Fokkerij in genomics tijdperk Johan van Arendonk Animal Breeding and Genomics Centre Wageningen University

Animal Breeding and Genomics Centre (WU) Our Mandate: training and research on the role and sustainable utilisation of genetic variation in farm and companion animals. Expertise in quantitative and molecular genetics. Staff: Scientists, 8 Postdocs and 25 PhD students  Funding Strong scientific position Partnership with industry International orientation

Breeding: utilizing genetic variation Creating genetic change Selecting the best animals Using the best animals to produce next generation Aim: produce animals that perform better Challenges: Understanding the impact of genetic variation Developing tools to find the genetically best animals

Current breeding schemes BLUP breeding values: Evolved from sire models to animal model From single to multiple trait analysis Optimally combines phenotypic information Emphasis on traits that can be recorded relatively easy (growth rate, milk production, longevity)

Application of Molecular genetics Objective Finding genes (QTL) that contribute to genetic variation Molecular Markers Differences in DNA Can be measured

Principle QTL mapping Sire AB 50% A 50% B Difference: information on location and size of QTL Genetic markers: make it possible to follow transmission from parent to offspring bad good

QTL mapping: central role in gene detection QTL Development of molecular tools Phenotypes and Genotypes Candidate genes: Comparative Mapping Data mining Physiology Gene expression QTL analysis Gene Identification

Genome sequence: increase in molecular tools Large increase in number of markers Chicken genome sequence: 2.8 million SNPs identified based Improvement of comparative map Opportunity to exploit knowledge from other species

Chicken – human comparative maps

Genomics and breeding for product quality

Milk Genomics Initiative: started in 2004 Goal: Determine opportunities to change milk composition through breeding Three activities 1. Measure milk composition of 2000 cows 2. Determine the amount of genetic variation 3. Mapping QTL/genes involved in some components Partners:

Design of experiment 5 large families 50 small families 1000 heifers 50* heifers 5*200 Collection of samples on 400 farms: 1.3 milk samples 2.Blood sample for DNA analysis

Finding genes using molecular genetic information Measure performance Analysis of DNA Family structure Collection of information

Results on milk fat composition 1.Substantial variation in milk fat composition 2.Large genetic variation between cows 3.DGAT1: gene with a large contribution to genetic variation

Milk Genomics Initiative Unique combination of: Disciplines: from dairy science to genomics Industry and University: science for impact Partners in the chain: from cow to product Milk Genomics Initiative=Team effort

Utilization of genomic information 1. Gene technology: production of GM animals Application to livestock hindered by many factors including technology Break through needed to enable site-specific insertion 2. Use genomic information to better exploit natural genetic variation Marker assisted selection Genomic selection Pedigree reconstruction (e.g. high health chip in pigs)

High health chip Pig production chain: no information on pedigree of finishing pigs Large number of DNA markers: opportunity to trace line and father of origin Opportunities to improve selection for improved health and carcass quality

Research infrastructure Maintaining up-to-date research infrastructure ABI 3730 sequencerIllumina BeadXpress X-tractor Gene Large scale genotyping: outsourced (Utrecht) External funding essential

Successful collaboration with Industry Long-term partnership important (no quick wins) Recognition of the interest of industry and university (understand the driving forces) Contributes to publications in leading journals

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