Ranking non-synonymous single nucleotide polymorphisms based on disease concepts
© Shihab et al.; licensee BioMed Central Ltd. 2014
Received: 19 February 2014
Accepted: 21 June 2014
Published: 30 June 2014
As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are ‘disease agnostic’ but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk.
KeywordsSNV nsSNPs Disease-causing Disease-specific FATHMM HMMs SIFT PolyPhen Bioinformatics
The average human exome harbours around 20,000 single nucleotide variants (SNVs), of which approximately half are annotated as non-synonymous single nucleotide polymorphisms (nsSNPs) . However, characterizing the functional consequences of nsSNPs by direct laboratory experimentation is both time consuming and expensive. Therefore, computational prediction algorithms capable of predicting and/or prioritizing putatively functional variants for further experimentation are becoming increasingly important.
There is a plethora of computational prediction algorithms capable of analysing the functional consequences of nsSNPs . One of these methods is the Functional Analysis through Hidden Markov Models (FATHMM) algorithm : a sequence-based method which combines evolutionary conservation in homologous (both orthologous and/or paralogous) sequences with ‘pathogenicity weights’, representing the overall tolerance of proteins (and their component domains) to mutations. Using our original weighting scheme (adjusted for inherited disease mutations), we observed an improved predictive performance over alternative computational prediction algorithms using a ‘gold-standard’ benchmark . Nonetheless, these algorithms, including our own, were not designed to discriminate between nsSNPs influencing a specific disease (disease-specific) and other putative disease-causing/functional mutations (non-specific). For example, when tasked with discriminating between cancer-associated and other germline polymorphisms, these algorithms are capable of identifying a high proportion of cancer-promoting mutations. However, a large proportion of putative disease-causing (non-neoplastic) mutations are misclassified as having a role in carcinogenesis . In both a clinical and a research context, these tools are commonly used to investigate the aetiology of specific diseases. We therefore believe that there is a significant need for disease-specific functional variant predictions.
To the best of our knowledge, computational prediction algorithms have been explored exclusively in a gene-specific manner, e.g. predicting the effects of nsSNPs in mismatch repair proteins [6, 7]. The sole context in which disease-specific predictions have been developed is in the prediction of cancer-associated mutations [8–11]. In our previous work, we adapted our original algorithm by means of a cancer-specific weighting scheme and observed improved predictive performances over alternative (cancer-specific) computational prediction algorithms when predicting the functional consequences of cancer-associated nsSNPs . We have now extended this concept to a novel and more comprehensive ‘disease-specific’ weighting scheme to investigate whether such an approach is capable of prioritizing nsSNPs based on 17 disease concepts/categories.
Performance of computational prediction algorithms when discriminating between disease-specific variants and other disease-causing/neutral variants
In the above, tp, fp, tn and fn refer to the number of true positives, false positives, true negatives and false negatives observed, respectively. Accuracy, precision, specificity, sensitivity, negative predictive value (NPV) and Matthew's correlation coefficient (MCC) were calculated using normalized numbers. Italic font corresponds to the best performing method for a given statistic.
As our weighting scheme was derived using the same mutation data used to assess our method (albeit using a leave-one-out analysis), we recognize the potential for bias. Therefore, we also performed a 20-fold cross-validation analysis (see Table 1 and Additional file 1: Supp. Info 2–18). We observed no significant deviations in the performance measures reported and therefore concluded that the performance of our disease-specific approach is not an artefact of over-fitting. We also recognize that most of our algorithm's predictive power comes from our weighting scheme, i.e. it is the weighting scheme that allows us to differentiate between disease-associated variants and other disease-causing mutations. Therefore, we also compared our approach to a naive weighting scheme. Here, we used our weighting scheme (omitting sequence conservation) to derive a prediction score. Proteins, and their constituent domains, with a higher proportion of disease-associated mutations would predict all variants falling within them as disease, and those with a higher proportion of other disease-causing mutations/neutral polymorphisms would predict all variants as neutral. Overall, we observed a similar performance to that of our algorithm (see Additional file 1: Supp. Info 19–36). However, it should be noted that a naive approach is incapable of reliably discriminating between disease-associated mutations and other disease-causing variants as the weighting scheme becomes more balanced, whereas our disease-specific approach (which incorporates sequence conservation for prediction) appears to be less susceptible to balanced weights.
In order to facilitate the replication of our work, we have annotated SwissProt/TrEMBL disease variants (Release 2014_06) with the disease concepts used in our analysis and make this resource publically available at our website (http://fathmm.biocompute.org.uk). Using this dataset to train and test our algorithm, we observed similar performances to those reported above (see Additional file 2).
There is a plethora of computational prediction algorithms available to predict the functional consequences of nsSNPs . However, these algorithms are not designed to distinguish between mutations related to a specific disease, or a group of related diseases (disease-specific), and other putative disease-causing (non-specific) mutations. As the cost of whole-exome/whole-genome sequencing falls, making these methods more amenable to use in a research or clinical context, the challenge of filtering true disease-causing candidate variants from other putative functional variants is likely to become increasingly important. In this work, we assessed the potential benefits of making disease-specific predictions (relevant to 17 disease categories) using the Functional Analysis through Hidden Markov Models (FATHMM) framework and observed an overall reduction in the number of false positives identified, thereby leading to improved specificity over traditional algorithms. However, we also observed an increase in the number of false negatives identified and conclude that additional work is needed to improve sensitivity and enhance the utility of our disease-specific approach. Nevertheless, there is potential to extend this approach to more specific categories for the purposes of enhancing clinical prediction.
An important consideration when evaluating the performance of computational prediction algorithms is the cross-validation dataset. Here, the performance of such algorithms should be trained and tested using different datasets (cross-validation). In order to alleviate the potential for bias in our results, we performed a 20-fold cross-validation procedure across our 17 disease concepts. From this analysis, we observed no significant deviations in the reported performance measures and therefore conclude that the performances observed were not an artefact of our disease-specific weighting scheme.
One of the major limitations of our disease-specific approach is that, in extreme cases, there is potential for dominating pathogenicity weights which could bias or exaggerate the effects of variants, e.g. when prioritizing variants in proteins and/or domains which have very strong associations with the disease concept under investigation. Here, the pathogenicity weights used could dominate the underlying amino acid probabilities (used to measure sequence conservation) and therefore bias the prediction. For example, when these weights are biased towards the disease concept, neutral polymorphisms falling within diverse regions of a protein/domain would be classified as ‘damaging’ as opposed to being classified as ‘benign’. As a consequence, our disease-specific models are best suited as a whole-genome/whole-exome prioritization method (hypothesis-free) and should be used with caution when prioritizing variants in a gene-specific manner. In an attempt to alleviate the potential effects of dominating pathogenicity weights, measures of sequence conservation are presented alongside our rankings so that spurious predictions can be assessed and ignored.
An alternative approach to our disease-specific weighting scheme is to filter putative disease-causing nsSNPs using the Gene Ontology . However, this approach is dependent upon protein annotations being made available whereas our algorithm does not require prior information on protein function. Furthermore, users adopting this approach are required to select from a range of technical phrases, e.g. ‘negative regulation of cellular macromolecule biosynthetic process’ (GO: 2000113). In contrast, our disease-specific models do not require any formal knowledge on GO terms and biological processes, just an understanding of which model/concept best represents the disease under investigation. Our disease-specific models, including a high-throughput web-based implementation of our algorithm and a standalone software package, are available at http://fathmm.biocompute.org.uk.
Predicting the functional consequences of nsSNPs
In Equation 1, Pw and Pm represent the probabilities for the wild-type and mutant amino acid residues, respectively, whereas Wd and Wn represent the relative frequencies of disease-associated and functionally neutral nsSNPs mapping onto the corresponding model, respectively. Here, we use inherited disease-causing nsSNPs annotated as DMs (damaging mutations) in the Human Gene Mutation Database (HGMD Pro 12.4 ) and putative neutral polymorphisms from the SwissProt/TrEMBL database  (Release 2013_04) to derive Wd and Wn, respectively. The effect of our weighting scheme is as follows: when using pure conservation-based prediction methods, nsSNPs falling within diverse regions of the protein (or domain) are typically considered ‘neutral/benign’. However, our weighting scheme assesses the tolerance of the corresponding model (representing a protein or domain) to mutation and then adjusts a conservation-based prediction accordingly. For example, nsSNPs falling within P53 (a well-established cancer gene) are penalized according to the gene's intolerance to mutation whereas nsSNPs falling within MHC (known to contain hypervariable regions) are not penalized given the gene's apparent tolerance to mutation.
Incorporating a disease-specific weighting scheme
Summary of nsSNPs used in our disease-specific mutation datasets
Number of proteins
Number of amino acid substitutions
Human Gene Mutation Database (HGMD)
Ear, nose and throat
Putative neutral polymorphisms
In the aforementioned data, tp and fp refer to the number of true positives and false positives reported, respectively, whereas tn and fn refer to the number of true negatives and false negatives reported, respectively. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses were performed using the ROCR software suite .
This work was supported by the UK Medical Research Council (MRC) [MC_UU_12013/8 and G1000427/1] and was carried out in the Bristol Centre for Systems Biomedicine (BCSBmed) Doctoral Training Centre (director INMD) using the computational facilities of the Advanced Computing Research Centre, University of Bristol—http://www.bris.ac.uk/acrc. JG’s contribution was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) [BB/G022771]. MM and DNC gratefully acknowledge the financial support of BIOBASE GmbH.
- Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, Shendure J: Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011, 12: 745-755.View ArticlePubMedGoogle Scholar
- Thusberg J, Olatubosun A, Vihinen M: Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat. 2011, 32: 358-368.View ArticlePubMedGoogle Scholar
- Shihab HA, Gough J, Cooper DN, Stenson PD, Barker GLA, Edwards KJ, Day INM, Gaunt TR: Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Hum Mutat. 2013, 34: 57-65.PubMed CentralView ArticlePubMedGoogle Scholar
- Sasidharan Nair P, Vihinen M: VariBench: a benchmark database for variations. Hum Mutat. 2013, 34: 42-49.View ArticlePubMedGoogle Scholar
- Kaminker JS, Zhang Y, Waugh A, Haverty PM, Peters B, Sebisanovic D, Stinson J, Forrest WF, Bazan JF, Seshagiri S, Zhang Z: Distinguishing cancer-associated missense mutations from common polymorphisms. Cancer Res. 2007, 67: 465-473.View ArticlePubMedGoogle Scholar
- Ali H, Olatubosun A, Vihinen M: Classification of mismatch repair gene missense variants with PON-MMR. Hum Mutat. 2012, 33: 642-650.View ArticlePubMedGoogle Scholar
- Thompson BA, Greenblatt MS, Vallee MP, Herkert JC, Tessereau C, Young EL, Adzhubey IA, Li B, Bell R, Feng B, Mooney SD, Radivojac P, Sunyaev SR, Frebourg T, Hofstra RM, Sijmons RH, Boucher K, Thomas A, Goldgar DE, Spurdle AB, Tavtigian SV: Calibration of multiple in silico tools for predicting pathogenicity of mismatch repair gene missense substitutions. Hum Mutat. 2013, 34: 255-265.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaminker JS, Zhang Y, Watanabe C, Zhang Z: CanPredict: a computational tool for predicting cancer-associated missense mutations. Nucleic Acids Res. 2007, 35: W595-W598.PubMed CentralView ArticlePubMedGoogle Scholar
- Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, Vogelstein B, Karchin R: Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res. 2009, 69: 6660-6667.PubMed CentralView ArticlePubMedGoogle Scholar
- Reva B, Antipin Y, Sander C: Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011, 39: e118-PubMed CentralView ArticlePubMedGoogle Scholar
- Gonzalez-Perez A, Deu-Pons J, Lopez-Bigas N: Improving the prediction of the functional impact of cancer mutations by baseline tolerance transformation. Genome Med. 2012, 4: 89-PubMed CentralView ArticlePubMedGoogle Scholar
- Shihab HA, Gough J, Cooper DN, Day INM, Gaunt TR: Predicting the functional consequences of cancer-associated amino acid substitutions. Bioinformatics. 2013, 29: 1504-1510.PubMed CentralView ArticlePubMedGoogle Scholar
- Ng PC, Henikoff S: Predicting deleterious amino acid substitutions. Genome Res. 2001, 11: 863-874.PubMed CentralView ArticlePubMedGoogle Scholar
- Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR: A method and server for predicting damaging missense mutations. Nat Methods. 2010, 7: 248-249.PubMed CentralView ArticlePubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29.PubMed CentralView ArticlePubMedGoogle Scholar
- Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O'Donovan C, Redaschi N, Yeh LS: UniProt: the Universal Protein knowledgebase. Nucleic Acids Res. 2004, 32: D115-D119.PubMed CentralView ArticlePubMedGoogle Scholar
- Eddy SR: A new generation of homology search tools based on probabilistic inference. Genome Inform. 2009, 23: 205-211.PubMedGoogle Scholar
- Gough J, Karplus K, Hughey R, Chothia C: Assignment of homology to genome sequences using a library of hidden Markov models that represent all proteins of known structure. J Mol Biol. 2001, 313: 903-919.View ArticlePubMedGoogle Scholar
- Sonnhammer EL, Eddy SR, Durbin R: Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins. 1997, 28: 405-420.View ArticlePubMedGoogle Scholar
- Kullback S, Leibler RA: On information and sufficiency. Ann Math Stat. 1951, 22: 79-86.View ArticleGoogle Scholar
- Stenson PD, Mort M, Ball EV, Howells K, Phillips AD, Thomas NS, Cooper DN: The Human Gene Mutation Database: 2008 update. Genome Med. 2009, 1: 13-PubMed CentralView ArticlePubMedGoogle Scholar
- Lindberg DA, Humphreys BL, McCray AT: The Unified Medical Language System. Methods Inf Med. 1993, 32: 281-291.PubMedGoogle Scholar
- Mort M, Evani US, Krishnan VG, Kamati KK, Baenziger PH, Bagchi A, Peters BJ, Sathyesh R, Li B, Sun Y, Xue B, Shah NH, Kann MG, Cooper DN, Radivojac P, Mooney SD: In silico functional profiling of human disease-associated and polymorphic amino acid substitutions. Hum Mutat. 2010, 31: 335-346.PubMed CentralView ArticlePubMedGoogle Scholar
- Vihinen M: Guidelines for reporting and using prediction tools for genetic variation analysis. Hum Mutat. 2013, 34: 275-282.View ArticlePubMedGoogle Scholar
- Sing T, Sander O, Beerenwinkel N, Lengauer T: ROCR: visualizing classifier performance in R. Bioinformatics. 2005, 21: 3940-3941.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.