Evidence map›Paper›PMID 32033531›Full record

ArticleGenetics, selection, evolution : GSE2020

pCADD: SNV prioritisation in Sus scrofa.

Christian Groß, Martijn Derks, Hendrik-Jan Megens, Mirte Bosse, Martien A M Groenen, Marcel Reinders, Dick de Ridder

Abstract readEvaluation Study
In one paragraph

Article in Genetics, selection, evolution : GSE, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Evaluating the Performance ofCombinatorial chemistry & high throughput screening · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Christian GroßDelft Bioinformatics Lab, University of Technology Delft, 2600GA, Delft, The Netherlands. c.gross@tudelft.nl.ORCID http://orcid.org/0000-0002-9482-4259
Martijn DerksAnimal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands.
Hendrik-Jan MegensAnimal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands.
Mirte BosseAnimal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands.
Martien A M GroenenAnimal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands.
Marcel ReindersDelft Bioinformatics Lab, University of Technology Delft, 2600GA, Delft, The Netherlands.
Dick de RidderBioinformatics Group, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands.

Funding

TTW-Breed4Food 14283
6 · The paper itself

Abstract

backgroundIn animal breeding, identification of causative genetic variants is of major importance and high economical value. Usually, the number of candidate variants exceeds the number of variants that can be validated. One way of prioritizing probable candidates is by evaluating their potential to have a deleterious effect, e.g. by predicting their consequence. Due to experimental difficulties to evaluate variants that do not cause an amino-acid substitution, other prioritization methods are needed. For human genomes, the prediction of deleterious genomic variants has taken a step forward with the introduction of the combined annotation dependent depletion (CADD) method. In theory, this approach can be applied to any species. Here, we present pCADD (p for pig), a model to score single nucleotide variants (SNVs) in pig genomes.

resultsTo evaluate whether pCADD captures sites with biological meaning, we used transcripts from miRNAs and introns, sequences from genes that are specific for a particular tissue, and the different sites of codons, to test how well pCADD scores differentiate between functional and non-functional elements. Furthermore, we conducted an assessment of examples of non-coding and coding SNVs, which are causal for changes in phenotypes. Our results show that pCADD scores discriminate between functional and non-functional sequences and prioritize functional SNVs, and that pCADD is able to score the different positions in a codon relative to their redundancy. Taken together, these results indicate that based on pCADD scores, regions with biological relevance can be identified and distinguished according to their rate of adaptation.

conclusionsWe present the ability of pCADD to prioritize SNVs in the pig genome with respect to their putative deleteriousness, in accordance to the biological significance of the region in which they are located. We created scores for all possible SNVs, coding and non-coding, for all autosomes and the X chromosome of the pig reference sequence Sscrofa11.1, proposing a toolbox to prioritize variants and evaluate sequences to highlight new sites of interest to explain biological functions that are relevant to animal breeding.

Indexed as

Polymorphism, Single NucleotideAnimalsFemaleGenetic VariationGenomeGenomicsMaleMicroRNAsModels, GeneticSwineX ChromosomeMicroRNAs

Identifiers

PMID32033531
PMCPMC7006094

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.