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35Th International Society for Animal Genetics Conference 7 35th INTERNATIONAL SOCIETY FOR ANIMAL GENETICS CONFERENCE 7. 23.16 – 7.27. 2016 Salt Lake City, Utah ABSTRACT BOOK https://www.asas.org/meetings/isag2016 INVITED SPEAKERS S0100 – S0124 https://www.asas.org/meetings/isag2016 epigenetic modifications, such as DNA methylation, and measuring different proteins and cellular metab- INVITED SPEAKERS: FUNCTIONAL olites. These advancements provide unprecedented ANNOTATION OF ANIMAL opportunities to uncover the genetic architecture GENOMES (FAANG) ASAS-ISAG underlying phenotypic variation. In this context, the JOINT SYMPOSIUM main challenge is to decipher the flow of biological information that lies between the genotypes and phe- notypes under study. In other words, the new challenge S0100 Important lessons from complex genomes. is to integrate multiple sources of molecular infor- T. R. Gingeras* (Cold Spring Harbor Laboratory, mation (i.e., multiple layers of omics data to reveal Functional Genomics, Cold Spring Harbor, NY) the causal biological networks that underlie complex traits). It is important to note that knowledge regarding The ~3 billion base pairs of the human DNA rep- causal relationships among genes and phenotypes can resent a storage devise encoding information for be used to predict the behavior of complex systems, as hundreds of thousands of processes that can go on well as optimize management practices and selection within and outside a human cell. This information is strategies. Here, we describe a multi-step procedure revealed in the RNAs that are composed of 12 billion for inferring causal gene-phenotype networks underly- nucleotides, considering the strandedness and allelic ing complex phenotypes integrating multi-omics data. content of each of the diploid copies of the genome. We initially assess marginal associations among geno- Results stemming from the efforts to catalog and ana- types and either intermediate phenotypes (such as gene lyze the RNA products made by cells in the human expression) and endpoint phenotypes (such as carcass (ENCODE), fly-worm (modENCODE), and mouse fat deposition and muscularity), and then, in those ENCODE projects have shed light on both the func- genomic regions where multiple, significant hits co-lo- tional content and how this information is organized by calize, we attempt to reconstruct molecular networks various genomes. In human cells, a total of ~161,000 using causal structural learning algorithms. These transcripts present within ~50,000 genic regions rep- algorithms attempt to infer networks, assuming that the resent our previously best manually curated annota- pattern of conditional independencies observed in the tion (based on v7 Gencode) of the transcriptome. The joint probability distribution of these sets of correlated results from the ENCODE project point to consider- variables are compatible with the unknown causal able supplementation of these data. Analyses of these model. As a proof of principle of the significance of transcriptome data sets have resulted in important and this integrative approach, we show the construction of underappreciated lessons, such as pervasive, genome- causal molecular networks underlying economically wide transcription prompts a need to redefine the def- relevant meat quality traits in pigs, using multi-omics inition of a gene; expression ranges follow transcript data obtained from an F2 Duroc × Pietrain resource types and subcellular localization; expression of iso- population. Interestingly, our findings shed light on the forms of a gene by a cell do not follow a minimalis- mechanisms underlying some known antagonist rela- tic strategy; and genomic characteristics of potential tionships between important phenotypes, for instance, trans-acting enhancer regions are distinguishable from carcass fat deposition and meat lean content. Gener- other types of cis-acting regulatory regions. These and ally, the proposed methodology allows further learning other lessons drawn from the landscape of both coding regarding phenotypic and molecular causal structures and non-coding RNAs present in eukaryotic cells have underlying complex traits in farm species. been used to assist in understanding and organizing Key Words: causal inference, graphical models, what is often seen as dauntingly complex genomes. systems biology Key Words: annotation, ENCODE, transcriptome S0102 Genotypes to phenotypes: Lessons from S0101 Causal inference of molecular networks functional variation in the human genome integrating multi-omics data. F. Peñagaricano* and transcriptome. B. E. Stranger* (Section (University of Florida, Gainesville, FL) of Genetic Medicine, Department of Medicine, Recent developments of massively parallel technol- Institute of Genomics and Systems Biology, ogies allow assaying different biological molecules Center for Data Intensive Sciences, University at very high throughput rates, including sequenc- of Chicago, Chicago, IL) ing and genotyping of DNA, quantifying whole-ge- Complex trait association mapping in humans has nome gene expression, including measuring mRNA successfully identified genetic loci influencing trait and microRNA abundance, identifying genome-wide 2 variation for hundreds of different phenotypes, includ- pairs located on the same chromosome and relatively ing disease. The vast majority of associated loci local- near each other (<100 kb), therefore pointing to poly- ize to non-coding regions of the genome, suggesting merase read-through. However, interchromosomal possible effects on gene regulatory mechanisms. chimeras are also observed, pointing to trans-splicing. Without a clear understanding of the regulatory code Importantly, these recurrent chimeras tend to maintain of the human genome, deep characterization of the an open reading frame and could therefore generate molecular function(s) of genetic variants in the human chimeric proteins. We also observed that not only the genome has become increasingly important for defin- gene-to-gene connection is conserved, but strikingly ing that code and understanding genetic associations so are specific junction sites. The genes connected in to complex traits. Studies of the human transcriptome, common chimeras tend to be involved in morphogen- its complexity, and its relation to genetic variation in esis and body plan formation, and consistently tend to a variety of contexts have proven highly informative be detected in cell lines of embryonic origin. Valida- for understanding genome function and for suggest- tion of human chimeras by RT-PCR yielded a success ing testable hypotheses involving candidate genes for rate of 50% and subsequent cloning and sequencing complex traits and the functional mechanisms though revealed novel transcript structures, of which some which they may act. These approaches are increas- preserve the domains from the 2 parents’ genes. Apply- ingly leading to successful functional characterization ing this method to multiple animal species and breeds of trait-associated variants, in some cases, suggesting will help us understand chimera evolution, as well as possible targets for trait manipulation. Finally, these reveal some links between genotype and phenotype. characterizations are being used to build models pre- [1] Gingeras, T.R. Implications of chimeric non-co-linear transcripts. dicting variant function, further extending possible Nature, 2009. applications. [2] Kaessmann, H. Origins, evolution, and phenotypic impact of new genes. Genome Re, 2010. Key Words: genome function, non-coding [3] Djebali, S. et al. Evidence for transcript networks composed of chi- variants, regulatory mechanisms meric RNAs in human cells. PLoS One, 2012. [4] Mitelman F., Johansson B. & Mertens F., The impact of translo- cations and gene fusions on cancer causation, Nature Rev Cancer, 2007 S0103 Recurrent chimeric transcripts in human [5] Houseley, J. & Tollervey, D. Apparent non-canonical trans-splicing and mouse. S. Djebali*1,2,3, B. Rodríguez Martín2,3, is generated by reverse transcriptase in vitro. PLoS One, 2010. 2,3 2,3 [6] Li, H., Wang, J., Mor, G. & Sklar, J. A neoplastic gene fusion mim- E. Palumbo , D. D. Pervouchine , ics trans-splicing of RNAs in normal human cells. Science, 2008. A. Breschi2,3, C. Davis4, A. Dobin4, G. Alonso5, [7] Wu, C.S. et al. Integrative transcriptome sequencing identifies A. Rastrojo5, B. Aguado5, T. R. Gingeras4, trans-splicing events with important roles in human embryonic stem cell pluripotency. Genome Res, 2013. R. Guigó2,3 (1GenPhySE, INRA, Castanet-Tolosan, France, 2Universitat Pompeu Fabra (UPF), Key Words: chimeras, transcripts, trans-splicing Barcelona, Spain, 3Bioinformatics and Genomics Program, Centre for Genomic Regulation (CRG), Barcelona, Spain, 4Cold Spring Harbor Laboratory, S0104 Improving genomic selection across Functional Genomics, Cold Spring Harbor, NY, breeds and across generations with functional *1 2 5Centro de Biología Molecular Severo Ochoa (CSIC- annotation. B. Hayes , A. J. Chamberlain , 3 2 4 UAM), Madrid, Spain) H. Daetwyler , C. J. Vander Jagt , M. E. Goddard (1Department of Economic Development, The formation of chimeric transcripts (chimeras) has Melbourne, Australia, 2Dairy Futures Cooperative been widely reported [1,2,3]. Some of them reflect Research Centre, Bundoora, Australia, 3Department underlying chromosomal rearrangements [4] or are of Economic Development, Jobs, Transport, and the results of the propensity of reverse transcriptase to Resources, Bundoora, Australia, 4Department of engage in template switching [5]. However, a proportion Primary
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