Sharing dog exome/genome variant and phenotype data in Varda and LOVD3 - Peter Taschner

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We hypothesize that efficient sharing of genetic variants and phenotype data from other species may help overcoming the main hurdle in current whole exome (WES) and whole genome sequencing (WGS) studies aimed at cracking rare disease: finding sufficient evidence to prove causality. Roughly, studies give a yield of 1/3 proven causality, 1/3 likely causality and 1/3 unresolved cases. Obviously, using other world-wide resources to find additional positive or negative evidence for candidate variants remaining might improve overall yield. An important obstacle here is that information from other organisms is often not easy to find and access in an interoperable manner. Furthermore, lack of standards and metadata may interfere with reuse of data. Reference protein sequences from other organisms are already used to predict amino acid conservation scores [1, 2]. These might be improved if protein variants in individual races of certain species could be included in the calculations. When these variants would be annotated with the frequency and their functional effects, this information would be even more valuable. Variants causing disease in animals are currently stored in Online Mendelian Inheritance in Animals (OMIA, http://omia.angis.org.au/). For several species, including dogs, the research community has partially adopted standards resembling HGNC and HGVS nomenclature. Further standardization may contribute to data from other species becoming more FAIR (Findable, Accessible, Interoperable, and Re-usable) (FAIR data, http://www.dtls.nl/fair-data/). As a first step, we have established instances of the variant frequency database Varda (Dog Varda, http://varda.generade.nl) and the LOVD3 gene variant database platform [3] for the dog (Dog LOVD, http://databases. generade.nl/dog/). We demonstrate that variants from VCF files can be stored and annotated with their frequency in Varda. Information about the functional effects of variants and the resulting phenotypes in individual animals can be stored in the Canis familiaris LOVD. We hope that this example will be followed by research and diagnostic centers world-wide for the dog and other species. [1] PolyPhen-2, PMID:20354512, http://genetics.bwh.harvard.edu/pph2/ [2] SIFT, PMID:11337480, http://sift.bii.a-star.edu.sg/ [3] LOVD, Leiden Open-source Variation Database, PMID:21520333, http://www.LOVD.nl

Wissenschaft

Sharing dog exome/genome
variant and phenotype data
in Varda and LOVD3
Human Variome Project Meeting, Paris, June 2, 2016
Peter Taschner
taschner@generade.nl

Serious health problems: inbreeding
Hereditary disorders in pedigree dog breeds
Selection reduces genetic diversity
Small genetic basis: founder effects
Exome / genome sequencing:
Selection of dogs to maintain genetic diversity and reduce carrier load
Variants in disease-causing genes: candidate genes for human disease
Dog variants: evidence for causal variants in human patients?
2
Why share dog data?

Nature 438: 803 (2005)
Genetic bottleneck: loss of variation

5
5 dogs per breed
Panel 96 markers
Variation between 85 dog breeds

OMIA
http://omia.angis.org.au
672 dog traits/disorders
201 causative variant known
378 disease models

OMIA: dog CLN6
c.829C>T p.(Trp277Arg)
Reference sequence?
More information in
causal variant tables

Monarch database: dog CLN6
https://monarchinitiative.org/disease/OMIA:001443-9615

Dog exome capture kit design
Raw read data in SRA
No variant details provided
No dbSNP submissions

11
Clinical exome sequencing
Varda
Re-analyze raw dog data
VCF variant file
Annotate using VEP
Import into Varda
Import filtered set into LOVD

12
Varda – dog variant frequencies
https://varda.generade.nl/aule/
DMD gene
Other tools available for dogs:
Mutalyzer:
Name Checker
Position Converter
Mapping database for dog and mouse

13
Variant Effect Predictor
Canine gene symbols follow HGNC systematics
Variants described in HGVS format
Challenges:
Many predicted transcripts and gene models
LOVD uses human-oriented resources
Adaptation/manual intervention necessary
http://databases.generade.nl/dog

14
Acknowledgments
Bioinformatics, UAS Leiden
Thomas Olivier
Koen van Diemen
Rosa Hegi
Olga Veth
Bo Blanckenburg
Floyd Wittink
LUMC, Human Genetics
Ivo Fokkema
Martijn Vermaat
LUMC, Sequence Analysis Support Core
Wibowo Arindrarto
University Gent
Bart Broeckx
OMIA
Frank Nicholas
Varda dog: https://varda.generade.nl/aule/
Dog LOVD: http://databases.generade.nl/dog

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