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- Lequarre, Anne-Sophie, et al.
(författare)
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LUPA : A European initiative taking advantage of the canine genome architecture for unravelling complex disorders in both human and dogs
- 2011
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Ingår i: The Veterinary Journal. - : Elsevier BV. - 1090-0233 .- 1532-2971. ; 189:2, s. 155-159
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Forskningsöversikt (refereegranskat)abstract
- The domestic dog offers a unique opportunity to explore the genetic basis of disease, morphology and behaviour. Humans share many diseases with our canine companions, making dogs an ideal model organism for comparative disease genetics. Using newly developed resources, genome-wide association studies in dog breeds are proving to be exceptionally powerful. Towards this aim, veterinarians and geneticists from 12 European countries are collaborating to collect and analyse the DNA from large cohorts of dogs suffering from a range of carefully defined diseases of relevance to human health. This project, named LUPA, has already delivered considerable results. The consortium has collaborated to develop a new high density single nucleotide polymorphism (SNP) array. Mutations for four monogenic diseases have been identified and the information has been utilised to find mutations in human patients. Several complex diseases have been mapped and fine mapping is underway. These findings should ultimately lead to a better understanding of the molecular mechanisms underlying complex diseases in both humans and their best friend.
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| 2. |
- Meadows, Jennifer, et al.
(författare)
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Genome sequencing of 2000 canids by the Dog10K consortium advances the understanding of demography, genome function and architecture
- 2023
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Ingår i: Genome Biology. - : BioMed Central (BMC). - 1465-6906 .- 1474-760X. ; 24
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Tidskriftsartikel (refereegranskat)abstract
- Background: The international Dog10K project aims to sequence and analyze several thousand canine genomes. Incorporating 20 x data from 1987 individuals, including 1611 dogs (321 breeds), 309 village dogs, 63 wolves, and four coyotes, we identify genomic variation across the canid family, setting the stage for detailed studies of domestication, behavior, morphology, disease susceptibility, and genome architecture and function.Results: We report the analysis of > 48 M single-nucleotide, indel, and structural variants spanning the autosomes, X chromosome, and mitochondria. We discover more than 75% of variation for 239 sampled breeds. Allele sharing analysis indicates that 94.9% of breeds form monophyletic clusters and 25 major clades. German Shepherd Dogs and related breeds show the highest allele sharing with independent breeds from multiple clades. On average, each breed dog differs from the UU_Cfam_GSD_1.0 reference at 26,960 deletions and 14,034 insertions greater than 50 bp, with wolves having 14% more variants. Discovered variants include retrogene insertions from 926 parent genes. To aid functional prioritization, single-nucleotide variants were annotated with SnpEff and Zoonomia phyloP constraint scores. Constrained positions were negatively correlated with allele frequency. Finally, the utility of the Dog10K data as an imputation reference panel is assessed, generating high-confidence calls across varied genotyping platform densities including for breeds not included in the Dog10K collection.Conclusions: We have developed a dense dataset of 1987 sequenced canids that reveals patterns of allele sharing, identifies likely functional variants, informs breed structure, and enables accurate imputation. Dog10K data are publicly available.
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| 3. |
- Wucher, Valentin, et al.
(författare)
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FEELnc : a tool for long non-coding RNA annotation and its application to the dog transcriptome
- 2017
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 45:8
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Tidskriftsartikel (refereegranskat)abstract
- Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. One of the main bottlenecks, however, is to correctly identify the different classes of RNAs among the plethora of reconstructed transcripts, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program that accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-the-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE data sets. The program also provides specific modules that enable the user to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to identify lncRNAs even in the absence of a training set of non-coding RNAs. We used FEELnc on a real data set comprising 20 canine RNA-seq samples produced by the European LUPA consortium to substantially expand the canine genome annotation to include 10 374 novel lncRNAs and 58 640 mRNA transcripts. FEELnc moves beyond conventional coding potential classifiers by providing a standardized and complete solution for annotating lncRNAs and is freely available at https://github.com/tderrien/FEELnc.
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