A. Cecchinato

Invited review: Genetics and modeling of milk coagulation properties

Milk coagulation properties (MCP) are conventionally measured using computerized renneting meters, mechanical or optical devices that record curd firmness over time (CF(t)). The traditional MCP are rennet coagulation time (RCT, min), curd firmness (a(30), mm), and curd-firming time (k(20), min). The milk of different ruminant species varies in terms of CF(t) pattern. Milk from Holstein-Friesian and some Scandinavian cattle breeds yields higher proportions of noncoagulating samples, samples with longer RCT and lower a(30), and samples for which k(20) is not estimable, than does milk from Brown Swiss, Simmental, and other local Alpine breeds. The amount, proportion, and genetic variants (especially κ-casein) of milk protein fractions strongly influence MCP and explain variable proportions of the observed differences among breeds and among individuals of the same breed. In addition, other major genes have been shown to affect MCP. Individual repeatability of MCP is high, whereas any herd effect is low; thus, the improvement of MCP should be based principally on selection. Exploitable additive genetic variation in MCP exists and has been assessed using different breeds in various countries. Several models have been formulated that either handle noncoagulating samples or not. The heritability of MCP is similar to that of other milk quality traits and is higher than the heritability of milk yield. Rennet coagulation time and a(30) are highly correlated, both phenotypically and genetically. This means that the use of a(30) data does not add valuable information to that obtainable from RCT; both traits are genetically correlated mainly with milk acidity. Moreover, a(30) is correlated with casein content. The major limitations of traditional MCP can be overcome by prolonging the observation period and by using a novel CF(t) modeling, which uses all available information provided by computerized renneting meters and allows the estimation of RCT, the potential asymptotic curd firmness, the curd-firming rate, and the syneresis rate. Direct measurements of RCT obtained from both mechanical and optical devices show similar heritabilities and exhibit high phenotypic and genetic correlations. Moreover, mid-infrared reflectance spectroscopy can predict MCP. The heritabilities of predicted MCP are higher than those of measured MCP, and the 2 sets of values are strongly correlated. Therefore, mid-infrared reflectance spectroscopy is a reliable and cheap method whereby MCP can be improved at the population level; this is because such spectra are already routinely acquired from the milk of cows enrolled in milk recording schemes.

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk.

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a(30), mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900 cm(-1). Prediction models for RCT and a(30) based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a(30), respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a(30) were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a(30) ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.

Genetic parameters of coagulation properties, milk yield, quality, and acidity estimated using coagulating and noncoagulating milk information in Brown Swiss and Holstein-Friesian cows

The aim of this study was to estimate heritabilities of rennet coagulation time (RCT) and curd firmness (a(30)) and their genetic correlations with test-day milk yield, composition (fat, protein, and casein content), somatic cell score, and acidity (pH and titratable acidity) using coagulating and noncoagulating (NC) milk information. Data were from 1,025 Holstein-Friesian (HF) and 1,234 Brown Swiss (BS) cows, which were progeny of 54 HF and 58 BS artificial insemination sires, respectively. Milk coagulation properties (MCP) of each cow were measured once using a computerized renneting meter and samples not exhibiting coagulation within 31 min after rennet addition were classified as NC milk. For NC samples, RCT was unobserved. Multivariate analyses, using Bayesian methodology, were performed to estimate the genetic relationships of RCT or a(30) with the other traits and statistical inference was based on the marginal posterior distributions of parameters of concern. For analyses involving RCT, a right-censored Gaussian linear model was used and records of NC milk samples, being censored records, were included as unknown parameters in the model implementing a data augmentation procedure. Rennet coagulation time was more heritable [heritability (h(2))=0.240 and h(2)=0.210 for HF and BS, respectively] than a(30) (h(2)=0.148 and h(2)=0.168 for HF and BS, respectively). Milk coagulation properties were more heritable than a single test-day milk yield (h(2)=0.103 and h(2)=0.097 for HF and BS, respectively) and less heritable than milk composition traits whose heritability ranged from 0.275 to 0.275, with the only exception of fat content of BS milk (h(2)=0.108). A negative genetic correlation, lower than -0.85, was estimated between RCT and a(30) for both breeds. Genetic relationships of MCP with yield and composition were low or moderate and favorable. The genetic correlation of somatic cell score with RCT in BS cows was large and positive and even more positive were those of RCT with pH and titratable acidity in both breeds, ranging from 0.80 to 0.94. Including NC milk information in the data affected the estimated correlations and decreased the uncertainty associated with the estimation process. On the basis of the estimated heritabilities and genetic correlations, enhancement of MCP through selective breeding with no detrimental effects on yield and composition seems feasible in both breeds. Milk acidity may play a role as an indicator trait for indirect enhancement of MCP.

Effectiveness of mid-infrared spectroscopy to predict fatty acid composition of Brown Swiss bovine milk

Mid-infrared spectroscopy (MIR) is used to predict fatty acid (FA) composition of individual milk samples (n=267) of Brown Swiss cows. FAs were analyzed by gas chromatography as a reference method. Samples were scanned (4000 to 900 cm-1) by MIR, and predictive models were developed using modified partial least squares regressions with full cross-validation. The methods using a first derivative or multiplicative scatter corrected plus first derivative resulted, on average, in the best predictions. Coefficients of correlation between measured and predicted C8:0, C10:0, C12:0, C14:0, anteiso-C17:0, c9-C18:1, and medium- and long-chain FA, and saturated, monounsaturated and unsaturated FA ranged from 0.71 to 0.77, suggesting that prediction models can be implemented in milk recording schemes to routinely collect information on FA composition from the whole Brown Swiss population for breeding purposes.

Genetic analysis of rennet coagulation time, curd-firming rate, and curd firmness assessed over an extended testing period using mechanical and near-infrared instruments

The aims of this study were (1) to analyze rennet coagulation time (RCT), curd-firming rate, and curd firmness obtained by extending the standard 30-min testing time to 45 min; (2) to estimate heritabilities of the aforementioned traits determined by mechanical (Formagraph; Foss Electric, Hillerød, Denmark) and near-infrared optical (Optigraph; Ysebaert, Frépillon, France) instruments, and to assess the statistical relevance of their genetic background by using the Bayes factor procedure, the deviance information criterion, and the mean squared error; (3) to estimate phenotypic and genetic relationships between instruments within trait and between traits within instrument; and (4) to obtain correlations for sire rankings based on the used instruments. Individual milk samples were collected from 913 Brown Swiss cows reared in 63 herds located in Trento Province (Italy). Milk coagulation properties (MCP) were measured using 2 different instruments: Formagraph and Optigraph. Both instruments were housed in the same laboratory and operated by the same technician. Each sample was analyzed simultaneously on each instrument. All experimental conditions (milk temperature and the concentration and type of rennet) were identical. For the analysis, univariate and bivariate animal models were implemented using Bayesian methods. Univariate analyses were conducted to test the hypothesis that the traits showed additive genetic determination. Deviance information criterion, Bayes factor, and mean squared error were used as model choice criteria. The main results were that (1) RCT could be measured on all samples by extending the observation time to 45 min, and its genetic parameters (h(2)=0.23) and breeding values could be estimated while avoiding the bias of noncoagulating samples; (2) curd-firming rate could be measured on almost all milk samples, and its genetic parameters could be estimated for the first time on a field data set (h(2)=0.21); (3) for the first time, genetic parameters of curd firmness 45 min after rennet addition (h(2)=0.12) were estimated, and they were compared with curd firmness 30 min after rennet addition (h(2)=0.17); and (4) MCP estimated using the Optigraph appeared to be genetically different from those determined by Formagraph, with the partial exception of RCT (genetic correlation=0.97). Breeding strategies for the improvement of MCP must be planned with caution. Currently, the high throughput, ease of use, and reduced costs of analysis make predictions obtained from mid-infrared spectroscopy (MIRS) on untreated milk samples a promising alternative to produce relevant data at the population level. The use of mechanical lactodynamographs to establish reference data for MIRS calibrations have been already studied, whereas the use of near-infrared optical lactodynamographs as a reference method for MIRS calibrations needs to be investigated.

Near-infrared reflectance spectroscopy predictions as indicator traits in breeding programs for enhanced beef quality.

The aims of this study were 1) to investigate the potential application of near-infrared spectroscopy (NIRS) to predict beef quality (BQ) traits, 2) to assess genetic variations of BQ measures and their predictions obtained by NIRS, and 3) to infer the genetic relationship between measures of BQ and their predictions. Young Piedmontese bulls (n = 1,230) were raised and fattened on 124 farms and slaughtered at the same commercial abattoir. The BQ traits evaluated were shear force (SF, kg), cooking loss (CL, %), drip loss (DL, %), lightness (L*), redness (a*), yellowness (b*), saturation index (SI), and hue angle. Near-infrared spectra were collected using a Foss NIRSystems 5000 instrument over a spectral range of 1,100 to 2,498 nm every 2 nm, in reflectance mode. After editing, prediction models were developed on a calibration subset (n = 268) using partial least squares regressions, followed by application of these models to the validation subset (n = 940). Estimations of (co)variance for measures of BQ and NIRS-based predictions were obtained through a set of bivariate Bayesian analyses on the validation subset. Near-infrared predictions were satisfactory for measurements of L* (R(2) = 0.64), a* (R(2) = 0.68), hue angle (R(2) = 0.81), and saturation index (R(2) = 0.59), but not for b*, DL, CL, and SF. The loss of additive genetic variance of predicted vs. measured L*, a*, DL, CL, and SF was generally high and was similar to the loss of residual variance, being a function of the calibration parameter R(2). As a consequence, estimated heritabilities of measures and predictions of BQ were similar for traits with high calibration R(2) values. Genetic correlations between BQ measures and predictions were high for all color traits and DL, and were greater than the corresponding phenotypic correlations, whereas both the phenotypic and genetic correlations for SF and CL were nil. Results suggest that NIRS-based predictions for color features and DL may be used as indicator traits to improve meat quality of the Piedmontese breed.

Genetic analysis of the Fourier-transform infrared spectra of bovine milk with emphasis on individual wavelengths related to specific chemical bonds

Fourier-transform infrared (FTIR) spectra are used to predict the fat, protein, casein, and lactose contents of milk. These estimates are currently used to predict the individual estimated breeding values of animals. The objective of the present study was to estimate the genetic variation and heritabilities of the milk transmittance spectrum at each individual FTIR wave. Milk was sampled once per cow from a total of 1,064 Italian Brown Swiss cows from 30 herds, sired by 50 artificial insemination sires. The FTIR spectra of all samples were collected within 3 h of sampling from 25 mL of milk. The obtained spectral range comprised wavenumbers 5,000 to 930×cm(-1), corresponding to wavelengths 2.00 to 10.76 μm and frequencies from 149.9 to 27.9 THz, for a total of 1,056 waves. These were acquired using a MilkoScan FT120 FTIR interferometer (Foss Electric A/S, Hillerød, Denmark). Each spectral data point was treated as a single trait and analyzed using an animal model REML method. The results indicated that the transmittance of the bovine milk FTIR spectrum was heritable for most individual waves in the wavenumber interval from 5,000 to 930×cm(-1). Moreover, the transmittance of contiguous FTIR waves was much more highly correlated in terms of the average value and phenotypic variation, compared with genetic variation. In the present study, we characterized 5 regions of the FTIR spectrum that were relevant to the analysis of milk; 2 regions, one in the transition area between the short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) divisions of the electromagnetic spectrum (SWIR-MWIR region) and another very short region in the MWIR division (MWIR-2 region), were characterized by very high phenotypic variability in the transmittance of individual milk samples within each wave. This was caused by the absorption peaks of water, which can mask the effects of other important milk components. These regions also showed high genetic variability in transmittance, and the heritability estimates of individual waves were generally very low (with some exceptions). The 3 other identified regions contained many transmittance peaks that represented important chemical bonds; these showed much lower phenotypic and genetic variability in terms of individual waves, but relatively higher and less variable heritability estimates. Among them, the SWIR region (near-infrared) showed a peculiar cyclic pattern of the heritability coefficients of transmittance, the MWIR-1 region was particularly important for the estimation of fat, and the MWIR-LWIR region (also known also as the fingerprint region) had 3 areas of relatively high heritability. In summary, we found that the transmittance data from the FTIR spectra of milk have genetic variability that may prove useful for the direct genetic improvement of dairy species, rather than only through indirect phenotypic predictions of individual milk quality and technological traits.

Survival analysis of preweaning piglet survival in a dry-cured ham-producing crossbred line

The aim of this study was to investigate piglet preweaning survival and its relationship with a total merit index (TMI) used for selection of Large White terminal boars for dry-cured ham production. Data on 13,924 crossbred piglets (1,347 litters), originated by 189 Large White boars and 328 Large White-derived crossbred sows, were analyzed under a frailty proportional hazards model, assuming different baseline hazard functions and including sire and nursed litter as random effects. Estimated hazard ratios (HR) indicated that sex, cross-fostering, year-month of birth, parity of the nurse sow, size of the nursed litter, and class of TMI were significant effects for piglet preweaning survival. Female piglets had less risk of dying than males (HR = 0.81), as well as cross-fostered piglets (HR = 0.60). Survival increased when piglets were nursed by sows of third (HR = 0.85), fourth (HR = 0.76), and fifth (HR = 0.79) parity in comparison with first and second parity sows. Piglets of small (HR = 3.90) or very large litters (HR >1.60) had less chance of surviving in comparison with litters of intermediate size. Class of TMI exhibited an unfavorable relationship with survival (HR = 1.20 for the TMI top class). The modal estimates of sire variance under different baseline hazard functions were 0.06, whereas the variance for the nursed litter was close to 0.7. The estimate of the nursed litter effect variance was greater than that of the sire, which shows the importance of the common environment generated by the nurse sow. Relationships between sire rankings obtained from different survival models were high. The heritability estimate in equivalent scale was low and reached a value of 0.03. Nevertheless, the exploitable genetic variation for this trait justifies the inclusion of piglet preweaning survival in the current breeding program for selection of Large White terminal boars for dry-cured ham production.

Genetic analysis of beef fatty acid composition predicted by near-infrared spectroscopy

The aims of this study were 1) to investigate the potential application of near-infrared spectroscopy (NIRS) to predict intramuscular fat (IMF) and fatty acid (FA) composition of individual meat samples, 2) to estimate heritability of IMF and FA NIRS-based predictions, and 3) to assess the statistical relevance of the genetic background of such predictions by using the Bayes factor (BF) procedure. Young Piemontese bulls (n = 1,298) were raised and fattened on 124 farms, and slaughtered at the same commercial abattoir. Intramuscular fat content and FA composition were analyzed on a random subset of 148 samples of minced and homogenized longissimus thoracis muscle. Near-infrared spectroscopy spectra were collected on all samples (n = 1,298) in reflectance mode between 1,100 and 2,498 nm (every 2 nm) using fresh minced meat samples. Calibration models developed from the random subset of 148 samples were used to predict IMF and FA contents of the remaining 1,150 samples. Intramuscular fat content and FA predictions were analyzed under a Bayesian univariate animal linear models, and the statistical relevance of heritability estimates was assessed through BF; the model with polygenic additive effects was favored when BF > 1. In general, satisfactory results (R(2) > 0.60) were obtained for 6 out of the 8 major FA (C14:0, C:16:0, C16:1, C18:0, C18:1n-9 cis/trans, and C18:1n-11 trans), 6 out of the 19 minor FA (C10:0, C12:0, C17:0, C17:1, C18:2 cis-9,trans-11, and C20:2), and the total SFA, MUFA, and PUFA. Bayes factors between models with and without a genetic component provided values greater than 1 for IMF, C14:0, C16:0, C18:1n-9 cis/trans, C17:0, C17:1, C20:2, SFA, MUFA, and PUFA. The greatest BF was reached by C20:2 (BF >10), suggesting strong evidence of genetic determinism, whereas IMF, C18:1n-9 cis/trans, C17:0, C17:1, MUFA, and PUFA showed substantial evidence favoring the numerator model (3.16 < BF < 10). Point estimates of heritabilities for FA predicted by NIRS were low to moderate (0.07 to 0.21). Results support that NIRS is a useful technique to satisfactorily predict some FA of meat. The existence of an important genetic determinism affecting FA profile has been confirmed, suggesting that FA composition of meat can be genetically modified.

The relevance of purebred information for predicting genetic merit of survival at birth of crossbred piglets.

The objective of this study was to infer (co)variance components for piglet survival at birth in purebred and crossbred pigs. Data were from 13,643 (1,213 litters) crossbred and 30,919 (3,162 litters) purebred pigs, produced by mating the same 168 purebred boars to 460 Large White-derived crossbred females and 1,413 purebred sows, respectively. The outcome variable was piglet survival at birth as a binary trait. A Bayesian bivariate threshold model was implemented via Gibbs sampling. Flat priors were assigned to the effects of sex, parity of the dam, litter size, and year-month of birth. Gaussian priors were assigned to litter, dam, and sire effects. Marginal posterior means (SD) of the sire and dam variances for liability of piglet survival in purebred were 0.018 (0.008) and 0.077 (0.020), respectively. For crossbred, sire and dam variance estimates were 0.030 (0.018) and 0.120 (0.034), respectively. The posterior means (SD) of the heritability of liability of survival in purebred and crossbred and of the genetic correlation between these traits were 0.049 (0.023), 0.091 (0.054), and 0.248 (0.336), respectively. The greatest 95% confidence region (-0.406, 0.821) for the genetic correlation between purebred and crossbred liabilities of piglet survival included zero. Results suggest that the expected genetic progress for piglet survival in crossbreds when selection is based on purebred information may be nil.