Functional Annotation of Salmonids Workshop

Transcription

1 Functional Annotation of Salmonids Workshop June 2016 Toronto FAANG (I) some history (ii) underlying principles (iii) how different communities are self-organizing funding, (iv) some best practices/ recommendations/insights inc role of industry

2 The FAANG Initiative from genome to phenome in farmed animals. Graham Plastow and the FAANG Consortium

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5 Why FAANG? A coordinated effort.to minimize redundancy and leverage existing activity will enable significant progress in a cost-effective manner provides leverage of funding $$ Collecting, storing and sharing samples for future additional assays. Expert labs and standard protocols Common Data Infrastructure (@ Roslin & EBI) Standardized bioinformatics pipelines

6 Stage 1 Sampling biological replicates representing neonatal and mature phases Core tissues Core assays

7 Stage 2 Biological questions Species specific questions (tissues, stages) Additional assays ( specific needs and research interests ) expanding genetic diversity within a species Species-specific USPs

8 Data sharing Paramount Contribute to Share FAANG-members (Toronto principles) Regular public release of archived raw data (Ft Lauderdale principles) Steering Committee oversight All members can and will continue to do work outside of FAANG and other data is not required to meet the same data sharing expectations

9 The Agri-food Grand Challenge Population +1.2% per annum Food +100% by 2050 Opportunity!! Energy +1.8% per annum Climate +2 o C by 2050

10 Meat on the Menu Meat Dairy Cereals Starchy Roots Global food demand forecast, 1961=100. Based on The Economist with data from the Food and Agriculture Organization. 3 billion people trying to move into the middle class in emerging economies will drive meat demand.

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12 Candidate Genes and WGA Is there still a need for candidate gene approaches in the the era black of genome-wide box of association studies? quantitative Candidate genetics genes Stefan Wilkening, Bowang Chen, Justo Lorenzo Bermejo, Federico Canzian Genomics 93 (2009) G = h 2 * sd * a * (i/t) the breeders equation

13 Genomics and Livestock Knowledge of the genetic variants that affect livestock health and productivity can assist with breeding efforts aimed at improving these traits. Or with precision management by genotype

14 Genomic selection Meuwissen et al. (2001) no need to first detect single markers or genes before we can utilize genomic information in selection we need many markers covering the whole genome and use all of them (no distinction based on significance) to predict breeding values genomic selection October 2007, BovineSNP50 BeadChip Technically and practically it became possible to genotype one animal for SNPs at a reasonable price F. Miglior, 2015

15 Genomic selection 1 Genomic selection: reliability 0.70 reliability 0 birth own performance progeny F. Miglior, 2015 Selection of young bulls based on Sire and Dam EBV : reliability ~ 0.40 Age Selection on own performance : reliability ~ 0.55 Selection of proven bulls based on milk production information of 100 daughters: reliability = 0.95

16 Context Annual net benefits to the industry due to genetics progress Period Before genomics (before 2009) After genomics ( ) Expected in Average rate of progress per year (Pro $) Net annual economic value to the industry 1 84 $265 Million/year 171 $540 Million/year 237 $748 Million/year 1 Based on 0.9M milking cows; net returns per cow using 2014 Valacta figures for milk income, feed costs, heifer rearing costs, and cow survival to 6 years of age; benefits account for the cumulative nature of genetic change, and are discounted at 5% per year. F. Miglior, 2015

17 Solutions Identify causative mutations work consistently across populations

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19 Finding the Causative Mutation amongst many candidate genes Identified DE genes between genotypes (AA vs AG) 3 genes were DE: ** *** ** Only one was DE across multiple time points * NS NS **

20 A SNP introduces a new acceptor splice site, which causes five nucleotides to be added to the transcript The 5bp transcript causes a frame-shift in the protein that introduced an early stop codon. The susceptible genotype is missing the C-terminus of the protein Koltes et al. BMC Genomics 16:412.

21 Solutions Identify causative mutations work consistently across populations

22 Combining coding sequence variants with dense SNP markers in Bayes RC Increased power to detect causal variants and increased accuracy of genomic prediction Most apparent in populations not closely related to the reference population

23 Gathering On Functional Annotation of ANimal Genomes (GO-FAANG Workshop) Washington DC, October 7-8

24 Gathering On Functional Annotation of ANimal Genomes (GO-FAANG Workshop) Washington DC, October 7-8 FAANG: Status on Data creation Elisabetta Giuffra INRA- French National Institute for Agricultural Research Jouy-en-Josas, France

25 Biological targets and assays for data creation Andersson et al. 2015; PMID: Members committed* to collecting, storing and sharing samples for first data Single developmental stage ( adult ) Minimize genetic diversity within species Focus on tissues/primary cells Use well-established assays (white paper- Box 1) *see FAANG Consortium policies GO-FAANG workshop, Washington, DC Oct. 7-8, 2015

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27 Transcribed loci (RNAseq), Chromatin accessibility and architecture (e.g. ATACseq, ChIPseq), Histone modification marks (H3K4me3, H3K27me3, H3K27Ac, H3K4me1, and CTCF).

28 The Animals/Samples/Assays (ASA) Committee Aims: - Propose, develop and standardize animal/tissue collection protocols, storage practices, and assay protocols for the FAANG community ( pilot projects ). - Foster collaborations, avoid redundancies and enhance synergies faang-samples@animalgenome.org (presently: 52 members) GO-FAANG workshop, Washington, DC Oct. 7-8, 2015

29 New Project Genetic variations associated with feed efficiency and methane yield in beef cattle 1) collaboration with the lead groups in the FAANG consortium (at UC Davis, INRA and the Roslin Institute) to develop a reference annotation for cattle. 2) helping develop the sample and assay protocols to expand the characterization of regulatory elements (and chromatin context) in beef cattle (this will integrate Canadian efforts to expand the information available on different biological states and development stages); and 3) collecting samples from Canadian beef animals to create reference information for feed efficiency and methane yield.

30 % Change in greenhouse gas emissions and global warning potential achieved through genetic improvement ( ) Species CH 4 NH 3 N 2 O GWP 100 Chickens layers Chickens broilers Pigs Cattle dairy Cattle beef Sheep CARBON FOOTPRINT (CO 2 e/kg product); Pork kg; Chicken ; Dairy1.3 kg; Beef kg Sources: Project for DEFRA by Genesis Faraday Partnership and Cranfield University (AC0204) from Hume et al. (2011), J. Ag. Sci., doi: /s Slide courtesy of Chris Warkup Biosciences KTN

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32 Funding Ben Hayes (Australia) we have received funding for contributing to FAANG. The funding will be used to perform RNAseq and ChIPseq for H3K27ac, H3K27me3, H3K4me1, H3K4me3, CTCF on a large number of tissues (39) collected from two cows and foetus' from these cows. We have enough tissue stored to be able to share with the FAANG community after the above assays are performed, and all samples are being loaded into BioSamples in accordance with requirements of the samples committee. USDA-AFRI Foundational RFP One of the opportunities is the A1201 Program Area (due date Aug 3). One of the Priority areas listed is FAANG, and the USDA has provided the following additional instructions: Projects related to FAANG must follow the guidelines developed by the FAANG community for selecting animals, tissues, assays, experimental protocols, data sharing and data analysis ( Applicants should address major knowledge gaps and avoid duplication of efforts.

33 Other initiatives On April 26, 2016, a seven year-old female U.S. Rambouillet sheep (Benz 2616) was humanely euthanized and 102 unique tissues were collected from this animal as part of the ovine FAANG project. Genome DNA from the ewe had been previously used for a new de novo assembly of the sheep genome using 70-fold, PacBio long-read sequencing. Thus, data generated from the FAANG core assays can be annotated directly onto the new ovine reference genome. Samples from all tissues were either snap frozen in liquid nitrogen and can be used for RNA-seq, ChIP-seq and other FAANG assays, or slowly frozen or processed into DNA for chromatin accessibility assays. Thirty people were involved in the tissue collection including ovine FAANG members Noelle Cockett, Alisha Massa, Brian Sayre, Michelle Mousel and Brenda Murdoch, as well as Utah State University pathologists and veterinarians including Tom Baldwin, Rusty Stott, Arnaud Van Wettere, Gordon Hullinger, Jaqueline LaRose, Holly Mason, and Kerry Rood. Special thanks to Tracy Hadfield for organizing the collection event, Alisha Massa for aligning the sample collections, Codie Durfee (with assistance from Brenda Murdoch and Brian Sayre) for processing DNA for ATAC-seq assays and Michelle Mousel for coordinating blood and immune cell collections. Other FAANG members who participated in the planning process included Kim Worley, Stephen White, Brian Dalrymple, James Kijas, Tim Smith, and Mike Heaton. Please contact Noelle Cockett (noelle.cockett@usu.edu), Stephen White (swhite@vetmed.wsu.edu) or Tim Smith (Tim.Smith@ARS.USDA.GOV) if you are interested in utilizing these tissues for core FAANG assays.

34 Funding Large scale International Leverage Applied

35 Funding Large scale International Leverage Applied

36 Field Day July 20 th 2016 Conference Oct 18/19th 2016