| 1. |
- Tenghe, Amabel, et al.
(författare)
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Accuracy of genomic breeding values from endocrine and traditional fertility traits in dairy cows
- 2016
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Ingår i: Annual meeting of the European Association for Animal Production. - 1382-6077. ; 22, s. 287-287
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Endocrine fertility traits defined from progesterone concentration levels in milk have been suggested as alternative indicators for fertility in dairy cows because they are less biased by farm management decisions and more directly reflect a cow's reproductive physiology than traditional fertility traits. With the aim of enabling the use of endocrine fertility traits in genomic selection, we evaluated the added value (accuracy) of using endocrine fertility traits in genomic prediction of fertility. Endocrine and traditional fertility records were available for 2,447 Holstein cows with 5,339 lactations from Ireland, the Netherlands, Sweden, and the United Kingdom. The endocrine traits were commencement of luteal activity (CLA) and proportion of samples in luteal activity (PLA), and the traditional trait was calving to first service (CFS). Genomic estimated breeding values (GEBV) were derived using genomic BLUP in univariate and bivariate analysis, with 85,485 single nucleotide polymorphisms. The accuracies of GEBV were evaluated by 5-fold cross-validation. Accuracies of GEBV ranged from 0.04 to 0.15 across all traits for univariate analysis, and 0.02 to 0.49 for bivariate analysis, indicating low to modest predictive ability. Improved accuracies of GEBV for CFS were achieved in bivariate analysis where endocrine and traditional fertility traits were used, and there was a better predictive ability of CFS in bivariate analysis with CLA than with PLA. This first study on genomic predictions for fertility using endocrine traits suggests some improvement over using only the traditional traits. Further studies with larger training populations may show bigger improvements.
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| 2. |
- Tenghe, Amabel, et al.
(författare)
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Genome-wide association study for endocrine fertility traits using single nucleotide polymorphism arrays and sequence variants in dairy cattle
- 2016
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Ingår i: Journal of Dairy Science. - : American Dairy Science Association. - 0022-0302 .- 1525-3198. ; 99, s. 5470-5485
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Tidskriftsartikel (refereegranskat)abstract
- Endocrine fertility traits, which are defined from progesterone concentration levels in milk, are interesting indicators of dairy cow fertility because they more directly reflect the cows own reproductive physiology than classical fertility traits, which are more biased by farm management decisions. The aim of this study was to detect quantitative trait loci (QTL) for 7 endocrine fertility traits in dairy cows by performing a genomewide association study with 85k single nucleotide polymorphisms (SNP), and then fine-map targeted QTL regions, using imputed sequence variants. Two classical fertility traits were also analyzed for QTL with 85k SNP. The association between a SNP and a phenotype was assessed by single-locus regression for each SNP, using a linear mixed model that included a random polygenic effect. A total of 2,447 Holstein Friesian cows with 5,339 lactations with both phenotypes and genotypes were used for association analysis. Heritability estimates ranged from 0.09 to 0.15 for endocrine fertility traits and 0.03 to 0.10 for classical fertility traits. The genome-wide association study identified 17 QTL regions for endocrine fertility traits on Bos taurus autosomes (BTA) 2, 3, 8, 12, 15, 17, 23, and 25. The highest number (5) of QTL regions from the genome-wide association study was identified for the endocrine trait "proportion of samples with luteal activity." Overlapping QTL regions were found between endocrine traits on BTA 2, 3, and 17. For the classical trait calving to first service, 3 QTL regions were identified on BTA 3, 15, and 23, and an overlapping region was identified on BTA 23 with endocrine traits. Fine-mapping target regions for the endocrine traits on BTA 2 and 3 using imputed sequence variants confirmed the QTL from the genome-wide association study, and identified several associated variants that can contribute to an index of markers for genetic improvement of fertility. Several potential candidate genes underlying endocrine fertility traits were also identified in the target regions and are discussed. However, due to high linkage disequilibrium, it was not possible to specify genes or polymorphisms as causal factors for any of the regions. Key words: quantitative trait loci, milk progesterone, dairy cattle, fertility.
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| 3. |
- Tenghe, Amabel, et al.
(författare)
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Improving accuracy of bulls' predicted genomic breeding values for fertility using daughters' milk progesterone profiles
- 2018
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Ingår i: Journal of Dairy Science. - : American Dairy Science Association. - 0022-0302 .- 1525-3198. ; 101, s. 5177-5193
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Tidskriftsartikel (refereegranskat)abstract
- The main objective of this study was to investigate the benefit of accuracy of genomic prediction when combining records for an intermediate physiological phenotype in a training population with records for a traditional phenotype. Fertility was used as a case study, where commencement of luteal activity (C-LA) was the physiological phenotype, whereas the interval from calving to first service and calving interval were the traditional phenotypes. The potential accuracy of across-country genomic prediction and optimal recording strategies of C-LA were also investigated in terms of the number of farms and number of repeated records for C-LA. Predicted accuracy was obtained by estimating population parameters for the traits in a data set of 3,136 Holstein Friesian cows with 8,080 lactations and using a deterministic prediction equation. The effect of genetic correlation, heritability, and reliability of C-LA on the accuracy of genomic prediction were investigated. When the existing training population was 10,000 bulls with reliable estimated breeding value for the traditional trait, predicted accuracy for the physiological trait increased from 0.22 to 0.57 when 15,000 cows with C-LA records were added to the bull training population; but, when the interest was in predicting the traditional trait, we found no benefit from the additional recording. When the genetic correlation was higher between the physiological and traditional traits (0.7 instead of 0.3), accuracy increased less when adding the 15.000 cows with C-LA (from 0.51 to 0.63). In across-country predictions, we observed little to no increase in accuracy of the intermediate physiological phenotype when the training population from Sweden was large, but when accuracy increased the training population was small (200 cows), from 0.19 to 0.31 when 15,000 cows were added from the Netherlands (genetic correlation of 0.5 between countries), and from 0.19 to 0.48 for genetic correlation of 0.9. The predicted accuracy initially increased substantially when recording on the same farm was extended and multiple C-LA records per cow were used in prediction compared with single records; that is, accuracy increased from 0.33 with single records to 0.38 with multiple records (on average 1.6 records per cow) from 2 yr of recording C-LA. But, when the number C-LA per cow increased beyond 2 yr of recording, we noted no substantial benefit in accuracy from multiple records. For example, for 5 yr of recording (on average 2.5 records per cow), accuracy was 0.47; on doubling the recording period to 10 yr (on average 3.1 records per cow), accuracy increased by 0.07 units, whereas when C-LA was recorded for 15 yr (on average 3.3 records per cow) accuracy increased only by 0.05 units. Therefore, for genomic prediction using expensive equipment to record traits for training populations, it is important to optimize the recording strategy. The focus should be on recording more cows rather than continuous recording on the same cows.
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| 4. |
- Tenghe, Amabel
(författare)
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Milk progesterone measures to improve genomic selection for fertility in dairy cows
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Improved reproductive performance has a substantial benefit for the overall profitability of dairy cattle farming by decreasing insemination and veterinary treatment costs, shortening calving intervals, and lowering the rate of involuntary culling. Unfortunately, the low heritability of classical fertility traits derived from calving and insemination data makes genetic improvement by traditional animal breeding slow. Therefore, there is an interest in finding novel measures of fertility that have a higher heritability or using genomic information to aid genetic selection for fertility. The overall objective of this thesis was to explore the use of milk progesterone (P4) records and genomic information to improve selection for fertility in dairy cows. In a first step, the use of in-line milk progesterone records to define endocrine fertility traits was investigated, and genetic parameters estimated. Several defined endocrine fertility traits were heritable, and showed a reasonable repeatability. Also, the genetic correlation of milk production traits with endocrine fertility traits were considerably lower than the correlations of milk production with classical fertility traits. In the next step 17 quantitative trait loci (QTL) associated with endocrine fertility traits, were identified on Bos taurus autosomes (BTA) 2, 3, 8, 12, 15, 17, 23, and 25 in a genome-wide association study with single nucleotide polymorphisms. Further, fine-mapping of target regions on BTA 2 and 3, identified several associated variants and potential candidate genes underlying endocrine fertility traits. Subsequently, the optimal use of endocrine fertility traits in genomic evaluations was investigated; using empirical and theoretical predictions for single-trait models, I showed that endocrine fertility traits have more predictive ability than classical fertility traits. The accuracy of genomic prediction was also substantially improved when endocrine and classical fertility traits were combined in multi-trait genomic prediction. Finally, using deterministic predictions, the potential accuracy of multi-trait genomic selection when combining a cow training population measured for the endocrine trait commencement of luteal activity (C-LA), with a training population of bulls with daughter observations for a classical fertility trait was investigated. Results showed that for prediction of fertility, there is no benefit of investing in a cow training population when the breeding goal is based on classical fertility traits. However, when considering a more biological breeding goal for fertility like C-LA, accuracy is substantially improved when endocrine traits are available from a limited number of farms.
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| 5. |
- Tenghe, Amabel, et al.
(författare)
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Opportunities for genomic prediction for fertility using endocrine and classical fertility traits in dairy cattle
- 2016
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Ingår i: Journal of Animal Science. - : Oxford University Press (OUP). - 0021-8812 .- 1525-3163. ; 94, s. 3645-3654
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Tidskriftsartikel (refereegranskat)abstract
- Endocrine fertility traits, defined from progesterone concentration levels in milk, have been suggested as alternative indicators for fertility in dairy cows because they are less biased by farm management decisions and more directly reflect a cow's reproductive physiology than classical traits derived from insemination and calving data. To determine the potential use of endocrine fertility traits in genomic evaluations, the improvement in accuracy from using endocrine fertility traits concurrent with classical traits in the genomic prediction of fertility was quantified. The impact of recording all traits on all training animals was also investigated. Endocrine and classical fertility records were available on 5,339 lactations from 2,447 Holstein cows in Ireland, the Netherlands, Sweden, and the United Kingdom. The endocrine traits were commencement of luteal activity (C-LA]) and proportion of samples with luteal activity (PLA); the classical trait was the interval from calving to first service (CFS). The interval from C-LA to first service (C-LAFS), which is a combination of an endocrine trait and a classical trait, was also investigated. The target (breeding goal) trait for fertility was CFS or C-LAFS, whereas C-LA and PLA served as predictor traits. Genomic EBV (GEBV) for fertility were derived using genomic BLUP in bivariate models with 85,485 SNP. Genomic EBV for the separate fertility traits were also computed, in univariate models. The accuracy of GEBV was evaluated by 5-fold cross-validation. The highest accuracy of GEBV was achieved using bivariate predictions, where both an endocrine fertility trait and the classical fertility trait were used. Accuracy of GEBV for predicting adjusted phenotypes for CFS in the univariate model was 0.04, but when predicting CFS using a bivariate model with C-LA, the accuracy increased to 0.14 when all training animals were phenotyped for C-LA and (or not) for CFS. On phenotyping all training animals for both C-LA and CFS, accuracy for CFS increased to 0.18; however, when validation animals were also phenotyped for C-LA, there was no substantial increase in accuracy. When predicting CFS in bivariate analysis with PLA, accuracy ranged from 0.07 to 0.14. This first study on genomic predictions for fertility using endocrine traits suggests some improvement in the accuracy of prediction over using only the classical traits. Further studies with larger training populations may show greater improvements.
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