Phenome-scale causal network discovery with bidirectional mediated Mendelian randomization

Inference of directed biological networks from observational genomics datasets is a crucial but notoriously difficult challenge. Modern population-scale biobanks, containing simultaneous measurements of traits, biomarkers, and genetic variation, offer an unprecedented opportunity to study biological networks. Mendelian randomization (MR) has received attention as a class of methods for inferring causal effects in observational data that uses genetic variants as instrumental variables, but MR methods rely on assumptions that limit their application to complex traits at the biobank-scale. Moreover, MR estimates the total effect of one trait on another, which may be mediated by other factors. Biobanks include measurements of many potential mediators, in principle enabling the conversion of MR estimates into direct effects representing a causal network. Here, we show that this can be accomplished by a flexible two stage procedure we call bidirectional mediated Mendelian randomization (bimmer). First, bimmer estimates the effect of every trait on every other. Next, bimmer finds a parsimonious network that explains these effects using direct and mediated causal paths. We introduce novel methods for both steps and show via extensive simulations that bimmer is able to learn causal network structures even in the presence of non-causal genetic correlation. We apply bimmer to 405 phenotypes from the UK biobank and demonstrate that learning the network structure is invaluable for interpreting the results of phenome-wide MR, while lending causal support to several recent observational studies.

Genetic characterization of Echinococcus isolates from various intermediate hosts in the Qinghai-Tibetan Plateau Area, China

This study examined Echinococcus spp. genotypes and genetic variants isolated from humans as well as domestic and wild animals from the Qinghai-Tibetan Plateau Area using the cox1 gene. All samples except the pika isolates were identified as the Echinococcus granulosus sensu stricto. Sixteen different haplotypes with considerable intraspecific variation were detected and characterized in mitochondrial cox1 sequences. The parsimonious network of cox1 haplotypes showed star-like features, and the neutrality indexes computed via Tajima's D and Fu's Fs tests showed high negative values in E. granulosus s. s., indicating deviations from neutrality; the Fst values were low among the populations, implying that the populations were not genetically differentiated. The pika isolates were identified as E. multilocularis and E. shiquicus. Only one haplotype was recognized in the pika isolates. E. granulosus s. s. was the predominant species found in animals and humans, followed by E. multilocularis and E. shiquicus, with high genetic diversity circulating among the animals and humans in this area. Further studies are needed to cover many sample collection sites and larger numbers of pathogen isolates, which may reveal abundant strains and/or other haplotypes in the hydatid cysts infecting human and animal populations of the QTPA, China.

Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic approaches, supporting online lifelong prognostics without the requirement of a retraining or reconfiguration phase. The PANFIS method is applied to normal-to-failure bearing vibration data collected for 139 days to predict the time-domain features of vibration slew bearing signals. The performance of the proposed method is compared to some established methods, such as ANFIS, eTS, and Simp_eTS. From the results, it is suggested that PANFIS offers an outstanding performance compared to those methods.

Improving the Discriminatory Capabilities of a Neural Classifier by Using a Biased-Wavelet Layer

In the context of wavelet neural networks (WNN's), two modifications to the basic training algorithms are proposed, namely the introduction of a bias component in the wavelets and the adoption of a weight decay policy. A problem of ECG segment classification is used for illustration purposes. Results suggest that bias improves the discriminatory capabilities of the WNN, which is also compared favourably to a conventional perceptron classifier. The use of weight decay during training, followed by pruning, resulted in a more parsimonious network, which also turned out to be a more conservative classifier. The knowledge embedded in the wavelet layer is interpreted with basis on the concept of super-wavelets.