Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

The objective of this study was to predict genomic breeding values for milk yield of crossbred dairy cattle under different scenarios using single-step genomic BLUP (ssGBLUP). The data set included 13,880,217 milk yield measurements on 6,830,415 cows. Genotypes of 89,558 Holstein, 40,769 Jersey, and 22,373 Holstein-Jersey crossbred animals were used, of which all Holstein, 9,313 Jersey, and 1,667 crossbred animals had phenotypic records. Genotypes were imputed to 45K SNP markers. The SNP effects were estimated from single-breed evaluations for Jersey (JE), Holstein (HO) and crossbreds (CROSS), and multibreed evaluations including all Jersey and Holstein (JE_HO) or approximately equal proportions of Jersey, Holstein, and crossbred animals (MIX). Indirect predictions (IP) of the validation animals (358 crossbred animals with phenotypes excluded from evaluations) were calculated using the resulting SNP effects. Additionally, breed proportions (BP) of crossbred animals were applied as a weight when IP were estimated based on each pure breed. The predictive ability of IP was calculated as the Pearson correlation between IP and phenotypes of the validation animals adjusted for fixed effects in the model. Regression of adjusted phenotypes on IP was used to assess the inflation of IP. The predictive ability of IP for CROSS, JE, HO, JE_HO, and MIX scenario was 0.50, 0.50, 0.47, 0.50, and 0.46, respectively. Using BP was the least successful, with a predictive ability of 0.32. The inflation of the IP for crossbred animals using CROSS, JE, HO, JE_HO, MIX, and BP scenarios were 1.17, 0.65, 0.55, 0.78, 1.00, and 0.85, respectively. The IP of crossbred animals can be predicted using single-step GBLUP under a scenario that includes purebred genotypes.     

Association between milk protein gene variants and protein composition traits in dairy cattle

The objective of this study was to identify DNA markers in the 4 casein genes (CSN1S1, CSN1S2, CSN2, and CSN3) and the 2 major whey protein genes (LALBA and LGB) that show associations with milk protein profile measured by reverse-phase HPLC. Fifty-three single nucleotide polymorphisms (SNP) were genotyped for cows in a unique resource population consisting of purebred Holstein and (Holstein × Jersey) × Holstein crossbred animals. Seven traits were analyzed, including concentrations of α(S)-casein (CN), β-CN, κ-CN, α-lactalbumin, β-lactoglobulin, and 2 additional secondary traits, the total concentration of the above 5 milk proteins and the α(S)-CN to β-CN ratio. A substantial fraction of phenotypic variation could be explained by the additive genetic component for the 7 milk protein composition traits studied. Moreover, several SNP were significantly associated with all examined traits at an experiment-wise error rate of 0.05, except for α-lactalbumin. Importantly, the significant SNP explained a large proportion of the phenotypic variation of milk protein composition. Our findings could be used for selecting animals that produce milk with desired composition or desired processing and manufacturing properties.     

牛乳酪蛋白遗传多态性研究

牛乳酪蛋白遗传多态性影响着牛乳的组成和干酪制作,同时是研究牛品种特性和系统进化的有用工具.就牛乳中酪蛋白的遗传多态性、乳酪蛋白遗传多态性的研究方法以及酪蛋白多态性与乳品质之间的相关性等进行综述,旨在阐述当前牛乳酪蛋白遗传多态性研究的现状,为今后该方向研究提供参考.     

牛乳清蛋白遗传多态性研究

就牛乳清蛋白遗传多态性及其研究方法以及乳清蛋白多样性与乳品质之间的相关性作以阐述,旨在揭示当前该方向研究现状,并为今后研究提供参考。     

Estimation of genetic parameters and heterosis for longevity in crossbred Danish dairy cattle

Crossbreeding has been shown to improve the longevity of dairy cattle in countries across the world. The aim of this study was to estimate heterosis, breed effects, and genetic parameters for longevity in crossbred dairy cattle among Danish Holstein (DH), Danish Red (DR), and Danish Jersey (DJ) breeds. Data were provided from 119 Danish commercial herds that use systematic crossbreeding (i.e., rotational crossbreeding). Additional data from 11 mixed-breed herds with DH and DJ were included to estimate reliable breed effects for DJ. Survival information on 73,741 cows was analyzed with a linear animal model using the artificial insemination–REML algorithm in the DMU package. Five longevity (L) traits were defined: days from first calving until the end of first lactation or culling (L1), days from first calving until the end of second lactation or culling (L2), days from first calving until the end of third lactation or culling (L3), days from first calving until the end of fourth lactation or culling (L4), and days from first calving until the end of fifth lactation or culling (L5). Heritabilities ranged between 0.022 and 0.090. Additive breed effects in units of days were estimated relative to DH for DR as ?0.5 (L1), +10.5 (L2), +18.5 (L3), +11.9 (L4), and +28.6 (L5), and corresponding figures for DJ were +2.0, +0.5, +14.2, +27.7, and +44.0. Heterosis effects in L1 were low (1.2%) but favorable in crosses between DH and DR, whereas negative heterosis effects were estimated for crosses between DH and DJ (?2.5%) and DR and DJ (?1.2%). The largest heterosis effects for L2, L3, L4, and L5 were found in DH × DR and were favorable (+3.3, +5.7, +7.7, and +8.5%, respectively). Corresponding figures for heterosis effects in DH × DJ and DR × DJ were favorable as well: +2.3, +4.1, +5.6, and +6.2% in DH × DJ and +3.1, +7.3, +6.9, and +7.2% in DR × DJ. The favorable heterosis effects show that crossbreeding is an efficient tool for improving longevity in Danish dairy cattle.     

Estimation of genetic (co)variances for milk yield in first three lactations using an animal model and restricted maximum likelihood

Genetic relationships among lactation records are of interest because most selection of bulls is on first lactations. Selection also complicates estimation of genetic parameters. Techniques unbiased by selection should be used. Estimation of genetic and environmental (co)variances was done using restricted maximum likelihood with an expectation-maximization algorithm for an animal model. The algorithm involved solving mixed model equations by direct inversion of coefficient matrix that became feasible by neglecting relationships across herds. From data consisting of first to third lactation milk records of New York Holsteins, two computationally manageable subsets were selected of 15 herds each totaling 3070 and 2900 cows. Each cow had a recorded first lactation and a recorded second lactation if she had a recorded third record. Herds were chosen according to frequency of related animals and about 200 cows per herd. After 18 rounds of iteration, changes in estimates between successive rounds were constantly decreasing and small. Estimates averaged from both subsets gave heritabilities of h1(2) = .33, h2(2) = .33, h3(2) = .34, genetic correlations of rg12 = .86, rg13 = .85, rg23 = .87, and phenotypic correlations of rp12 = .57, rp13 = .52, rp23 = .65.     

Estimation of genetic parameters for milk fat depression in dairy cattle

The objective of this study was to apply reaction norm models to milk recording data to investigate genetic variation in and environmental sensitivity of susceptibility to milk fat depression (MFD). Data comprised 556,276 test-day records of 80,493 heifers in 1043 herds. Breeding values and genetic variances for fat percentage and fat yield were estimated by applying random regression models to average herd-test-day fat percentage. Genetic and permanent environmental correlations between fat yield expressed in different environments ranged, respectively, from 0.83 to 1.00 and from 0.29 to 1.00. Genetic and permanent environmental correlations between fat percentage expressed in different environments ranged, respectively, from 0.87 to 1.00 and from -0.05 to 0.99. Two traits were defined for MFD. The first trait reflected variation of milk fat percentage of animals within lactation after correction for year-season, herd-test-day, age-at-calving, and stage-of-lactation. This trait had an estimated heritability of about 5% and a genetic correlation between the fifth and 95th percentile of the data of 0.50. The second trait reflected the deviation of an animal's fat percentage on a test-day from its expected fat percentage based on fat percentage on the first test-day. This trait had an estimated heritability of about 4% and a genetic correlation between the fifth and 95th percentile of the data of 0.43. The correlation between estimated breeding values of sires for the 2 MFD traits was -0.3. Our results suggest that genetic variation in susceptibility to MFD is present and that selection for reduced susceptibility to MFD is possible.     

Lactation failure in crossbred Sahiwal Friesian cattle

Milk producers in Malaysia make extensive use of crossbred Sahiwal Friesian dairy cattle. These animals have, however, been found susceptible to lactation failure. A survey of cows in an experimental herd of F1 Sahiwal Friesian animals indicated that, in 30% of animals, milk yield decreased to negligible levels within the first 8 weeks post partum. Lactation failure was associated with a progressive increase in the amount of residual milk left in the udder after normal milking. By week 3 of lactation, residual milk volume was significantly greater than that in animals that, based on previous lactation history were not susceptible to lactation failure, and accounted for up to 30% of milk available at the morning milking. The cellular consequences of residual milk accumulation were evident in the activities of acetyl-CoA carboxylase, fatty acid synthetase and galactosyltransferase, key enzyme markers of cellular differentiation, which decreased in glands undergoing lactation failure and were lower than values measured in tissue of control cows. Mammary cell number, estimated by tissue DNA content, was also reduced in animals undergoing lactation failure. These indices of mammary development indicate that lactation failure is the result of premature involution in susceptible animals. Premature involution is a predictable consequence of progressive milk stasis in failing lactation, and attributable to an increase in autocrine feedback by inhibitory milk constituents. The progressive increase in residual milk is, on the other hand, unlikely to be attributable to impaired mammary development. Measurements of milk storage during milk accumulation showed no differences between control and lactation failure cows in the distribution of milk between alveolar and cisternal storage compartments. We conclude that lactation failure in Sahiwal Friesian cows is due to a failure of milk removal, and probably the result of an impaired milk ejection reflex rather than to the glands' milk storage characteristics.     

Quantitative trait loci mapping for dairy cattle production traits using a maximum likelihood method

A maximum likelihood method was developed for QTL mapping in half-sib designs and compared to the regression method in analyses of both field and simulated data. The field data consisted of milk production evaluations of 433 progeny tested sons of 6 sires and 64 microsatellite markers distributed over 12 chromosomes. Based on permutation tests, 5 significant QTL were detected in the field data by the regression method compared with 10 by the maximum likelihood method (P < 0.05). In field data analysis, the maximum likelihood method detected more significant QTL and had a smaller residual variance than the regression method. The simulation included 9 scenarios differing in number of families, family size, QTL variance, and marker density, each replicated 100 times. The simulation results suggested that, as for the regression method, the precision of estimating QTL from the maximum likelihood method improves with increasing number of sons per sire, increasing the ratio of QTL to phenotypic variance, and decreasing marker interval. The maximum likelihood method had a smaller dispersion of estimated QTL positions than the regression method in 6 of 9 scenarios simulated. Overall, the maximum likelihood method shows potential advantage in QTL detection over the regression method, especially in the situations with less favorable conditions for QTL detection.     

焦磷酸测序法分析中国荷斯坦牛、娟姗牛及 水牛的乳蛋白基因多态性

目的 本研究旨在分析和比较中国荷斯坦牛、娟姗牛和水牛的乳蛋白基因多态性。方法 根据奶牛多态位点设计引物, 采用焦磷酸测序法分析乳蛋白基因多态性, 并采用高效液相色谱法进行验证。结果 荷斯坦牛和娟姗牛的乳蛋白(β-casein, κ-casein和β-lactoglobulin)均存在基因多态性, 而水牛仅在κ-casein上存在多态性且多态位点与另两种奶牛不同。结论 中国荷斯坦牛、娟姗牛和水牛的乳蛋白基因均存在多态性, 焦磷酸测序法能高通量、快速测定乳蛋白基因多态性。     

Effect of quantitative trait loci for milk protein percentage on milk protein yield and milk yield in Israeli Holstein dairy cattle

Although numerous quantitative trait loci (QTL) mapping studies involving milk protein percent (PP), milk yield (MY), and protein yield (PY) have been carried out, there has not been any systematic evaluation of the effects of individual QTL on these 3 interrelated traits. Consequently, the aim of the present study was to investigate the effects on MY and PY of QTL for PP previously mapped in various laboratories. The study, based on selective DNA pooling of milk samples, included 10 Israeli Holstein artificial insemination bulls, each the sire of 1,800 or more milk-recorded daughters. For each sire-trait combination across the 10 sires, milk samples of the highest and lowest daughters with respect to estimated breeding values for PP, PY, and MY were collected for pooling. A total of 134 dinucleotide microsatellites distributed over 25 bovine autosomes were used. An empirical standard error for marker-QTL linkage testing was calculated based on the variation among split samples within the same tail. Threshold comparison-wise error rate P-values were set to control proportion of false positives at P = 0.10 level for declaring significant effects at the marker-trait level. Estimates of the number of true null hypotheses for each trait were obtained from the histogram of marker comparison-wise error rate P-values. Based on these estimates, effective power of the experiment at the marker-trait level was estimated as 0.75, 0.41, and 0.73 for PP, PY, and MY. The proportion of heterozygosity at the QTL was estimated as 0.46, 0.39, and 0.40, respectively. After correcting for incomplete power and proportion of false positives, it was estimated that 38.7 and 37.5% of the markers affecting PP and MY, respectively, also affected PY. Of the markers affecting PY, 68.9 and 76.5%, respectively, also affected PP and MY. Apparently, none of the significant markers affected PY exclusively, and only 6.5 and 16.0%, respectively, affected PP or MY exclusively. Thus, almost all significant markers, and by inference almost all QTL, had effects on at least 2 of the 3 traits.     

Whole-genome association study for milk protein composition in dairy cattle

Our objective was to perform a genome-wide association study for content in bovine milk of α_S1-casein (α_S1-CN), α_S2-casein (α_S2-CN), β-casein (β-CN), κ-casein (κ-CN), α-lactalbumin (α-LA), β-lactoglobulin (β-LG), casein index, protein percentage, and protein yield using a 50K single nucleotide polymorphism (SNP) chip. In total, 1,713 Dutch Holstein-Friesian cows were genotyped for 50,228 SNP and a 2-step association study was performed. The first step involved a general linear model and the second step used a mixed model accounting for all family relationships. Associations with milk protein content and composition were detected on 20 bovine autosomes. The main genomic regions associated with milk protein composition or protein percentage were found on chromosomes 5, 6, 11, and 14. The number of chromosomal regions showing significant (false discovery rate <0.01) effects ranged from 3 for β-CN and 3 for β-LG to 12 for α_S2-CN. A genomic region on Bos taurus autosome (BTA) 6 was significantly associated with all 6 major milk proteins, and a genomic region on BTA 11 was significantly associated with the 4 caseins and β-LG. In addition, regions were detected that only showed a significant effect on one of the milk protein fractions: regions on BTA 13 and 22 with effects on α_S1-CN; regions on BTA 1, 9, 10, 17, 19, and 28 with effects on α_S2-CN; a region on BTA 6 with an effect on β-CN; regions on BTA 13 and 21 with effects on κ-CN; regions on BTA 1, 5, 9, 16, 17, and 26 with effects on α-LA; and a region on BTA 24 with an effect on β-LG. The proportion of genetic variance explained by the SNP showing the strongest association in each of these genomic regions ranged from <1% for α_S1-CN on BTA 22 to almost 100% for casein index on BTA 11. Variation associated with regions on BTA 6, 11, and 14 could in large part but not completely be explained by known protein variants of β-CN (BTA 6), κ-CN (BTA 6), and β-LG (BTA 11) or DGAT1 variants (BTA 14). Our results indicate 3 regions with major effects on milk protein composition, in addition to several regions with smaller effects involved in the regulation of milk protein composition.     

Genetic parameters for clinical mastitis, somatic cell counts, and milk production estimated by multiple-trait restricted maximum likelihood

Heritabilities and genetic correlations for mastitis, SCC, and milk production in first lactation were calculated using data from the Swedish disease recording system. Genetic parameters were estimated from sire components of variance and covariance obtained from a multiple-trait restricted maximum likelihood procedure. Data were stratified into three subsets according to breed and AI stud. The numbers of cows were 46,431, 25,373, and 25,201, respectively. Heritabilities of mastitis and SCC averaged .014 and .080, but breed differences were seen. Genetic correlations between mastitis and SCC were moderately high, averaging .6. Milk production showed a slightly unfavorable genetic correlation with mastitis and SCC.     

Genotype imputation in a tropical crossbred dairy cattle population

The objective of this study was to investigate different strategies for genotype imputation in a population of crossbred Girolando (Gyr × Holstein) dairy cattle. The data set consisted of 478 Girolando, 583 Gyr, and 1,198 Holstein sires genotyped at high density with the Illumina BovineHD (Illumina, San Diego, CA) panel, which includes ～777K markers. The accuracy of imputation from low (20K) and medium densities (50K and 70K) to the HD panel density and from low to 50K density were investigated. Seven scenarios using different reference populations (RPop) considering Girolando, Gyr, and Holstein breeds separately or combinations of animals of these breeds were tested for imputing genotypes of 166 randomly chosen Girolando animals. The population genotype imputation were performed using FImpute. Imputation accuracy was measured as the correlation between observed and imputed genotypes (CORR) and also as the proportion of genotypes that were imputed correctly (CR). This is the first paper on imputation accuracy in a Girolando population. The sample-specific imputation accuracies ranged from 0.38 to 0.97 (CORR) and from 0.49 to 0.96 (CR) imputing from low and medium densities to HD, and 0.41 to 0.95 (CORR) and from 0.50 to 0.94 (CR) for imputation from 20K to 50K. The CORR_anim exceeded 0.96 (for 50K and 70K panels) when only Girolando animals were included in RPop (S1). We found smaller CORR_anim when Gyr (S2) was used instead of Holstein (S3) as RPop. The same behavior was observed between S4 (Gyr + Girolando) and S5 (Holstein + Girolando) because the target animals were more related to the Holstein population than to the Gyr population. The highest imputation accuracies were observed for scenarios including Girolando animals in the reference population, whereas using only Gyr animals resulted in low imputation accuracies, suggesting that the haplotypes segregating in the Girolando population had a greater effect on accuracy than the purebred haplotypes. All chromosomes had similar imputation accuracies (CORR_snp) within each scenario. Crossbred animals (Girolando) must be included in the reference population to provide the best imputation accuracies.     

Relationships between milk protein composition, milk protein variants, and cow fertility traits in Dutch Holstein-Friesian cattle

Selective breeding can change milk protein composition to improve the manufacturing properties of milk. However, the effects of such breeding strategies on other economically important traits should be investigated before implementation. The objectives of this study were to examine the association between cow fertility traits and (1) milk protein composition and (2) milk protein variants (β-lactoglobulin, β-casein, κ-casein, and β-κ-casein) in commercial Dutch Holstein-Friesian cattle. Data on 1,644 first-lactation cows were analyzed by fitting linear mixed models. Greater relative concentration of α_S1-casein within total milk protein had a positive phenotypic relationship with nonreturn rates and calving rate after first insemination. Furthermore, results showed virtually no significant relationship between cow fertility and concentration of other milk proteins or milk protein variants. Results of this study can be used to assess the correlated effects of breeding for improved milk protein composition on reproduction, thereby allowing for better evaluation of breeding programs before implementation. Our findings suggest that selecting cows based on milk protein composition or milk protein variants for improved manufacturing properties would have no negative influence on reproductive performance.     

Genetic parameters for first milk production and composition traits for Holsteins using multivariate restricted maximum likelihood

Heritabilities and genetic and phenotypic correlations among yields of milk, fat, protein, and percentages of fat and protein were estimated from 40,984 first lactation records of daughters of 488 young and 75 proven Holstein sires using multivariate REML and a sire model accounting for relationships and sire groups. Proven sires were treated as fixed effects. Heritabilities for yields of milk, fat, protein, and percentages of fat and protein were .29, .31, .25, .65, and .61, respectively. Genetic correlations of milk with yields of fat, protein, and percentages of fat and protein and correlations of fat yield with fat percentage were .45, .79, -.49, -.54 and .56, respectively. Genetic correlations among yields and among percentage of fat and protein were the same (.62). Genetic and phenotypic correlations of protein percentage with fat and protein yields and correlations of fat percentages with protein yield were small (-.13 to .11). Phenotypic correlations were .73 to .90 among yields of milk, fat, and protein; -.31 for milk and fat percentage; -.39 for milk and protein percentage; and .38 for fat yield and fat percentage. Estimates were consistent with an earlier study utilizing data from the same population and also with other reports.     

Influence of dietary protein concentration on milk production by dairy cattle during early lactation

In a 3 X 2 factorial experiment 75 Holstein cows in first, second, or third lactation were fed rations containing either 12.2% or 16.2% crude protein in total ration dry matter. On the average, 26% of dry matter intake was from corn silage, 22% from alfalfa-grass hay, and 52% from a grain mix. Protein was controlled by feeding a 13.7% crude protein grain mix with 1.4% urea for the 12% ration and a 19.8% crude protein grain mix with natural protein for the 16% ration. Average daily milk production (kg/day) for wk 2 through 12 of lactation for 12% and 16% rations by lactations were: first, 21.6 and 21.9; second, 25.7 and 31.5; and third, 27.5 and 34.0. Dry matter intakes by lactations were .42, 1.18, and 2.05 kg/day higher for cows fed the high protein compared to low protein rations. Milk composition was not influenced by protein treatment. The markedly different response to protein supplementation in milk production between heifers in first lactation and more mature cows is unexplained.     

Annual rhythms of milk and milk fat and protein production in dairy cattle in the United States

An annual pattern of milk composition has been well recognized in dairy cattle, with the highest milk fat and protein concentration observed during the winter and lowest occurring in the summer; however, rhythms of milk yield and composition have not been well quantified. Cosinor rhythmometry is commonly used to model repeating daily and annual rhythms and allows determination of the amplitude (peak to mean), acrophase (time at peak), and period (time between peaks) of the rhythm. The objective of this study was to use cosinor rhythmometry to characterize the annual rhythms of milk yield and milk fat and protein concentration and yield using both national milk market and cow-level data. First, 10 yr of monthly average milk butterfat and protein concentration for each Federal Milk Marketing Order were obtained from the US Department of Agriculture Agricultural Marketing Service database. Fat and protein concentration fit a cosine function with a 12-mo period in all milk markets. We noted an interaction between milk marketing order and milk fat and protein concentration. The acrophase (time at peak) of the fat concentration rhythm ranged from December 4 to January 19 in all regions, whereas the rhythm of protein concentration peaked between December 27 and January 6. The amplitude (peak to mean) of the annual rhythm ranged from 0.07 to 0.14 percentage points for milk fat and from 0.08 to 0.12 percentage points for milk protein. The amplitude of the milk fat rhythm generally was lower in southern markets and higher in northern markets. Second, the annual rhythm of milk yield and milk fat and protein yield and concentration were analyzed in monthly test day data from 1,684 cows from 11 tiestall herds in Pennsylvania. Fat and protein concentration fit an annual rhythm in all herds, whereas milk and milk fat and protein yield only fit rhythms in 8 of the 11 herds. On average, milk yield peaked in April, fat and protein yield peaked in February, fat concentration peaked in January, and protein concentration peaked in December. Amplitudes of milk, fat, and protein yield averaged 0.82 kg, 55.3 g, and 30.4 g, respectively. Milk fat and protein concentration had average amplitudes of 0.12 and 0.07, respectively, similar to the results of the milk market data. Generally, milk yield and milk components fit annual rhythm regardless of parity or diacylglycerol O-acyltransferase 1 (DGAT1) K232A polymorphism, with only cows of the low-frequency AA genotype (5.2% of total cows) failing to fit rhythm of milk yield. In conclusion, the yearly rhythms of milk yield and fat and protein concentration and yield consistently occur regardless of region, herd, parity, or DGAT1 genotype and supports generation by a conserved endogenous annual rhythm.     

Effects of milk protein loci on first lactation production in dairy cattle

A total of 920 cows of Holstein-based H line, Ayrshire-based A line, and cross-bred C line between H and A lines was used to determine the genotypic and gene frequencies of milk protein types and to study the relationships of milk protein loci to first lactation yields. Effects of milk protein loci on first lactation performance were examined using classification and gene substitution models. Gene frequencies at the five milk protein loci studied were similar to those reported in the literature. Gene substitution at alpha s1-casein locus showed the greatest effects on first lactation yields compared to those at other milk protein loci. Unfortunately, the favorable B allele at this locus is almost fixed (the frequency of the B allele = .955), a result of long-term selection for high milk production in dairy cattle. The extremely high frequency of a favorable allele at the alpha s1-casein locus imposes a limitation for further genetic improvement at this locus unless a more favorable mutation can be induced. Although favorable alleles at beta-casein, kappa-casein, and beta-lactoglobulin loci exerted smaller effects on first lactation performance than those at the alpha s1-casein locus, their moderate frequencies in the current population can be raised to improve lactation yields through milk protein typing. The combined contribution of the four milk protein loci accounted for 8.9% of phenotypic variance in milk yield, 8.6% in protein yield, ad 5.0% in fat yield.