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A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep.

ABSTRACT: Sheep milk is mainly intended to manufacture a wide variety of high-quality cheeses. The ovine cheese industry would benefit from an improvement, through genetic selection, of traits related to the milk coagulation properties (MCPs) and cheese yield-related traits, broadly denoted as "cheese-making traits." Considering that routine measurements of these traits needed for genetic selection are expensive and time-consuming, this study aimed to evaluate the accuracy of a cheese-making phenotype imputation method based on the information from official milk control records combined with the pH of the milk. For this study, we analyzed records of milk production traits, milk composition traits, and measurements of cheese-making traits available from a total of 1,145 dairy ewes of the Spanish Assaf sheep breed. Cheese-making traits included five related to the MCPs and two cheese yield-related traits. The milk and cheese-making phenotypes were adjusted for significant effects based on a general linear model. The adjusted phenotypes were used to define a multiple-phenotype imputation procedure for the cheese-making traits based on multivariate normality and Markov chain Monte Carlo sampling. Five of the seven cheese-making traits considered in this study achieved a prediction accuracy of 0.60 computed as the correlation between the adjusted phenotypes and the imputed phenotypes. Particularly the logarithm of curd-firming time since rennet addition (logK20) (0.68), which has been previously suggested as a potential candidate trait to improve the cheese ability in this breed, and the logarithm of the ratio between the rennet clotting time and the curd firmness at 60 min (logRCT/A60) (0.65), which has been defined by other studies as an indicator trait of milk coagulation efficiency. This study represents a first step toward the possible use of the phenotype imputation of cheese-making traits to develop a practical methodology for the dairy sheep industry to impute cheese-making traits only based on the analysis of a milk sample without the need of pedigree information. This information could be also used in future planning of specific breeding programs considering the importance of the cheese-making efficiency in dairy sheep and highlights the potential of phenotype imputation to leverage sample size on expensive, hard-to-measure phenotypes.

SUBMITTER: Marina H

PROVIDER: S-EPMC7751019 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Sheep production in Israel has improved by crossing the fat-tailed local Awassi breed with the East Friesian and later, with the Booroola Merino breed, which led to the formation of the highly prolific Afec-Assaf strain. This strain differs from its parental Awassi breed in morphological traits such as tail and horn size, coat pigmentation and wool characteristics, as well as in production, reproductive and health traits. To identify major genes associated with the formation of the Afec-Assaf strain, we genotyped 41 Awassi and 141 Afec-Assaf sheep using the Illumina Ovine SNP50 BeadChip array, and analyzed the results with PLINK and EMMAX software. The detected variable genomic regions that differed between Awassi and Afec-Assaf sheep (variable genomic regions; VGR) were compared to selection signatures that were reported in 48 published genome-wide association studies in sheep. Because the Afec-Assaf strain, but not the Awassi breed, carries the Booroola mutation, association analysis of BMPR1B used as the test gene was performed to evaluate the ability of this study to identify a VGR that includes such a major gene.Of the 20 detected VGR, 12 were novel to this study. A ~7-Mb VGR was identified on Ovies aries chromosome OAR6 where the Booroola mutation is located. Similar to other studies, the most significant VGR was detected on OAR10, in a region that contains candidate genes affecting horn type (RXFP2), climate adaptation (ALOX5AP), fiber diameter (KATNAl1), coat pigmentation (FRY) and genes associated with fat distribution. The VGR on OAR2 included BNC2, which is also involved in controlling coat pigmentation in sheep. Six other VGR contained genes that were shown to be involved in coat pigmentation by analyzing their mammalian orthologues. Genes associated with fat distribution in humans, including GRB14 and COBLL1, were located in additional VGR. Sequencing DNA from Awassi and Afec-Assaf individuals revealed non-synonymous mutations in some of these candidate genes.Our results highlight VGR that differentiate the Awassi breed from the Afec-Assaf strain, some of which may include genes that confer an advantage to Afec-Assaf and Assaf over Awassi sheep with respect to intensive sheep production under Mediterranean conditions.

Project description:BackgroundThe use of whole-genome sequence (WGS) data for genomic prediction and association studies is highly desirable because the causal mutations should be present in the data. The sequencing of 935 sheep from a range of breeds provides the opportunity to impute sheep genotyped with single nucleotide polymorphism (SNP) arrays to WGS. This study evaluated the accuracy of imputation from SNP genotypes to WGS using this reference population of 935 sequenced sheep.ResultsThe accuracy of imputation from the Ovine Infinium® HD BeadChip SNP (~ 500 k) to WGS was assessed for three target breeds: Merino, Poll Dorset and F1 Border Leicester × Merino. Imputation accuracy was highest for the Poll Dorset breed, although there were more Merino individuals in the sequenced reference population than Poll Dorset individuals. In addition, empirical imputation accuracies were higher (by up to 1.7%) when using larger multi-breed reference populations compared to using a smaller single-breed reference population. The mean accuracy of imputation across target breeds using the Minimac3 or the FImpute software was 0.94. The empirical imputation accuracy varied considerably across the genome; six chromosomes carried regions of one or more Mb with a mean imputation accuracy of < 0.7. Imputation accuracy in five variant annotation classes ranged from 0.87 (missense) up to 0.94 (intronic variants), where lower accuracy corresponded to higher proportions of rare alleles. The imputation quality statistic reported from Minimac3 (R2) had a clear positive relationship with the empirical imputation accuracy. Therefore, by first discarding imputed variants with an R2 below 0.4, the mean empirical accuracy across target breeds increased to 0.97. Although accuracy of genomic prediction was less affected by filtering on R2 in a multi-breed population of sheep with imputed WGS, the genomic heritability clearly tended to be lower when using variants with an R2 ≤ 0.4.ConclusionsThe mean imputation accuracy was high for all target breeds and was increased by combining smaller breed sets into a multi-breed reference. We found that the Minimac3 software imputation quality statistic (R2) was a useful indicator of empirical imputation accuracy, enabling removal of very poorly imputed variants before downstream analyses.

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A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep.

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