The Impact of the 1000 Bull Genomes Project and its Future

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Presentation transcript:

The Impact of the 1000 Bull Genomes Project and its Future Amanda Chamberlain, Christy Vander Jagt, Ruidong Xiang, Mike Goddard, Iona MacLeod, Robert Schnabel, Ben Hayes, Hans Daetwyler Founded in 2011 at Sir Mark Oliphant Genomics Conference

Hayes and Daetwyler 2019. Annual Review of Animal Biosciences. 7:89-102 1000 bull genomes Founded in 2011 at Sir Mark Oliphant Genomics Conference Largest impact on discovery of deleterious mutations Glass-eyed albino Dominant red Neurocristopathy Osteogenesis imperfecta type 2 Bulldog calf syndrome Holstein Haplotype 3 Weaver syndrome Progressive retinal degeneration Oculocutaneous albinism Lethal chondrodysplasia Belted Familial renal syndrome Recessive embryonic lethal

Hayes and Daetwyler 2019. Annual Review of Animal Biosciences. 7:89-102 1000 bull genomes Founded in 2011 at Sir Mark Oliphant Genomics Conference Largest impact on discovery of deleterious mutations Facilitated GWAS for complex traits using imputed sequence data Accurate identification of causative genes Improvements to accuracy of genomic predictions Largest gains in multibreed populations Best results from preselecting variants based on GWAS Bigger improvements will come from identification of mutations of small effect Computationally efficient methods that can utilise full sequence Utilising biological information

Run 7 – New Assembly ARS-UCD1.2 + Btau4 Y

Run 7 – New Pipeline GATKv3.8 best practises .bam and g.vcf submission

40 Partner Institutes

3817 animals Average 12x

Taurus Taurus-Indicus 106 breeds 171 breeds 3103 animals 3817 animals 13.5 million INDEL 90.2 million SNP Taurus-Indicus 171 breeds 3817 animals 19 million INDEL 132.3 million SNP 2012 2014 2013 2015 2017 2019

1.9%

Identifying Causative Variants for Improved Genomic Prediction (Run6) https://www.pnas.org/content/early/2019/09/06/1904159116 Identifying Causative Variants for Improved Genomic Prediction (Run6) 30 sources of multi-omic information: Phenotypes from 44,000+ dairy cattle with 17.7 million variants: Priority ‘FAETH’ List Chr19:26729990 Chr4:114884928 Chr14:2235149 Chr23:31296580 Chr10:20381129 … 17495271. Chr5:114343758 40K 10K-XT

Per-variant Impact on 34 Dairy Traits Conserved sites across 100 vertebrate species

Per-variant Impact on 34 Dairy Traits Polar lipid content mQTL

Per-variant Impact on 34 Dairy Traits Expression QTL

Genomic Prediction: 50K-XT vs 50K Genotypes: - real & imputed Standard 50K genotypes - imputed XT-50K genotypes (disadvantaged! VS. Std 50K) Phenotypes: - milk, fat & protein yield - fertility & Somatic Cell Count (SCS) Reference/Training set = Aust. mixed-breed, Bulls & Cows: 29,200 animals (~26,000 Hol, ~3000 Jer, ~400 Aust Red) Validation = animals not in Ruidong’s analyses: Holstein, Jersey, Aust Red (bulls and cows) NZ Crossbreds

Genomic Prediction: 50K-XT vs 50K

Acknowledgements

Acknowledgements 1000 bulls pipeline Christy Vander Jagt Hans Daetwyler Robert Schnabel Variant Selection for Australian Dairy Ruidong Xiang Iona Macleod Mike Goddard