Differential shrinkage as a way of integrating prior knowledge in a Bayesian model to improve the analysis of genetic association studies

We propose a method of integrating external biological information about SNPs in a Bayesian hierarchical shrinkage model that jointly estimates SNP effects with the aim of increasing the power to detect variants in genetic association studies. Our method induces shrinkage on the SNP effects that is inversely proportional to prior information: SNPs with more information are subject to little shrinkage and more likely to be detected, while SNPs without prior information are strongly shrunk towards zero (no effect). The performance of the method was tested in a simulation study with 1000 datasets, each with 500 subjects and ∼1200 SNPs, divided in 10 Linkage Disequilibrium (LD) blocks. One LD block was simulated to be truly associated with the outcome. The method was further tested on an empirical example using BMI as the outcome and data from the European Community Respiratory Health Survey: 1,829 subjects and 2,614 SNPs from 30 blocks, 6 of which known to be truly associated with BMI. Prior knowledge was retrieved using the bioinformatic tool Dintor and incorporated in the model. The Bayesian model with inclusion of prior information outperformed the classical analysis. In the simulation study, the mean ranking of the true LD block was 2.8 for the Bayesian model vs. 3.6 for the classical analysis. Similarly, the mean ranking of the six true blocks in the empirical example was 8.3 vs. 11.7 in the classical analysis. These results suggest that our method represents a more powerful approach to detect new variants in genetic association studies

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects.

With the aim of improving detection of novel single-nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected. The performance of the method was tested in a simulation study (1,000 datasets, 500 subjects with ∼200 SNPs in 10 linkage disequilibrium (LD) blocks) using a continuous and a binary outcome. It was further tested in an empirical example on body mass index (continuous) and overweight (binary) in a dataset of 1,829 subjects and 2,614 SNPs from 30 blocks. Biological knowledge was retrieved using the bioinformatics tool Dintor, which queried various databases. The joint Bayesian model with inclusion of prior information outperformed the standard analysis: in the simulation study, the mean ranking of the true LD block was 2.8 for the Bayesian model versus 3.6 for the standard analysis of individual SNPs; in the empirical example, the mean ranking of the six true blocks was 8.5 versus 9.3 in the standard analysis. These results suggest that our method is more powerful than the standard analysis. We expect its performance to improve further as more biological information about SNPs becomes available

Challenges in the determination of the binding modes of non-standard ligands in X-ray crystal complexes

Despite its central role in structure based drug design the determination of the binding mode (position, orientation and conformation in addition to protonation and tautomeric states) of small heteromolecular ligands in protein:ligand complexes based on medium resolution X-ray diffraction data is highly challenging. In this perspective we demonstrate how a combination of molecular dynamics simulations and free energy (FE) calculations can be used to correct and identify thermodynamically stable binding modes of ligands in X-ray crystal complexes. The consequences of inappropriate ligand structure, force field and the absence of electrostatics during X-ray refinement are highlighted. The implications of such uncertainties and errors for the validation of virtual screening and fragment-based drug design based on high throughput X-ray crystallography are discussed with possible solutions and guidelines. © Springe