Expected early genetic gain from selection for milk yield in dairy cattle

Summary A matrix program to predict short term genetic gain from single trait selection for milk yield was developed. Rate of genetic gain was calculated as the annual change in the mean breeding value of all producing females. Several parameters sets representing various selection policies were used to examine situations pertinent to dairy populations of the United States. Approach to the asymptotic rates of genetic gain within the model varied with the choice of parameters, but even with consistent selection policies, predicted total genetic gain in the first 10 years was only half of the expected from classical theory. Considerable year to year variation in the rate of gain occurred. Early gains were more dependent on female selection decisions than gains during the steady state. In a two-phase model, the approach to the linear rate of gain in the second phase was accelerated by starting with an ongoing improvement program, but considerable delays still existed. Selection for sex- limited traits such as milk yield, which require pedigree selection and a waiting time for progeny test results reached asymptotic rates more slowly than previously assumed.     

Genetic correlations between production and disease traits during first lactation in Holstein cows

The aim of this study was to estimate genetic correlations between milk yield, somatic cell score (SCS), mastitis, and claw and leg disorders (CLDs) during first lactation in Holstein cows by using a threshold–linear random regression test-day model. We used daily records of milk, fat and protein yields; somatic cell count (SCC); and mastitis and CLD incidences from 46 771 first-lactation Holstein cows in Hokkaido, Japan, that calved between 2000 and 2009. A threshold animal model for binary records (mastitis and CLDs) and linear animal model for yield traits were applied in our multiple trait analysis. For both liabilities and yield traits, additive genetic effects were used as random regression on cubic Legendre polynomials of days on milk. The highest positive genetic correlations between yields and disease incidences (0.36 for milk and mastitis, 0.56 for fat and mastitis, 0.24 for protein and mastitis, 0.32 for milk and CLD, 0.44 for fat and CLD and 0.31 for protein and CLD) were estimated at about the time of peak milk yield (36 to 65 days in milk). Selection focused on early lactation yield may therefore increase the risk of mastitis and CLDs. The positive genetic correlations of SCS with mastitis or CLD incidence imply that selection to reduce SCS in the early stages of lactation would decrease the incidence of both mastitis and CLD.     

Evaluation of reference lactation length in Chios dairy sheep

Definition and establishment of a fixed reference lactation length could provide useful tools for on-farm comparison of ewes and flock management as well as genetic evaluations for the breeding programme. The objectives of this study were to (i) evaluate different reference lactation lengths for the Chios dairy sheep and (ii) define the most suitable reference length for the breed. A total of 260 042 test-day milk records from 24 474 ewes in 130 flocks collected between 2003 and 2014 were used; 15 different lactation lengths were evaluated ranging from 120 to 260 days, defined at 10-day intervals as reference for the Chios sheep. The evaluation criteria included: (a) heritability and repeatability of milk yield in each reference lactation, (b) genetic correlation of reference lactation milk yield with actual lactation milk yield and yield at first test-day record and (c) correlated response in reference lactation milk yield from selection based on first test-day milk yield. The latter emulates genetic gains achieved for milk yield based on early lactation selection. Heritability and repeatability estimates of reference lactation milk yield and genetic correlation with actual lactation yield favoured long reference lactations (180 to 230 days). On the contrary, correlation with first test-day record milk yield was higher for short lactations (120 to 170 days). Moreover, selection on first test-day record milk yield would lead to a correlated response in reference yield in 220 days equal to 85% of the highest estimate achieved in the maximum reference length of 260 days (190 days when only considering first lactation milk yield). Based on the results of the present study, an overall reference lactation length for the Chios breed of 220 days post-lambing and a first lactation reference length of 190 days post-lambing are recommended.     

Genetic parameters for production,health, fertility and longevity traits in dairy cows

Milk production, fertility, longevity and health records, were extracted from databases of two milk recording organisations in the United Kingdom for the first three lactations of the Holstein–Friesian breed. These included data related to health events (mastitis and lameness), voluntarily recorded on a proportion of farms. The data were analysed to calculate disease incidence levels and to estimate genetic parameters for health traits and their relationships with production and other functional traits. The resulting dataset consisted of 124 793 lactations from 75 137 animals of 1586 sires, recorded in 2434 herds. Incidence of health events increased with parity. The overall incidence of mastitis (MAS) and lameness (LAM), defined as binary traits, were 17% and 16%, respectively. Heritability estimates for MAS and LAM were 0.04 and 0.02, respectively, obtained from repeatability linear sire models. Heritability estimates of mastitis and lameness as count traits were slightly higher, 0.05 and 0.03, respectively. Genetic correlations were obtained by bivariate analyses of all pair-wise combinations between milk 305-day yield (MY), protein 305-day yield (PY), fat 305-day yield (FY), lactation average log_e transformed lactation average somatic cell count (SCS), calving interval (CI), days to first service (DFS), non-return at 56 days (NR56), number of inseminations (NINS), mastitis (MAS), number of mastitis episodes (NMAS), lameness (LAM), number of lameness episodes (NLAM) and lifespan score (LS). As expected, MAS was correlated most strongly with SCS (0.69), which supports the use of SCS as an indicator trait for mastitis. Genetic correlations between MAS and yield and fertility traits were of similar magnitude ranging from 0.27 to 0.33. Genetic correlations between MAS with LAM and LS were 0.38 and −0.59, respectively. Not all genetic correlations between LAM and other traits were significant because of fewer numbers of lameness records. LAM had significant genetic correlations with MY (0.38), PY (0.28), CI (0.35), NINS (0.38) and LS (−0.53). The heritability estimates of mastitis and lameness were low; therefore, genetic gain through direct selection alone would be slow, yet still positive and cumulative. Direct selection against mastitis and lameness as additional traits should reduce incidence of both diseases, and simultaneously improve fertility and longevity. However, both health traits had antagonistic relationships with production traits, thus genetic gain in production would be slower.     

Relationships between conception rate in Holstein heifers and cows and milk yield at various stages of lactation

We investigated the relationships between conception rates (CRs) at first service in Japanese Holstein heifers (i.e. animals that had not yet had their first calf) and cows and their test-day (TD) milk yields. Data included records of artificial insemination (AI) for heifers and cows that had calved for the first time between 2000 and 2008 and their TD milk yields at 6 through 305 days in milk (DIM) from first through third lactations. CR was defined as a binary trait for which first AI was a failure or success. A threshold-linear animal model was applied to estimate genetic correlations between CRs of heifers or cows and TD milk yield at various lactation stages. Two-trait genetic analyses were performed for every combination of CR and TD milk yield by using the Bayesian method with Gibbs sampling. The posterior means of the heritabilities of CR were 0.031 for heifers, 0.034 for first-lactation cows and 0.028 for second-lactation cows. Heritabilities for TD milk yield increased from 0.324 to 0.433 with increasing DIM but decreased slightly after 210 DIM during first lactation. These heritabilities from the second and third lactations were higher during late stages of lactation than during early stages. Posterior means of the genetic correlations between heifer CR and all TD yields were positive (range, 0.082 to 0.287), but those between CR of cows and milk yields during first or second lactation were negative (range, −0.121 to −0.250). Therefore, during every stage of lactation, selection in the direction of increasing milk yield may reduce CR in cows. The genetic relationships between CR and lactation curve shape were quite weak, because the genetic correlations between CR and TD milk yield were constant during the lactation period.     

Effect of lactation length adjustment procedures on genetic parameter estimates for buffalo milk yield

The objectives of this study were to estimate the genetic parameters for milk yield unadjusted and adjusted for days in milk and, subsequently, to assess the influence of adjusting for days in milk on sire rank. Complete lactations from 90 or 150 days of lactation to 270 or 350 days in milk were considered in these analyses. Milk yield was adjusted for days in milk by multiplicative correction factors, or by including lactation length as a covariable in the model. Milk yields adjusted by different procedures were considered as different traits. Heritability estimates varied from 0.17 to 0.28. Genetic correlation estimates between milk yields unadjusted and adjusted for days in milk were greater than 0.82. Adjusting for days in milk affected the parameter estimates. Multiplicative correction factors produced the highest heritability estimates. More reliable breeding value estimates can be expected by including short length lactation records in the analyses and adjusting the milk yields for days in milk, regardless of the method used for the adjustment. High selection intensity coupled to the inclusion of short length lactations and adjustment with multiplicative factors can change the sire rank..     

Data transformation for rank reduction in multi-trait MACE model for international bull comparison

Since many countries use multiple lactation random regression test day models in national evaluations for milk production traits, a random regression multiple across-country evaluation (MACE) model permitting a variable number of correlated traits per country should be used in international dairy evaluations. In order to reduce the number of within country traits for international comparison, three different MACE models were implemented based on German daughter yield deviation data and compared to the random regression MACE. The multiple lactation MACE model analysed daughter yield deviations on a lactation basis reducing the rank from nine random regression coefficients to three lactations. The lactation breeding values were very accurate for old bulls, but not for the youngest bulls with daughters with short lactations. The other two models applied principal component analysis as the dimension reduction technique: one based on eigenvalues of a genetic correlation matrix and the other on eigenvalues of a combined lactation matrix. The first one showed that German data can be transformed from nine traits to five eigenfunctions without losing much accuracy in any of the estimated random regression coefficients. The second one allowed performing rank reductions to three eigenfunctions without having the problem of young bulls with daughters with short lactations.     

Multiple-trait multiple-country genetic evaluation of Holstein bulls for female fertility and milk production traits

The aim of this study was to investigate the effect of including milk yield data in the international genetic evaluation of female fertility traits to reduce or eliminate a possible bias because of across-country selection for milk yield. Data included two female fertility traits from Great Britain, Italy and the Netherlands, together with milk yield data from the same countries and from the United States, because the genetic trends in other countries may be influenced by selection decisions on bulls in the United States. Potentially, female fertility data had been corrected nationally for within-country selection and management biases for milk yield. Using a multiple-trait multiple across-country evaluation (MT-MACE) for the analysis of female fertility traits with milk yield, across-country selection patterns both for female fertility and milk yield can be considered simultaneously. Four analyses were performed; one single-trait multiple across-country evaluation analysis including only milk yield data, one MT-MACE analysis including only female fertility traits, and one MT-MACE analysis including both female fertility and milk yield traits. An additional MT-MACE analysis was performed including both female fertility and milk yield traits, but excluding the United States. By including milk yield traits to the analysis, female fertility reliabilities increased, but not for all bulls in all the countries by trait combinations. The presence of milk yield traits in the analysis did not considerably change the genetic correlations, genetic trends or bull rankings of female fertility traits. Even though the predicted genetic merits of female fertility traits hardly changed by including milk yield traits to the analysis, the change was not equally distributed to the whole data. The number of bulls in common between the two sets of Top 100 bulls for each trait in the two analyses of female fertility traits, with and without the four milk yield traits and their rank correlations were low, not necessarily because of the absence of the US milk yield data. The joint international genetic evaluation of female fertility traits with milk yield is recommended to make use of information on several female fertility traits from different countries simultaneously, to consider selection decisions for milk yield in the genetic evaluation of female fertility traits for obtaining more accurate estimating breeding values (EBV) and to acquire female fertility EBV for bulls evaluated for milk yield, but not for female fertility.     

Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle (Open Access publication)

The efficiency of the French marker-assisted selection (MAS) was estimated by a simulation study. The data files of two different time periods were used: April 2004 and 2006. The simulation method used the structure of the existing French MAS: same pedigree, same marker genotypes and same animals with records. The program simulated breeding values and new records based on this existing structure and knowledge on the QTL used in MAS (variance and frequency). Reliabilities of genetic values of young animals (less than one year old) obtained with and without marker information were compared to assess the efficiency of MAS for evaluation of milk, fat and protein yields and fat and protein contents. Mean gains of reliability ranged from 0.015 to 0.094 and from 0.038 to 0.114 in 2004 and 2006, respectively. The larger number of animals genotyped and the use of a new set of genetic markers can explain the improvement of MAS reliability from 2004 to 2006. This improvement was also observed by analysis of information content for young candidates. The gain of MAS reliability with respect to classical selection was larger for sons of sires with genotyped progeny daughters with records. Finally, it was shown that when superiority of MAS over classical selection was estimated with daughter yield deviations obtained after progeny test instead of true breeding values, the gain was underestimated.     

Effects of stage of pregnancy on variance components,daily milk yields and 305-day milk yield in Holstein cows,as estimated by using a test-day model

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open to maximize lifetime productivity in dairy cows.     

Integrating genomic selection into dairy cattle breeding programmes: a review

Extensive genetic progress has been achieved in dairy cattle populations on many traits of economic importance because of efficient breeding programmes. Success of these programmes has relied on progeny testing of the best young males to accurately assess their genetic merit and hence their potential for breeding. Over the last few years, the integration of dense genomic information into statistical tools used to make selection decisions, commonly referred to as genomic selection, has enabled gains in predicting accuracy of breeding values for young animals without own performance. The possibility to select animals at an early stage allows defining new breeding strategies aimed at boosting genetic progress while reducing costs. The first objective of this article was to review methods used to model and optimize breeding schemes integrating genomic selection and to discuss their relative advantages and limitations. The second objective was to summarize the main results and perspectives on the use of genomic selection in practical breeding schemes, on the basis of the example of dairy cattle populations. Two main designs of breeding programmes integrating genomic selection were studied in dairy cattle. Genomic selection can be used either for pre-selecting males to be progeny tested or for selecting males to be used as active sires in the population. The first option produces moderate genetic gains without changing the structure of breeding programmes. The second option leads to large genetic gains, up to double those of conventional schemes because of a major reduction in the mean generation interval, but it requires greater changes in breeding programme structure. The literature suggests that genomic selection becomes more attractive when it is coupled with embryo transfer technologies to further increase selection intensity on the dam-to-sire pathway. The use of genomic information also offers new opportunities to improve preservation of genetic variation. However, recent simulation studies have shown that putting constraints on genomic inbreeding rates for defining optimal contributions of breeding animals could significantly reduce achievable genetic gain. Finally, the article summarizes the potential of genomic selection to include new traits in the breeding goal to meet societal demands regarding animal health and environmental efficiency in animal production.     

Quantitative trait loci for carbon isotope discrimination are repeatable across environments and wheat mapping populations

Wheat productivity is commonly limited by a lack of water essential for growth. Carbon isotope discrimination (Delta), through its negative relationship with transpiration efficiency, has been used in selection of higher wheat yields in breeding for rainfed environments. The potential also exists for selection of increased Delta for improved adaptation to irrigated and high rainfall environments. Selection efficiency of Delta would be enhanced with a better understanding of its genetic control. Three wheat mapping populations (Cranbrook/Halberd, Sunco/Tasman and CD87/Katepwa) containing between 161 and 190 F(1)-derived, doubled-haploid progeny were phenotyped for Delta and agronomic traits in 3-5 well-watered environments. The range for Delta was large among progeny (c. 1.2-2.3 per thousand), contributing to moderate-to-high single environment (h (2) = 0.37-0.91) and line-mean (0.63-0.86) heritabilities. Transgressive segregation was large and genetic control complex with between 9 and 13 Delta quantitative trait loci (QTL) identified in each cross. The Delta QTL effects were commonly small, accounting for a modest 1-10% of the total additive genetic variance, while a number of chromosomal regions appeared in two or more populations (e.g. 1BL, 2BS, 3BS, 4AS, 4BS, 5AS, 7AS and 7BS). Some of the Delta genomic regions were associated with variation in heading date (e.g. 2DS, 4AS and 7AL) and/or plant height (e.g. 1BL, 4BS and 4DS) to confound genotypic associations between Delta and grain yield. As a group, high Delta progeny were significantly (P < 0.10-0.01) taller and flowered earlier but produced more biomass and grain yield in favorable environments. After removing the effect of height and heading date, strong genotypic correlations were observed for Delta and both yield and biomass across populations (r (g) = 0.29-0.57, P < 0.05) as might be expected for the favorable experimental conditions. Thus selection for Delta appears beneficial in increasing grain yield and biomass in favorable environments. However, care must be taken to avoid confounding genotypic differences in Delta with stature and development time when selecting for improved biomass and yield especially in environments experiencing terminal droughts. Polygenic control and small size of individual QTL for Delta may reduce the potential for QTL in marker-assisted selection for improved yield of wheat.     

Genetic parameters for lactation traits of milking ewes: protein content and composition,fat, somatic cells and individual laboratory cheese yield

The effects of some environmental variation factors and the genetic parameters for total milk traits (fat content, protein content, casein content, serum protein content, lactation mean of individual laboratory cheese yield (LILCY), lactation mean of somatic cell count (LSCC), and milk yield) were estimated from the records of 1 111 Churra ewes. Genetic parameters were estimated by multivariate REML. Heritability for fat content was low (0.10) as is usually found in the Churra breed. Heritabilities for protein content, casein content, serum protein content, LILCY, milk yield and somatic cell count were 0.31, 0.30, 0.22, 0.09, 0.26 and 0.11, respectively. The highest heritability estimates were for protein and casein contents. Casein content is not advisable as an alternative to protein content as a selection criterion for cheese yield improvement; it does not have any compelling advantages and its measurement is costly. Our results for LSCC indicated that efforts should focus on improving the level of management rather than selecting for somatic cells, in the actual conditions of the Churra breed.     

Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle

Phenotypic variation in milk production traits has been described over the course of a lactation as well as between different parities. The objective of this study was to investigate whether variation in production is affected by different loci across lactations. A genome-wide association study (GWAS) using a 50-k SNP chip was conducted in 152 divergent German Holstein Friesian cows to test for association with milk production traits over different lactations. The first four lactations were analysed regarding milk yield, fat, protein, lactose, milk urea nitrogen yield and content as well as somatic cell score. Two approaches were used: (i) Wilmink curve parameters were used to assess the genetic effects over the course of a lactation and (ii) test-day yield deviations (YD) were used as a normative approach for a GWAS. The significant effects were largest for markers affecting curve parameters for which there was a statistical power <0.8 of detection even in this small design. While significant markers for YDs were detected in this study, the power to detect effects of a similar magnitude was only 0.11, suggesting that many loci may have been missed with this approach in the present design. Furthermore, all significant effects were specific for a single lactation, leading to the conclusion that the variance explained by a certain locus changes from lactation to lactation. We confirm the common evidence that most production traits vary in the degree of persistency after the peak as a result of genetic influence.     

Genetic and phenotypic parameters for test day milk yield of Sahiwal cattle in the semi-arid tropics

A total of 19 376 test day (TD) milk yield records from the first three lactations of 1618 cows daughters of 162 sires were used to estimate genetic and phenotypic parameters and determine the relationship between daily milk yield and lactation milk yield in the Sahiwal cattle in Kenya. Variance components were estimated using animal models based on a derivative free restricted maximum likelihood procedure. Variance components were estimated using various univariate and multi-trait fixed regression test day models (TDM) that defined contemporary groups either based on the year-season of calving (YSCV) or on the year-season of TD milk sampling (YSTD). Variance components were influenced by CG which resulted in differences in heritability and repeatability estimates between TDM. Models considering YSTD resulted in higher additive genetic variances and lower residual variances compared with models in which YSCV was considered. Heritability estimates for daily yield ranged from 0.28 to 0.46, 0.38 to 0.52 and 0.33 to 0.52 in the first, second and third lactation, respectively. In the first and second lactation, the heritability estimates were highest between TD 2 and TD 4. Genetic correlations among daily milk yields ranged from 0.41 to 0.93, 0.50 to 0.83 and 0.43 to 86 in the first, second and third lactation, respectively. The phenotypic correlations were correspondingly lower. Genetic correlations were different from unit when fitting multi-trait TDM. Therefore, a multiple trait model would be more ideal in determining the genetic merit of dairy sires and bulls based on daily yield records. Genetic and phenotypic correlations between daily yield and lactation yields were high and positive. Genetic correlations ranged from 0.84 to 0.99, 0.94 to 1.00 and 0.94 to 0.97 in the first, second and third lactations, respectively. The corresponding phenotypic correlation estimates ranged from 0.50 to 0.85, 0.50 to 0.83 and 0.53 to 0.87. The high genetic correlation between daily yield and lactation yield imply that both traits are influenced by similar genes. Therefore daily yields records could be used in genetic evaluation in the Sahiwal cattle breeding programme.     

Genetic and phenotypic parameters of lactations longer than 305 days (extended lactations)

Test-day milk yield and somatic cell count data over extended lactation (lactation to 540-600 days) were analysed considering part lactations as different traits and fitting random regression (RR) models. Data on Australian Jersey and Holstein Friesian (HF) were used to demonstrate the shape of the lactation curve and data on HF were used for genetic study. Test-day data from about 100 000 cows that calved between 1998 and 2005 were used for this study. In all analyses, a sire model was used.When part lactations were considered as different traits, protein yield early in the lactation (e.g. first 2 months) had a genetic correlation of about 0.8 with protein yield produced after 300 days of lactation. Genetic correlations between lactation stages that are adjacent to each other were high (0.9 or more) within parity. Across parities, genetic correlations were high for both protein and milk yield if they are within the same stage of lactation. Phenotypic correlations were lower than genetic correlations. Heritability of milk-yield traits estimated from the RR model varied from 0.15 at the beginning of the lactation to as high as 0.37 by the 4th month of lactation. All genetic correlations between different days in milk were positive, with the highest correlations between adjacent days in milk and decreasing correlations with increasing time-span. The pattern of genetic correlations between milk yield in the second 300 days (301 to 600 days of lactation) do not markedly differ from the pattern in the first 300 days of lactation. The lowest estimated genetic correlation was 0.15 between milk yield on days 45 and 525 of lactation. The result from this study shows that progeny of bulls with high estimated breeding values for yield traits and those that produce at a relatively high level in the first few months are the most likely candidates for use in herds favouring extended lactations.     

Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle

Background

Estimates of dominance variance in dairy cattle based on pedigree data vary considerably across traits and amount to up to 50% of the total genetic variance for conformation traits and up to 43% for milk production traits. Using bovine SNP (single nucleotide polymorphism) genotypes, dominance variance can be estimated both at the marker level and at the animal level using genomic dominance effect relationship matrices. Yield deviations of high-density genotyped Fleckvieh cows were used to assess cross-validation accuracy of genomic predictions with additive and dominance models. The potential use of dominance variance in planned matings was also investigated.

Results

Variance components of nine milk production and conformation traits were estimated with additive and dominance models using yield deviations of 1996 Fleckvieh cows and ranged from 3.3% to 50.5% of the total genetic variance. REML and Gibbs sampling estimates showed good concordance. Although standard errors of estimates of dominance variance were rather large, estimates of dominance variance for milk, fat and protein yields, somatic cell score and milkability were significantly different from 0. Cross-validation accuracy of predicted breeding values was higher with genomic models than with the pedigree model. Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values. Additive and dominance SNP effects for milk yield and protein yield were estimated with a BLUP (best linear unbiased prediction) model and used to calculate expectations of breeding values and total genetic values for putative offspring. Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield.

Conclusions

Estimated dominance variance was substantial for most of the analyzed traits. Due to small dominance effect relationships between cows, predictions of individual dominance deviations were very inaccurate and including dominance in the model did not improve prediction accuracy in the cross-validation study. Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain.     

Milk yield persistency in Brazilian Gyr cattle based on a random regression model

With the objective of evaluating measures of milk yield persistency, 27,000 test-day milk yield records from 3362 first lactations of Brazilian Gyr cows that calved between 1990 and 2007 were analyzed with a random regression model. Random, additive genetic and permanent environmental effects were modeled using Legendre polynomials of order 4 and 5, respectively. Residual variance was modeled using five classes. The average lactation curve was modeled using a fourth-order Legendre polynomial. Heritability estimates for measures of persistency ranged from 0.10 to 0.25. Genetic correlations between measures of persistency and 305-day milk yield (Y305) ranged from -0.52 to 0.03. At high selection intensities for persistency measures and Y305, few animals were selected in common. As the selection intensity for the two traits decreased, a higher percentage of animals were selected in common. The average predicted breeding values for Y305 according to year of birth of the cows had a substantial annual genetic gain. In contrast, no improvement in the average persistency breeding value was observed. We conclude that selection for total milk yield during lactation does not identify bulls or cows that are genetically superior in terms of milk yield persistency. A measure of persistency represented by the sum of deviations of estimated breeding value for days 31 to 280 in relation to estimated breeding value for day 30 should be preferred in genetic evaluations of this trait in the Gyr breed, since this measure showed a medium heritability and a genetic correlation with 305-day milk yield close to zero. In addition, this measure is more adequate at the time of peak lactation, which occurs between days 25 and 30 after calving in this breed.     

Evolutionary analysis of a key floral trait in aquilegia canadensis (ranunculaceae): genetic variation in herkogamy and its effect on the mating system

The mating system of flowering plant populations evolves through selection on genetically based phenotypic variation in floral traits. The physical separation of anthers and stigmas within flowers (herkogamy) is expected to be an important target of selection to limit self-fertilization. We investigated the pattern of phenotypic and genetic variation in herkogamy and its effect of self-fertilization in a broad sample of natural populations of Aquilegia canadensis, a species that is highly selfing despite strong inbreeding depression. Within natural populations, plants exhibit substantial phenotypic variation in herkogamy caused primarily by variation in pistil length rather than stamen length. Compared to other floral traits, herkogamy is much more variable and a greater proportion of variation is distributed among rather than within individuals. We tested for a genetic component of this marked phenotypic variation by growing naturally pollinated seed families from five populations in a common greenhouse environment. For three populations, we detected a significant variation in herkogamy among families, and a positive regression between parental herkogamy measured in the field and progeny herkogamy in the greenhouse, suggesting that there is often genetic variation in herkogamy within natural populations. We estimated levels of self-fertilization for groups of flowers that differed in herkogamy and show that, as expected, herkogamy was associated with reduced selfing in 13 of 19 populations. In six of these populations, we performed floral emasculations to show that this decrease in selfing is due to decreased autogamy (within-flower selfing), the mode of selfing that herkogamy should most directly influence. Taken together, these results suggest that increased herkogamy should be selected to reduce the production of low-quality selfed seed. The combination of high selfing and substantial genetic variation for herkogamy in A. canadensis is enigmatic, and reconciling this observation will require a more integrated analysis of how herkogamy influences not only self-fertilization, but also patterns of outcross pollen import and export.     

Prediction of first test day milk yield using historical records in dairy cows

The transition between two lactations remains one of the most critical periods during the productive life of dairy cows. In this study, we aimed to develop a model that predicts the milk yield of dairy cows from test day milk yield data collected in the previous lactation. In the past, data routinely collected in the context of herd improvement programmes on dairy farms have been used to provide insights in the health status of animals or for genetic evaluations. Typically, only data from the current lactation is used, comparing expected (i.e., unperturbed) with realised milk yields. This approach cannot be used to monitor the transition period due to the lack of unperturbed milk yields at the start of a lactation. For multiparous cows, an opportunity lies in the use of data from the previous lactation to predict the expected production of the next one. We developed a methodology to predict the first test day milk yield after calving using information from the previous lactation. To this end, three random forest models (nextMILK_FULL, nextMILK_PH, and nextMILK_P) were trained with three different feature sets to forecast the milk yield on the first test day of the next lactation. To evaluate the added value of using a machine-learning approach against simple models based on contemporary animals or production in the previous lactation, we compared the nextMILK models with four benchmark models. The nextMILK models had an RMSE ranging from 6.08 to 6.24 kg of milk. In conclusion, the nextMILK models had a better prediction performance compared to the benchmark models. Application-wise, the proposed methodology could be part of a monitoring tool tailored towards the transition period. Future research should focus on validation of the developed methodology within such tool.