variants is of central importance in cancer research. However, count statistics on discovered somatic insertions and deletions (indels) indicate that large amounts of discoveries are missed because of the quantification of uncertainties related to gap and alignment ambiguities, twilight zone indels, cancer heterogeneity, sample purity, sampling, and strand bias. We provide a unifying statistical model whose dependency structures enable accurate quantification of all inherent uncertainties in short time. Consequently, false discovery rate (FDR) in somatic indel discovery can now be controlled at utmost accuracy, increasing the amount of true discoveries while safely suppressing the FDR.