CARDOSO, F. F.

RCAAP - Repositório Científico de Acesso Aberto de Portugal

Quantitative genomic methods applied to animal breeding.

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nomica aplicada ao melhoramento animal: presente e futuroor: Alexandre Rodrigues Caetano, CENARGEN/EMBRAPA, BrasOia, OF, Brasilrramentas gen6micas aplicadas ao"melhoramento animal. (Genomic Tools Applied to Animal Breeding)the 1980's large efforts have been devoted by severaltional research groups to identify informative molecular markersbuild genetic maps for animal species with economic importance.tic maps were built using different techniques with the objectivedeveloping the necessary tools for identifying and characterizing"the underlying genes responsible for the genetic variation observed.h traits related to productivity, product quality, disease resistance,.etc. Several resource populations were generated by crossingbreeds with extreme phenotypes, and also by using structuredpopulations found in standard genetic evaluation programs, tomap Quantitative Trait Loci (QTL) for eventual identification of theunderlying genes by positional cloning. Research objectives aimedat identifying DNA markers for use as breeding tools in MarkerAssisted Selection (MAS) procedures and also at the developmentof innovative biotechnological tools (transgenics, knock-outs, etc)based on the physiological role of newly identified genes. However,over two decades of work resulted in the identification of only a smallnumber of major genes that affect production traits and a few genesresponsible for heritable diseases. Commercial molecular markerpanels were assembled with the generated information and marketedas breeding tools for cattle and pigs but, at the most, can explainonly a small fraction of the estimated additive genetic variance forany given trait, hindering their use as highly questionable. The Bovine'(~ome Project, starting in 2003, spearheaded efforts to massivelytify and use SNP (Single Nucleotide Polymorph isms) markers·agricultural species. The identification of hundreds of thousands·SNPs and the assembly of a reference sequence allowed for the• . tion of then newly developed genotyping methods that can. data for hundreds of thousands of SNPs in parallel, whichht about a revolution in methods and objectives of molecularing research. Methods have been developed to simultaneously"'" _ 'mate the effects of all genomic regions that affect a particllilar traitJfermed Genomic Selection) which have shown correlations >70%',With EBVs (Estimated Breeding Values) estimated with traditional.,fnethOds. In addition, these new tools have also made a greattribution to research aiming at identifying genes responsible for. Ie diseases, turning projects that would cost hundreds ofds of dollars and last for years irito experiments that can beout in weeks at a fraction of the costs and that require little'iI1vestment in laboratory infrastructure. These innovations haveed the potential for higher rates of genetic gains at reduced costsgreat improvements are still expected in the near future as news that use Next Generation Sequencing technologies are fullyrated into genetic evaluation and breeding programs. We willw how Embrapa's animal genetics and improvement researchps have been at the forefront of these developments and haveto pioneer research activities in the country.Quantitative genomic methods applied toanimal breeding ••Major advances have been made in technologies for prospectinggenes related to complex biological traits such as disease resistance,production efficiency and product quality. The aim of this work is topresent quantitative methods applied to animal breeding by Embrapa'sAnimal Genomics Research Network, using two distinct approaches:structural and functional. The structural approach is based on usingmolecular markers to relate DNA sequence changes (polymorphisms)to population structure and to observed variation in phenotypes ofinterest. Currently, Single Nucleotide Polymorphisms (SNP), a singlebase change in the genetic sequence of an individual with respectto a reference genome, due to their abundance in the genome andsuitability for automated large scale processing are the most powerfulmarkers for genomic studies. Methods to use dense SNP genotypesto estimate linkage disequilibrium (LD), to infer effective populationsize and to design whole genome association studies and genomicevaluation schemes are presented and demonstrated with livestockdata. Additionally, from a functional perspective, gene expressionprofiling using oligonucleotide microarrays represents another largescale alternative to identify genes that contribute to economicallyimportant phenotypic variation in livestock. In these experiments, theeffect of a treatment, condition or genotype on transcript abundanceis simultaneously measured for thousands of genes, facilitating theidentification of gene regulatory networks and metabolic pathways.The processing of samples to assess gene expression involves severalsteps and there are embedded procedures to verify the quality of theRNA sample and experimental procedures. Initially, quality controlcriteria are used to evaluate array images for artifacts, raw datadistributions consistency, correlations between arrays, percentageof detected genes, background and noise values, scale factorsand control probes signals. Next, robust multiarray average (RMA)is employed for background adjustment, quantile normalization,log2-transformation and summarization of the multiple correctedprobes intensities to a single signal value. Finally, the differentiallyexpressed genes identification is based on a two-step single-genemixed model analysis and a modified F test combining informationacross genes, using permutations to calculate significance and falsediscovery rate to adjust p-value for multiple testing. Despite usingdifferent methodology, structural and functional approaches areboth of prospective nature, providing insights for the associationsbetween groups of genes and physiological traits, links between thegenome and the biological processes involved in the manifestation ofthe phenotype and may generate direct information for the geneticbackground of economically important traits or new hypotheses tobe tested in subsequent studies, using more specific or targetingapproaches.Financial Support: Embrapa SEG Projeto 01.06.09.01.04.00.

Quantitative genomic methods applied to animal breeding.

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RCAAP - Repositório Científico de Acesso Aberto de Portugal

Repository Open Access to Scientific Information from Embrapa