Launching SheepGenomesDB: 100 million variants from nearly 500 sheep genomes

Transcription

1 Launching : 00 million variants from nearly 500 sheep genomes James Kijas, Rudiger Brauning, Shannon Clarke, Alan McCulloch, Noelle Cockett 3, Gary Saunders 4, Marina Naval Sanchez, Sean McWilliam, Hans Daetwyler

2 International Sheep Genomics Consortium: now

3 SGD Aims: : Implement a single harmonized workflow for variant detection, and apply it to publically available sheep genome sequences : Prepare raw and filtered variant collections Genome wide 3: Make project data available to the user community in multiple ways to accommodate varied project requirements Chromosome wide Region specific Gene specific

4 SGD Architecture and Dataflow: FastQ Sequence Read Archive Standardised Pipeline for: QC, Mapping to OARV3. FastQ VCF European Bioinformatics Institute European Variation Archive (EVA) - Variants accessioned Data Available to User Community Variant Detection - SNP - indels - dbsnp IDs assigned -SS/ RS numbers - Functional annotation - tools for user download Web Interface - Individual animal VCF files -Tools to cut and carry the data - Project updates

5 Animals in Run Total of 453 genomes in Run 56 Populations, broad geographic representation 6.4 animals / population, large variation Unclassified Timahdite Texel Sardinian Black 4 94 Romney D Man New Zealand Composite Coopworth Breed N Breed N AFRICAN WHITE DORPER MERINO 3 AFSHARI MERINO HORNED 4 AWASSI 3 MERINO POLLED 6 BANGLADESHI MILK LACAUNE BANGLADESHI GAROLE MORADA NOVA BENI GUIL 7 NAMAQUA AFRIKANER BORDER LEICESTER NORDUZ BOUJAD NORWEGIAN WHITE SHEEP BRAZILIAN CREOLE OJALADA CASTELLANA OULED DJELLAL 8 CHANGTHANGI ROMNEY 47 CHEVIOT RONDERIB AFRIKANER CHURRA SAKIZ CINE CAPARI SALZ COMPOSITE NZ 98 SANTA INES COOPWORTH 38 SARDINIAN ANCESTRAL BLACK 4 D'MAN 7 SCOTTISH BLACKFACE DOLLGELLAU WELSH MOUNTAIN SUFFOLK BLACKFACE DORSET SUMATRAN DORSET POLLED SWISS MIRROR ETHIOPIAN MENZ SWISS WHITE ALPINE 4 FINNSHEEP 4 TEXEL 0 GARUT TIBETAN GULF COAST NATIVE TIMAHDITE 5 INDIAN GAROLE TREGAON WELSH MOUNTAIN KARAKAS UNCLASSIFIED 94 KARYA VALAIS BLACK NOSE MEAT LACAUNE WELSH HARDY SPECKLED FACE WILTSHIRE

6 A Variant Calling: Two Methods. UNFILTERED Variants SAMtools filters SAMtools GATK GATK filters Comparison of Variant Callers GATK SAMtools Variants Raw Filtered %_Reduction Raw Filtered %_Reduction SNP 77,69,95 5,035, ,05,56 48,37,67 9 Indels 7,905,455 4,44, ,73,047 3,350, Total Variants 85,55,406 55,476, ,738,303 5,73,6 3. FILTERED Variants 6,000,000 5,000,000 GATK Filtered SNP SAMtools Filtered SNP 600, ,000 GATK Filtered Indels SAMtools Filtered Indels 4,000, ,000 3,000, ,000,000,000 00,000,000,000 00,000 Independent callers used GATK consistently more permissive Filtering removed 9 50% of variant calls MT X MT X

7 Variant Calling: Merging A B Union. UNFILTERED Variants SAMtools filters SAMtools GATK GATK filters merge Intersect 3. RAW Variants RAW variants. FILTERED Variants merge Two sets of variants obtained after merging, both into EVA. Around 75% of variants called by BOTH callers. Interest % elevated in the high quality variant collection. 4. HIGH QUALITY Variants HIGH QUALITY variants

8 European Variant Archive

9 Variants are annotated Accessioned Gene Search Filter by Variant Type Download Genotypes

10 What Next? Genome wide Lets put the data to work!. Control genomes for confirming disease mutations Chromosome wide. Identifying gene specific variants 3. New SNP genotyping platforms Region specific Gene specific 4. Identification of CNV 5. Explore consequences of domestication and selection 6. Selection sweeps for breed specific traits 7. Marina Naval Sanchez et al. S05

11 What Next? Run - Planned for later this year. FastQ data in SRA is the starting point animals from Australian commercial breeds more NZ animals. - Irish, Chinese, US and Scottish animals likely - Contacts: Rudi Brauning Hans Daetwyler James Kijas Rudiger.Brauning@agresearch.co.nz Hans.Daetwyler@ecodev.vic.gov.au James.Kijas@csiro.au

12 Acknowledgements Rudiger Brauning, Shannon Clarke, Alan McCulloch Noelle Cockett Gary Saunders Marina Naval Sanchez, Sean McWilliam Amanda Chamberlain, Hans Daetwyler Funding: USDA award USU sub ward # We thank the NEXTGEN project, FarmIQ, B+LNZG, NZAGRC-PGGRC PGGRC, Ovita and the SheepCRC for the generation of genomes.