SsGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca.

Juan Pablo Gutiérrez,Maria Wurzinger,Jonathan Alejandro Morón,Alonso Burgos,Alan Cruz,Betsy Mancisidor,Gustavo Gutiérrez

doi:10.3390/ani11113052

Abstract

Simple SummaryAlpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future.Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs.

Highlights

The heritabilities for fiber diameter (FD) were moderate at 0.334 ± 0.001 for Best Linear Unbiased Prediction (BLUP) and 0.336 ± 0.001 for single-step Genomic BLUP (ssGBLUP)
The heritabilities for standard deviation (SD) were moderate at 0.381 ± 0.001 for BLUP and 0.382 ± 0.001 for ssGBLUP, and moderate to low for percentage of medullation (PM) at 0.158 ± 0.001 for BLUP and 0.160 ± 0.001 for ssGBLUP
This study showed that heritabilities estimated by BLUP and ssGBLUP were similar to those reported by Cruz et al [2], in which they analyzed the FD, SD, and PM traits, but jointly with other production traits

Summary

Introduction

The textile industry seeks quality fibers [2], measured by the fineness and the low variability of its diameter [3]. Improving the fiber’s textile characteristics consists of producing a finer fiber with low variability and a lower itching factor. In alpaca breeding, most advanced improvement programs rely on quantitative genetics using Best Linear Unbiased Prediction (BLUP) methods with a multi-trait animal model with repeated measurements [2]. Fiber traits such as fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) can be measured multiple times in the animal, usually at shearing time. Fiber diameter tends to increase with the animal’s age [3] but is affected by the stage of pregnancy and lactation [4]

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