Mergeomics : multidimensional data integration to identify pathogenic perturbations to biological systems

Background: Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. Results: We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. Conclusion: Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.Peer reviewe

Opposite risk patterns for autism and schizophrenia are associated with normal variation in birth size:phenotypic support for hypothesized diametric gene-dosage effects

Opposite phenotypic and behavioural traits associated with copy number variation and disruptions to imprinted genes with parent-of-origin effects have led to the hypothesis that autism and schizophrenia share molecular risk factors and pathogenic mechanisms, but a direct phenotypic comparison of how their risks covary has not been attempted. Here, we use health registry data collected on Denmark's roughly 5 million residents between 1978 and 2009 to detect opposing risks of autism and schizophrenia depending on normal variation (mean ± 1 s.d.) in adjusted birth size, which we use as a proxy for diametric gene-dosage variation in utero. Above-average-sized babies (weight, 3691–4090 g; length, 52.8–54.3 cm) had significantly higher risk for autism spectrum (AS) and significantly lower risk for schizophrenia spectrum (SS) disorders. By contrast, below-average-sized babies (2891–3290 g; 49.7–51.2 cm) had significantly lower risk for AS and significantly higher risk for SS disorders. This is the first study directly comparing autism and schizophrenia risks in the same population, and provides the first large-scale empirical support for the hypothesis that diametric gene-dosage effects contribute to these disorders. Only the kinship theory of genomic imprinting predicts the opposing risk patterns that we discovered, suggesting that molecular research on mental disease risk would benefit from considering evolutionary theory

Constraints on the coevolution of contemporary human males and females

Because autosomal genes in sexually reproducing organisms spend on average half their time in each sex, and because the traits that they influence encounter different selection pressures in males and females, the evolutionary responses of one sex are constrained by processes occurring in the other sex. Although intralocus sexual conflict can restrict sexes from reaching their phenotypic optima, no direct evidence currently supports its operation in humans. Here, we show that the pattern of multivariate selection acting on human height, weight, blood pressure and glucose, total cholesterol, and age at first birth differs significantly between males and females, and that the angles between male and female linear (77.8 ± 20.5°) and nonlinear (99.1 ± 25.9°) selection gradients were closer to orthogonal than zero, confirming the presence of sexually antagonistic selection. We also found evidence for intralocus sexual conflict demonstrated by significant changes in the predicted male and female responses to selection of individual traits when cross-sex genetic covariances were included and a significant reduction in the angle between male- and female-predicted responses when cross-sex covariances were included (16.9 ± 15.7°), compared with when they were excluded (87.9 ± 31.6°). We conclude that intralocus sexual conflict constrains the joint evolutionary responses of the two sexes in a contemporary human population