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      <dc:title>Genetic Marker Discovery in Complex Traits: A Field Example on Fat Content and Composition in Pigs</dc:title>
      <dc:creator>Pena i Subirà, Ramona Natacha</dc:creator>
      <dc:creator>Ros Freixedes, Roger</dc:creator>
      <dc:creator>Tor i Naudí, Marc</dc:creator>
      <dc:creator>Estany Illa, Joan</dc:creator>
      <dc:subject>Meat quality</dc:subject>
      <dc:subject>Intramuscular fat</dc:subject>
      <dc:subject>Candidate gene</dc:subject>
      <dc:subject>Pork</dc:subject>
      <dc:subject>Oleic acid</dc:subject>
      <dc:subject>Monounsaturated fatty acid</dc:subject>
      <dc:description>Among the large number of attributes that define pork quality, fat content and composition&#xd;
have attracted the attention of breeders in the recent years due to their interaction with human health&#xd;
and technological and sensorial properties of meat. In livestock species, fat accumulates in different&#xd;
depots following a temporal pattern that is also recognized in humans. Intramuscular fat deposition&#xd;
rate and fatty acid composition change with life. Despite indication that it might be possible to&#xd;
select for intramuscular fat without affecting other fat depots, to date only one depot-specific genetic&#xd;
marker (PCK1 c.2456C>A) has been reported. In contrast, identification of polymorphisms related&#xd;
to fat composition has been more successful. For instance, our group has described a variant in the&#xd;
stearoyl-coA desaturase (SCD) gene that improves the desaturation index of fat without affecting&#xd;
overall fatness or growth. Identification of mutations in candidate genes can be a tedious and costly&#xd;
process. Genome-wide association studies can help in narrowing down the number of candidate&#xd;
genes by highlighting those which contribute most to the genetic variation of the trait. Results from&#xd;
our group and others indicate that fat content and composition are highly polygenic and that very&#xd;
few genes explain more than 5% of the variance of the trait. Moreover, as the complexity of the&#xd;
genome emerges, the role of non-coding genes and regulatory elements cannot be disregarded.&#xd;
Prediction of breeding values from genomic data is discussed in comparison with conventional best&#xd;
linear predictors of breeding values. An example based on real data is given, and the implications&#xd;
in phenotype prediction are discussed in detail. The benefits and limitations of using large SNP&#xd;
sets versus a few very informative markers as predictors of genetic merit of breeding candidates are&#xd;
evaluated using field data as an example.</dc:description>
      <dc:description>Research partially funded by the Spanish Ministry of Economy and Competitiveness and the European Union Regional Development Funds (AGL2015-65846-R).</dc:description>
      <dc:date>2024-12-05T21:37:43Z</dc:date>
      <dc:date>2024-12-05T21:37:43Z</dc:date>
      <dc:date>2017-01-20T11:11:49Z</dc:date>
      <dc:date>2017-01-20T11:11:49Z</dc:date>
      <dc:date>2016</dc:date>
      <dc:type>article</dc:type>
      <dc:type>publishedVersion</dc:type>
      <dc:identifier>http://hdl.handle.net/10459.1/59061</dc:identifier>
      <dc:relation>Reproducció del document publicat a https://doi.org/10.3390/ijms17122100</dc:relation>
      <dc:relation>International Journal o f Molecular Sciences, 2016, vol. 17, núm. 12, p 1-17</dc:relation>
      <dc:rights>cc-by (c) Estany Illa, Joan., et al. 2016</dc:rights>
      <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
      <dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
      <dc:publisher>MDPI</dc:publisher>
   </ow:Publication>
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