Common sequence variants affect molecular function more than rare variants?

Mahlich, Yannick; Reeb, Jonas; Hecht, Maximilian; Schelling, Maria; Beer, Tjaart de; Bromberg, Yana; Rost, Burkhard

doi:10.1038/s41598-017-01054-2

Cited by 26 publications

(36 citation statements)

References 27 publications

(37 reference statements)

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“…Additionally, we find that the properties of rare variants remain close to those of common variants. These findings contrast with other observations [9], which suggest that common variants have more impact on molecular function than rare variants. In this study, only for a few proteomics properties, such as the thermal stability and abundance of the affected proteins, common variants appear closer in character to disease-associated variants than to rare variants.…”

Section: Rare Variants Are Similar To Common Variantscontrasting

confidence: 99%

“…Thus our results indicate two competing trends for disease-associated variants: (i) disease-associated variants tend to localise to more abundant and stable proteins, which may suggest that these proteins are more sensitive to perturbation by variants; (ii) disease-associated variants in protein cores tend to localise to less stable proteins, which is consistent with the idea that such proteins might be more easily destabilised to a degree at which function is deleteriously impacted (see Discussion). gnomAD common data also show negative correlations with protein stability, for variants occurring at the core; this could potentially support the argument presented by Mahlich and colleagues [9] that common variants could affect molecular function more than rare variants. However, we believe this is more likely to be due to the fact that very few common variants localise to protein cores, as shown by Figure 5B, resulting in sparse statistics (i.e.…”

Section: Proteomics and Transcriptomics Features Associate With Variasupporting

confidence: 78%

“…The boundary separating disease-causing from neutral variants can be fluid: for example, a number of missense variants thought to lead to severe Mendelian childhood disease were identified in nominally healthy individuals in the ExAC database [8]. Moreover, whether common population variants have more functional impact than rare variants is hotly debated [9,10]. A better understanding of the molecular principles which underlie differences between disease-associated and population variants is necessary to improve variant classification, and define more clearly the boundaries which distinguish variants in health and disease.…”

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confidence: 99%

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Missense variants in health and disease affect distinct functional pathways and proteomics features

Laddach

Fraternali

2019

Preprint

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Missense variants are present amongst the healthy population, but some of them are causative of human diseases. Therefore, a classification of variants associated with "healthy" or "diseased" states is not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences, is essential to better distinguish pathogenic from population variants. Here we quantify variant enrichment across full-length proteins, their domains and 3D-structure defined regions. We integrate this with available transcriptomic and proteomic (protein half-life, thermal stability, abundance) data. Using this approach we have mined a rich set of molecular features which enable us to understand the differences underlying pathogenic and population variants: pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing, localise to protein cores and interaction interfaces, and are enriched in more abundant proteins. In terms of their molecular properties, we find that common population variants and pathogenic variants show the greatest contrast. Additionally, in contrary to other studies, we find that rare population variants display features closer to common than pathogenic variants. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants. Such details could be harnessed to predict variant deleteriousness, and prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at http://fraternalilab. kcl.ac.uk/ZoomVar. It allows users to programmatically annotate a large number of missense variants with protein structural information, and to calculate variant enrichment in different protein structural regions. Significance StatementOne of the greatest challenges in understanding the genetic basis of diseases is to discriminate between likely harmless and potentially disease-causing sequence variants. To better evaluate the pathogenic potential of missense variants, we developed a strategy to quantitatively measure the enrichment of both disease and non disease-related variants within a protein based on its structural and domain organisation. By integrating available transcriptomics and proteomics data, our approach distinguishes pathogenic from population variants far more clearly than previously possible, and reveals hitherto unknown details of how different proteins exhibit resilience and/or sensitivity towards genetic variants. Our results will help to prioritise variant-enriched proteins for therapeutic targeting; we have created the ZoomVar database, accessible at http://fraternalilab.kcl.ac.uk/ZoomVar, for programmatic mapping of user-defined variants to protein structural and domain information.

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Section: Rare Variants Are Similar To Common Variantscontrasting

confidence: 99%

Section: Proteomics and Transcriptomics Features Associate With Variasupporting

confidence: 78%

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confidence: 99%

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Missense variants in health and disease affect distinct functional pathways and proteomics features

Laddach

Fraternali

2019

Preprint

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“…Previous efforts to compare and contrast predictors of general variant impact have found that methods are correlated in their predictions, largely due to similar design objectives, overlapping training sets, and/or feature sets (Ioannidis et al, ; Mahlich et al, ). Interestingly, for predictors of mutation‐induced stability change, correlations were found to be small in previous evaluations (Khan & Vihinen, ; Potapov et al, ).…”

Section: Discussionmentioning

confidence: 99%

Assessment of methods for predicting the effects of PTEN and TPMT protein variants

et al. 2019

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Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation‐induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation, we explored the utility of high‐throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state‐of‐the‐art predictors against over 7,000 nonsynonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerging as top performers depending on the metric, it is nontrivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability‐affecting. Furthermore, it is unclear as to what extent VSP abundance scores were reasonable proxies for the stability‐related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

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“…Single amino acid variants (SAVs) have been found to be relevant for the molecular function of proteins [1]. The impact of different SAVs has been predicted in silico showing that, within human, more common SAVs (observed in more than 5% of the population) tend to have a larger impact on protein function than the rare ones (observed in less than 1% of the population [1]. The effect of common SAVs (gain or loss of functionality) on the survival of the species remains, at least partially, unknown.…”

Section: Introductionmentioning

confidence: 99%

An exhaustive analysis of single amino acid variants in helical transmembrane proteins

Llorian-Salvador¹,

Bernhofer²,

Mahlich

et al. 2019

Preprint

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Single nucleotide variants (SNVs) have been widely studied in the past due to being the main source of human genetic variation. Less is known about the effect of single amino acid variants (SAVs) due to the immense resources required for comprehensive experimental studies. In contrast, in silico methods predicting the effects of sequence variants upon molecular function and upon the organism are readily available and have contributed unexpected suggestions, e.g. that SAVs common to a human population (shared by >5% of the population) have, on average, more significant impact on the molecular function of proteins than do rare SAVs (shared by <1% of the population). Here, we investigated the impact of variants in a human population upon helical transmembrane proteins (TMPs). Three main results stood out. Firstly, common SAVs, on average, have stronger effects than rare SAVs for TMPs, and are enriched, in particular, in the membrane helices. Secondly, proteins with seven transmembrane helices (7TM, including GPCRs, i.e. G protein-coupled receptors) are depleted of SAVs in comparison to other proteins, possibly due to increased evolutionary constraints in these important proteins. Thirdly, rare SAVs with strong effect are significantly absent (over common SAVs) in signal peptide regions. Key words: helical transmembrane proteins, single amino acid variants, functional features prediction, human proteome, topology prediction.Abbreviations used: GPCR, G protein-coupled receptors (7-pass TMPs); non-TMP, nontransmembrane proteins (due to dedication errors, these may include some beta-barrel membrane proteins); SAV, single amino acid variant; SNV, single nucleotide variant; TMH, transmembrane helix; TMP, transmembrane protein.

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Common sequence variants affect molecular function more than rare variants?

Cited by 26 publications

References 27 publications

Missense variants in health and disease affect distinct functional pathways and proteomics features

Missense variants in health and disease affect distinct functional pathways and proteomics features

Assessment of methods for predicting the effects of PTEN and TPMT protein variants

An exhaustive analysis of single amino acid variants in helical transmembrane proteins

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