<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2007-1124</journal-id>
<journal-title><![CDATA[Revista mexicana de ciencias pecuarias]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. de cienc. pecuarias]]></abbrev-journal-title>
<issn>2007-1124</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-11242023000100172</article-id>
<article-id pub-id-type="doi">10.22319/rmcp.v14i1.6182</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Regresión cuantil para predicción de caracteres complejos en bovinos Suizo Europeo usando marcadores SNP y pedigrí]]></article-title>
<article-title xml:lang="en"><![CDATA[Quantile regression for prediction of complex traits in Braunvieh cattle using SNP markers and pedigree]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valerio-Hernández]]></surname>
<given-names><![CDATA[Jonathan Emanuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Rodríguez]]></surname>
<given-names><![CDATA[Paulino]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruíz-Flores]]></surname>
<given-names><![CDATA[Agustín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma Chapingo Posgrado en Producción Animal ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Colegio de Postgraduados  ]]></institution>
<addr-line><![CDATA[Texcoco Estado de México]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2023</year>
</pub-date>
<volume>14</volume>
<numero>1</numero>
<fpage>172</fpage>
<lpage>189</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-11242023000100172&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-11242023000100172&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-11242023000100172&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los modelos de predicción genómica generalmente suponen que los errores se distribuyen como variables aleatorias normales, independientes e idénticamente distribuidas con media cero e igual varianza. Esto no siempre se cumple, además puede haber fenotipos distantes de la media poblacional, los que usualmente se eliminan al realizar la predicción. La regresión cuantil (QR) afronta aspectos estadísticos como alta dimensionalidad, multicolinealidad y distribución fenotípica diferente de la normal. El objetivo de este trabajo fue comparar QR utilizando información de marcadores y pedigrí con los métodos alternativos tales como mejor predicción lineal insesgada genómica (GBLUP) y mejor predicción lineal insesgada genómica en un solo paso (ssGBLUP) para analizar los pesos al nacimiento (PN), destete (PD) y al año (PA) de bovinos Suizo Europeo y datos simulados con diferente grado de asimetría y proporción de datos atípicos. La capacidad predictiva de los modelos se evaluó mediante validación cruzada. El desempeño predictivo de QR tanto sólo con información de marcadores como con marcadores más pedigrí, con el conjunto de datos reales, fue mejor que las metodologías GBLUP y ssGBLUP para PD y PA. Para PN GBLUP y ssGBLUP fueron mejores, sin embargo, solo se utilizaron los cuantiles 0.25, 0.50 y 0.75, y la distribución de PN no fue asimétrica. En el experimento de datos simulados, las correlaciones entre efectos de marcador &#8220;verdadero&#8221; y efectos estimados, así como las correlaciones de señales &#8220;verdaderas&#8221; y estimadas fueron más altas cuando se usó QR comparado con GBLUP. Las ventajas de QR fueron más notorias con distribución asimétrica de los fenotipos y con mayor proporción de datos atípicos, como fue el caso del conjunto de datos simulados.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Genomic prediction models generally assume that errors are distributed as normal, independent, and identically distributed random variables with zero mean and equal variance. This is not always true, in addition there may be phenotypes distant from the population mean, which are usually removed when making the prediction. Quantile regression (QR) deals with statistical aspects such as high dimensionality, multicollinearity and phenotypic distribution different from the normal one. The objective of this work was to compare QR using marker and pedigree information with alternative methods such as genomic best linear unbiased prediction (GBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) to analyze the birth (BW), weaning (WW) and yearling (YW) weights of Braunvieh cattle and simulated data with different degrees of asymmetry and proportion of outliers. The predictive capacity of the models was assessed by cross-validation. The predictive performance of QR both with marker information alone and with information of markers plus pedigree, with the actual dataset, was better than the GBLUP and ssGBLUP methodologies for WW and YW. For BW, GBLUP and ssGBLUP were better, however, only quantiles 0.25, 0.50 and 0.75 were used, and the BW distribution was not asymmetric. In the simulated data experiment, correlations between &#8220;true&#8221; marker effects and estimated effects, as well as &#8220;true&#8221; and estimated signal correlations were higher when QR was used compared to GBLUP. The advantages of QR were more noticeable with asymmetric distribution of phenotypes and with a higher proportion of outliers, as was the case with the simulated dataset.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Regresión cuantil]]></kwd>
<kwd lng="es"><![CDATA[GBLUP]]></kwd>
<kwd lng="es"><![CDATA[ssGBLUP]]></kwd>
<kwd lng="en"><![CDATA[Quantile regression]]></kwd>
<kwd lng="en"><![CDATA[GBLUP]]></kwd>
<kwd lng="en"><![CDATA[ssGBLUP]]></kwd>
</kwd-group>
</article-meta>
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