Volume 13, Issue 5

As genomic sequencing technologies continue to advance, researchers are furthering their understanding of the relationships between genetic variants and expressed traits. However, missing data can significantly limit the power of a genetic study. Here, the use of a regularized generalized linear model, denoted by GLMNET, is proposed to impute missing genotypes. The method aims to address certain limitations of earlier regression approaches in regards to genotype imputation, particularly the specification of the number of neighboring SNPs to be included for imputing the missing genotype. The performance of GLMNET-based method is compared to the conventional multinomial regression method and two phase-based methods: fastPHASE and BEAGLE. Two simulation scenarios are evaluated: a sparse-missing model, and a small-panel expansion model. The sparse-missing model simulates a scenario where SNPs were missing in a random fashion across the genome. In the small-panel expansion model, a set of individuals is only genotyped at a subset of the SNPs of the large panel. Each imputation method is tested in the context of two data-sets: Canadian Holstein cattle data and human HapMap CEU data. Results show that the proposed GLMNET method outperforms the other methods in the small panel expansion scenario and fastPHASE performs slightly better than the GLMNET method in the sparse-missing scenario.

In this paper we develop a Bayesian statistical inference approach to the unified analysis of isobaric labelled MS/MS proteomic data across multiple experiments. An explicit probabilistic model of the log-intensity of the isobaric labels’ reporter ions across multiple pre-defined groups and experiments is developed. This is then used to develop a full Bayesian statistical methodology for the identification of differentially expressed proteins, with respect to a control group, across multiple groups and experiments. This methodology is implemented and then evaluated on simulated data and on two model experimental datasets (for which the differentially expressed proteins are known) that use a TMT labelling protocol.

We discuss the use of modified Kolmogorov-Smirnov (KS) statistics in the context of gene set analysis and review corresponding null and alternative hypotheses. Especially, we show that, when enhancing the impact of highly significant genes in the calculation of the test statistic, the corresponding test can be considered to infer the classical self-contained null hypothesis. We use simulations to estimate the power for different kinds of alternatives, and to assess the impact of the weight parameter of the modified KS statistic on the power. Finally, we show the analogy between the weight parameter and the genesis and distribution of the gene-level statistics, and illustrate the effects of differential weighting in a real-life example.

Genome-wide association studies have been successful in uncovering novel genetic variants that are associated with disease status or cross-sectional phenotypic traits. Researchers are beginning to investigate how genes play a role in the development of a trait over time. Linear mixed effects models (LMM) are commonly used to model longitudinal data; however, it is unclear if the failure to meet the models distributional assumptions will affect the conclusions when conducting a genome-wide association study. In an extensive simulation study, we compare coverage probabilities, bias, type 1 error rates and statistical power when the error of the LMM is either heteroscedastic or has a non-Gaussian distribution. We conclude that the model is robust to misspecification if the same function of age is included in the fixed and random effects. However, type 1 error of the genetic effect over time is inflated, regardless of the model misspecification, if the polynomial function for age in the fixed and random effects differs. In situations where the model will not converge with a high order polynomial function in the random effects, a reduced function can be used but a robust standard error needs to be calculated to avoid inflation of the type 1 error. As an illustration, a LMM was applied to longitudinal body mass index (BMI) data over childhood in the ALSPAC cohort; the results emphasised the need for the robust standard error to ensure correct inference of associations of longitudinal BMI with chromosome 16 single nucleotide polymorphisms.

In molecular phylogenetics, standard models of sequence evolution generally assume that sequence composition remains constant over evolutionary time. However, this assumption is violated in many datasets which show substantial heterogeneity in sequence composition across taxa. We propose a model which allows compositional heterogeneity across branches, and formulate the model in a Bayesian framework. Specifically, the root and each branch of the tree is associated with its own composition vector whilst a global matrix of exchangeability parameters applies everywhere on the tree. We encourage borrowing of strength between branches by developing two possible priors for the composition vectors: one in which information can be exchanged equally amongst all branches of the tree and another in which more information is exchanged between neighbouring branches than between distant branches. We also propose a Markov chain Monte Carlo (MCMC) algorithm for posterior inference which uses data augmentation of substitutional histories to yield a simple complete data likelihood function that factorises over branches and allows Gibbs updates for most parameters. Standard phylogenetic models are not informative about the root position. Therefore a significant advantage of the proposed model is that it allows inference about rooted trees. The position of the root is fundamental to the biological interpretation of trees, both for polarising trait evolution and for establishing the order of divergence among lineages. Furthermore, unlike some other related models from the literature, inference in the model we propose can be carried out through a simple MCMC scheme which does not require problematic dimension-changing moves. We investigate the performance of the model and priors in analyses of two alignments for which there is strong biological opinion about the tree topology and root position.

Graphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the “wisdom of crowds” network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.