Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows.

This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 males), were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.

Genome-wide association study for selected cheese-making properties in Dual-Purpose Belgian Blue cows.

This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from -0.61 to -0.41 and from -0.55 to -0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.

[Comparative analysis of the brain distribution of [18F]FDG in populations of patients with Alzheimer's disease with or without family history of dementia]. / Analyse comparative de la distribution cérébrale du [18F]FDG dans une population de patients souffrant de maladie d'Alzheimer présentant ou non des antécédents familiaux de démence.

Hereditary forms of Alzheimer's disease (AD) and early-onset forms have more brain damage than sporadic or late-onset forms at the time of diagnosis (1, 2). Data in the literature are contradictory concerning familial forms without known heredity or mutation. The aim of this study was to compare the brain distribution of FDG between two populations of patients with a clinical diagnosis of sporadic AD according to the presence or not of a first degree family history of dementia. We retrospectively included 243 patients with clinical diagnosis of AD who underwent brain FDG PET imaging between 2012 and 2017. SPM12 was used to compare the FDG brain distribution in 199 patients with AD and no familial history of dementia and 43 patients with AD and first degree familial history of dementia. Compared to a database of 22 healthy control subjects, both groups of AD patients showed a significant decrease of FDG distribution in temporo-parietal, posterior cingulate and posterior left frontal cortex with respect to the controls (p inferior to 0.05 corrected for the family-wise error, pFWE-corr). There were no significant differences between the two AD groups (pFWE-corr superior to 0.05 and p superior to 0.001 uncorrected for multiple comparisons) that present the same brain metabolic pathology.

Les formes héréditaires de la maladie d'Alzheimer (MA) et les formes à début précoce présentent une atteinte cérébrale plus importante que les formes sporadiques ou celles à début tardif au moment du diagnostic (1, 2). Les données de la littérature sont contradictoires en ce qui concerne les formes familiales sans hérédité ni mutation connue. L'objectif de cette étude était de comparer la distribution cérébrale du [18F]fluoro-2-deoxy-D-glucose ([18F]FDG) entre deux populations de patients présentant un diagnostic clinique de la MA sporadique selon la présence, ou non, d'une histoire familiale de démence au premier degré. Dans cette étude rétrospective, nous avons inclus 243 patients vus entre 2012 et 2017. Le logiciel SPM12 a été utilisé pour comparer la distribution cérébrale du FDG entre 199 patients souffrant de MA, sans histoire familiale et 43 patients souffrant de MA avec une histoire familiale de démence au premier degré. Comparés à une base de données de 22 sujets contrôles sains, chacun des deux groupes de patients présentait une réduction significative de la distribution du FDG au niveau du cortex temporo-pariétal, cingulaire postérieur et frontal postérieur gauche (p inf�rieur a 0,05 corrigé pour le family-wise error, pFWE-corr), caractéristique de la maladie. Il n'y avait pas de différence significative entre les deux groupes MA (pFWE-corr sup�rieur a 0,05 et p sup�rieur a 0,001 non corrigé, pour des comparaisons multiples) qui présentent donc la même altération métabolique cérébrale.

Genetic evaluation for birth and conformation traits in dual-purpose Belgian Blue cattle using a mixed inheritance model.

The segregation of the causal mutation () in the muscular hypertrophy gene in dual-purpose Belgian Blue (dpBB) cattle is considered to result in greater calving difficulty (dystocia). Establishing adapted genetic evaluations might overcome this situation through efficient selection. However, the heterogeneity of dpBB populations at the locus implies separating the major gene and other polygenic effects in complex modeling. The use of mixed inheritance models may be an interesting option because they simultaneously assume both influences. A genetic evaluation in dpBB based on a mixed inheritance model was developed for birth and conformation traits: gestation length (GL), calving difficulty (CD), birth weight (BiW), and body conformation score (BC). A total of 27,362 animals having records were used for analyses. The total number of animals in the pedigree used to build the numerator relationship matrix was 62,617. Genotypes at the locus were available for 2,671 animals. Missing records at this locus were replaced with genotype probabilities. A total of 13,221 (48.3%) were registered as dpBB, 1,287 (4.7%) as beef Belgian Blue, and 12,854 (47.0%) were unknown. From those 13,221 dpBB animals, 650, 849, and 534 had double or single copies or no copy, respectively, of the causal mutation () in the muscular hypertrophy gene, whereas 11,188 had missing genotypes. This heterogeneity at the locus may be the reason for high variability in the studied traits, that is, high heritability estimates of 0.33, 0.30, 0.38, and 0.43 for GL, CD, BiW, and BC, respectively. In general, additive ( < 0.05) and dominance ( < 0.001) allele substitution for calves and dams had significant impact for all traits. The moderate coefficient of genetic variation (27.80%) and high direct heritability (0.28) for CD suggested genetic variability in dpBB and possible genetic improvement through selection. This variability has allowed dpBB breeders to successfully apply mass selection in the past. Genetic trend means from 1988 to 2016 showed that sire selection for CD within genotype was progressively applied by breeders. The selection intensity was more important for CD in double-muscled lines than in segregated lines. Our study illustrated the possible confusion caused by the use of major genes in selection and the importance of fitting appropriate models such as mixed inheritance models that combine polygenic and gene content information.

Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models.

An increasing number of models are being developed to provide information from milk Fourier transform mid-infrared (FT-MIR) spectra on fine milk composition, technological properties of milk, or even cows' physiological status. In this context, and to take advantage of these existing models, the purpose of this work was to evaluate whether a spectral standardization method can enable the use of multiple equations within a network of different FT-MIR spectrometers. The piecewise direct standardization method was used, matching "slave" instruments to a common reference, the "master." The effect of standardization on network reproducibility was assessed on 66 instruments from 3 different brands by comparing the spectral variability of the slaves and the master with and without standardization. With standardization, the global Mahalanobis distance from the slave spectra to the master spectra was reduced on average from 2,655.9 to 14.3, representing a significant reduction of noninformative spectral variability. The transfer of models from instrument to instrument was tested using 3 FT-MIR models predicting (1) the quantity of daily methane emitted by dairy cows, (2) the concentration of polyunsaturated fatty acids in milk, and (3) the fresh cheese yield. The differences, in terms of root mean squared error, between master predictions and slave predictions were reduced after standardization on average from 103 to 17 g/d, from 0.0315 to 0.0045 g/100 mL of milk, and from 2.55 to 0.49 g of curd/100 g of milk, respectively. For all the models, standard deviations of predictions among all the instruments were also reduced by 5.11 times for methane, 5.01 times for polyunsaturated fatty acids, and 7.05 times for fresh cheese yield, showing an improvement of prediction reproducibility within the network. Regarding the results obtained, spectral standardization allows the transfer and use of multiple models on all instruments as well as the improvement of spectral and prediction reproducibility within the network. The method makes the models universal, thereby offering opportunities for data exchange and the creation and use of common robust models at an international level to provide more information to the dairy sector from direct analysis of milk.

Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra.

Changes in milk production traits (i.e., milk yield, fat, and protein contents) with the pregnancy stage are well documented. To our knowledge, the effect of pregnancy on the detailed milk composition has not been studied so far. The mid-infrared (MIR) spectrum reflects the detailed composition of a milk sample and is obtained by a nonexhaustive and widely used method for milk analysis. Therefore, this study aimed to investigate the effect of pregnancy on milk MIR spectrum in addition to milk production traits (milk yield, fat, and protein contents). A model including regression on the number of days pregnant was applied on milk production traits (milk yield, fat, and protein contents) and on 212 spectral points from the MIR spectra of 9,757 primiparous Holstein cows from Walloon herds. Effects of pregnancy stage were expressed on a relative scale (effect divided by the squared root of the phenotypic variance); this allowed comparisons between effects on milk traits and on 212 spectral points. Effect of pregnancy stage on production traits were in line with previous studies indicating that the model accounted well for the pregnancy effect. Trends of the relative effect of the pregnancy stage on the 212 spectral points were consistent with known and observed effect on milk traits. The highest effect of the pregnancy was observed in the MIR spectral region from 968 to 1,577 cm-1. For some specific wavenumbers, the effect was higher than for fat and protein contents in the beginning of the pregnancy (from 30 to 90 or 120 d pregnant). In conclusion, the effect of early pregnancy can be observed in the detailed milk composition through the analysis of the MIR spectrum of bovine milk. Further analyses are warranted to explore deeply the use of MIR spectra of bovine milk for breeding and management of dairy cow pregnancy.

Invited review: Opportunities for genetic improvement of metabolic diseases.

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.

Changes throughout lactation in phenotypic and genetic correlations between methane emissions and milk fatty acid contents predicted from milk mid-infrared spectra.

The aim of this study was to estimate phenotypic and genetic correlations between methane production (Mp) and milk fatty acid contents of first-parity Walloon Holstein cows throughout lactation. Calibration equations predicting daily Mp (g/d) and milk fatty acid contents (g/100 dL of milk) were applied on milk mid-infrared spectra related to Walloon milk recording. A total of 241,236 predictions of Mp and milk fatty acids were used. These data were collected between 5 and 305 d in milk in 33,555 first-parity Holstein cows from 626 herds. Pedigree data included 109,975 animals. Bivariate (i.e., Mp and a fatty acid trait) random regression test-day models were developed to estimate phenotypic and genetic parameters of Mp and milk fatty acids. Individual short-chain fatty acids (SCFA) and groups of saturated fatty acids, SCFA, and medium-chain fatty acids showed positive phenotypic and genetic correlations with Mp (from 0.10 to 0.16 and from 0.23 to 0.30 for phenotypic and genetic correlations, respectively), whereas individual long-chain fatty acids (LCFA), and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed null to positive phenotypic and genetic correlations with Mp (from -0.03 to 0.13 and from -0.02 to 0.32 for phenotypic and genetic correlations, respectively). However, these correlations changed throughout lactation. First, de novo individual and group fatty acids (i.e., C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, SCFA group) showed low phenotypic or genetic correlations (or both) in early lactation and higher at the end of lactation. In contrast, phenotypic and genetic correlations between Mp and C16:0, which could be de novo synthetized or derived from blood lipids, were more stable during lactation. This fatty acid is the most abundant fatty acid of the saturated fatty acid and medium-chain fatty acid groups of which correlations with Mp showed the same pattern across lactation. Phenotypic and genetic correlations between Mp and C17:0 and C18:0 were low in early lactation and increased afterward. Phenotypic and genetic correlations between Mp and C18:1 cis-9 originating from the blood lipids were negative in early lactation and increased afterward to become null from 18 wk until the end of lactation. Correlations between Mp and groups of LCFA, monounsaturated fatty acids, and unsaturated fatty acids showed a similar or intermediate pattern across lactation compared with fatty acids that compose them. Finally, these results indicate that correlations between Mp and milk fatty acids vary following lactation stage of the cow, a fact still often ignored when trying to predict Mp from milk fatty acid profile.

Development of Fourier transform mid-infrared calibrations to predict acetone, ß-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network.

To manage negative energy balance and ketosis in dairy farms, rapid and cost-effective detection is needed. Among the milk biomarkers that could be useful for this purpose, acetone and ß-hydroxybutyrate (BHB) have been proved as molecules of interest regarding ketosis and citrate was recently identified as an early indicator of negative energy balance. Because Fourier transform mid-infrared spectrometry can provide rapid and cost-effective predictions of milk composition, the objective of this study was to evaluate the ability of this technology to predict these biomarkers in milk. Milk samples were collected in commercial and experimental farms in Luxembourg, France, and Germany. Acetone, BHB, and citrate contents were determined by flow injection analysis. Milk mid-infrared spectra were recorded and standardized for all samples. After edits, a total of 548 samples were used in the calibration and validation data sets for acetone, 558 for BHB, and 506 for citrate. Acetone content ranged from 0.020 to 3.355mmol/L with an average of 0.103mmol/L; BHB content ranged from 0.045 to 1.596mmol/L with an average of 0.215mmol/L; and citrate content ranged from 3.88 to 16.12mmol/L with an average of 9.04mmol/L. Acetone and BHB contents were log-transformed and a part of the samples with low values was randomly excluded to approach a normal distribution. The 3 edited data sets were then randomly divided into a calibration data set (3/4 of the samples) and a validation data set (1/4 of the samples). Prediction equations were developed using partial least square regression. The coefficient of determination (R(2)) of cross-validation was 0.73 for acetone, 0.71 for BHB, and 0.90 for citrate with root mean square error of 0.248, 0.109, and 0.70mmol/L, respectively. Finally, the external validation was performed and R(2) obtained were 0.67 for acetone, 0.63 for BHB, and 0.86 for citrate, with respective root mean square error of validation of 0.196, 0.083, and 0.76mmol/L. Although the practical usefulness of the equations developed should be further verified with other field data, results from this study demonstrated the potential of Fourier transform mid-infrared spectrometry to predict citrate content with good accuracy and to supply indicative contents of BHB and acetone in milk, thereby providing rapid and cost-effective tools to manage ketosis and negative energy balance in dairy farms.

Capitalizing on fine milk composition for breeding and management of dairy cows.

The challenge of managing and breeding dairy cows is permanently adapting to changing production circumstances under socio-economic constraints. If managing and breeding address different timeframes of action, both need relevant phenotypes that allow for precise monitoring of the status of the cows, and their health, behavior, and well-being as well as their environmental impact and the quality of their products (i.e., milk and subsequently dairy products). Milk composition has been identified as an important source of information because it could reflect, at least partially, all these elements. Major conventional milk components such as fat, protein, urea, and lactose contents are routinely predicted by mid-infrared (MIR) spectrometry and have been widely used for these purposes. But, milk composition is much more complex and other nonconventional milk components, potentially predicted by MIR, might be informative. Such new milk-based phenotypes should be considered given that they are cheap, rapidly obtained, usable on a large scale, robust, and reliable. In a first approach, new phenotypes can be predicted from MIR spectra using techniques based on classical prediction equations. This method was used successfully for many novel traits (e.g., fatty acids, lactoferrin, minerals, milk technological properties, citrate) that can be then useful for management and breeding purposes. An innovation was to consider the longitudinal nature of the relationship between the trait of interest and the MIR spectra (e.g., to predict methane from MIR). By avoiding intermediate steps, prediction errors can be minimized when traits of interest (e.g., methane, energy balance, ketosis) are predicted directly from MIR spectra. In a second approach, research is ongoing to detect and exploit patterns in an innovative manner, by comparing observed with expected MIR spectra directly (e.g., pregnancy). All of these traits can then be used to define best practices, adjust feeding and health management, improve animal welfare, improve milk quality, and mitigate environmental impact. Under the condition that MIR data are available on a large scale, phenotypes for these traits will allow genetic and genomic evaluations. Introduction of novel traits into the breeding objectives will need additional research to clarify socio-economic weights and genetic correlations with other traits of interest.

On the role of mid-infrared predicted phenotypes in fertility and health dairy breeding programs.

Fertility and health traits are of prime importance in dairy breeding programs. However, these traits are generally complex, difficult to record, and lowly heritable (<0.10), thereby hampering genetic improvement in disease resistance and fertility. Hence, indicators are useful in the prediction of genetic merit for fertility and health traits as long as they are easier to measure than direct fitness traits, heritable, and genetically correlated. Considering that changes in (fine) milk composition over a lactation reflect the physiological status of the cow, mid-infrared (MIR) analysis of milk opens the door to a wide range of potential indicator traits of fertility and health. Previous studies investigated the phenotypic and genetic relationships between fertility and MIR-predicted phenotypes, most being related to negative postpartum energy balance and body fat mobilization (e.g., fat:protein ratio, urea, fatty acids profile). Results showed that a combination of various fatty acid traits (e.g., C18:1 cis-9 and C10:0) could be used to improve fertility. Furthermore, occurrence of (sub)clinical ketosis has been related to milk-based phenotypes such as fat:protein ratio, fatty acids, and ketone bodies. Hence, MIR-predicted acetone and ß-hydroxybutyrate contents in milk could be useful for breeding cows less susceptible to ketosis. Although studies investigating the genetic association among mastitis and MIR-predicted phenotypes are scarce, a wide range of traits, potentially predicted by MIR spectrometry, are worthy of consideration. These include traits related to the disease response of the cow (e.g., lactoferrin), reduced secretory activity (e.g., casein), and the alteration of the blood-milk barrier (e.g., minerals). Moreover, direct MIR prediction of fertility and health traits should be further considered. To conclude, MIR-predicted phenotypes have a role to play in the improvement of dairy cow fertility and health. However, further studies are warranted to (1) grasp underlying associations among MIR-predicted indicator and fitness traits, (2) estimate the genetic parameters, and (3) include these traits in broader breeding strategies.

Genetic analysis of heat stress effects on yield traits, udder health, and fatty acids of Walloon Holstein cows.

Genetic parameters that considered tolerance for heat stress were estimated for production, udder health, and milk composition traits. Data included 202,733 test-day records for milk, fat, and protein yields, fat and protein percentages, somatic cell score (SCS), 10 individual milk fatty acids (FA) predicted by mid-infrared spectrometry, and 7 FA groups. Data were from 34,468 first-lactation Holstein cows in 862 herds in the Walloon region of Belgium and were collected between 2007 and 2010. Test-day records were merged with daily temperature-humidity index (THI) values based on meteorological records from public weather stations. The maximum distance between each farm and its corresponding weather station was 21km. Linear reaction norm models were used to estimate the intercept and slope responses of 23 traits to increasing THI values. Most yield and FA traits had phenotypic and genetic declines as THI increased, whereas SCS, C18:0, C18:1 cis-9, and 4 FA groups (unsaturated FA, monounsaturated FA, polyunsaturated FA, and long-chain FA) increased with THI. Moreover, the latter traits had the largest slope-to-intercept genetic variance ratios, which indicate that they are more affected by heat stress at high THI levels. Estimates of genetic correlations within trait between cold and hot environments were generally high (>0.80). However, lower estimates (<=0.67) were found for SCS, fat yield, and C18:1 cis-9, indicating that animals with the highest genetic merit for those traits in cold environments do not necessarily have the highest genetic merit for the same traits in hot environments. Among all traits, C18:1 cis-9 was the most sensitive to heat stress. As this trait is known to reflect body reserve mobilization, using its variations under hot conditions could be a very affordable milk biomarker of heat stress for dairy cattle expressing the equilibrium between intake and mobilization under warm conditions.

Brain Metabolic Dysfunction in Capgras Delusion During Alzheimer's Disease: A Positron Emission Tomography Study.

Capgras delusion is characterized by the misidentification of people and by the delusional belief that the misidentified persons have been replaced by impostors, generally perceived as persecutors. Since little is known regarding the neural correlates of Capgras syndrome, the cerebral metabolic pattern of a patient with probable Alzheimer's disease (AD) and Capgras syndrome was compared with those of 24-healthy elderly participants and 26 patients with AD without delusional syndrome. Comparing the healthy group with the AD group, the patient with AD had significant hypometabolism in frontal and posterior midline structures. In the light of current neural models of face perception, our patients with Capgras syndrome may be related to impaired recognition of a familiar face, subserved by the posterior cingulate/precuneus cortex, and impaired reflection about personally relevant knowledge related to a face, subserved by the dorsomedial prefrontal cortex.

Early neuropsychological detection of Alzheimer's disease.

Lifestyle modification offers a promising way of preventing or delaying Alzheimer's disease (AD). In particular, nutritional interventions can contribute to decrease the risk of dementia. The efficacy of such interventions should be assessed in individuals thought to be prone to AD. It is therefore necessary to identify markers that may help detecting AD as early as possible. This review will focus on subtle neuropsychological changes that may already exist in the predementia phase, and that could point to individuals at risk of dementia. Episodic memory decline appears consistently as the earliest sign of incipient typical AD. An episodic memory test that ensures deep encoding of information and assesses retrieval with free as well as cued recall appears as a useful tool to detect patients at an early stage of AD. Beyond the memory domain, category verbal fluency has been shown to decline early and to predict progression to AD. Moreover, in line with current diagnosis criteria for prodromal AD, combining neuropsychological scores and neuroimaging data allows a better discrimination of future AD patients than neuroimaging or neuropsychological data alone. Altogether, the detection of cognitive changes that are predictive of the typical form of probable AD already in the predementia stage points to at risk people who are the best target for therapeutic interventions, such as nutrition or physical exercise counseling or dietary interventions.

Non-genetic sources of variation of milk production and reproduction and interactions between both classes of traits in Sicilo-Sarde dairy sheep.

This work aimed to study the sources of variation in productive and reproductive traits of the dairy Sicilo-Sarde ewes and to further investigate the interaction between both classes of traits. After edits, a database containing 5935 lactation records collected during 6 successive years in eight dairy flocks in the North of Tunisia was used. Total milked milk (TMM) in the milking-only period was retained as productive trait. The interval from the start of the mating period to the subsequent lambing (IML) and the lambing status (LS) were designed as reproductive traits. Sicilo-Sarde ewes had an average TMM of 60.93 l (±44.12) during 132.8 days (±46.6) after a suckling period of 100.4 days (±24.9). Average IML was 165.7 days. In a first step, the major factors influencing milk production and reproductive traits were determined. The significant sources of variation identified for TMM were: flock, month of lambing, year of lambing, parity, suckling length, litter size and milking-only length. Flock×month of the start of the mating period, parity, year of mating and litter size were identified as significant factors of variation for IML, while flock×month of the start of the mating period, parity and year of mating were identified as significant sources of variation for LS. In a second step, variance components were estimated using a three traits threshold mixed model, which combined LS as categorical trait and TMM and IML as continuous traits. Repeatability estimates were 0.21 (±0.03) for TMM, 0.09 (±0.02) for IML, and 0.10 (±0.05) for LS. Moreover, TMM and IML were found to be favorably associated for the flock× year of lambing effect (-0.45±0.18) but unfavorably associated for the animal effect (0.20±0.09).

Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle.

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × test-day had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01×10(-3) and 4.17×10(-3) for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × test-day effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also contributed substantially to micro-environmental sensitivity. Addition of random regressions to the mean model did not reduce heterogeneity in residual variance and that genetic heterogeneity of residual variance was not simply an effect of an incomplete mean model.

Development of a genetic evaluation for body condition score for Canadian Holsteins.

Valacta (Sainte-Anne-de-Bellevue, Québec, Canada) is the Canadian Dairy Herd Improvement organization responsible for milk recording in Québec and Atlantic provinces. Up to 14 first-lactation body condition score (BCS) records were collected per cow (average of 2.5 records per cow), allowing the trait to be described by a random regression animal model so that animals could be ranked by the shape of their BCS curve. However, Valacta's BCS are available from Québec herds only and the long-term objective of this research is to develop a nationwide genetic evaluation of sires and cows for BCS. Alternatively, Holstein Canada (Brantford, Ontario, Canada) collects type trait records nationwide, primarily for first-lactation cows. Holstein Canada typically collects a single record per trait, so that selection for Holstein Canada BCS would be based on overall BCS level rather than the shape of the BCS curve. Several different methods of genetically evaluating Valacta's BCS were investigated, including consideration of average BCS level across lactation, the amount of fluctuation in the BCS curve during lactation, and combinations of BCS level and BCS fluctuation. Sires with ≥ 25 daughters were compared (as opposed to comparing cows) because their BCS estimated breeding values (EBV) are based on more information, and so should be more reliable. Of the different methods of calculating Valacta BCS EBV, ranking bulls based on overall BCS level gave the best results in that their daughter phenotypic BCS curves showed limited loss in early lactation BCS and replenished condition by the end of lactation. Whereas Valacta's BCS were analyzed using a random regression animal model, Holstein Canada only needs to collect 1 BCS record per cow at classification and the resulting BCS EBV was strongly correlated with Valacta's BCS EBV. Furthermore, because Holstein Canada's BCS are collected nationally and Valacta's BCS are not, a national genetic evaluation for Holstein Canada's BCS is more convenient. The results of this study do not eliminate the possibility of a genetic evaluation of BCS as a longitudinal trait, but indicate that other methods of calculating Valacta BCS EBV should be explored. Until that time, genetically evaluating Holstein Canada's BCS is simple, easily implemented, and may be effective in altering the level and shape of the genetic BCS curve.

Genetic parameters of milk production traits and fatty acid contents in milk for Holstein cows in parity 1-3.

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields, fat and protein contents, somatic cell count, and 17 groups and individual milk fatty acid (FA) contents predicted by mid-infrared spectrometry for first-, second- and third-parity Holstein cows. Edited data included records collected in the Walloon region of Belgium from 37,768 cows in parity 1,22,566 cows in parity 2 and 8221 in parity 3. A total of 69 (23 traits for three parities) single-trait random regression animal test-day models were run. Approximate genetic correlations among traits were inferred from pairwise regressions among estimated breeding values of cow having observations. Heritability and genetic correlation estimates from this study reflected the origins of FA: de novo synthetized or originating from the diet and the body fat mobilization. Averaged daily heritabilities of FA contents in milk ranged between 0.18 and 0.47. Average daily genetic correlations (averaged across days in milk and parities) among groups and individual FA contents in milk ranged between 0.31 and 0.99. The genetic variability of FAs in combination with the moderate to high heritabilities indicated that FA contents in milk could be changed by genetic selection; however, desirable direction of change in these traits remains unclear and should be defined with respect to all issues of importance related to milk FA.

Relationship between body condition score and health traits in first-lactation Canadian Holsteins.

The objective of this research was to estimate daily genetic correlations between longitudinal body condition score (BCS) and health traits by using a random regression animal model in first-lactation Holsteins. The use of indicator traits may increase the rate of genetic progress for functional traits relative to direct selection for functional traits. Indicator traits of interest are those that are easier to record, can be measured early in life, and are strongly genetically correlated with the functional trait of interest. Several BCS records were available per cow, and only 1 record per health trait (1=affected; 0=not affected) was permitted per cow over the lactation. Two bivariate analyses were performed, the first between BCS and mastitis and the second between BCS and metabolic disease (displaced abomasum, milk fever, and ketosis). For the first analysis, 217 complete herds were analyzed, which included 28,394 BCS records for 10,715 cows and 6,816 mastitis records for 6,816 cows. For the second analysis, 350 complete herds were analyzed, which included 42,167 BCS records for 16,534 cows and 13,455 metabolic disease records for 13,455 cows. Estimation of variance components by a Bayesian approach via Gibbs sampling was performed using 400,000 samples after a burn-in of 150,000 samples. The average daily heritability (posterior standard deviation) of BCS was 0.260 (0.026) and the heritabilities of mastitis and metabolic disease were 0.020 (0.007) and 0.041 (0.012), respectively. Heritability estimates were similar to literature values. The average daily genetic correlation between BCS and mastitis was -0.730 (0.110). Cows with a low BCS during the lactation are more susceptible to mastitis, and mastitic cows are likely to have low BCS. Daily estimates of genetic correlations between BCS and mastitis were moderate to strong throughout the lactation, becoming stronger as the lactation progressed. The average daily genetic correlation between BCS and metabolic disease was -0.438 (0.125), and was consistent throughout the lactation. A lower BCS during the lactation is genetically associated with the occurrence of mastitis and metabolic disease.