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Gillis, Jesse; Pavlidis, Paul — 2019-03-05 Gene networks are commonly interpreted as encoding functional information in their connections. An extensively validated principle called guilt by association states that genes which are associated or interacting are more likely to share function. Guilt by association provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. In this work, we show that functional information within gene networks is typically concentrated in only a very few interactions whose properties cannot be reliably related to the rest of the network. In effect, the apparent encoding of function within networks has been largely driven by outliers whose behaviour cannot even be generalized to individual genes, let alone to the network at large. While experimentalist-driven analysis of interactions may use prior expert knowledge to focus on the small fraction of critically important data, large-scale computational analyses have typically assumed that high-performance cross-validation in a network is due to a generalizable encoding of function. Because we find that gene function is not systemically encoded in networks, but dependent on specific and critical interactions, we conclude it is necessary to focus on the details of how networks encode function and what information computational analyses use to extract functional meaning. We explore a number of consequences of this and find that network structure itself provides clues as to which connections are critical and that systemic properties, such as scale-free-like behaviour, do not map onto the functional connectivity within networks.

Mistry, Meeta; Gillis, Jesse; Pavlidis, Paul — 2019-03-05 Numerous studies have examined gene expression profiles in post-mortem human brain samples from individuals with schizophrenia compared to healthy controls, to gain insight into the molecular mechanisms of the disease. While some findings have been replicated across studies,there is a general lack of consensus of which genes or pathways are affected. It has been unclear if these differences are due to the underlying cohorts, or methodological considerations. Here we present the most comprehensive analysis to date of expression patterns in the prefrontal cortex of schizophrenic compared to unaffected controls. Using data from seven independent studies, we assembled a data set of 153 affected and 153 control individuals. Remarkably, we identified expression differences in the brains of schizophrenics that are validated by up to seven laboratories using independent cohorts. Our combined analysis revealed a signature of 39 probes that are up-regulated in schizophrenia and 86 down-regulated. Some of these genes were previously identified in studies that were not included in our analysis, while others are novel to our analysis. In particular, we observe gene expression changes associated with various aspects of neuronal communication, and alterations of processes affected as a consequence of changes in synaptic functioning. A gene network analysis predicted previously unidentified functional relationships among the signature genes. Our results provide evidence for a common underlying expression signature in this heterogeneous disorder.

Gillis, Jesse; Pavlidis, Paul — 2019-03-11 <p>MOTIVATION:<br> Gene networks have been used widely in gene function prediction algorithms, many based on complex extensions of the 'guilt by association' principle. We sought to provide a unified explanation for the performance of gene function prediction algorithms in exploiting network structure and thereby simplify future analysis.<p> <p>RESULTS:<br> We use co-expression networks to show that most exploited network structure simply reconstructs the original correlation matrices from which the co-expression network was obtained. We show the same principle works in predicting gene function in protein interaction networks and that these methods perform comparably to much more sophisticated gene function prediction algorithms.<p> <p>AVAILABILITY AND IMPLEMENTATION:<br> Data and algorithm implementation are fully described and available at http://www.chibi.ubc.ca/extended. Programs are provided in Matlab m-code.<p> <p>CONTACT:<br> paul@chibi.ubc.ca<p>

Gillis, Jesse; Pavlidis, Paul — 2019-03-11 Many previous studies have shown that by using variants of "guilt-by-association", gene function predictions can be made with very high statistical confidence. In these studies, it is assumed that the "associations" in the data (e.g., protein interaction partners) of a gene are necessary in establishing "guilt". In this paper we show that multifunctionality, rather than association, is a primary driver of gene function prediction. We first show that knowledge of the degree of multifunctionality alone can produce astonishingly strong performance when used as a predictor of gene function. We then demonstrate how multifunctionality is encoded in gene interaction data (such as protein interactions and coexpression networks) and how this can feed forward into gene function prediction algorithms. We find that high-quality gene function predictions can be made using data that possesses no information on which gene interacts with which. By examining a wide range of networks from mouse, human and yeast, as well as multiple prediction methods and evaluation metrics, we provide evidence that this problem is pervasive and does not reflect the failings of any particular algorithm or data type. We propose computational controls that can be used to provide more meaningful control when estimating gene function prediction performance. We suggest that this source of bias due to multifunctionality is important to control for, with widespread implications for the interpretation of genomics studies.

Gillis, Jesse; Pavlidis, Paul — 2019-03-11 <p>BACKGROUND: Differential coexpression is a change in coexpression between genes that may reflect 'rewiring' of transcriptional networks. It has previously been hypothesized that such changes might be occurring over time in the lifespan of an organism. While both coexpression and differential expression of genes have been previously studied in life stage change or aging, differential coexpression has not. Generalizing differential coexpression analysis to many time points presents a methodological challenge. Here we introduce a method for analyzing changes in coexpression across multiple ordered groups (e.g., over time) and extensively test its validity and usefulness.<p> <p>RESULTS: Our method is based on the use of the Haar basis set to efficiently represent changes in coexpression at multiple time scales, and thus represents a principled and generalizable extension of the idea of differential coexpression to life stage data. We used published microarray studies categorized by age to test the methodology. We validated the methodology by testing our ability to reconstruct Gene Ontology (GO) categories using our measure of differential coexpression and compared this result to using coexpression alone. Our method allows significant improvement in characterizing these groups of genes. Further, we examine the statistical properties of our measure of differential coexpression and establish that the results are significant both statistically and by an improvement in semantic similarity. In addition, we found that our method finds more significant changes in gene relationships compared to several other methods of expressing temporal relationships between genes, such as coexpression over time.<p> <p>CONCLUSION: Differential coexpression over age generates significant and biologically relevant information about the genes producing it. Our Haar basis methodology for determining age-related differential coexpression performs better than other tested methods. The Haar basis set also lends itself to ready interpretation in terms of both evolutionary and physiological mechanisms of aging and can be seen as a natural generalization of two-category differential coexpression.<p> <p>CONTACT: paul@bioinformatics.ubc.ca.<p>

Melka, Melkaye G.; Pavlidis, Paul; Gillis, Jesse; Bernard, Manon; Abrahamowicz, Michal; Chakravarty, M. Maller; Leonard, Gabriel T.; Perron, Michel; Richer, Louis; Veillette, Suzanne; Banaschewski, Tobias; Barker, Gareth J.; Buchel, Christian; Conrod, Patricia; Flor, Herta; Heinz, Andreas; Garavan, Hugh; Bruhl, Rudiger; Mann, Karl; Artiges, Eric; Lourdusamy, Anbarasu; Lathrop, Mark; Loth, Eva; Schwartz, Yannick; Frouin, Vincent; Rietschel, Marcella; Smolka, Michael N.; Strohle, Andreas; Gallinat, Jurge; Struve, Maren; Lattka, Eva; Waldenberger, Melanie; Schumann, Gunter; Gaudet, Daniel; Paus, Tomas; Pausova, Zdenka — 2019-03-05 Genetic variations in fat mass- and obesity (FTO)-associated gene, a well-replicated gene locus of obesity, appear to be associated also with reduced regional brain volumes in elderly. Here, we examined whether FTO is associated with total brain volume in adolescence, thus exploring possible developmental effects of FTO. We studied a population-based sample of 598 adolescents recruited from the French Canadian founder population in whom we measured brain volume by magnetic resonance imaging. Total fat mass was assessed with bioimpedance and body mass index was determined with anthropometry. Genotype-phenotype associations were tested with Merlin under an additive model. We found that the G allele of FTO (rs9930333) was associated with higher total body fat [TBF (P = 0.002) and lower brain volume (P = 0.005)]. The same allele was also associated with higher lean body mass (P = 0.03) and no difference in height (P = 0.99). Principal component analysis identified a shared inverse variance between the brain volume and TBF, which was associated with FTO at P = 5.5 × 10(-6). These results were replicated in two independent samples of 413 and 718 adolescents, and in a meta-analysis of all three samples (n = 1729 adolescents), FTO was associated with this shared inverse variance at P = 1.3 × 10(-9). Co-expression networks analysis supported the possibility that the underlying FTO effects may occur during embryogenesis. In conclusion, FTO is associated with shared inverse variance between body adiposity and brain volume, suggesting that this gene may exert inverse effects on adipose and brain tissues. Given the completion of the overall brain growth in early childhood, these effects may have their origins during early development.

Gillis, Jesse; Pavlidis, Paul — 2019-03-11 <p>MOTIVATION:<p> <p>The Gene Ontology (GO) is heavily used in systems biology, but the potential for redundancy, confounds with other data sources and problems with stability over time have been little explored.<p> <p>RESULTS:<p> <p>We report that GO annotations are stable over short periods, with 3% of genes not being most semantically similar to themselves between monthly GO editions. However, we find that genes can alter their 'functional identity' over time, with 20% of genes not matching to themselves (by semantic similarity) after 2 years. We further find that annotation bias in GO, in which some genes are more characterized than others, has declined in yeast, but generally increased in humans. Finally, we discovered that many entries in protein interaction databases are owing to the same published reports that are used for GO annotations, with 66% of assessed GO groups exhibiting this confound. We provide a case study to illustrate how this information can be used in analyses of gene sets and networks. <p> <p>AVAILABILITY:<p> <p>Data available at http://chibi.ubc.ca/assessGO.<p>

Lohia, Ruchi; Fox, Nathan; Gillis, Jesse — 2022 Additional file 2 Table S1. Details of each individual Hi-C project used for building human meta-Hi-C network. Table S2. Details of each individual Hi-C project used for building mouse meta-Hi-C network. Table S3. Details of each individual Hi-C project used for building fly meta-Hi-C network. https://creativecommons.org/licenses/by/4.0/legalcode