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Background: Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk., Results: We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cis lactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways., Conclusions: Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds., (© 2024. The Author(s).)

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Background: Milk samples from 10,641 dairy cattle were screened by a mass spectrometry method for extreme concentrations of the A or B isoforms of the whey protein, β-lactoglobulin (BLG), to identify causative genetic variation driving changes in BLG concentration., Results: A cohort of cows, from a single sire family, was identified that produced milk containing a low concentration of the BLG B protein isoform. A genome-wide association study (GWAS) of BLG B protein isoform concentration in milk from AB heterozygous cows, detected a group of highly significant single nucleotide polymorphisms (SNPs) within or close to the BLG gene. Among these was a synonymous G/A variation at position + 78 bp in exon 1 of the BLG gene (chr11:103256256G > A). The effect of the A allele of this SNP (which we named B') on BLG expression was evaluated in a luciferase reporter assay in transfected CHO-K1 and MCF-7 cells. In both cell types, the presence of the B' allele in a plasmid containing the bovine BLG gene from -922 to + 898 bp (relative to the transcription initiation site) resulted in a 60% relative reduction in mRNA expression, compared to the plasmid containing the wild-type B sequence allele. Examination of a mammary RNAseq dataset (n = 391) identified 14 heterozygous carriers of the B' allele which were homozygous for the BLG B protein isoform (BB'). The level of expression of the BLG B' allele was 41.9 ± 1.0% of that of the wild-type BLG B allele. Milk samples from three cows, homozygous for the A allele at chr11:103,256,256 (B'B'), were analysed (HPLC) and showed BLG concentrations of 1.04, 1.26 and 1.83 g/L relative to a mean of 4.84 g/L in milk from 16 herd contemporaries of mixed (A and B) BLG genotypes. The mechanism by which B' downregulates milk BLG concentration remains to be determined., Conclusions: High-throughput screening and identification of outliers, enabled the discovery of a synonymous G > A mutation in exon 1 of the B allele of the BLG gene (B'), which reduced the milk concentration of β-lactoglobulin B protein isoform, by more than 50%. Milk from cows carrying the B' allele is expected to have improved processing characteristics, particularly for cheese-making., (© 2022. The Author(s).)

Background: Fourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk., Results: Separate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk., Conclusions: This study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents., (© 2021. The Author(s).)

The objective of this study was to estimate genetic correlations among milk fatty acid (FA) concentrations in New Zealand dairy cattle. Concentrations of each of the most common FA, expressed as a percentage of the total FA, were determined by gas chromatography on a specific cohort of animals. Using this data set, prediction equations were derived using mid-infrared (MIR) spectroscopy data collected from the same samples. These prediction equations were applied to a large data set of MIR measurements in 34,141 milk samples from 3,445 Holstein-Friesian, 2,935 Jersey, and 3,609 crossbred Holstein-Friesian × Jersey cows, sampled an average of 3.42 times during the 2007-2008 season. Data were analyzed using univariate and bivariate repeatability animal models. Heritability of predicted FA concentration in milk fat ranged from 0.21 to 0.42, indicating that genetic selection could be used to change the FA composition of milk. The de novo synthesized FA (C6:0, C8:0, C10:0, C12:0, and C14:0) showed strong positive genetic correlations with each other, ranging from 0.24 to 0.99. Saturated FA were negatively correlated with unsaturated (-0.93) and polyunsaturated (-0.84) FA. The saturated FA were positively correlated with milk fat yield and fat percentage, whereas the unsaturated FA were negatively associated with fat yield and fat percentage. Our results indicate that bovine milk FA composition can be changed through genetic selection using MIR as a phenotypic proxy., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: Lactose provides an easily-digested energy source for neonates, and is the primary carbohydrate in milk in most species. Bovine lactose is also a key component of many human food products. However, compared to analyses of other milk components, the genetic control of lactose has been little studied. Here we present the first GWAS focussed on analysis of milk lactose traits., Results: Using a discovery population of 12,000 taurine dairy cattle, we detail 27 QTL for lactose concentration and yield, and subsequently validate the effects of 26 of these loci in a distinct population of 18,000 cows. We next present data implicating causative genes and variants for these QTL. Fine mapping of these regions using imputed, whole genome sequence-resolution genotypes reveals protein-coding candidate causative variants affecting the ABCG2, DGAT1, STAT5B, KCNH4, NPFFR2 and RNF214 genes. Eleven of the remaining QTL appear to be driven by regulatory effects, suggested by the presence of co-locating, co-segregating eQTL discovered using mammary RNA sequence data from a population of 357 lactating cows. Pathway analysis of genes representing all lactose-associated loci shows significant enrichment of genes located in the endoplasmic reticulum, with functions related to ion channel activity mediated through the LRRC8C, P2RX4, KCNJ2 and ANKH genes. A number of the validated QTL are also found to be associated with additional milk volume, fat and protein phenotypes., Conclusions: Overall, these findings highlight novel candidate genes and variants involved in milk lactose regulation, whose impacts on membrane transport mechanisms reinforce the key osmo-regulatory roles of lactose in milk.

A major pleiotropic quantitative trait locus (QTL) located at ~25 Mbp on bovine chromosome 14 affects a myriad of growth and developmental traits in Bos taurus and indicus breeds. These QTL have been attributed to two functional variants in the bidirectional promoter of PLAG1 and CHCHD7. Although PLAG1 is a good candidate for mediating these effects, its role remains uncertain given that these variants are also associated with expression of five additional genes at the broader locus. In the current study, we conducted expression QTL (eQTL) mapping of this region using a large, high depth mammary RNAseq dataset representing 375 lactating cows. Here we show that of the seven previously implicated genes, only PLAG1 and LYN are differentially expressed by QTL genotype, and only PLAG1 bears the same association signature of the growth and body weight QTLs. For the first time, we also report significant association of PLAG1 genotype with milk production traits, including milk fat, volume, and protein yield. Collectively, these data strongly suggest PLAG1 as the causative gene underlying this diverse range of traits, and demonstrate new effects for the locus on lactation phenotypes.

The mammary gland is a prolific lipogenic organ, synthesising copious amounts of triglycerides for secretion into milk. The fat content of milk varies widely both between and within species, and recent independent genome-wide association studies have highlighted a milk fat percentage quantitative trait locus (QTL) of large effect on bovine chromosome 5. Although both EPS8 and MGST1 have been proposed to underlie these signals, the causative status of these genes has not been functionally confirmed. To investigate this QTL in detail, we report genome sequence-based imputation and association mapping in a population of 64,244 taurine cattle. This analysis reveals a cluster of 17 non-coding variants spanning MGST1 that are highly associated with milk fat percentage, and a range of other milk composition traits. Further, we exploit a high-depth mammary RNA sequence dataset to conduct expression QTL (eQTL) mapping in 375 lactating cows, revealing a strong MGST1 eQTL underpinning these effects. These data demonstrate the utility of DNA and RNA sequence-based association mapping, and implicate MGST1, a gene with no obvious mechanistic relationship to milk composition regulation, as causally involved in these processes.

The main objectives of this study were to establish the relative value of milk yields under twice-daily milking (TDM) as a predictor of yield and yield loss under once-daily milking (ODM), and to understand the role of residual milk and udder storage capacity-related traits in regulating yield and yield loss during ODM. A Holstein-Friesian × Jersey crossbred herd was established over 2 seasons (years), as 2 individual cohorts on the same farm, managed on a pasture-based system over 4 lactations. Short-term (1-wk) ODM studies, with a starting total of 690 cows, were undertaken in mid- and late-lactation in lactation 2 and in mid-lactation in lactation 3 for each cohort. A 10-wk study of ODM performance began in mid-lactation in lactation 3, whereas lactation 4 was a full-lactation assessment of ODM. In the short-term studies, milk yield under ODM was well predicted (R(2)=0.7 to 0.8 in 5 of 6 studies) by the daily yield under TDM in the week before ODM. Yield loss (kg/d) increased with increasing milk yield and with increasing somatic cell count (SCC), although predictions were relatively poor (R(2)=0.09 to 0.30). Yield loss (%) decreased with increasing TDM yield in 3 of the 6 studies and was positively correlated with SCC during ODM. Nevertheless, ODM yield loss, in absolute or percentage terms, was a poorly repeatable trait in grazing cows. Part of the variation in yield loss percentage (30%) was positively associated with residual milk (%), measured pretrial, during measurement of functional udder capacity in lactation 3. Total production (kg of milk) over the full-lactation ODM study in lactation 4 was correlated with total production in the 10-wk trial in lactation 3 (r=0.72 and 0.63 for cohorts 1 and 2, respectively). Identifying the highest- and lowest-producing 10% of animals during the full lactation of ODM indicated that poor production was associated with high yields of residual milk (measured in lactation 3) and, conversely, high production was associated with low yields of residual milk, relative to the other 80% of animals. These same "high" and "low" production groups from lactation 4 had similar differences in performance in the earlier short-term studies and a larger or smaller percentage yield loss associated with the residual milk measurement. Breeding strategies for ODM may benefit, therefore, from greater emphasis on selecting for a low residual milk fraction to optimize milking performance. Nevertheless, the level of milk production under TDM is a strong phenotypic predictor of milk production under ODM., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Selective breeding has strongly reduced the genetic diversity in livestock species, and contemporary breeding practices exclude potentially beneficial rare genetic variation from the future gene pool. Here we test whether important traits arising by new mutations can be identified and rescued in highly selected populations. We screened milks from 2.5 million cows to identify an exceptional individual which produced milk with reduced saturated fat content, and improved unsaturated and omega-3 fatty acid concentrations. The milk traits were transmitted dominantly to her offspring, and genetic mapping and genome sequencing revealed a new mutation in a previously unknown splice enhancer of the DGAT1 gene. Homozygous carriers show features of human diarrheal disorders, and may be useful for the development of therapeutic strategies. Our study demonstrates that high-throughput phenotypic screening can uncover rich genetic diversity even in inbred populations, and introduces a novel strategy to develop novel milks with improved nutritional properties.

The objective of this study was to estimate heritability and crossbreeding parameters (breed and heterosis effects) of various fatty acid (FA) concentrations in milk fat of New Zealand dairy cattle. For this purpose, calibration equations to predict concentration of each of the most common FAs were derived with partial least squares (PLS) using mid-infrared (MIR) spectral data from milk samples (n=850) collected in the 2003-04 season from 348 second-parity crossbred cows during peak, mid and late lactation. The milk samples produced both, MIR spectral data and concentration of the most common FAs determined using gas chromatography (GC). The concordance correlation coefficients (CCC) between the concentration of a FA determined by GC and the PLS equation ranged from 0.63 to 0.94, suggesting that some prediction equations can be considered to have substantial predictive ability. The PLS calibration equations were then used to predict the concentration of each of the fatty acids in 26,769 milk samples from 7385 cows that were herd-tested during the 2007-08 season. Data were analysed using a single-trait repeatability animal model. Shorter chain FA (16:0 and below) were significantly higher (P<0.05) in Jersey cows, while longer chain, including unsaturated longer chain FA were higher in Holstein-Friesian cows. The estimates of heritabilities ranged from 0.17 to 0.41 suggesting that selective breeding could be used to ensure milk fat composition stays aligned to consumer, market and manufacturing needs.

Milk is composed of a complex mixture of lipids, proteins, carbohydrates and various vitamins and minerals as a source of nutrition for young mammals. The composition of milk varies between individuals, with lipid composition in particular being highly heritable. Recent reports have highlighted a region of bovine chromosome 27 harbouring variants affecting milk fat percentage and fatty acid content. We aimed to further investigate this locus in two independent cattle populations, consisting of a Holstein-Friesian x Jersey crossbreed pedigree of 711 F2 cows, and a collection of 32,530 mixed ancestry Bos taurus cows. Bayesian genome-wide association mapping using markers imputed from the Illumina BovineHD chip revealed a large quantitative trait locus (QTL) for milk fat percentage on chromosome 27, present in both populations. We also investigated a range of other milk composition phenotypes, and report additional associations at this locus for fat yield, protein percentage and yield, lactose percentage and yield, milk volume, and the proportions of numerous milk fatty acids. We then used mammary RNA sequence data from 212 lactating cows to assess the transcript abundance of genes located in the milk fat percentage QTL interval. This analysis revealed a strong eQTL for AGPAT6, demonstrating that high milk fat percentage genotype is also additively associated with increased expression of the AGPAT6 gene. Finally, we used whole genome sequence data from six F1 sires to target a panel of novel AGPAT6 locus variants for genotyping in the F2 crossbreed population. Association analysis of 58 of these variants revealed highly significant association for polymorphisms mapping to the 5'UTR exons and intron 1 of AGPAT6. Taken together, these data suggest that variants affecting the expression of AGPAT6 are causally involved in differential milk fat synthesis, with pleiotropic consequences for a diverse range of other milk components.

Regulation of milk synthesis and secretion is controlled mostly through local (intramammary) mechanisms. To gain insight into the molecular pathways comprising this response, an analysis of mammary gene expression was conducted in 12 lactating cows shifted from twice daily to once daily milking. Tissues were sampled by biopsy from adjacent mammary quarters of these animals during the two milking frequencies, allowing changes in gene expression to be assessed within each animal. Using bovine-specific, oligonucleotide arrays representing 21,495 unique transcripts, a range of differentially expressed genes were found as a result of less frequent milk removal, constituting transcripts and pathways related to apoptotic signaling (NF-kappaB, JUN, ATF3, IGFBP5, TNFSF12A) mechanical stress and epithelial tight junction synthesis (CYR61, CTGF, THBS1, CLDN4, CLDN8), and downregulated milk synthesis (LALBA, B4GALT1, UGP2, CSN2, GPAM, LPL). Quantitative real-time PCR was used to assess the expression of 13 genes in the study, and all 13 of these were correlated (P < 0.05) with values derived from array analysis. It can be concluded that the physiological changes that occur in the bovine mammary gland as a result of reduced milk removal frequency likely comprise the earliest stages of the involution response and that mechano-signal transduction cascades associated with udder distension may play a role in triggering these events.

Aim: To identify quantitative trait loci (QTL) affecting the concentration of beta-lactoglobulin in milk, and to evaluate the effect of beta-lactoglobulin genetic variants on the concentration of fat, protein and casein in bovine milk., Methods: A herd of 850 F2 Holstein-Friesian x Jersey crossbred cows was produced through mating six Holstein-Friesian x Jersey F1 bulls of high genetic merit with F1 cows from the national herd. A total of 1,610 herd-test records from 556 second-parity crossbreds were analysed. The concentration of fat, protein and casein in milk was measured at peak, mid- and late lactation, during the production seasons of 2003-2004 and 2004-2005. Liveweight was measured daily. DNA from the F2 animals, their F1 dams and sires, and selected grandsires was genotyped across the genome, initially with 285 microsatellite markers, and subsequently with 6,634 single nucleotide polymorphisms (SNP)., Results: A highly significant QTL for the concentration of beta-lactoglobulin in milk was identified, which coincided with the position of the beta-lactoglobulin gene on bovine Chromosome 11. No other consistently significant QTL for the concentration of beta-lactoglobulin in milk were detected. Cows with the BB beta-lactoglobulin genotype produced milk with a 30% lower concentration of beta-lactoglobulin than cows with the AA genotype. The beta-lactoglobulin polymorphism also explained variation in the proportion of casein in total protein. In addition, the percentage of fat was higher for BB than AA animals, whereas the percentage of total protein, mean daily milk yield and liveweight did not differ between AA and BB animals., Conclusions: A significant QTL determining the concentration of beta-lactoglobulin in milk was identified. Selection of animals for the beta-lactoglobulin B-allele may enable the production of milk naturally enriched for casein, thus allowing a potential increase in the yield of cheese. There may be additional future value in production of bovine milk more like human milk, where decreasing the concentration of beta-lactoglobulin is desirable.

beta-Carotene biochemistry is a fundamental process in mammalian biology. Aberrations either through malnutrition or potentially through genetic variation may lead to vitamin A deficiency, which is a substantial public health burden. In addition, understanding the genetic regulation of this process may enable bovine improvement. While many bovine QTL have been reported, few of the causative genes and mutations have been identified. We discovered a QTL for milk beta-carotene and subsequently identified a premature stop codon in bovine beta-carotene oxygenase 2 (BCO2), which also affects serum beta-carotene content. The BCO2 enzyme is thereby identified as a key regulator of beta-carotene metabolism.

Five traits of major economic importance in the New Zealand dairy industry are milk volume, milk fat, milk protein, live weight, and survival. This study evaluated the impact of live weight as a trait in the selection objectives for the New Zealand dairy industry. Live weight of the lactating cow is an important measure because it reflects feeding costs via maintenance feed and salvage values of cows to be culled. In addition, selection responses were evaluated for differing relative economic values for milk protein and milk fat, and selection indexes that included or excluded phenotypic and genotypic correlations between traits were compared. Inclusion of live weight, with a negative economic value in a four-trait selection index with milk, milk fat, and protein resulted in higher economic response. Protein response to selection was not more than 2% when the relative economic value for the ratio of protein to milk fat exceeded 5:1 in a two-trait model; however, milk fat response decreased by over 10%. When a negative relative economic value was assigned to milk fat, economic returns were lower because of lower milk fat responses and the lack of higher protein responses compared with the same ratio for relative economic value but a positive weight for milk fat. Accounting for phenotypic and genetic correlations in deriving selection index weight improved economic response 5%.