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Genetic analysis of production traits and body size measurements and their relationships with metabolic diseases in German Holstein cattle

Open AccessPublished:November 21, 2022DOI:https://doi.org/10.3168/jds.2022-22363

      ABSTRACT

      This study sheds light on the genetic complexity and interplay of production, body size, and metabolic health in dairy cattle. Phenotypes for body size-related traits from conformation classification (130,166 animals) and production (101,562 animals) of primiparous German Holstein cows were available. Additionally, 21,992, 16,641, and 7,096 animals were from herds with recordings of the metabolic diseases ketosis, displaced abomasum, and milk fever in first, second, and third lactation. Moreover, all animals were genotyped. Heritabilities of traits and genetic correlations between all traits were estimated and GWAS were performed. Heritability was between 0.240 and 0.333 for production and between 0.149 and 0.368 for body size traits. Metabolic diseases were lowly heritable, with estimates ranging from 0.011 to 0.029 in primiparous cows, from 0.008 to 0.031 in second lactation, and from 0.037 to 0.052 in third lactation. Production was found to have negative genetic correlations with body condition score (BCS; −0.279 to −0.343) and udder depth (−0.348 to −0.419). Positive correlations were observed for production and body depth (0.138–0.228), dairy character (DCH) (0.334–0.422), and stature (STAT) (0.084–0.158). In first parity cows, metabolic disease traits were unfavorably correlated with production, with genetic correlations varying from 0.111 to 0.224, implying that higher yielding cows have more metabolic problems. Genetic correlations of disease traits in second and third lactation with production in primiparous cows were low to moderate and in most cases unfavorable. While BCS was negatively correlated with metabolic diseases (−0.255 to −0.470), positive correlations were found between disease traits and DCH (0.269–0.469) as well as STAT (0.172–0.242). Thus, the results indicate that larger and sharper animals with low BCS are more susceptible to metabolic disorders. Genome-wide association studies revealed several significantly associated SNPs for production and conformation traits, confirming previous findings from literature. Moreover, for production and conformation traits, shared significant signals on Bos taurus autosome (BTA) 5 (88.36 Mb) and BTA 6 (86.40 to 87.27 Mb) were found, implying pleiotropy. Additionally, significant SNPs were observed for metabolic diseases on BTA 3, 10, 14, 17, and 26 in first lactation and on BTA 2, 6, 8, 17, and 23 in third lactation. Overall, this study provides important insights into the genetic basis and interrelations of relevant traits in today's Holstein cattle breeding programs, and findings may help to improve selection decisions.

      Key words

      INTRODUCTION

      During the past few decades, gross efficiency of milk production has considerably improved because of increased productivity of animals (
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      The impact of genetic selection for increased milk yield on the welfare of dairy cows.
      ). Dilution of maintenance, which describes the distribution of a cow's maintenance requirement to a larger amount of produced milk, has been the main driver of enhanced efficiency so far (
      • Bauman D.E.
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      Sources of variation and prospects for improvement of productive efficiency in the dairy cow: A review.
      ;
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      ). However, additional productivity improvements in cows are not expected to significantly increase efficiency further (
      • VandeHaar M.J.
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      • Spurlock D.M.
      • Tempelman R.J.
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      Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency.
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      Between-cow variation in the components of feed efficiency.
      ).
      Efficiency is determined by the proportion of resources used to produce milk (
      • Bach A.
      • Terré M.
      • Vidal M.
      Symposium review: Decomposing efficiency of milk production and maximizing profit.
      ). From an economic point of view, the most relevant resource in milk production is feed (
      • Connor E.E.
      Invited review: Improving feed efficiency in dairy production: challenges and possibilities.
      ;
      • Moallem U.
      Future consequences of decreasing marginal production efficiency in the high-yielding dairy cow.
      ). For this reason, improving feed efficiency has recently drawn great attention (
      • Pryce J.E.
      • Wales W.J.
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      • Veerkamp R.F.
      • Hayes B.J.
      Genomic selection for feed efficiency in dairy cattle.
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      • Bertics S.J.
      • Contreras-Govea F.E.
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      Considerations when combining data from multiple nutrition experiments to estimate genetic parameters for feed efficiency.
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      • Lund P.
      Selecting for improved feed efficiency and reduced methane emissions in dairy cattle.
      ). For direct improvement of feed efficiency, however, feed intake measurements of individual cows are needed. The lack of such records, as well as the ongoing debate about an appropriate definition of feed efficiency, hinder the implementation of this trait in dairy cattle breeding goals (
      • Hurley A.M.
      • López-Villalobos N.
      • McParland S.
      • Lewis E.
      • Kennedy E.
      • O'Donovan M.
      • Burke J.L.
      • Berry D.P.
      Genetics of alternative definitions of feed efficiency in grazing lactating dairy cows.
      ;
      • Tempelman R.J.
      • Lu Y.
      Symposium review: Genetic relationships between different measures of feed efficiency and the implications for dairy cattle selection indexes.
      ).
      Instead of traits that directly improve feed efficiency, traits that indirectly enhance efficiency of milk production can be considered in breeding goals. Body size and live weight of cows are associated with the maintenance requirement of an animal, which is a major energy sink in dairy cattle (
      • Banos G.
      • Coffey M.P.
      Technical note. Prediction of liveweight from linear conformation traits in dairy cattle.
      ;
      • VandeHaar M.J.
      • Armentano L.E.
      • Weigel K.
      • Spurlock D.M.
      • Tempelman R.J.
      • Veerkamp R.
      Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency.
      ). For instance,
      • Vallimont J.E.
      • Dechow C.D.
      • Daubert J.M.
      • Dekleva M.W.
      • Blum J.W.
      • Barlieb C.M.
      • Liu W.
      • Varga G.A.
      • Heinrichs A.J.
      • Baumrucker C.R.
      Short communication: Heritability of gross feed efficiency and associations with yield, intake, residual intake, body weight, and body condition score in 11 commercial Pennsylvania tie stalls.
      found that body weight and BCS are unfavorably correlated with efficiency traits. Consequently, they concluded that smaller and thinner cows are more efficient compared with larger and heavier animals. The body size of dairy cows has steadily increased in the past few decades, partly owing to selection for milk traits (
      • Pryce J.E.
      • Wales W.J.
      • de Haas Y.
      • Veerkamp R.F.
      • Hayes B.J.
      Genomic selection for feed efficiency in dairy cattle.
      ;
      • Beavers L.
      • Van Doormaal B.
      A closer look at stature.
      ;
      • VandeHaar M.J.
      • Armentano L.E.
      • Weigel K.
      • Spurlock D.M.
      • Tempelman R.J.
      • Veerkamp R.
      Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency.
      ). To constrain further increases in body size and thus restrict maintenance feed costs of animals, increasingly more countries include live weight or body size measurements with a negative economic value in their national selection indices (
      • Cole J.B.
      • VanRaden P.M.
      Symposium review: Possibilities in an age of genomics: The future of selection indices.
      ). Given rising feed prices and other production costs, considering the body size of a cow in breeding goals may become even more important in the future.
      Currently, beside efficiency of milk production, animal health and the prevention of production diseases are major concerns in dairy cattle breeding. Particularly in the first weeks after calving, dairy cows are at high risk for metabolic disorders such as ketosis (KET), milk fever (MF), or displaced abomasum (DA). The occurrence of metabolic diseases is tightly linked to the negative energy balance of cows due to an insufficient supply of energy requirements in early lactation (
      • Collard B.L.
      • Boettcher P.J.
      • Dekkers J.C.M.
      • Petitclerc D.
      • Schaeffer L.R.
      Relationships between energy balance and health traits of dairy cattle in early lactation.
      ). Previous studies provided evidence that selection for more efficient animals adversely affects their energy balance (
      • Spurlock D.M.
      • Dekkers J.C.M.
      • Fernando R.
      • Koltes D.A.
      • Wolc A.
      Genetic parameters for energy balance, feed efficiency, and related traits in Holstein cattle.
      ;
      • Hurley A.M.
      • Lopez-Villalobos N.
      • McParland S.
      • Lewis E.
      • Kennedy E.
      • O'Donovan M.
      • Burke J.L.
      • Berry D.P.
      Characteristics of feed efficiency within and across lactation in dairy cows and the effect of genetic selection.
      ;
      • Seymour D.J.
      • Cánovas A.
      • Chud T.C.S.
      • Cant J.P.
      • Osborne V.R.
      • Baes C.F.
      • Schenkel F.S.
      • Miglior F.
      The dynamic behavior of feed efficiency in primiparous dairy cattle.
      ). Therefore, it is essential to ensure that selection for efficiency does not exacerbate nutritional deficits in early lactation and further promote the occurrence of metabolic diseases.
      Knowledge about the genetic basis of a trait and its genetic correlation with other traits is necessary to adequately incorporate them into breeding objectives. This knowledge allows the prediction of correlated selection response and the avoidance of possible undesirable side effects such as compromised health. Despite the high relevance of this topic, studies investigating the genetic relationships between production, body size measurements of cows, and the occurrence of metabolic diseases in dairy cattle are still lacking. To bridge this gap, the aim of this study was to genetically characterize various traits related to production, body size, and metabolic health in German Holstein cattle, using large-scale representative data sets. Specifically, variance components of traits and genetic correlations between traits were estimated. Additionally, GWAS were carried out to gain a better understanding of the genetic architecture of traits.

      MATERIALS AND METHODS

      No animals were used in this study, and ethical approval for the use of animals was not necessary.

      Animals and Phenotypes

      Three data sets were analyzed. Data set (DS) 1 included phenotypes of 130,166 primiparous cows for the 6 conformation traits BCS, body depth (BD), chest width (CW), dairy character (DCH), stature (STAT), and udder depth (UD), which were recorded during routine conformation classification in Germany according to the International Committee for Animal Recording (ICAR) guidelines (
      • ICAR
      ICAR Guidelines for Conformation Recording of Dairy Cattle, Beef Cattle, Dual Purpose Cattle and Dairy Goats. Section 5—Conformation Recording.
      ). Definitions of conformation traits are given in Supplemental Table S1 (http://doi.org/10.5281/zenodo.6874388). Animals were born between January 2013 and December 2018 and originated from 534 farms, with 244 observations per farm on average.
      Among the 130,166 cows in DS1, 101,562 animals had phenotypes for the traits milk yield (MKG), fat yield (FKG), and protein yield (PKG) in first lactation (DS2). Production traits were recorded over a lactation length of 280 to 305 DIM. Additionally, milk energy yield (MEY) was calculated according to
      • Nostitz B.
      • Mielke H.
      Vergleich verschiedener Methoden der Bestimmung des Milchenergiegehaltes beim Schwarzbunten Milchrind.
      using the following formula:
      MEY (MJ) = 0.802 × milk yield + 38.4 × fat yield + 23.6 × protein yield.


      The number of animals, number of observations, and descriptive statistics of all traits in DS1 and DS2 are shown in Table 1.
      Table 1Abbreviations, number of animals (N), and descriptive statistics for studied production and conformation traits in primiparous cows
      TraitAbbreviationNMeanSDMinimumMaximum
      Milk yield, kgMKG101,5629,358.71,593.93,098.017,144.0
      Fat yield, kgFKG101,562362.456.2110.0625.0
      Protein yield, kgPKG101,562318.750.6109.0547.0
      Milk energy yield, MJMEY101,56228,958.84,335.89,549.847,525.4
      Body condition scoreBCS133,1665.21.219
      Body depth scoreBD133,1666.21.219
      Chest width scoreCW133,1665.51.319
      Dairy character scoreDCH133,1665.81.319
      Stature, cmSTAT133,166150.23.7120180
      Udder depth scoreUD133,1666.11.219
      For some of the farms, records on the metabolic disease traits KET, DA, and MF were available (DS3). Moreover, an additional trait (META) indicating whether an animal had any of the metabolic diseases during lactation (i.e., KET, DA, or MF) was analyzed. Disease traits were recorded by veterinarians and herd managers following the official recording guidelines for health traits (
      • Stock K.F.
      • Cole J.
      • Pryce J.
      • Gengler N.
      • Bradley A.J.
      • Andrews L.
      • Heringstad B.
      • Egger-Danner C.
      Standardization of health data—ICAR guidelines including health key.
      ). Metabolic diseases were binary coded as 0 (animal had no disease during lactation) or 1 (animal had at least one disease during lactation). Only clinical disease cases were considered, and information on repeated cases of the same disorder during lactation were not available. Data set 3 consisted of 21,992 cows from 33 farms with disease records in first lactation. Phenotypic records for metabolic diseases of 16,641 and 7,096 animals in second and third lactation were available, respectively. Incidences of diseases in first lactation were low, particularly for MF (Table 2). Thus, MF was evaluated only in second and third lactation.
      Table 2Incidence of diseases
      DA = displaced abomasum; KET = ketosis; MF = milk fever; META = all metabolic diseases.
      defined as the proportion of animals with at least one case of disease during lactation and estimated genomic heritabilities on the observed scale (hobs2) and heritabilities transformed to the underlying scale (hund2) using the Dempster-Lerner equation (
      • Dempster E.R.
      • Lerner I.M.
      Heritability of threshold characters.
      ) in first, second, and third lactation
      Values in parentheses are standard errors.
      LactationKETMFDAMETA
      FirstIncidence, %1.150.080.781.82
      hobs20.011 (0.003)0.029 (0.005)0.019 (0.004)
      hund20.138 (0.038)0.488 (0.084)0.170 (0.036)
      SecondIncidence, %3.390.921.664.62
      hobs20.027 (0.005)0.008 (0.004)0.030 (0.006)0.031 (0.006)
      hund20.156 (0.029)0.119 (0.059)0.287 (0.057)0.146 (0.028)
      ThirdIncidence, %6.484.043.159.45
      hobs20.052 (0.010)0.047 (0.009)0.037 (0.009)0.049 (0.010)
      hund20.197 (0.038)0.241 (0.046)0.225 (0.055)0.148 (0.030)
      1 DA = displaced abomasum; KET = ketosis; MF = milk fever; META = all metabolic diseases.
      2 Values in parentheses are standard errors.

      Precorrection of Phenotypic Data in DS1 and DS2

      Before genetic analyses, raw phenotypes of DS1 and DS2 were adjusted for systematic effects. The precorrection was done separately for the 2 data sets using the following linear models:
      DS1 (conformation):
      yijkl=μ+AFCi+AGE_CLj+DIM_CLk+HYSl+eijkl,


      where yijkl is the phenotypic observation of each trait (BCS, BD, CW, DCH, STAT, UD), µ represents the overall mean, AFCi is the fixed effect of age at first calving (5 classes), AGE_CLj is the fixed effects of age at classification (8 classes), DIM_CLk is the fixed effect of the days in milk at classification (5 classes), HYSl represents the fixed effect of the herd-year-season (5,770 levels), and eijkl is the random residual effect.
      DS2 (production):
      yijk=μ+AFCi+DIMj+HYSk+eijk,


      where yijk is the phenotypic observation of each trait (MKG, FKG, PKG, MEY), µ represents the overall mean, AFCi is the fixed effect of the age at first calving, DIMj represents the fixed effect of days in milk, HYSk is the fixed effect of the herd-year-season (4,521 levels), and eijk is the random residual effect.
      The statistical precorrection was performed using the lm()-function of statistical software R (
      • R Core Team
      R: A Language and Environment for Statistical Computing.
      ). For subsequent genetic analyses, residuals of the linear models were used. Owing to the binary coding of disease traits in DS3, correction for fixed effects was done during genetic analyses.

      Genotypes and Quality Control

      From routine genetic evaluation, genotypes (45,613 SNPs) were available for all 130,166 animals with phenotypic observations. For further analysis, only genetic variants on autosomes were considered. Quality control of data was carried out using PLINK v1.09 (
      • Purcell S.
      • Neale B.
      • Todd-Brown K.
      • Thomas L.
      • Ferreira M.A.R.
      • Bender D.
      • Maller J.
      • Sklar P.
      • de Bakker P.I.W.
      • Daly M.J.
      • Sham P.C.
      PLINK: A tool set for whole-genome association and population-based linkage analyses.
      ). Single nucleotide polymorphisms with a minor allele frequency lower than 1% and SNPs with a deviation from Hardy-Weinberg equilibrium at a threshold of P < 1 × 10−5 were removed. After quality control, a final data set including 130,166 animals and 44,144 SNPs remained.

      Variance Components and Genetic Correlations

      Genetic and residual variances for traits in DS1 and DS2 were estimated with genome-based restricted maximum likelihood implemented in the software GCTA (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      ), using the following model:
      y=1nμ+Zg+e,


      where y is a vector of preadjusted phenotypic records for production or conformation traits, 1n is a vector of ones, μ is the overall mean, Z is a design matrix, and g is the vector of additive genetic effects distributed g ∼ N(0,G σ2g) where G represents the genomic relationship matrix constructed from all SNPs (n = 44,144) following
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      , and σ2g is the additive genetic variance. Further, e is a vector including the random residual terms distributed as e ∼ N (0, Iσ2e) where I is an identity matrix and σ2e is the residual variance. To estimate heritabilities of binary disease traits in DS3, the linear model was extended by a vector and the respective design matrix containing the fixed effects age at first calving (5 classes) and herd-year-season (427, 357, and 235 levels for first, second, and third parity, respectively). Additionally, estimated heritabilities of binary disease traits on the observed scale were transformed to the underlying scale using the equation from
      • Dempster E.R.
      • Lerner I.M.
      Heritability of threshold characters.
      .
      For estimation of genetic correlations between production and conformation traits, bivariate models for pairwise combinations of all preadjusted phenotypes were used in the same manner as described above. Similarly, genetic correlations between metabolic diseases and other traits were estimated. Fixed effects were included in the model depending on the combination of traits studied.

      Genome-Wide Association Studies

      Single-trait GWAS for each phenotype were performed using GCTA and the following single SNP regression mixed linear model:
      y=1nμ+Zg+Wv+e,


      where y is the vector of preadjusted phenotypes (production or conformation traits); 1n is a vector of ones; μ is the overall mean; g is the vector of polygenic effects with g ∼ N (0, Gσ2g where G represents the genomic relationship matrix and σ2g is the polygenic additive genetic variance; v is the vector of SNP effects; and e is the vector of random residuals. Z and W are incidence matrices for g and v, respectively. For binary disease traits, the model was extended by the fixed effects age at first calving-class and herd-year-season.
      Genetic variants were considered to be significantly associated with the trait of interest at a Bonferroni-corrected threshold of P < 1.13 × 10−6 [(0.05/44,144), −log10(P) ≈ 5.95]. For GWAS of disease traits, a further less conservative threshold for suggestive associations was set to P < 2.27 × 10−5 [(1/44,144), −log10(P) ≈ 4.64]. To assess model quality, quantile-quantile plots were visually inspected to compare the observed and expected distributions of −log10(P) under the assumption of no association. Additionally, genomic inflation factors λ (
      • Devlin B.
      • Roeder K.
      Genomic control for association studies.
      ) were computed.

      RESULTS

      Variance Components and Correlations

      Variance components and genomic heritabilities of production and conformation traits are shown in Table 3. The heritability of production traits in first lactation was 0.333 (±0.012) for MKG, 0.279 (±0.012) for FKG, and 0.240 (±0.011) for PKG and MEY. The heritability of conformation traits was lowest for CW (0.149 ± 0.010) and highest for STAT (0.368 ± 0.020).
      Table 3Phenotypic variance hund2 additive genetic variance (σa2), residual variance (σe2), and SNP-based heritability (h2 SNP) of production
      FKG = fat yield; MEY = milk energy yield; MKG = milk yield; PKG = protein yield.
      and conformation traits
      BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      in first lactation
      Values in parentheses are standard errors.
      Traitσp2σa2σe2h2 SNP
      MKG2,386,561.7 (21,765.89)795,068.8 (21,530.66)1,591,492.8 (11,292.81)0.333 (0.01)
      FKG2,909.4 (25.58)811.3 (24.83)2,097.5 (14.78)0.279 (0.01)
      PKG2,290.7 (19.55)549.5 (18.62)1,741.2 (12.24)0.240 (0.01)
      MEY17,018,193.2 (143,684.73)4,075,059.4 (136,759.35)12,943,133.8 (90,718.48)0.240 (0.01)
      BCS1.52 (0.01)0.40 (0.01)1.12 (0.01)0.263 (0.01)
      BD1.37 (0.01)0.27 (0.01)1.10 (0.01)0.197 (0.01)
      CW1.54 (0.01)0.23 (0.01)1.31 (0.01)0.149 (0.01)
      DCH1.67 (0.01)0.42 (0.01)1.26 (0.01)0.242 (0.01)
      STAT13.13 (0.13)4.83 (0.13)8.30 (0.06)0.368 (0.02)
      UD1.33 (0.01)0.39 (0.01)0.94 (0.01)0.293 (0.01)
      1 FKG = fat yield; MEY = milk energy yield; MKG = milk yield; PKG = protein yield.
      2 BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      3 Values in parentheses are standard errors.
      Genetic correlations between production traits and conformation traits are presented in Table 4. Regarding production, all traits were highly correlated with each other, showing correlations between 0.538 (±0.020, MKG and FKG) and 0.906 (±0.004, PKG and MEY). Among conformation traits, the highest positive genetic correlation was found for BCS and CW (0.704 ± 0.023). The strongest negative correlation was between BCS and DCH (−0.855 ± 0.008). Stature showed low to medium correlations with other conformation traits, and the strongest correlation was with UD (0.338 ± 0.019).
      Table 4Genetic correlations between production
      FKG = fat yield; MEY = milk energy yield; MKG = milk yield; PKG = protein yield.
      and conformation traits
      BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      in first lactation
      Standard errors of estimates ranged between 0.011 and 0.030.
      TraitFKGPKGMEYBCSBDCWDCHSTATUD
      MKG0.5380.8780.833−0.2760.138−0.0120.3340.106−0.357
      FKG0.6040.859−0.2900.224−0.0400.3450.084−0.348
      PKG0.906−0.2780.2040.0440.3380.158−0.380
      MEY−0.3430.228−0.0120.4220.130−0.419
      BCS0.2090.704−0.855−0.0720.166
      BD0.6200.4220.130−0.206
      CW−0.4980.2790.051
      DCH0.230−0.125
      STAT0.338
      1 FKG = fat yield; MEY = milk energy yield; MKG = milk yield; PKG = protein yield.
      2 BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      3 Standard errors of estimates ranged between 0.011 and 0.030.
      Genetic correlations between production traits and BCS were all negative, ranging from −0.276 (±0.033, MKG and BCS) to −0.343 (±0.021, MEY and BCS). Similarly, negative genetic correlations were found for production and UD, with correlations varying between −0.348 (±0.024, FKG and UD) and −0.419 (±0.019, MEY and UD), meaning that cows with deep udders showed higher production. By contrast, positive correlations were observed for production and the conformation traits BD, DCH, and STAT. The strongest positive correlations were identified between production traits and DCH, ranging from 0.334 (±0.019, MKG and DCH) to 0.422 (±0.020, MEY and DCH), implying that sharp animals tend to have higher production. Genetic correlations for production and BD varied from 0.138 (±0.022, MKG and BD) to 0.228 (±0.023, MEY and BD). Stature had somewhat lower genetic correlations with production traits, with values ranging from 0.084 (±0.020, FKG and STAT) to 0.158 (±0.021, PKG and STAT).
      In primiparous cows, heritabilities of metabolic diseases on the observed scale were 0.011 (±0.003) for KET, 0.029 (±0.005) for DA, and 0.017 (±0.004) for META (Table 2). In second lactation, heritability estimates varied between 0.008 (±0.004, MF) and 0.031 (±0.006, META), whereas in third lactation, the estimate ranged from 0.037 (±0.009, DA) to 0.052 (±0.010, KET). After transformation to the underlying scale, approximate heritabilities for metabolic diseases were between 0.119 (±0.059, MF in second lactation) and 0.488 (±0.084, DA in first lactation).
      Genetic correlations of disease traits KET, DA, and META in first lactation with production and conformation traits are presented in Figure 1 and Supplemental Table S2 (http://doi.org/10.5281/zenodo.6874388). Owing to the low prevalence of MF, correlations between MF and other traits could not be estimated. Other disease traits were positively correlated with production traits, showing correlations from 0.111 (±0.061, MEY and DA) to 0.224 (±0.057, PKG and DA). Moderate negative genetic correlations were found between BCS and KET (−0.470 ± 0.102), DA (−0.255 ± 0.061), and META (−0.417 ± 0.072), as well as between CW and KET (−0.219 ± 0.103), DA (−0.080 ± 0.072), and META (−0.186 ± 0.081). In contrast, DCH was positively correlated with KET (0.469 ± 0.091), DA (0.269 ± 0.062), and META (0.459 ± 0.072). Genetic correlations between STAT and metabolic diseases were 0.234 (±0.090), 0.172 (±0.061), and 0.242 (±0.073) for KET, DA, and META, respectively. Body depth and UD were weakly correlated with disease traits in first lactation, and estimates were characterized by large standard errors.
      Figure thumbnail gr1
      Figure 1Genetic correlations and respective standard errors of metabolic diseases displaced abomasum (DA), ketosis (KET), and all metabolic diseases (META) in first lactation with production (MKG = milk yield; FKG = fat yield; PKG = protein yield; MEY = milk energy yield) and conformation traits (BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth).
      Genetic correlations between body size measurements and metabolic disease traits in second lactation are given in Table 5. In general, correlations between traits were less pronounced than in first lactation. However, BD showed moderate correlations with DA (0.309 ± 0.071) and META (0.201 ± 0.072) in second lactation. Clear negative genetic correlations were found for UD and KET (−0.230 ± 0.067), MF (−0.232 ± 0.115), and META (−0.238 ± 0.066) in second lactation. Genetic correlation between STAT and DA (0.240 ± 0.064) was more unfavorable in second than in first lactation. For most trait combinations in third lactation, genetic correlations were low and estimates were attached to large standard errors due to the small sample size (Table 6).
      Table 5Genetic correlations between body size measurements
      BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      from first lactation and metabolic disease traits
      DA = displaced abomasum; KET = ketosis; META = all metabolic diseases; MF = milk fever.
      in second lactation
      Standard errors in parentheses.
      ItemKETDAMFMETA
      BCS−0.358 (0.070)−0.142 (0.068)−0.167 (0.117)−0.319 (0.068)
      BD0.077 (0.073)0.309 (0.071)0.068 (0.121)0.201 (0.072)
      CW−0.188 (0.079)−0.077 (0.078)−0.042 (0.129)−0.109 (0.078)
      DCH0.273 (0.070)0.212 (0.067)0.178 (0.119)0.346 (0.069)
      STAT0.167 (0.067)0.240 (0.064)−0.141 (0.112)0.165 (0.064)
      UD−0.230 (0.067)−0.086 (0.067)−0.232 (0.115)−0.238 (0.066)
      1 BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      2 DA = displaced abomasum; KET = ketosis; META = all metabolic diseases; MF = milk fever.
      3 Standard errors in parentheses.
      Table 6Genetic correlations between body size measurements
      BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      from first lactation and metabolic disease traits
      DA = displaced abomasum; KET = ketosis; META = all metabolic diseases; MF = milk fever.
      in third lactation
      Standard errors in parentheses.
      ItemKETDAMFMETA
      BCS−0.189 (0.073)−0.173 (0.088)−0.164 (0.079)−0.221 (0.079)
      BD0.024 (0.077)0.056 (0.093)0.046 (0.079)0.038 (0.082)
      CW−0.113 (0.081)−0.014 (0.098)−0.083 (0.087)−0.159 (0.087)
      DCH0.173 (0.073)0.203 (0.089)0.174 (0.078)0.230 (0.079)
      STAT0.097 (0.069)0.103 (0.083)−0.004 (0.009)0.014 (0.071)
      UD−0.156 (0.071)−0.145 (0.088)−0.089 (0.076)−0.167 (0.078)
      1 BD = body depth; CW = chest width; DCH = dairy character; STAT = stature; UD = udder depth.
      2 DA = displaced abomasum; KET = ketosis; META = all metabolic diseases; MF = milk fever.
      3 Standard errors in parentheses.

      Genome-Wide Association Studies

      Quantile-quantile plots and genomic inflation factors for all traits are shown in Supplemental Figure S1 and S2 (http://doi.org/10.5281/zenodo.6874388). Genomic inflation factors were slightly deflated for production (λ = 0.93–0.98) and conformation traits (λ = 0.94–0.98) but were generally in an acceptable range. For metabolic disease traits in all lactation numbers, genomic inflation factors were close to 1.

      Production Traits

      Figure 2 shows Manhattan plots for production traits MKG, FKG, PKG, and MEY, and Supplemental Table S3 (http://doi.org/10.5281/zenodo.6874388) shows identified SNPs. In GWAS, clear peaks for production traits were found on BTA 3, 5, 6, 11, 14, 19, 20, 27, and 29. On BTA 14, a pronounced peak from 0.49 to 1.85 Mb, including several signals for MKG, FKG, and PKG, was observed with the most significant SNP ARS-BFGL-NGS-4939. Additionally, a strong hit was found on BTA 6 from 85.49 to 87.27 Mb comprising SNPs associated with all production traits. Moreover, several signals were identified on BTA 5 (91.20–94.35 Mb) for FKG and on BTA 20 (29.36–31.91 Mb) for MKG.
      Figure thumbnail gr2
      Figure 2Manhattan plots showing −log10(P) of GWAS for production traits (lactation yield) milk kilograms (MKG), fat kilograms (FKG), protein kilograms (PKG), and milk energy yield (MEY) in first lactation. The red line indicates the genome-wide threshold at −log10(P) = 5.95.

      Body Size Traits

      For all conformation traits studied, several SNPs reached the genome-wide significance threshold (Figure 3, Supplemental Table S4, http://doi.org/10.5281/zenodo.6874388). For traits BCS, CW, DCH, and UD, the analysis uncovered marked peaks on BTA 6 from 85.90 to 88.33 Mb. On BTA 5 (88.36 Mb), a shared signal was found between BCS, DCH, and UD. For STAT, hits on various chromosomes (BTA 2, 5, 7, 8, 11, and 19) were observed, with the strongest peaks on BTA 5 (105.35–105.78 Mb) and BTA 11 (78.08–79.41 Mb). Similarly, for UD, various genome-wide significant SNPs were located on BTA 4, 5, 6, 8, 11, and 19.
      Figure thumbnail gr3
      Figure 3Manhattan plots showing −log10(P) of GWAS for conformation traits BCS, body depth (BD), chest width (CW), dairy character (DCH), stature (STAT), and udder depth (UD). The red line indicates the genome-wide threshold at −log10(P) = 5.95.
      Interestingly, we detected shared signals for production and conformation traits on BTA 5 (88.36 Mb) and BTA 6 (86.40–87.27 Mb).

      Metabolic Diseases

      Manhattan plots for metabolic disease traits in first and third lactation are given in Figure 4, Figure 5, and significant SNPs are presented in Supplemental Tables S5 and S6 (http://doi.org/10.5281/zenodo.6874388).
      Figure thumbnail gr4
      Figure 4Manhattan plots showing −log10(P) of GWAS for metabolic disease traits displaced abomasum (DA), ketosis (KET), and all metabolic diseases (META) in first lactation. The red line indicates the genome-wide threshold at −log10(P) = 5.95, and the blue line indicates the suggestive threshold at −log10(P) = 4.64.
      Figure thumbnail gr5
      Figure 5Manhattan plots showing −log10(P) of GWAS for metabolic disease traits displaced abomasum (DA), ketosis (KET), milk fever (MF), and all metabolic diseases (META) in third lactation. The red line indicates the genome-wide threshold at −log10(P) = 5.95, and the blue line indicates the suggestive threshold at −log10(P) = 4.64.
      For disease traits in first lactation, only one SNP for DA (BTA10, 18.25 Mb) reached the Bonferroni-corrected threshold. Two more SNPs on BTA 3 (111.68 Mb) and BTA 26 (123.69 Mb) were significant for DA at the suggestive threshold. For META, signals at the suggestive level were found on BTA 14 (26.89 Mb) and BTA 17 (28.95 Mb). For KET, none of the SNPs reached significance in first lactation.
      Similarly, no associated loci were detected for metabolic disease traits in second lactation (Supplemental Figure S3, http://doi.org/10.5281/zenodo.6874388). In third lactation, however, SNPs on BTA 2, 6, 8, 17, and 23 were identified (Figure 5 and Supplemental Table S6, http://doi.org/10.5281/zenodo.6874388). For KET, 2 SNPs were located on BTA 2 (71.44 Mb, 84.97 Mb). Additionally, 3 variants were found for KET on BTA 8 in the genomic region from 4.42 Mb to 4.66 Mb. For META, 3 significantly associated genetic variants were observed on BTA 6 at 86.92 Mb. Additionally, for MF, SNPs on BTA 17 and 23 were above the suggestive threshold.

      DISCUSSION

      Heritabilities

      Heritabilities for production and most conformation traits were in agreement with findings from previous studies (
      • Van Dorp T.E.
      • Dekkers J.C.M.
      • Martin S.W.
      • Noordhuizen J.P.T.M.
      Genetic parameters of health disorders, and relationships with 305-day milk yield and conformation traits of registered Holstein cows.
      ;
      • Dechow C.D.
      • Rogers G.W.
      • Sander-Nielsen U.
      • Klei L.
      • Lawlor T.J.
      • Clay J.S.
      • Freeman A.E.
      • Abdel-Azim G.
      • Kuck A.
      • Schnell S.
      Correlations among body condition scores from various sources, dairy form, and cow health from the United States and Denmark.
      ;
      • Kadarmideen H.N.
      Genetic correlations among body condition score, somatic cell score, milk production, fertility and conformation traits in dairy cows.
      ;
      • Stoop W.M.
      • van Arendonk J.A.M.
      • Heck J.M.L.
      • van Valenberg H.J.F.
      • Bovenhuis H.
      Genetic parameters for major milk fatty acids and milk production traits of Dutch Holstein-Friesians.
      ;
      • Dadpasand M.
      • Zamiri M.J.
      • Atashi H.
      • Akhlaghi A.
      Genetic relationship of conformation traits with average somatic cell score at 150 and 305 days in milk in Holstein cows of Iran.
      ;
      • Mehtiö T.
      • Pitkänen T.
      • Leino A.-M.
      • Mäntysaari E.A.
      • Kempe R.
      • Negussie E.
      • Lidauer M.H.
      Genetic analyses of metabolic body weight, carcass weight and body conformation traits in Nordic dairy cattle.
      ). For STAT, however, several studies reported higher heritabilities in different Holstein populations compared with our results, with values ranging between 0.43 and 0.71 (
      • Pérez-Cabal M.A.
      • Alenda R.
      Genetic relationships between lifetime profit and type traits in Spanish Holstein cows.
      ;
      • de Haas Y.
      • Janss L.L.G.
      • Kadarmideen H.N.
      Genetic and phenotypic parameters for conformation and yield traits in three Swiss dairy cattle breeds.
      ;
      • Haile-Mariam M.
      • Nieuwhof G.J.
      • Beard K.T.
      • Konstatinov K.V.
      • Hayes B.J.
      Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations.
      ;
      • Bilal G.
      • Cue R.I.
      • Hayes J.F.
      Genetic and phenotypic associations of type traits and body condition score with dry matter intake, milk yield, and number of breedings in first lactation Canadian Holstein cows.
      ;
      • Manzanilla-Pech C.I.V.
      • Veerkamp R.F.
      • Tempelman R.J.
      • van Pelt M.L.
      • Weigel K.A.
      • VandeHaar M.
      • Lawlor T.J.
      • Spurlock D.M.
      • Armentano L.E.
      • Staples C.R.
      • Hanigan M.
      • de Haas Y.
      Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations—The Netherlands and United States.
      ).
      Among studies, heritabilities for metabolic disorders are often inconsistent. The main reasons for this inconsistency are heterogeneous data sets with corresponding differences in incidence rates of diseases used for the estimation of genetic parameters and varied statistical models applied (linear vs. threshold models) (
      • Emanuelson U.
      Recording of production diseases in cattle and possibilities for genetic improvements: A review.
      ;
      • Zerbin I.
      • Lehner S.
      • Distl O.
      Genetics of bovine abomasal displacement.
      ;
      • Pryce J.E.
      • Parker Gaddis K.L.
      • Koeck A.
      • Bastin C.
      • Abdelsayed M.
      • Gengler N.
      • Miglior F.
      • Heringstad B.
      • Egger-Danner C.
      • Stock K.F.
      • Bradley A.J.
      • Cole J.B.
      Invited review: Opportunities for genetic improvement of metabolic diseases.
      ). Moreover,
      • Gianola D.
      Theory and analysis of threshold characters.
      demonstrated that heritability estimates are influenced by disease frequency when linear models are used for binary traits, which exacerbates the difficulty in comparing results across studies.
      Similar to our study,
      • Koeck A.
      • Miglior F.
      • Jamrozik J.
      • Kelton D.F.
      • Schenkel F.S.
      Genetic associations of ketosis and displaced abomasum with milk production traits in early first lactation of Canadian Holsteins.
      applied linear models and found low heritabilities for DA (0.04 ± 0.005) and KET (0.02 ± 0.006) in primiparous cows. Using threshold methodology,
      • Parker Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      Genomic selection for producer-recorded health event data in US dairy cattle.
      reported higher estimates of 0.22 (±0.03) for DA and 0.09 (±0.02) for KET in first lactation. The transformed heritabilities on the underlying scale in our study were much higher for DA (0.488 ± 0.084) but similar for KET (0.138 ± 0.038). The high estimate for DA was likely because of the low incidence of disease (
      • Stock K.F.
      • Hamann H.
      • Distl O.
      Estimation of genetic parameters for the prevalence of osseous fragments in limb joints of Hanoverian Warmblood horses.
      ).
      For MF, our results revealed heritabilities on the observed scale of 0.008 (±0.004) and 0.047 (±0.009) in second and third lactation, respectively.
      • Koeck A.
      • Jamrozik J.
      • Kistemaker G.J.
      • Schenkel F.S.
      • Moore R.K.
      • Lefebvre D.M.
      • Kelton D.F.
      • Miglior F.
      Development of genetic evaluation for metabolic disease traits for Canadian dairy cattle.
      found a low heritability of 0.011 (0.003) for MF in second to fifth parity estimated by linear models. The study of
      • Saborío-Montero A.
      • Vargas-Leitón B.
      • Romero-Zúñiga J.J.
      • Camacho-Sandoval J.
      Additive genetic and heterosis effects for milk fever in a population of Jersey, Holstein × Jersey, and Holstein cattle under grazing conditions.
      observed a heritability of 0.03 (±0.002) for MF, which was calculated with a linear animal model. By contrast,
      • Heringstad B.
      • Chang Y.M.
      • Gianola D.
      • Klemetsdal G.
      Genetic analysis of clinical mastitis, milk fever, ketosis, and retained placenta in three lactations of Norwegian red cows.
      estimated heritabilities of 0.09 (±0.02), 0.11 (±0.01), and 0.13 (±0.01) in first, second, and third lactation, respectively, for MF in Norwegian Red cattle, using a threshold model. These results corresponded with our approximated heritability on the underlying scale for MF in second lactation (0.119 ± 0.059). In third lactation, however, our study revealed a considerably higher estimate on the underlying scale (0.241 ± 0.046).
      Collectively, the results clearly showed that metabolic diseases are generally heritable, although only to a lower degree. In many countries, metabolic disease traits are already considered in national selection indices. However, reliabilities of genomic breeding values are yet rather low. Therefore, continuous data recording of metabolic disease traits is essential to estimate more accurate breeding values and thereby improve genetic progress in metabolic health of dairy cows.
      In the present study, only clinical cases of metabolic disorders were recorded and analyzed. Particularly for MF, however, undetected subclinical cases of disease are expected to be much more frequent than clinical cases with visible symptoms. According to
      • Reinhardt T.A.
      • Lippolis J.D.
      • McCluskey B.J.
      • Goff J.P.
      • Horst R.L.
      Prevalence of subclinical hypocalcemia in dairy herds.
      , subclinical hypocalcemia occurs in up to 50% of cows, depending on parity number. A similar situation occurs for ketosis, with subclinical cases appearing at high frequency and causing substantial economic losses in dairy cattle (
      • Mostert P.F.
      • Bokkers E.A.M.
      • van Middelaar C.E.
      • Hogeveen H.
      • de Boer I.J.M.
      Estimating the economic impact of subclinical ketosis in dairy cattle using a dynamic stochastic simulation model.
      ;
      • Steeneveld W.
      • Amuta P.
      • van Soest F.J.S.
      • Jorritsma R.
      • Hogeveen H.
      Estimating the combined costs of clinical and subclinical ketosis in dairy cows.
      ).

      Genetic Correlations

      Production and Body Size

      The results of our study revealed low to moderate positive genetic correlations of production with STAT and DCH.
      • Wasana N.
      • Cho G.
      • Park S.
      • Kim S.
      • Choi J.
      • Park B.
      • Park C.
      • Do C.
      Genetic relationship of productive life, production and type traits of Korean Holsteins at early lactations.
      observed similar genetic correlations for STAT with MKG (0.12) and PKG (0.14) in first-parity cows.
      • Ahlborn G.
      • Dempfle L.
      Genetic parameters for milk production and body size in New Zealand Holstein-Friesian and Jersey.
      reported higher genetic correlations between STAT and MKG (0.34 ± 0.105) and between FKG (0.25 ± 0.112) and PKG (0.32 ± 0.107) in primiparous Holstein Friesian cattle. From the literature, DCH is known to have a much stronger positive correlation with production yield than STAT (
      • Hansen M.
      • Lund M.S.
      • Sørensen M.K.
      • Christensen L.G.
      Genetic parameters of dairy character, protein yield, clinical mastitis, and other diseases in the Danish Holstein cattle.
      ;
      • Lassen J.
      • Hansen M.
      • Sørensen M.K.
      • Aamand G.P.
      • Christensen L.G.
      • Madsen P.
      Genetic relationship between body condition score, dairy character, mastitis, and diseases other than mastitis in first-parity Danish Holstein cows.
      ), which corresponds to our findings.
      The correlations for STAT and DCH with production imply that larger and sharper animals tend to have higher yield, which is well known. However, the body size of cows is not only related to milk production but also to feed intake.
      • Manzanilla-Pech C.I.V.
      • Veerkamp R.F.
      • Tempelman R.J.
      • van Pelt M.L.
      • Weigel K.A.
      • VandeHaar M.
      • Lawlor T.J.
      • Spurlock D.M.
      • Armentano L.E.
      • Staples C.R.
      • Hanigan M.
      • de Haas Y.
      Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations—The Netherlands and United States.
      reported considerable genetic correlations for DMI with STAT (0.57 ± 0.11), CW (0.61 ± 0.13), BD (0.49 ± 0.12), and BCS (0.46 ± 0.15) in US Holstein cows. In line with that,
      • Manafiazar G.
      • Goonewardene L.
      • Miglior F.
      • Crews Jr., D.H.
      • Basarab J.A.
      • Okine E.
      • Wang Z.
      Genetic and phenotypic correlations among feed efficiency, production and selected conformation traits in dairy cows.
      demonstrated significant genetic correlations for DMI and BD (0.44 ± 0.09), CW (0.68 ± 0.08), and STAT (0.45 ± 0.09), clearly indicating that larger cows consume more feed. The same study also revealed an antagonistic genetic relationship between STAT and gross energy efficiency (−0.32 ± 0.20). A similar tendency was found for BCS by
      • Vallimont J.E.
      • Dechow C.D.
      • Daubert J.M.
      • Dekleva M.W.
      • Blum J.W.
      • Barlieb C.M.
      • Liu W.
      • Varga G.A.
      • Heinrichs A.J.
      • Baumrucker C.R.
      Short communication: Heritability of gross feed efficiency and associations with yield, intake, residual intake, body weight, and body condition score in 11 commercial Pennsylvania tie stalls.
      . They investigated strong unfavorable genetic correlations between different efficiency parameters and BCS (−0.64 ± 0.17 to −0.70 ± 0.16) in dairy cattle. In accordance with that, cattle breeds of smaller body size such as Jersey have been demonstrated to generally exhibit better production efficiency than the larger Holstein Friesian breed (
      • Prendiville R.
      • Pierce K.M.
      • Buckley F.
      An evaluation of production efficiencies among lactating Holstein-Friesian, Jersey, and Jersey × Holstein-Friesian cows at pasture.
      ;
      • Lembeye F.
      • López-Villalobos N.
      • Burke J.L.
      • Davis S.R.
      • Richardson J.
      • Sneddon N.W.
      • Donaghy D.J.
      Comparative performance in Holstein-Friesian, Jersey and crossbred cows milked once daily under a pasture-based system in New Zealand.
      ). Even though production efficiency of dairy cattle has improved significantly because of higher production, benefits seem to be partly cancelled by higher feed intake. Consequently, results from our study and those in the literature suggest that larger cows generally have higher production levels, but not necessarily better efficiency.
      Beside the efficiency aspect, the environmental impact, in particular the carbon footprint of milk production, is becoming increasingly relevant.
      • Zetouni L.
      • Kargo M.
      • Norberg E.
      • Lassen J.
      Genetic correlations between methane production and fertility, health, and body type traits in Danish Holstein cows.
      analyzed genetic relationships of conformation traits and CH4 emissions and showed negative genetic correlations between CH4 and BCS (−0.28 ± 0.10) and CW (−0.20 ± 0.13), implying that larger animals produce less CH4. A different picture was revealed by
      • Manzanilla-Pech C.I.V.
      • Løvendahl P.
      • Mansan Gordo D.
      • Difford G.F.
      • Pryce J.E.
      • Schenkel F.
      • Wegmann S.
      • Miglior F.
      • Chud T.C.
      • Moate P.J.
      • Williams S.R.O.
      • Richardson C.M.
      • Stothard P.
      • Lassen J.
      Breeding for reduced methane emission and feed-efficient Holstein cows: An international response.
      , who observed a strong positive genetic correlation between CH4 and body weight (0.65 ± 0.07), suggesting that heavier animals have higher CH4 production. These findings are supported by the study from
      • López-Paredes J.
      • Goiri I.
      • Atxaerandio R.
      • García-Rodríguez A.
      • Ugarte E.
      • Jiménez-Montero J.A.
      • Alenda R.
      • González-Recio O.
      Mitigation of greenhouse gases in dairy cattle via genetic selection: 1. Genetic parameters of direct methane using noninvasive methods and proxies of methane.
      that demonstrated positive genetic correlations between CH4 and STAT (0.43), CW (0.26), and BCS (0.09). However,
      • Breider I.S.
      • Wall E.
      • Garnsworthy P.C.
      Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows.
      found no significant genetic correlations for CH4 and body weight in dairy cattle. Based on these inconsistent results, it becomes apparent that further research in this area is needed to verify genetic relationships between CH4 and conformation traits.

      Production, Body Size, and Metabolic Diseases

      All genetic correlations between production traits and metabolic diseases in first lactation were positive and low to moderate, indicating that higher producing cows are more susceptible to metabolic disorders. Although the genetic associations between production and metabolic health are of high importance, current research investigating them is still limited.
      • Parker Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      Genomic selection for producer-recorded health event data in US dairy cattle.
      reported genetic correlations close to 0 (0.02 ± 0.017) between milk yield and both DA and KET in first parity cows. Similarly,
      • Koeck A.
      • Miglior F.
      • Jamrozik J.
      • Kelton D.F.
      • Schenkel F.S.
      Genetic associations of ketosis and displaced abomasum with milk production traits in early first lactation of Canadian Holsteins.
      demonstrated that milk yield in early lactation was genetically uncorrelated with DA and KET. In line with our results,
      • Harder B.
      • Bennewitz J.
      • Hinrichs D.
      • Kalm E.
      Genetic parameters for health traits and their relationship to different persistency traits in German Holstein dairy cattle.
      computed genetic correlations between metabolic diseases and persistency of production traits in German Holstein cattle, with values ranging from 0.10 to 0.19, which points out antagonistic relationships between health and production.
      Metabolic diseases typically occur in the first weeks after calving. During this period, the cow faces physiological challenges, such as a pronounced energy deficit due to the imbalance of energy demand for production and energy supply from feed intake. According to
      • Sundrum A.
      Metabolic disorders in the transition period indicate that the dairy cows' ability to adapt is overstressed.
      , the duration and intensity of this imbalance is influenced by the level of milk production. High-yielding cows have greater difficulties with the required metabolic adaptations during early lactation, making them more susceptible to metabolic diseases (
      • van Knegsel A.T.M.
      • Hammon H.M.
      • Bernabucci U.
      • Bertoni G.
      • Bruckmaier R.M.
      • Goselink R.M.A.
      • Gross J.J.
      • Kuhla B.
      • Metges C.C.
      • Parmentier H.K.
      • Trevisi E.
      • Tröscher A.
      • van Vuuren A.M.
      Metabolic adaptation during early lactation: Key to cow health, longevity and a sustainable dairy production chain.
      ;
      • Gross J.J.
      • Bruckmaier R.M.
      Review: Metabolic challenges in lactating dairy cows and their assessment via established and novel indicators in milk.
      ). Collectively, the unfavorable genetic associations between production and metabolic diseases revealed in our study suggest that continued intensive selection for milk yield is likely to exacerbate this situation, leading to negative consequences for the metabolic health of dairy cows.
      In the current study, BCS was negatively correlated with KET, DA and META indicating that animals with greater BCS show lower diseases susceptibility. By contrast, DCH showed positive genetic correlations with disease traits, meaning that sharper animals are at higher risk for disease occurrence probably partly caused by higher production levels. These results are in good agreement with findings from previous research.
      • Dechow C.D.
      • Rogers G.W.
      • Sander-Nielsen U.
      • Klei L.
      • Lawlor T.J.
      • Clay J.S.
      • Freeman A.E.
      • Abdel-Azim G.
      • Kuck A.
      • Schnell S.
      Correlations among body condition scores from various sources, dairy form, and cow health from the United States and Denmark.
      reported genetic correlations of −0.48 (0.15) for BCS and DA and 0.65 (0.16) for dairy form and DA. Likewise, in Canadian Holstein cattle, an average genetic correlation between BCS and metabolic diseases of −0.438 (±0.125) was found (
      • Loker S.
      • Miglior F.
      • Koeck A.
      • Neuenschwander T. F.-O.
      • Bastin C.
      • Jamrozik J.
      • Schaeffer L.R.
      • Kelton D.
      Relationship between body condition score and health traits in first-lactation Canadian Holsteins.
      ).
      • Lassen J.
      • Hansen M.
      • Sørensen M.K.
      • Aamand G.P.
      • Christensen L.G.
      • Madsen P.
      Genetic relationship between body condition score, dairy character, mastitis, and diseases other than mastitis in first-parity Danish Holstein cows.
      obtained genetic relationships of −0.22 ± 0.10 and 0.43 ± 0.09 between digestive diseases (including KET, DA and MF) and BCS and DCH, respectively. According to the authors, the rather weak genetic correlation between BCS and diseases was due to the low disease incidence in first-parity cows.
      The association between specific conformation traits and metabolic health of animals is not surprising because BCS and DCH are proxies for the energetic status of animals (
      • Veerkamp R.F.
      • Brotherstone S.
      Genetic correlations between linear type traits, food intake, live weight and condition score in Holstein Friesian dairy cattle.
      ). Thin cows with limited amounts of energy reserves (i.e., animals with low BCS or high scores for DCH) cannot heavily rely on body tissue mobilization to supply the energy demands in early lactation. Consequently, those animals may go through a prolonged and more extreme negative energy balance, which leads to compromised metabolic health (
      • Collard B.L.
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      Relationships between energy balance and health traits of dairy cattle in early lactation.
      ;
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      ).
      With exception of MF, STAT was positively correlated with metabolic diseases, with the magnitude of genetic correlations decreasing from first to third lactation. These results demonstrate that cows of larger body size tend to have lower metabolic health, possibly driven by higher production performance.
      • Becker J.C.
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      Costs for health care of Holstein cows selected for large versus small body size.
      compared health care costs of Holstein cows selected for either small or large body size. They found significantly greater health costs in first and second lactation for larger animals owing to the higher incidence rates of diseases. Aside from metabolic diseases,
      • Schöpke K.
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      Relationships between bovine hoof disorders, body condition traits, and test-day yields.
      showed that animals with an intermediate body size and body weight had the least problems with claw and leg diseases. Overall, cattle with smaller body dimensions seem to show lower occurrence of diseases and thus better health stability.

      Genome-Wide Associations

      We performed GWAS using a large-scale and representative data set for German Holstein cattle, and the results revealed many significant signals in different genomic regions. However, the SNP array that was used had a limited marker density and did not allow for a reliable candidate gene analysis. Therefore, detected signals are described here, and potential candidate genes that have previously been reported in literature are discussed.
      For production traits, GWAS revealed several significant variants. As expected, a marked hit was observed on BTA 14 in the genomic region from 0.49 to 1.85 Mb, which is known for a high gene content and its association with milk production phenotypes in dairy cattle (
      • Clancey E.
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      Genome-wide association analysis and gene set enrichment analysis with SNP data identify genes associated with 305-day milk yield in Holstein dairy cows.
      ). Beside DGAT1 with its major effect on milk yield (
      • Grisart B.
      • Farnir F.
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      • Kim J.-J.
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      • Coppieters W.
      • Georges M.
      Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition.
      ;
      • Winter A.
      • Krämer W.
      • Werner F.A.O.
      • Kollers S.
      • Kata S.
      • Durstewitz G.
      • Buitkamp J.
      • Womack J.E.
      • Thaller G.
      • Fries R.
      Association of a lysine-232/alanine polymorphism in a bovine gene encoding acyl-CoA:diacylglycerol acyltransferase (DGAT1) with variation at a quantitative trait locus for milk fat content.
      ), previous studies identified VPS28 as potential candidate loci in this genomic region for milk, fat, and protein yield (
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • van Tassell C.P.
      • Yang J.
      • Wang S.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows.
      ;
      • Liu L.
      • Zhang Q.
      Identification and functional analysis of candidate gene VPS28 for milk fat in bovine mammary epithelial cells.
      ). Likewise,
      • Pedrosa V.B.
      • Schenkel F.S.
      • Chen S.-Y.
      • Oliveira H.R.
      • Casey T.M.
      • Melka M.G.
      • Brito L.F.
      Genomewide association analyses of lactation persistency and milk production traits in Holstein cattle based on imputed whole-genome sequence data.
      found VPS28 as well as PLEC and MAF1 in the same genomic area strongly affected milk traits in dairy cattle.
      Our study discovered several significant signals for conformation traits harboring many prominent genes that have already been reported in the literature.
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • van Tassell C.P.
      • Yang J.
      • Wang S.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows.
      identified OSR1 on BTA 11 as a candidate gene for STAT, which coincides with our findings. Significant variants for STAT were found between 105.35 and 105.78 Mb on BTA 5 in our study. A recent study from
      • Doyle J.L.
      • Berry D.P.
      • Veerkamp R.F.
      • Carthy T.R.
      • Walsh S.W.
      • Evans R.D.
      • Purfield D.C.
      Genomic regions associated with skeletal type traits in beef and dairy cattle are common to regions associated with carcass traits, feed intake and calving difficulty.
      detected the gene NDUFA9, which has an effect on STAT in Holstein Friesian cattle, in that genomic region. In close proximity, CCND2 has been confirmed as a candidate gene for STAT (
      • Abo-Ismail M.K.
      • Brito L.F.
      • Miller S.P.
      • Sargolzaei M.
      • Grossi D.A.
      • Moore S.S.
      • Plastow G.
      • Stothard P.
      • Nayeri S.
      • Schenkel F.S.
      Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle.
      ;
      • Bouwman A.C.
      • Daetwyler H.D.
      • Chamberlain A.J.
      • Ponce C.H.
      • Sargolzaei M.
      • Schenkel F.S.
      • Sahana G.
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      • Boitard S.
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      • Pausch H.
      • Brøndum R.F.
      • Bowman P.J.
      • Thomsen B.
      • Guldbrandtsen B.
      • Lund M.S.
      • Servin B.
      • Garrick D.J.
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      • Bagnato A.
      • Wang M.
      • Hoff J.L.
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      • Taylor J.F.
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      • Panitz F.
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      • Gredler B.
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      • Boussaha M.
      • Sanchez M.-P.
      • Rocha D.
      • Capitan A.
      • Tribout T.
      • Barbat A.
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      • Jagannathan V.
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      • Fries R.
      • Stothard P.
      • Veerkamp R.F.
      • Boichard D.
      • Goddard M.E.
      • Hayes B.J.
      Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals.
      ;
      • Jiang J.
      • Cole J.B.
      • Freebern E.
      • Da Y.
      • VanRaden P.M.
      • Ma L.
      Functional annotation and Bayesian fine-mapping reveals candidate genes for important agronomic traits in Holstein bulls.
      ).
      • Bouwman A.C.
      • Daetwyler H.D.
      • Chamberlain A.J.
      • Ponce C.H.
      • Sargolzaei M.
      • Schenkel F.S.
      • Sahana G.
      • Govignon-Gion A.
      • Boitard S.
      • Dolezal M.
      • Pausch H.
      • Brøndum R.F.
      • Bowman P.J.
      • Thomsen B.
      • Guldbrandtsen B.
      • Lund M.S.
      • Servin B.
      • Garrick D.J.
      • Reecy J.
      • Vilkki J.
      • Bagnato A.
      • Wang M.
      • Hoff J.L.
      • Schnabel R.D.
      • Taylor J.F.
      • Vinkhuyzen A.A.E.
      • Panitz F.
      • Bendixen C.
      • Holm L.-E.
      • Gredler B.
      • Hozé C.
      • Boussaha M.
      • Sanchez M.-P.
      • Rocha D.
      • Capitan A.
      • Tribout T.
      • Barbat A.
      • Croiseau P.
      • Drögemüller C.
      • Jagannathan V.
      • Vander Jagt C.
      • Crowley J.J.
      • Bieber A.
      • Purfield D.C.
      • Berry D.P.
      • Emmerling R.
      • Götz K.-U.
      • Frischknecht M.
      • Russ I.
      • Sölkner J.
      • van Tassell C.P.
      • Fries R.
      • Stothard P.
      • Veerkamp R.F.
      • Boichard D.
      • Goddard M.E.
      • Hayes B.J.
      Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals.
      additionally found GHR on BTA 20 affecting STAT. In the genomic region harboring GHR, we found a clear signal for MKG. In accordance with that, a major effect of GHR on milk yield was previously reported by
      • Blott S.
      • Kim J.-J.
      • Moisio S.
      • Schmidt-Küntzel A.
      • Cornet A.
      • Berzi P.
      • Cambisano N.
      • Ford C.
      • Grisart B.
      • Johnson D.
      • Karim L.
      • Simon P.
      • Snell R.
      • Spelman R.J.
      • Wong J.
      • Vilkki J.
      • Georges M.
      • Farnir F.
      • Coppieters W.
      Molecular dissection of a quantitative trait locus: A phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition.
      . Thus, results suggest pleiotropic effects of GHR for production and STAT. On BTA 5, we found an additional significant SNP for STAT at 47.85 Mb.
      • Pryce J.E.
      • Hayes B.J.
      • Bolormaa S.
      • Goddard M.E.
      Polymorphic regions affecting human height also control stature in cattle.
      reported HMGA2 as potential candidate gene for STAT in cattle near that site. This gene has also been shown to be involved in genetic determination of human height (
      • Weedon M.N.
      • Lettre G.
      • Freathy R.M.
      • Lindgren C.M.
      • Voight B.F.
      • Perry J.R.B.
      • Elliott K.S.
      • Hackett R.
      • Guiducci C.
      • Shields B.
      • Zeggini E.
      • Lango H.
      • Lyssenko V.
      • Timpson N.J.
      • Burtt N.P.
      • Rayner N.W.
      • Saxena R.
      • Ardlie K.
      • Tobias J.H.
      • Ness A.R.
      • Ring S.M.
      • Palmer C.N.A.
      • Morris A.D.
      • Peltonen L.
      • Salomaa V.
      • Davey Smith G.
      • Groop L.C.
      • Hattersley A.T.
      • McCarthy M.I.
      • Hirschhorn J.N.
      • Frayling T.M.
      A common variant of HMGA2 is associated with adult and childhood height in the general population.
      ;
      • Gudbjartsson D.F.
      • Walters G.B.
      • Thorleifsson G.
      • Stefansson H.
      • Halldorsson B.V.
      • Zusmanovich P.
      • Sulem P.
      • Thorlacius S.
      • Gylfason A.
      • Steinberg S.
      • Helgadottir A.
      • Ingason A.
      • Steinthorsdottir V.
      • Olafsdottir E.J.
      • Olafsdottir G.H.
      • Jonsson T.
      • Borch-Johnsen K.
      • Hansen T.
      • Andersen G.
      • Jorgensen T.
      • Pedersen O.
      • Aben K.K.
      • Witjes J.A.
      • Swinkels D.W.
      • den Heijer M.
      • Franke B.
      • Verbeek A.L.M.
      • Becker D.M.
      • Yanek L.R.
      • Becker L.C.
      • Tryggvadottir L.
      • Rafnar T.
      • Gulcher J.
      • Kiemeney L.A.
      • Kong A.
      • Thorsteinsdottir U.
      • Stefansson K.
      Many sequence variants affecting diversity of adult human height.
      ;
      • Visscher P.M.
      Sizing up human height variation.
      ).
      Interestingly, our study uncovered some shared signals for production and conformation traits indicating pleiotropy. On BTA 5 at 88.36 Mb, a clear signal for the traits BCS, DCH, and UD and all production traits was found.
      • Jiang J.
      • Cole J.B.
      • Freebern E.
      • Da Y.
      • VanRaden P.M.
      • Ma L.
      Functional annotation and Bayesian fine-mapping reveals candidate genes for important agronomic traits in Holstein bulls.
      performed GWAS in US Holsteins based on sequence data and observed pleiotropic effects of gene ABCC9 in that genomic region for production (milk and protein yield) and body-type traits (UD), which confirms our results. Likewise,
      • Nayeri S.
      • Sargolzaei M.
      • Abo-Ismail M.K.
      • May N.
      • Miller S.P.
      • Schenkel F.
      • Moore S.S.
      • Stothard P.
      Genome-wide association for milk production and female fertility traits in Canadian dairy Holstein cattle.
      reported ABCC9 to have effects on fat and protein yield as well as on calving to first service interval in Canadian Holstein cattle.
      Moreover, our results showed shared signals for all production and conformation traits BCS, CW, DCH, and UD on BTA 6 between 86.40 and 87.27 Mb. This genomic region was reported to harbor gene NPFFR2 associated with UD in Fleckvieh cattle (
      • Pausch H.
      • Emmerling R.
      • Schwarzenbacher H.
      • Fries R.
      A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle.
      ). Furthermore, NPFFR2 was detected as potential candidate loci for clinical mastitis in dairy cattle (
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Holm L.-E.
      • Panitz F.
      • Brøndum R.F.
      • Bendixen C.
      • Lund M.S.
      Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle.
      ;
      • Wu X.
      • Lund M.S.
      • Sahana G.
      • Guldbrandtsen B.
      • Sun D.
      • Zhang Q.
      • Su G.
      Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins.
      ). Nearby, we identified an additional significant SNP for BCS, DCH, CW, and production trait PKG. In same genomic area,
      • Sodeland M.
      • Grove H.
      • Kent M.
      • Taylor S.
      • Svendsen M.
      • Hayes B.J.
      • Lien S.
      Molecular characterization of a long range haplotype affecting protein yield and mastitis susceptibility in Norwegian Red cattle.
      observed gene SLC4A4 as a potential candidate for mastitis susceptibility in Norwegian Red cattle.
      • Pedrosa V.B.
      • Schenkel F.S.
      • Chen S.-Y.
      • Oliveira H.R.
      • Casey T.M.
      • Melka M.G.
      • Brito L.F.
      Genomewide association analyses of lactation persistency and milk production traits in Holstein cattle based on imputed whole-genome sequence data.
      detected SLC4A4 as being associated with milk and protein yield in Canadian Holstein cattle. All these results together imply some overlap in genetic determination of production, conformation, and health traits in dairy cattle caused by pleiotropic genes. As most relevant traits in dairy cattle have a complex genomic nature (
      • Hayes B.J.
      • Pryce J.
      • Chamberlain A.J.
      • Bowman P.J.
      • Goddard M.E.
      Genetic architecture of complex traits and accuracy of genomic prediction: Coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits.
      ;
      • Kemper K.E.
      • Goddard M.E.
      Understanding and predicting complex traits: Knowledge from cattle.
      ) and genes are spread throughout the entire genome, genetic overlap across traits is not surprising. However, deeper knowledge on causal genes with pleiotropic effects on different traits may be highly important for prospective breeding.
      Few studies have investigated the genetic complexity of metabolic health in dairy cattle. We identified several SNPs for different metabolic diseases, but most of them have not yet been reported in the literature. In primiparous cows, SNP BTB-01948148 on BTA 3 (111.68 Mb) was associated with DA. According to
      • Raschia M.A.
      • Nani J.P.
      • Carignano H.A.
      • Amadio A.F.
      • Maizon D.O.
      • Poli M.A.
      Weighted single-step genome-wide association analyses for milk traits in Holstein and Holstein × Jersey crossbred dairy cattle.
      , this region includes gene CSMD2, which affects milk yield in Holstein cattle. Moreover, a signal for META was found on BTA 17 (28.95 Mb). In close proximity, genes SCLT1 and JADE1 were reported as potential candidate genes for fatty acid composition of milk (
      • Duchemin S.I.
      • Bovenhuis H.
      • Megens H.-J.
      • van Arendonk J.A.M.
      • Visker M.H.P.W.
      Fine-mapping of BTA17 using imputed sequences for associations with de novo synthesized fatty acids in bovine milk.
      .
      In third-parity cows, clear signals were found for KET on BTA 2 (71.44 and 84.97 Mb). Previously, CFAP221 and HECW2 were identified in these genomic regions as potential candidate loci affecting SCS and milking speed in French Holstein cattle (
      • Marete A.
      • Sahana G.
      • Fritz S.
      • Lefebvre R.
      • Barbat A.
      • Lund M.S.
      • Guldbrandtsen B.
      • Boichard D.
      Genome-wide association study for milking speed in French Holstein cows.
      ). A further GWAS hit for KET was found on BTA 8 (4.42 to 4.66 Mb). In the same region, GALNTL6 was identified as a candidate gene in Holstein Friesian for the traits of cull cow carcass weight (
      • Doran A.G.
      • Berry D.P.
      • Creevey C.J.
      Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle.
      ), daughter pregnancy rate (
      • Parker Gaddis K.L.
      • Null D.J.
      • Cole J.B.
      Explorations in genome-wide association studies and network analyses with dairy cattle fertility traits.
      ), and semen quality in Holstein bulls (
      • Borowska A.
      • Szwaczkowski T.
      • Kamiński S.
      • Hering D.M.
      • Kordan W.
      • Lecewicz M.
      Identification of genome regions determining semen quality in Holstein-Friesian bulls using information theory.
      ).
      We identified SNP BTB-00133212 on BTA 6 (86.92 Mb) as being associated with META. In direct proximity, GC was found as a candidate gene for KET in US Holstein Friesian cattle (
      • Pralle R.S.
      • Schultz N.E.
      • White H.M.
      • Weigel K.A.
      Hyperketonemia GWAS and parity-dependent SNP associations in Holstein dairy cows intensively sampled for blood β-hydroxybutyrate concentration.
      . However,
      • Pacheco H.A.
      • da Silva S.
      • Sigdel A.
      • Mak C.K.
      • Galvão K.N.
      • Texeira R.A.
      • Dias L.T.
      • Peñagaricano F.
      Gene mapping and gene-set analysis for milk fever incidence in Holstein dairy cattle.
      showed that the genomic region containing GC has a substantial effect on the occurrence of MF. These results are not surprising because GC encodes for a vitamin D binding protein and thus possesses a significant role in the regulation of blood calcium concentration in dairy cows (
      • Horst R.L.
      • Goff J.P.
      • Reinhardt T.A.
      Calcium and vitamin D metabolism in the dairy cow.
      ;
      • Cavani L.
      • Poindexter M.B.
      • Nelson C.D.
      • Santos J.E.P.
      • Peñagaricano F.
      Gene mapping, gene-set analysis, and genomic prediction of postpartum blood calcium in Holstein cows.
      ).

      CONCLUSIONS

      This study showed that production is antagonistically correlated with the occurrence of metabolic disorders in dairy cattle and that larger cows tend to have lower metabolic health. Based on these findings, we recommend that more attention be paid to genetic progress in production yield and increase in body size because they are likely to have negative effects on cow health. However, this study also demonstrated that metabolic diseases are heritable, which means that genetic progress through direct selection is possible. In addition, the moderate genetic correlations of metabolic diseases with conformation traits such as BCS or DCH imply that these traits can serve as indicators for metabolic health stability of cows. Therefore, indirect selection could be an additional option to accelerate genetic progress and thereby improve metabolic health in dairy cattle.

      ACKNOWLEDGMENTS

      This study is part of the project QTCC: From Quantitative Trait Correlation to Causation in Dairy Cattle (project number 448536632) and was financially supported by the German Research Foundation (DFG). The authors have not stated any conflicts of interest.

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