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With the recent availability of tissue-specific gene expression data, e.g., provided by the GTEx Consortium, there is interest in comparing gene co-expression patterns across tissues. One promising approach to this problem is to use a multilayer network analysis framework and perform multilayer community detection. Communities in gene co-expression networks reveal groups of genes similarly expressed across individuals, potentially involved in related biological processes responding to specific environmental stimuli or sharing common regulatory variations. We construct a multilayer network in which each of the four layers is an exocrine gland tissue-specific gene co-expression network. We develop methods for multilayer community detection with correlation matrix input and an appropriate null model. Our correlation matrix input method identifies five groups of genes that are similarly co-expressed in multiple tissues (a community that spans multiple layers, which we call a generalist community) and two groups of genes that are co-expressed in just one tissue (a community that lies primarily within just one layer, which we call a specialist community). We further found gene co-expression communities where the genes physically cluster across the genome significantly more than expected by chance (on chromosomes 1 and 11). This clustering hints at underlying regulatory elements determining similar expression patterns across individuals and cell types. We suggest thatKRTAP3-1,KRTAP3-3, andKRTAP3-5share regulatory elements in skin and pancreas. Furthermore, we find thatCELA3AandCELA3Bshare associated expression quantitative trait loci in the pancreas. The results indicate that our multilayer community detection method for correlation matrix input extracts biologically interesting communities of genes. 

The persistence of versions of genes that cause severe disease in human populations has long perplexed scientists. It is common for many versions of a gene to exist. But scientists expect that over time natural selection will eliminate versions of genes harmful to human health. Sometimes, there are good reasons that a disease-causing gene may persist. For example, having two copies of a particular gene variant causes a condition, called sickle cell disease. But having one sickle cell-causing copy of the gene and one non-disease-causing copy protects against malaria. As a result, the version of the gene that causes sickle cell is more common in people from areas where malaria is prevalent despite the risks to people who end up with two copies. Scientists call this phenomenon balancing selection because trade-offs in the gene’s benefits and risks cause it to persist in the population. Aqil et al. show that balancing selection has likely caused many ancient gene variants to persist in human populations. In the experiments, Aqil et al. scoured the genomes of hundreds of modern humans from around the world and four groups of ancient human ancestors, including Neanderthals and Denisovans. The experiments looked for structural changes in genes, like deletions, that date back to more than 700,000 years ago – before modern humans split from their ancestors. They found large numbers of such ancient genes in modern humans. Using computer modeling, Aqil et al. showed that these ancient genes likely persist because of balancing selection. Many of these ancient genes regulate the immune response and metabolism. These genes may protect against infectious diseases outbreaks and starvation, which have occurred periodically throughout human history. But these same genes may cause immune or metabolic diseases in modern humans not currently facing these threats. The experiments show how such biological trade-offs have shaped human evolution and reveal that modern human populations, regardless of race or region of origin, share the same genetic variation that already our ancestors carried within them.