Whole Genome Sequencing of 75 Sheep for Variant Detection and Design of an HD Chip

Transcription

1 Whole Genome Sequencing of 75 Sheep for Variant Detection and Design of an HD Chip James Kijas on behalf of the International Sheep Genomics Consortium

2 Background Pace of discovery in sheep research has accelerated since early Successful GWAS into: -monogenic diseases (microphthalmia, skin disease, skeletal malformations ) - discrete traits (pigmentation, horn / poll, yellow fat.) - complex traits (lentivirus infection, wool and carcass characteristics ) 2. Signatures of Selection: - muscularity -absence of horns 3. Population History and Breed Diversity - relationship between individuals and breeds -divergence time, past N e etc Dwarfism and other defects: Shariflou et al Anim Genet (Early Online) ISGC HapMap paper (2012) PLoS Biology 10:e ISGC HapMap paper (2012) PLoS Biology 10:e

3 Next Step: Whole Genome Sequences Availability of the full genetic complement of an animal. The dream of geneticists / genomicists! Move from SNP to causal mutation Look inside signatures of selection Merge with transcriptomic data to better understand biological systems Eye Muscle Depth (at calculated common live weight - 30kg) Depth [mm] Population Mean = mm 10 MQ M1 M2 M3 M4 GR1GR2 P1 P2 P3 P4 R1 R2 R3 W1 W2 W3 W4 Muscle Parasite Repro Wool Sire Phenotypic variation Genotypic variation

4 ISGC Whole Genome Sequencing Experiment Aims for the project are: Identification of 50 M + SNP In depth survey of polymorphism beyond SNP (CNV, indels) Construction of an atlas of variability / community resource High Density (HD) SNP chip Wild sheep SNP chip Identification of disease causing mutation(s) Identification of genes impacted by domestication and/or subsequent selection

5 Rest of the Talk: 1. Workflow for the Sequencing and Analysis 2. Statistics From Sequence Production and Preliminary SNP calling 3. Working with the Data - Finding SNP around an example gene of interest - Can we find a mutation we know should be present? - Looking for an currently unknown disease mutation 4. HD Chip - SNP calling and QC - Design criteria for HD chip construction

6 1. Workflow for Sequencing and Analysis Animals Region Breeds Animals Asia 6 12 Africa 4 6 Middle East 7 13 Americas 4 8 UK 7 8 Europe Bighorn 3 Thinhorn 2 Diversity Panel from HapMap Experiment Animals from ISGC GWAS experiments (arthrogryposis, achondroplasia, muscular dystrophy, entropion) 6 sheep used for v1.0 SNP50 chip and CGH CNV Wild Sheep

7 1. Workflow for Sequencing and Analysis Animals Region Breeds Animals Asia 6 12 Africa 4 6 Middle East 7 13 Americas 4 8 UK 7 8 Europe Bighorn 3 Thinhorn 2 Kim Worley and her team Illumina GAII sequence Short insert, barcoded libraries Read mapping (> 2800 Gbof sequence) Texel reference genome v3.0 Atlas SNP2 variant detection.bam files Reference guided assemblies.vcf files SNP / indelcalls Groups are welcome to join McEwan Brauning Kijas et al Daetwyler Hayes

8 2. Statistics from Sequence Production and Preliminary SNP Calling SNP Count Animals Region Breeds Animals Asia 6 12 Africa 4 6 Middle East 7 13 Americas 4 8 UK 7 8 Europe Bighorn 3 Thinhorn 2 0e+00 2e+05 4e+05 6e+05 8e+05 1e+06 SNP read depth in one animal Variant Calling Statistics Sequence per animal Total Gb 38.4 Unique Aligned Gb 34.8 Mapped read coverage ~ 11 fold SNP (million) Average SNP called per animal Minimum number of SNP * 2.45 (UK Cheviot) Maximum number of SNP * (Bighorn) Unique SNP across 75 genomes 58.2 * Not corrected for the amount of sequence obtained per animal Read depth

9 3. Working with the data Broad s Integrated Genome Viewer (IGV) (Thorvaldsdottiret al Brief. Bioinform. Prepub19 th Apr)

10 3. Working with the data Define genomic region Searchable using elements in feature tracks Zoom.vcf files 10 animals.bam files 3 animals.gff files Feature tracks

11 3. Working with the data Harvest SNP around a gene / region of interest. Example MC1R 185 Kb MC1R

12 3. Working with the data 20 Kb 6 SNP in the gene 31 SNP within 5Kb MC1R

13 3. Working with the data 2.8 Kb Explore Individual SNP calls

14 3. Working with the data 181 bp Explore Individual SNP calls

15 3. Working with the data Can we find a mutation that we expect to be present? Tested using the MSTN(GDF8) mutation that underpins muscle hypertrophy Texel MSTN g+6723 G>A (3 UTR) OAR2 mirna target site ACATTCCA (Clopet al 2006) B GDF8 OAR2 OAR2 GDF8 SNP number SNP number

16 3. Working with the data

17 3. Working with the data mirna target site ACATTCCA

18 3. Working with the data MSTN mutation present in the data Suggests other (currently unknown) mutations will also be present SNP50 GWAS for disease traits - Arthrogryposis (Luis Ibanez) - Achondroplasia(Steve Bishop) - Progressive muscular dystrophy (Kristen Nowak) - Entropion(Cord Drogemuller) Arthrogryposis 13 cases 22 controls

19 3. Working with the data Sheep Chromosome 5 2 Cases 1 Carrier

20 3. Working with the data (summary) Dataset will be invaluable for researchers wanting quick access to SNP in their target region Depth of coverage appears sufficient to find things we know should be there More work to do for disease mutations in broad critical intervals

21 4. High Density SNP Chip Design Why it is worth making an HD chip? 1. Genomic Selection - increased marker density ensures better marker trait association - increased prospect for successful across breed prediction -part of tool kit (+ WGS + SNP50 + 5K + imputation) to drive genotyping efficiency 2. Superior Design: Broader Experimental Possibilities -strong exomebased content: 3 5 SNP per gene (K s and K n ) plus splice site variants - facilitates merging with gene expression studies: eqtl -rare SNP - dedicated CNV content depending on platform 3. Some advantages over Genotype By Sequencing (GBS) Approaches - chip has back compatibility to existing SNP50 datasets - can build forward compatibility with GBS into SNP chip design - chips are informatically lean, highly accurate and scalable to large populations

22 4. High Density SNP Chip Design Update on development: - SNP calling using three independent approaches - loci submitted for assay design scores in August - Design scheduled for completion in October - Design criteria include: Evenly spaced SNP spanning the genome Inclusion of low MAF SNP expected to tag rare haplotypes 50, ,000 SNP residing within exons/ UTR Loss of function SNP all existing SNP50 content to ensure back compatibility SNP in regions accessible to emerging GBS approaches wild sheep SNP special interest SNP (PNRP, MHC, disease mutations.) Please write to James.Kijas@csiro or John.McEwan@AgResearch.co.nz to assist in the design process

23 Acknowledgements WGS Project Team Brian Dalrymple Sean McWilliam Jiang Yu Hans Daetwyler Ben Hayes John McEwan Rudi Brauning Shannon Clarke Marty Kardos Kim Worley Richard Gibbs Jeffrey Reid Fuli Yu Sandra Lee Yuanqing Wu Donna Muzny Wider ISGC Activities and Support Frank Nicholas Hutton Oddy Noelle Cockett Tracy Hadfield Josh Miller Dave Coltman Mike Heaton

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