Improving accuracy of bulls' predicted genomic breeding values for fertility using daughters' milk progesterone profiles.

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.

Genome-wide association study for endocrine fertility traits using single nucleotide polymorphism arrays and sequence variants in dairy cattle.

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 genome-wide 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.

Estimating genetic parameters for fertility in dairy cows from in-line milk progesterone profiles.

The aim of this study was to define endocrine fertility traits from in-line milk progesterone (P4) records and to estimate genetic parameters for these traits. Correlations of classical fertility (calving interval and calving to first service) and milk production traits with endocrine fertility traits were also estimated. In-line milk P4 records (n=160,952) collected from June 2009 through November 2013 for 2,273 lactations of 1,561 Holstein-Friesian cows in 12 commercial herds in the Netherlands were analyzed for (the log of) the number of days from calving till commencement of luteal activity (lnC-LA), proportion of samples between 25 and 60 d in milk with luteal activity (PLA), presence or absence of luteal activity for a cow between 25 and 60 d in milk, interval from commencement of luteal activity to first service (CLAFS), first luteal phase length, length of first interluteal interval, and length of first interovulatory interval. Milk P4 records were sampled, on average, every 2 d. Genetic parameters were estimated using a mixed linear animal model. Heritability estimates (±SE) of endocrine fertility traits were 0.12±0.05 for lnC-LA, 0.12±0.05 for PLA, and 0.11±0.06 for CLAFS, and their repeatability estimates were 0.29±0.04, 0.21±0.04, and 0.15±0.06, respectively. The genetic correlation of lnC-LA with PLA was -0.91±0.06 and with CLAFS was -0.56±0.25. The genetic correlations of lnC-LA were 0.26±0.33 with calving interval and 0.37±0.21 with calving to first service. Genetic correlations of the milk production traits with lnC-LA ranged from 0.04 to 0.18 and 0.07 to 0.65 with classical fertility traits. The phenotypic correlations of all endocrine fertility traits with milk production traits were close to zero (0.01 to 0.07). This study shows that in-line P4 records can be used to define and explore several heritable endocrine fertility traits in dairy cows and might help in selection for improved fertility.

Opportunities for genomic prediction for fertility using endocrine and classical fertility traits in dairy cattle.

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.