Predicting the proportion of essential genes in mouse duplicates based on biased mouse knockout genes.
ABSTRACT In the yeast or nematode, the proportion of essential genes in duplicates is lower than in singletons (single-copy genes), due to the functional redundancy. One may expect that it should be the same in the mouse genome. However, based on the publicly available mouse knockout data, it was observed that the proportion of essential genes in duplicates is similar to that in singletons. The most straightforward interpretation, as claimed in a recent study, is that duplicate genes may have a negligible role in the mouse genetic robustness. Here we show that in the current mouse knockout dataset, recently duplicated genes have been highly underrepresented, leading to an overestimation of the proportion of essential genes in duplicates. After estimating the duplication time of mouse duplication events, we have developed a simple bias-correcting procedure and shown that the bias-corrected proportion of essential genes in mouse duplicates is significantly lower than that in singletons.
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ABSTRACT: Genes are characterized as essential if their knockout is associated with a lethal phenotype, and these "essential genes" play a central role in biological function. In addition, some genes are only essential when deleted in pairs, a phenomenon known as synthetic lethality. Here we consider genes displaying synthetic lethality as "essential pairs" of genes, and analyze the properties of yeast essential genes and synthetic lethal pairs together. As gene duplication initially produces an identical pair or sets of genes, it is often invoked as an explanation for synthetic lethality. However, we find that duplication explains only a minority of cases of synthetic lethality. Similarly, disruption of metabolic pathways leads to relatively few examples of synthetic lethality. By contrast, the vast majority of synthetic lethal gene pairs code for proteins with related functions that share interaction partners. We also find that essential genes and synthetic lethal pairs cluster in the protein-protein interaction network. These results suggest that synthetic lethality is strongly dependent on the formation of protein-protein interactions. Compensation by duplicates does not usually occur mainly because the genes involved are recent duplicates, but is more commonly due to functional similarity that permits preservation of essential protein complexes. This unified view, combining genes that are individually essential with those that form essential pairs, suggests that essentiality is a feature of physical interactions between proteins protein-protein interactions, rather than being inherent in gene and protein products themselves.PLoS ONE 01/2013; 8(4):e62866. · 3.73 Impact Factor
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ABSTRACT: When a duplicate gene has no apparent loss-of-function phenotype, it is commonly considered that the phenotype has been masked as a result of functional redundancy with the remaining paralog. This is supported by indirect evidence showing that multi-copy genes show loss-of-function phenotypes less often than single-copy genes and by direct tests of phenotype masking using select gene sets. Here we take a systematic genome-wide RNA interference approach to assess phenotype masking in paralog pairs in the Caenorhabditis elegans genome. Remarkably, in contrast to expectations, we find that phenotype masking makes only a minor contribution to the low knockdown phenotype rate for duplicate genes. Instead, we find that non-essential genes are highly over-represented among duplicates, leading to a low observed loss-of-function phenotype rate. We further find that duplicate pairs derived from essential and non-essential genes have contrasting evolutionary dynamics: whereas non-essential genes are both more often successfully duplicated (fixed) and lost, essential genes are less often duplicated but upon successful duplication are maintained over longer periods. We expect the fundamental evolutionary duplication dynamics presented here to be broadly applicable.PLoS Genetics 05/2013; 9(5):e1003330. · 8.52 Impact Factor
Article: How old is my gene?[Show abstract] [Hide abstract]
ABSTRACT: Gene functions, interactions, disease associations, and ecological distributions are all correlated with gene age. However, it is challenging to estimate the intricate series of evolutionary events leading to a modern-day gene and then to reduce this history to a single age estimate. Focusing on eukaryotic gene families, we introduce a framework that can be used to compare current strategies for quantifying gene age, discuss key differences between these methods, and highlight several common problems. We argue that genes with complex evolutionary histories do not have a single well-defined age. As a result, care must be taken to articulate the goals and assumptions of any analysis that uses gene age estimates. Recent algorithmic advances offer the promise of gene age estimates that are fast, accurate, and consistent across gene families. This will enable a shift to integrated genome-wide analyses of all events in gene evolutionary histories in the near future.Trends in Genetics 07/2013; · 9.77 Impact Factor