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Workshop: From Animal Genomics to Functional Analysis
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2016-11-23: University of Copenhagen, Frederiksberg Campus, Grønnegårdsvej 7, building 1-04, 2nd floor library from 13:00-16:05. Speakers: Ted Kalbfleisch, Mogens Sandø Lund, Alan Archibald and Merete Fredholm

Registration is not necessary.  

Programme:

13:00 - 13:05	Welcome
13:05 - 13:45	Improving the Reference Genome of the Domestic Horse Ted Kalbfleisch,University of Louisville
13:45 - 14:25	Can functional information improve genomic prediction? Mogens Sandø Lund, Aarhus University
14:25 - 14:40	Coffee Break
14:40 - 15:20	From Phenotype to Genotype and Back Again — Animal Genomics Enabling Prediction Alan Archibald, Edinburgh university
15:20 - 16:00	Dissecting the genetics underlying obesity and obesity related metabolic traits using the pig as a model Merete Fredholm,University of Copenhagen
16:00 - 16:05	Closing remarks

Improving the Reference Genome of the Domestic Horse
Ted Kalbfleisch, University of Louisville

An updated version of the reference genome for the domestic horse, EquCab3, is nearing completion.  The previous version, EquCab2, was completed in 2007 and published in 2009 (Wade et al., Science 2009).  The 2007 version was derived entirely from 30 million Sanger reads comprising ~6.8X genomic coverage.   It has since served well as the foundation for nearly all genetic and genomic studies for most Equid species. However, it was known to have shortcomings, specifically sequence gaps in the gc-rich 5’ regulatory regions of annotated genes.  Advances in Next Generation Sequencing technology, including new PCR free library preps with less bias against gc-rich sequences, have made it possible for us to improve contiguity in excess of 10 fold measured by both contig and scaffold N50 values.  Additionally, we present evidence of improved contiguity near the 5’ end of annotated genes which should help significantly as we endeavor to understand the role of genetics in gene regulatory processes.  A pre-release version of EquCab3 is currently being evaluated by the Equine research community, and will be ready for final release to annotation groups in early 2017.

Can functional information improve genomic prediction?
Mogens Sandø Lund, Aarhus University

In genomic selection genome wide markers are used to predict genetic merit of individuals. A priori all SNPs are generally assumed to have the same probability of affecting the target trait. However, if we knew which causative variants affect the trait, genomic predictions would be more accurate, especially when prediction is performed over long genetic distances. It is therefore appealing to use functional knowledge to discriminate, which SNP a priori are more likely to have an effect on a given trait. One way to achieve this is to define features of different functional elements or genomic regions harboring genes that show expression patterns relevant to phenotypic trait variation. This presentation will show a couple of examples of this and discuss its potential to improve genomic predictions.

 

From Phenotype to Genotype and Back Again — Animal Genomics Enabling Prediction
Alan Archibald, Edinburgh University

Much of the value in biological research lies in predicting outcomes whether it is the efficacy of a drug, the consequences of ageing, susceptibility to infectious disease or the performance of the daughters of an elite dairy bull. Animals are complex systems in which prediction is challenging. However, quantitative geneticists and animal breeders have been remarkably successful at developing statistical animal models, initially based on phenotypic records and pedigree information, that are effective predictors of genotype and hence of performance of potential progeny.
Over the past 25 years, the characterisation of farm animal genomes and the development of the molecular and statistical tools to exploit genomic information have enabled improvements in the prediction of phenotypes from genotypes. Currently, high density SNP genotypes are used for genomic selection in the major livestock species, i.e. learning SNP associations from recorded data and using these to predict genomic breeding values of young animals from SNP data alone. Further improvements can be expected through the use of genome sequence data and by adding knowledge of the likely effects of the sequence variants whether coding or regulatory. The recently launched Functional Annotation of Animal Genomes (FAANG) initiative is concerned with defining these functional sequences.

Dissecting the genetics underlying obesity and obesity related metabolic traits using the pig as a model
Merete Fredholm, University of Copenhagen

We have performed comprehensive comparative analyses of QTLs in an F2 pig resource population specifically designed to elucidate the genetics involved in obesity and obesity related metabolic traits. The analyses demonstrate that similar genetic mechanisms drive obesity phenotypes in pigs and humans. Detailed studies of a QTL region on SSC3 have shown that this region comprises three different LD blocks influencing lipid metabolism in pigs. The orthologous region has also been implicated in lipoprotein metabolism in humans. Investigation of the haplotypes with the largest additive genetic effects shows that specific haplotypes are able to uphold a healthy lipid profile despite development of obesity comparable to the metabolic healthy obese (MHO) phenotype in humans. The evidence supporting these findings will be presented together with results from in depth studies of miRNA profiling and from studies of tissue specific gene expression and analyses of epigenetic signatures in adipose tissues.

 

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