Genome-wide search for exonic variants affecting translational efficiency

Quan, Li; Makri, Angeliki; Lü, Yang; Marchand, Luc; Grabs, Rosemarie; Rousseau, Marylène; Ounissi-Benkalha, Houria; Robert, Françis; Harmsen, Eef; Hudson, Thomas J.; Pastinen, Tomi; Polychronakos, Constantin; Qu, Hui

doi:10.1038/ncomms3260

Cited by 20 publications

(16 citation statements)

References 53 publications

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“…While the allelic RNA ratio methodology discovers the majority of rSNPs and srSNP, one can miss specific events where the RNA with altered function is sequestered differentially by interactions with cellular components without changing the total RNA content of the cell. For example, mRNAs engaged in active translation are enriched in polysomes compared to the total cells’ mRNA content (Li et al 2013). srSNPs affording enhanced or reduced translation (NAT1 and OPRM1, respectively (Wang et al 2011b; Zhang et al 2005)) appear to assemble polysomes at distinct rates, a process that can be detected by allelic mRNA ratio analysis of polysomal RNA (R. Mascarenhas et al, unpublished).…”

Section: How To Interpret Gwas Data To Address the ‘Missing Heritabilmentioning

confidence: 99%

Missing heritability of common diseases and treatments outside the protein-coding exome

et al. 2014

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Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the ‘missing heritability’. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an ‘off-well’ state, revealing adaptations to changing environmental conditions. In contrast, genome-wide association studies (GWAS) focus on deleterious variants conveying disease risk, accelerating the path from off-well to illness and finally specific diseases, while common ‘normal’ variants remain hidden in the noise. Second, epistasis (dynamic gene-gene interactions) likely assumes a central role in adaptations and evolution; yet, GWAS analyses currently are poorly designed to reveal epistasis. As gene regulation is germane to adaptation, we propose that epistasis among common normal regulatory variants, or between common variants and less frequent deleterious variants, can have strong protective or deleterious phenotypic effects. These gene-gene interactions can be highly sensitive to environmental stimuli and could account for large differences in drug response between individuals. Residing largely outside the protein-coding exome, common regulatory variants affect either transcription of coding and non-coding RNAs (regulatory SNPs, or rSNPs) or RNA functions and processing (structural RNA SNPs, or srSNPs). Third, with the vast majority of causative variants yet to be discovered, GWAS rely on surrogate markers, a confounding factor aggravated by the presence of more than one causative variant per gene and by epistasis. We propose that the confluence of these factors may be responsible to large extent for the observed heritability gap.

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Section: How To Interpret Gwas Data To Address the ‘Missing Heritabilmentioning

confidence: 99%

Missing heritability of common diseases and treatments outside the protein-coding exome

et al. 2014

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“…That is not to say such variation is unimportant; UTR variation can affect many aspects of regulation (e.g. mRNA stability [ 30 , 31 ] and protein translation [ 32 , 33 ]) and while the sequences underlying these processes are largely cryptic at the present time, we predict they will be considered a more significant source of functional variation in future. Similarly, processed-transcripts (and RefSeq 'NR' transcripts) within protein-coding genes are in fact likely to encode CDS in reality, whether they are full-length or targets for the NMD pathway.…”

Section: Discussionmentioning

confidence: 99%

Comparison of GENCODE and RefSeq gene annotation and the impact of reference geneset on variant effect prediction

et al. 2015

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BackgroundA vast amount of DNA variation is being identified by increasingly large-scale exome and genome sequencing projects. To be useful, variants require accurate functional annotation and a wide range of tools are available to this end. McCarthy et al recently demonstrated the large differences in prediction of loss-of-function (LoF) variation when RefSeq and Ensembl transcripts are used for annotation, highlighting the importance of the reference transcripts on which variant functional annotation is based.ResultsWe describe a detailed analysis of the similarities and differences between the gene and transcript annotation in the GENCODE and RefSeq genesets. We demonstrate that the GENCODE Comprehensive set is richer in alternative splicing, novel CDSs, novel exons and has higher genomic coverage than RefSeq, while the GENCODE Basic set is very similar to RefSeq. Using RNAseq data we show that exons and introns unique to one geneset are expressed at a similar level to those common to both. We present evidence that the differences in gene annotation lead to large differences in variant annotation where GENCODE and RefSeq are used as reference transcripts, although this is predominantly confined to non-coding transcripts and UTR sequence, with at most ~30% of LoF variants annotated discordantly. We also describe an investigation of dominant transcript expression, showing that it both supports the utility of the GENCODE Basic set in providing a smaller set of more highly expressed transcripts and provides a useful, biologically-relevant filter for further reducing the complexity of the transcriptome.ConclusionsThe reference transcripts selected for variant functional annotation do have a large effect on the outcome. The GENCODE Comprehensive transcripts contain more exons, have greater genomic coverage and capture many more variants than RefSeq in both genome and exome datasets, while the GENCODE Basic set shows a higher degree of concordance with RefSeq and has fewer unique features. We propose that the GENCODE Comprehensive set has great utility for the discovery of new variants with functional potential, while the GENCODE Basic set is more suitable for applications demanding less complex interpretation of functional variants.

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“…The resulting information is a quantitative and qualitative description of the degree of polysomal engagement for every transcript (by which the molecular nature of this method), the so called translatome (Tebaldi et al, 2012 ); a calculation of translational efficiency can be done by this assay. The qualitative component of polysome profiling is given by computational approaches which allow us to investigate the differential association of mRNAs produced by the same gene locus (splice and 5′/3′ variants) with the polysomes (Frac-seq, Sterne-Weiler et al, 2013 ), or which measure the effect of single-nucleotide polymorphisms on translational efficiency (Li et al, 2013 ). Ribosome profiling (Ingolia, 2014 ) aims at providing a snapshot of mRNAs under translation by scoring the transcript regions which are protected from nuclease attack by ribosomes.…”

Section: Probing the Biological Status Of Whole Transcriptomesmentioning

confidence: 99%

Fingerprints of a message: integrating positional information on the transcriptome

Dassi

Quattrone

2014

Front. Cell Dev. Biol.

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The recent explosion of high-throughput sequencing methods applied to RNA molecules is allowing us to go beyond the description of sequence variants and their relative abundances, as measured by RNA-seq. We can now probe for RNA engagement in polysomes, for ribosomes, RNA binding proteins and microRNAs binding sites, for RNA secondary structure and for RNA methylation. These descriptors produce a steadily growing multidimensional array of positional information on RNA sequences, whose effective integration only would bring to decipher the regulatory interplay occurring between proteins, RNAs and their modifications on the transcriptome. This interplay ultimately dictates the degree of mRNA availability to translation, and thus the occurrence of cell phenotypes. However, several issues in data presentation are slowing down effective integration. A standardization effort for new dataset types produced should be urgently undertaken to solve these issues. Providing uniformed experimental details along with datasets processed to be directly usable and employing shared formats would greatly simplify integration efforts, strengthening hypotheses stemming from correlative observations and eventually bringing to mechanistic understanding.

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Genome-wide search for exonic variants affecting translational efficiency

Cited by 20 publications

References 53 publications

Missing heritability of common diseases and treatments outside the protein-coding exome

Missing heritability of common diseases and treatments outside the protein-coding exome

Comparison of GENCODE and RefSeq gene annotation and the impact of reference geneset on variant effect prediction

Fingerprints of a message: integrating positional information on the transcriptome

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