Above and beyond state-of-the-art approaches to investigate sequence data: summary of methods and results from the population-based association group at the Genetic Analysis Workshop 19

This paper summarizes the contributions from the Population-Based Association group at the Genetic Analysis Workshop 19. It provides an overview of the new statistical approaches tried out by group members in order to take best advantage of population-based sequence data.Although contributions were highly heterogeneous regarding the applied quality control criteria and the number of investigated variants, several technical issues were identified, leading to practical recommendations. Preliminary analyses revealed that Hurdle-negative binomial regression is a promising approach to investigate the distribution of allele counts instead of called genotypes from sequence data. Convergence problems, however, limited the use of this approach, creating a technical challenge shared by environment-stratified models used to investigate rare variant-environment interactions, as well as by rare variant haplotype analyses using well-established public software. Estimates of relatedness and population structure strongly depended on the allele frequency of selected variants for inference. Another practical recommendation was that dissenting probability values from standard and small-sample tests of a particular hypothesis may reflect a lack of validity of large-sample approximations. Novel statistical approaches that integrate evolutionary information showed some advantage to detect weak genetic signals, and Bayesian adjustment for confounding was able to efficiently estimate causal genetic effects. Haplotype association methods may constitute a valuable complement of collapsing approaches for sequence data. This paper reports on the experience of members of the Population-Based Association group with several novel, promising approaches to preprocessing and analyzing sequence data, and to following up identified association signals.     

Longitudinal analytical approaches to genetic data

Background

Longitudinal phenotypic data provides a rich potential resource for genetic studies which may allow for greater understanding of variants and their covariates over time. Herein, we review 3 longitudinal analytical approaches from the Genetic Analysis Workshop 19 (GAW19). These contributions investigated both genome-wide association (GWA) and whole genome sequence (WGS) data from odd numbered chromosomes on up to 4 time points for blood pressure–related phenotypes. The statistical models used included generalized estimating equations (GEEs), latent class growth modeling (LCGM), linear mixed-effect (LME), and variance components (VC). The goal of these analyses was to test statistical approaches that use repeat measurements to increase genetic signal for variant identification.

Results

Two analytical methods were applied to the GAW19: GWA using real phenotypic data, and one approach to WGS using 200 simulated replicates. The first GWA approach applied a GEE-based model to identify gene-based associations with 4 derived hypertension phenotypes. This GEE model identified 1 significant locus, GRM7, which passed multiple test corrections for 2 hypertension-derived traits. The second GWA approach employed the LME to estimate genetic associations with systolic blood pressure (SBP) change trajectories identified using LCGM. This LCGM method identified 5 SBP trajectories and association analyses identified a genome-wide significant locus, near ATOX1 (p?=?1.0E^?8). Finally, a third VC-based model using WGS and simulated SBP phenotypes that constrained the β coefficient for a genetic variant across each time point was calculated and compared to an unconstrained approach. This constrained VC approach demonstrated increased power for WGS variants of moderate effect, but when larger genetic effects were present, averaging across time points was as effective.

Conclusion

In this paper, we summarize 3 GAW19 contributions applying novel statistical methods and testing previously proposed techniques under alternative conditions for longitudinal genetic association. We conclude that these approaches when appropriately applied have the potential to: (a) increase statistical power; (b) decrease trait heterogeneity and standard error; (c) decrease computational burden in WGS; and (d) have the potential to identify genetic variants influencing subphenotypes important for understanding disease progression.

     

FLEXc: protein flexibility prediction using context-based statistics,predicted structural features,and sequence information

Background

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Results

In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.

Conclusions

Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

     

Predicting existing targets for new drugs base on strategies for missing interactions

Background

There has been paid more and more attention to supervised classification models in the area of predicting drug-target interactions (DTIs). However, in terms of classification, unavoidable missing DTIs in data would cause three issues which have not yet been addressed appropriately by former approaches. Directly labeled as negatives (non-DTIs), missing DTIs increase the confusion of positives (DTIs) and negatives, aggravate the imbalance between few positives and many negatives, and are usually discriminated as highly-scored false positives, which influence the existing measures sharply.

Results

Under the framework of local classification model (LCM), this work focuses on the scenario of predicting how possibly a new drug interacts with known targets. To address the first two issues, two strategies, Spy and Super-target, are introduced accordingly and further integrated to form a two-layer LCM. In the bottom layer, Spy-based local classifiers for protein targets are built by positives, as well as reliable negatives identified among unlabeled drug-target pairs. In the top layer, regular local classifiers specific to super-targets are built with more positives generated by grouping similar targets and their interactions. Furthermore, to handle the third issue, an additional performance measure, Coverage, is presented for assessing DTI prediction. The experiments based on benchmark datasets are finally performed under five-fold cross validation of drugs to evaluate this approach. The main findings are concluded as follows. (1) Both two individual strategies and their combination are effective to missing DTIs, and the combination wins the best. (2) Having the advantages of less confusing decision boundary at the bottom layer and less biased decision boundary at the top layer, our two-layer LCM outperforms two former approaches. (3) Coverage is more robust to missing interactions than other measures and is able to evaluate how far one needs to go down the list of targets to cover all the proper targets of a drug.

Conclusions

Proposing two strategies and one performance measure, this work has addressed the issues derived from missing interactions, which cause confusing and biased decision boundaries in classifiers, as well as the inappropriate measure of predicting performance, in the scenario of predicting interactions between new drugs and known targets.

     

Molecular mechanisms regulating formation,trafficking and processing of annular gap junctions

Internalization of gap junction plaques results in the formation of annular gap junction vesicles. The factors that regulate the coordinated internalization of the gap junction plaques to form annular gap junction vesicles, and the subsequent events involved in annular gap junction processing have only relatively recently been investigated in detail. However it is becoming clear that while annular gap junction vesicles have been demonstrated to be degraded by autophagosomal and endo-lysosomal pathways, they undergo a number of additional processing events. Here, we characterize the morphology of the annular gap junction vesicle and review the current knowledge of the processes involved in their formation, fission, fusion, and degradation. In addition, we address the possibility for connexin protein recycling back to the plasma membrane to contribute to gap junction formation and intercellular communication. Information on gap junction plaque removal from the plasma membrane and the subsequent processing of annular gap junction vesicles is critical to our understanding of cell-cell communication as it relates to events regulating development, cell homeostasis, unstable proliferation of cancer cells, wound healing, changes in the ischemic heart, and many other physiological and pathological cellular phenomena.     

The role of environmental factors for the composition of microbial communities of saline lakes in the Novosibirsk region (Russia)

Background

Nothing is currently known about microbial composition of saline lakes of the Novosibirsk region and its dependence on physical-chemical parameters of waters. We studied the structure of microbial communities of saline lakes of the Novosibirsk region and the effect of physical-chemical parameters of waters on microbial communities of these lakes.

Results

According to the ion content, the lakes were classified either as chloride or chloride-sulfate types. Water salinity ranges from 4.3 to 290 g L^?1. Many diverse microbial communities were found. Filamentous and colonial Cyanobacteria of the genera Scytonema, Aphanocapsa, and/or filamentous Algae dominated in littoral communities. Spatial and temporal organization of planktonic microbial communities and the quantities of Archaea and Bacteria were investigated using fluorescent in situ hybridization. We have found that the dominant planktonic component is represented by Archaea, or, less frequently, by Bacteria. Various phylogenetic groups (Bacteria, Archaea, Algae, and Cyanobacteria) are nonuniformly distributed. The principal component analysis was used to detect environmental factors that affect microorganism abundance. We found the principal components responsible for 71.1 % of the observed variation. It was demonstrated that two-block partial least squares was a better method than principal component analysis for analysis of the data. We observed general relationships between microbial abundance and water salinity.

Conclusions

We have performed the first-ever study of the structure of the microbial communities of eleven saline lakes in the Novosibirsk region along with their physical-chemical parameters of waters. Our study demonstrates that saline lakes in the Novosibirsk region contain a unique microbial communities that may become a prolific source of microorganisms for fundamental and applied studies in various fields of ecology, microbiology, geochemistry, and biotechnology, and deserve further metagenomic investigation.