Disease-related mutations predicted to impact protein function

By Christian Schaefer, Yana Bromberg, Dominik Achten, Burkhard Rost. 2012

24 citations recorded by the source corpus.

Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease. Most disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for disease-causing mutations were higher than the scores for the function-altering data set originally used for developing the prediction method (here SNAP). We might expect that diseases are caused by change-of-function mutations. However, it is surprising how well prediction methods developed for different purposes identify this link. Conversely, our predictions suggest that the set of nsSNPs not currently linked to diseases contains very few strong disease associations to be discovered. Firstly, annotations of disease-causing nsSNPs are on average so reliable that they can be used as proxies for functional impact. Secondly, disease-causing nsSNPs can be identified very well by methods that predict the impact of mutations on protein function. This implies that the existing prediction methods provide a very good means of choosing a set of suspect SNPs relevant for disease.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “Generally, today’s methods are able to distinguish between a set with 100 disease-causing and another with 100 less impacting variants [17,18].”
Neutral and weakly nonneutral sequence variants may define individuality (2013, background): “Disease-associated nsSNPs, on the other hand, are enriched in variants predicted to severely disrupt function (21).”
News from the protein mutability landscape. (2013, background): “For instance, mutations that directly cause a disease, such as those found in OMIM [47], are clearly identified by methods that predict the functional effect of variants [48].”
Collective judgment predicts disease-associated single nucleotide variants (2013, background): “This is an expected outcome, as not all functionally deleterious mutations are disease associated [8,28,29].”