Estimation of inbreeding, between-breed genomic relatedness and definition of sub-populations in red-pied cattle breeds.

Currently, enhancing the collaboration between related breeds is of main importance to increase the competitivity and the sustainability of local breeds. One type of collaboration is the development of an across-breed reference population that will allow a better management of local breeds. For this purpose, the genomic relatedness between the local target breed and possible breeds to be included in the reference population should be estimated. In Europe, there are several local red-pied cattle breeds that would benefit from this kind of collaboration. However, how different red-pied cattle breeds from the Benelux are related to each other and can collaborate is still unclear. The objectives of this study were therefore: (1) to estimate the level of inbreeding of the East Belgian Red and White (EBRW), the Red-Pied of the Ösling (RPO) and Dutch red-pied cattle breeds; (2) to determine the genomic relatedness of several red-pied cattle breeds, with a special focus on two endangered breeds: the EBRW and the RPO, and (3) based on the second objective, to detect animals from other breeds that were genomically close enough to be considered as advantageous in the creation of an across-breed reference population of EBRW or RPO. The estimated inbreeding levels based on runs of homozygosity were relatively low for almost all the studied breeds and especially for the EBRW and RPO. This would imply that inbreeding is currently not an issue in these two endangered breeds and that their sustainability is not threatened by their level of inbreeding. The results from the principal component analysis, the phylogenetic tree and the clustering all highlighted that the EBRW and RPO breeds were included in the genomic continuum of the studied red-pied cattle breeds and can be therefore considered as genomically close to Dutch red-pied cattle breeds, highlighting the possibility of a collaboration between these breeds. Especially, EBRW animals were closely related to Deep Red and Improved Red animals while, to a lesser extent, the RPO animals were closely related to the Meuse-Rhine-Yssel breed. Based on these results, we could use distance measures, based either on the principal component analysis or clustering, to detect animals from Dutch breeds that were genomically closest to the EBRW or RPO breeds. This will finally allow the building of an across-breed reference population for EBRW or RPO for further genomic evaluations, considering these genomically closest animals from other breeds.

High male specific contribution of the X-chromosome to individual global recombination rate in dairy cattle.

BACKGROUND: Meiotic recombination plays an important role in reproduction and evolution. The individual global recombination rate (GRR), measured as the number of crossovers (CO) per gametes, is a complex trait that has been shown to be heritable. The sex chromosomes play an important role in reproduction and fertility related traits. Therefore, variants present on the X-chromosome might have a high contribution to the genetic variation of GRR that is related to meiosis and to reproduction. RESULTS: We herein used genotyping data from 58,474 New Zealand dairy cattle to estimate the contribution of the X-chromosome to male and female GRR levels. Based on the pedigree-based relationships, we first estimated that the X-chromosome accounted for 30% of the total additive genetic variance for male GRR. This percentage was equal to 19.9% when the estimation relied on a SNP-BLUP approach assuming each SNP has a small contribution. We then carried out a haplotype-based association study to map X-linked QTL, and subsequently fine-mapped the identified QTL with imputed sequence variants. With this approach we identified three QTL with large effect accounting for 7.7% of the additive genetic variance of male GRR. The associated effects were equal to + 0.79, - 1.16 and + 1.18 CO for the alternate alleles. In females, the estimated contribution of the X-chromosome to GRR was null and no significant association with X-linked loci was found. Interestingly, two of the male GRR QTL were associated with candidate genes preferentially expressed in testis, in agreement with a male-specific effect. Finally, the most significant QTL was associated with PPP4R3C, further supporting the important role of protein phosphatase in double-strand break repair by homologous recombination. CONCLUSIONS: Our study illustrates the important role the X-chromosome can have on traits such as individual recombination rate, associated with testis in males. We also show that contribution of the X-chromosome to such a trait might be sex dependent.

A model-based approach to characterize individual inbreeding at both global and local genomic scales.

Inbreeding results from the mating of related individuals and may be associated with reduced fitness because it brings together deleterious variants in one individual. In general, inbreeding is estimated with respect to an arbitrary base population consisting of ancestors that are assumed unrelated. We herein propose a model-based approach to estimate and characterize individual inbreeding at both global and local genomic scales by assuming the individual genome is a mosaic of homozygous-by-descent (HBD) and non-HBD segments. The HBD segments may originate from ancestors tracing back to different periods in the past defining distinct age-related classes. The lengths of the HBD segments are exponentially distributed with class-specific parameters reflecting that inbreeding of older origin generates on average shorter stretches of observed homozygous markers. The model is implemented in a hidden Markov model framework that uses marker allele frequencies, genetic distances, genotyping error rates and the sequences of observed genotypes. Note that genotyping errors, low-fold sequencing or genotype-by-sequencing data are easily accommodated under this framework. Based on simulations under the inference model, we show that the genomewide inbreeding coefficients and the parameters of the model are accurately estimated. In addition, when several inbreeding classes are simulated, the model captures them if their ages are sufficiently different. Complementary analyses, either on data sets simulated under more realistic models or on human, dog and sheep real data, illustrate the range of applications of the approach and how it can reveal recent demographic histories among populations (e.g., very recent bottlenecks or founder effects). The method also allows to clearly identify individuals resulting from extreme consanguineous matings.

Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions.

Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.

Identification of large selective sweeps associated with major genes in cattle.

Selection for new favorable variants can lead to selective sweeps. However, such sweeps might be rare in the evolution of different species for which polygenic adaptation or selection on standing variation might be more common. Still, strong selective sweeps have been described in domestic species such as chicken lines or dog breeds. The goal of our study was to use a panel of individuals from 12 different cattle breeds genotyped at high density (800K SNPs) to perform a whole-genome scan for selective sweeps defined as unexpectedly long stretches of reduced heterozygosity. To that end, we developed a hidden Markov model in which one of the hidden states corresponds to regions of reduced heterozygosity. Some unexpectedly long regions were identified. Among those, six contained genes known to affect traits with simple genetic architecture such as coat color or horn development. However, there was little evidence for sweeps associated with genes underlying production traits.

LDSO: a program to simulate pedigrees and molecular information under various evolutionary forces.

Simulations are a major tool to evaluate new statistical methods and optimize experimental designs in the genomic era. However, this can only be achieved when the simulations are close enough to reality, as well as diverse enough to be realistic. For mapping studies, it is thus critical to re-create as much as possible the forces generating linkage (mutation, random drift, changes in population sizes, selection and pedigree structure) and the mechanisms producing trait genetic architecture (additivity, dominance, epistasis). We present here a computer program (ldso) simulating these phenomena. Optional outputs provide statistics on the linkage disequilibrium (LD) structure and the identity by descent between chromosomal segments, facilitating further data analyses. Furthermore, ldso enables the simulation of genomic data in known pedigrees, which sticks as precisely as possible to recent population history and structures of the long-range LD, allowing optimization of fine-mapping strategies.

Ancestral haplotype-based association mapping with generalized linear mixed models accounting for stratification.

MOTIVATION: In many situations, genome-wide association studies are performed in populations presenting stratification. Mixed models including a kinship matrix accounting for genetic relatedness among individuals have been shown to correct for population and/or family structure. Here we extend this methodology to generalized linear mixed models which properly model data under various distributions. In addition we perform association with ancestral haplotypes inferred using a hidden Markov model. RESULTS: The method was shown to properly account for stratification under various simulated scenari presenting population and/or family structure. Use of ancestral haplotypes resulted in higher power than SNPs on simulated datasets. Application to real data demonstrates the usefulness of the developed model. Full analysis of a dataset with 4600 individuals and 500 000 SNPs was performed in 2 h 36 min and required 2.28 Gb of RAM. AVAILABILITY: The software GLASCOW can be freely downloaded from www.giga.ulg.ac.be/jcms/prod_381171/software. CONTACT: francois.guillaume@jouy.inra.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle.

Genomic selection using 50,000 single nucleotide polymorphism (50k SNP) chips has been implemented in many dairy cattle breeding programs. Cheap, low-density chips make genotyping of a larger number of animals cost effective. A commonly proposed strategy is to impute low-density genotypes up to 50,000 genotypes before predicting direct genomic values (DGV). The objectives of this study were to investigate the accuracy of imputation for animals genotyped with a low-density chip and to investigate the effect of imputation on reliability of DGV. Low-density chips contained 384, 3,000, or 6,000 SNP. The SNP were selected based either on the highest minor allele frequency in a bin or the middle SNP in a bin, and DAGPHASE, CHROMIBD, and multivariate BLUP were used for imputation. Genotypes of 9,378 animals were used, from which approximately 2,350 animals had deregressed proofs. Bayesian stochastic search variable selection was used for estimating SNP effects of the 50k chip. Imputation accuracies and imputation error rates were poor for low-density chips with 384 SNP. Imputation accuracies were higher with 3,000 and 6,000 SNP. Performance of DAGPHASE and CHROMIBD was very similar and much better than that of multivariate BLUP for both imputation accuracy and reliability of DGV. With 3,000 SNP and using CHROMIBD or DAGPHASE for imputation, 84 to 90% of the increase in DGV reliability using the 50k chip, compared with a pedigree index, was obtained. With multivariate BLUP, the increase in reliability was only 40%. With 384 SNP, the reliability of DGV was lower than for a pedigree index, whereas with 6,000 SNP, about 93% of the increase in reliability of DGV based on the 50k chip was obtained when using DAGPHASE for imputation. Using genotype probabilities to predict gene content increased imputation accuracy and the reliability of DGV and is therefore recommended for applications of imputation for genomic prediction. A deterministic equation was derived to predict accuracy of DGV based on imputation accuracy, which fitted closely with the observed relationship. The deterministic equation can be used to evaluate the effect of differences in imputation accuracy on accuracy and reliability of DGV.

Genomic breeding value estimation using genetic markers, inferred ancestral haplotypes, and the genomic relationship matrix.

With the introduction of new single nucleotide polymorphism (SNP) chips of various densities, more and more genotype data sets will include animals genotyped for only a subset of the SNP. Imputation techniques based on unobserved ancestral haplotypes may be used to infer missing genotypes. These ancestral haplotypes may also be used in the genomic prediction model, instead of using the SNP. This may increase the reliability of predictions because the ancestral haplotype may capture more linkage disequilibrium with quantitative trait loci than SNP. The aim of this paper was to study whether using unobserved ancestral haplotypes in a genomic prediction model would provide more reliable genomic predictions than using SNP, and to determine how many loci in the genomic prediction model would be redundant. Genotypes of 8,960 bulls and cows for 39,557 SNP were analyzed with a hidden Markov model to associate each individual at each locus to 2 ancestral haplotypes. The number of ancestral haplotypes per locus was fixed at 10, 15, or 20. Subsequently, a validation study was performed in which the phenotypes of 3,251 progeny-tested bulls for 16 traits were used in a genomic prediction model to predict the estimated breeding values of at least 753 validation bulls. The squared correlation between genomic prediction and deregressed daughter performance estimated breeding value, when averaged across traits, was slightly higher when 15 or 20 ancestral haplotypes per locus were used in the prediction model instead of the SNP genotypes, whereas the prediction model using a genomic relationship matrix gave the lowest squared correlations. The number of redundant loci [i.e., loci that had less than 18 jumps (0.1%) from one ancestral haplotype to another ancestral haplotype at the next locus], was 18,793 (48%), which means that only 20,764 loci would need to be included in the genomic prediction model. This provides opportunities for greatly decreasing computer requirements of genomic evaluations with very large numbers of markers.

Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations.

The purpose of this study was to investigate the imputation error and loss of reliability of direct genomic values (DGV) or genomically enhanced breeding values (GEBV) when using genotypes imputed from a 3,000-marker single nucleotide polymorphism (SNP) panel to a 50,000-marker SNP panel. Data consisted of genotypes of 15,966 European Holstein bulls from the combined EuroGenomics reference population. Genotypes with the low-density chip were created by erasing markers from 50,000-marker data. The studies were performed in the Nordic countries (Denmark, Finland, and Sweden) using a BLUP model for prediction of DGV and in France using a genomic marker-assisted selection approach for prediction of GEBV. Imputation in both studies was done using a combination of the DAGPHASE 1.1 and Beagle 2.1.3 software. Traits considered were protein yield, fertility, somatic cell count, and udder depth. Imputation of missing markers and prediction of breeding values were performed using 2 different reference populations in each country: either a national reference population or a combined EuroGenomics reference population. Validation for accuracy of imputation and genomic prediction was done based on national test data. Mean imputation error rates when using national reference animals was 5.5 and 3.9% in the Nordic countries and France, respectively, whereas imputation based on the EuroGenomics reference data set gave mean error rates of 4.0 and 2.1%, respectively. Prediction of GEBV based on genotypes imputed with a national reference data set gave an absolute loss of 0.05 in mean reliability of GEBV in the French study, whereas a loss of 0.03 was obtained for reliability of DGV in the Nordic study. When genotypes were imputed using the EuroGenomics reference, a loss of 0.02 in mean reliability of GEBV was detected in the French study, and a loss of 0.06 was observed for the mean reliability of DGV in the Nordic study. Consequently, the reliability of DGV using the imputed SNP data was 0.38 based on national reference data, and 0.48 based on EuroGenomics reference data in the Nordic validation, and the reliability of GEBV using the imputed SNP data was 0.41 based on national reference data, and 0.44 based on EuroGenomics reference data in the French validation.

Imputation of genotypes from different single nucleotide polymorphism panels in dairy cattle.

Imputation of missing genotypes is important to join data from animals genotyped on different single nucleotide polymorphism (SNP) panels. Because of the evolution of available technologies, economical reasons, or coexistence of several products from competing organizations, animals might be genotyped for different SNP chips. Combined analysis of all the data increases accuracy of genomic selection or fine-mapping precision. In the present study, real data from 4,738 Dutch Holstein animals genotyped with custom-made 60K Illumina panels (Illumina, San Diego, CA) were used to mimic imputation of genotypes between 2 SNP panels of approximately 27,500 markers each and with 9,265 SNP markers in common. Imputation efficiency increased with number of reference animals (genotyped for both chips), when animals genotyped on a single chip were included in the training data, with regional higher marker densities, with greater distance to chromosome ends, and with a closer relationship between imputed and reference animals. With 0 to 2,000 animals genotyped for both chips, the mean imputation error rate ranged from 2.774 to 0.415% and accuracy ranged from 0.81 to 0.96. Then, imputation was applied in the Dutch Holstein population to predict alleles from markers of the Illumina Bovine SNP50 chip with markers from a custom-made 60K Illumina panel. A cross-validation study performed on 102 bulls indicated that the mean error rate per bull was approximately equal to 1.0%. This study showed the feasibility to impute markers in dairy cattle with the current marker panels and with error rates below 1%.

Marker imputation with low-density marker panels in Dutch Holstein cattle.

The availability of high-density bovine genotyping arrays made implementation of genomic selection possible in dairy cattle. Development of low-density single nucleotide polymorphism (SNP) panels will allow the extension of genomic selection to a larger portion of the population. Prediction of ungenotyped markers, called imputation, is a strategy that allows using the same low-density chips for all traits (and for different breeds). In the present study, we evaluated the accuracy of imputation with low-density genotyping arrays in the Dutch Holstein population. Five different sizes of genotyping arrays were tested, from 384 to 6,000 SNP. According to marker density, the overall allelic imputation error rate obtained with the program DAGPHASE, which relies on linkage disequilibrium and linkage, ranged from 11.7 to 2.0%, and that obtained with the program CHROMIBD, which relies on linkage and the set of all genotyped ancestors, ranged from 10.7 to 3.3%. However, imputation efficiency was influenced by the relationship between low-density and high-density genotyped animals. Animals with both parents genotyped had particularly low imputation error rates: <1% with 1,500 SNP or more. In summary, missing marker alleles can be predicted with 3 to 4% errors with approximately 1 SNP/Mb (approximately 3,000 markers). The CHROMIBD program proved more efficient than DAGPHASE only at lower marker densities or when several genotyped ancestors were available. Future studies are required to measure the effect of these imputation error rates on accuracy of genomic selection with low-density SNP panels.

Correlative responses for carcass and meat quality traits to selection for ovulation rate or prenatal survival in French Large White pigs.

Correlated effects of selection for components of litter size on carcass and meat quality traits were estimated using data from 3 lines of pigs derived from the same Large White base population. Two lines were selected for 6 generations on high ovulation rate at puberty (OR) or high prenatal survival corrected for ovulation rate in the first 2 parities (PS). The third line was an unselected control (CON). The 3 lines were kept for a 7th generation, but without any selection. Carcass and meat quality traits were recorded on the 5th to 7th generation of the experiment. Carcass traits included dressing percentage, carcass length (LGTH), average backfat thickness (ABT), estimated lean meat content, and 8 carcass joint weight traits. Meat quality traits included pH recorded 24 h after slaughter (pH24) of LM, gluteus superficialis (GS), biceps femoris (BF), and adductor femoris (AD) muscles, as well as reflectance and water-holding capacity (WHC) of GS and BF muscles. Heritabilities of carcass and meat quality traits and their genetic correlations with OR and PS were estimated using REML methodology applied to a multiple trait animal model. Correlated responses to selection were then estimated by computing differences between OR or PS and CON lines at generations 5 to 7 using least squares and mixed model methodology. Heritability (h(2)) estimates were 0.08 +/- 0.04, 0.58 +/- 0.10, 0.70 +/- 0.10, and 0.74 +/- 0.10 for dressing percentage, LGTH, ABT, and lean meat content, respectively, ranged from 0.28 to 0.72 for carcass joint traits, from 0.28 to 0.45 for pH24 and reflectance measurements, and from 0.03 to 0.11 for WHC measurements. Both OR and PS had weak genetic correlations with carcass (r(G) = -0.09 to 0.17) and most meat quality traits. Selection for OR did not affect any carcass composition or meat quality trait. Correlated responses to selection for PS were also limited, with the exception of a decrease in pH24 of GS and BF muscles (-0.12 to -0.14 after 6 generations; P < 0.05), in WHC of GS muscle (-18.9 s after 6 generations; P < 0.05) and a tendency toward an increase in loin weight (0.44 kg after 6 generations; P < 0.10) .

Estimation of genetic parameters and genome scan for 15 semen characteristics traits of Holstein bulls.

A QTL detection experiment was performed in French dairy cattle to search for QTL related to male fertility. Ten families, involving a total of 515 bulls, were phenotyped for ejaculated volume and sperm concentration, number of spermatozoa, motility, velocity, percentage of motile spermatozoa after thawing and abnormal spermatozoa. A set of 148 microsatellite markers were used to realize a genome scan. First, genetic parameters were estimated for all traits. Semen production traits were found to have moderate heritabilities (from 0.15 to 0.30) while some of the semen quality traits such as motility had high heritabilities (close to 0.60). Genetic correlations among traits showed negative relationships between volume and concentration and between volume and most quality traits such as motility or abnormal sperm while correlations between concentration and these traits were rather favourable. Percentages of abnormal sperm were negatively related to quality traits, especially with motility and velocity of spermatozoa. Three QTL related to abnormal sperm frequencies were significant at p < 0.01. In total, 11 QTL (p < 0.05) were detected. However, the number of QTL detected was within the range of expected false positives. Because of the lack of power to find QTL in this design further analyses are required to confirm these QTL.

Detection of quantitative trait loci affecting non-return rate in French dairy cattle.

The purpose of this study was to map quantitative trait loci (QTL) influencing female fertility estimated by non-return rate (NRR) in the French dairy cattle breeds Prim'Holstein, Normande and Montbeliarde. The first step was a QTL detection study on NRR at 281 days after artificial insemination on 78 half-sib families including 4993 progeny tested bulls. In Prim'Holstein, three QTL were identified on Bos taurus chromosomes BTA01, BTA02 and BTA03 (p < 0.01), whereas one QTL was identified in Normande on BTA01 (p < 0.05). The second step aimed at confirming these three QTL and refining their location by selecting and genotyping additional microsatellite markers on a sub-sample of 41 families from the three breeds using NRR within 56, 90 and 281 days after AI. Only the three QTL initially detected in Prim'Holstein were confirmed. Moreover, the analysis of NRR within 56, 90 and 281 days after AI allowed us to distinguish two FF QTL on BTA02 in Prim'Holstein, one for NRR56 and one for NRR90. Estimated QTL variance was 18%, 14%, 11.5% and 14% of the total genetic variance, respectively, for QTL mapping to BTA01, BTA02 (NRR90 and NRR56) and BTA03.

Short communication: correlations of marker-assisted breeding values with progeny-test breeding values for eight hundred ninety-nine French Holstein bulls.

French artificial insemination companies have been running a marker-assisted selection program since 2001 to determine which young bulls should be progeny tested. A first batch of 899 Holstein sires receiving their first proofs based on progeny daughters has been studied. Estimated breeding values with or without marker information were computed based on information available in April 2004, and correlated to daughter yield deviations available in 2007 for production traits. Marker-assisted estimated breeding values presented greater correlations with daughter yield deviations than those calculated using only pedigree index. The average improvement in correlation was 0.043 and ranged from +0.001 for protein yield to +0.103 for fat percentage. This gain was based on the initial and suboptimal conditions of the program and is expected to increase in the coming years because of several improvements implemented since the start of the marker-assisted selection program.

A Generalized Caprine-like Hypoplasia Syndrome is localized within a 6-cM interval on bovine chromosome 13 in the Montbéliarde breed.

Caprine-like Generalized Hypoplasia Syndrome (or SHGC) is a new hereditary disorder described in the Montbéliarde breed. We report here the characterization of this new disease, based on the visual examination of animals affected by SHGC, and on physiological and biochemical studies undertaken on samples of both SHGC and normal animals. Biological samples for more than 150 affected calves and their parents have been collected over the past 4 years within the framework of the Bovine Genetic Disease Observatory. First, pedigree analyses showed that the mode of inheritance is most probably autosomal recessive. Then, a genome scan with 113 animals and 140 microsatellite markers revealed a single locus within a 35-cM region on bovine chromosome 13. Genotypes of 261 animals for 18 new microsatellite markers from the region confirmed the localization of the disorder to a 6-cM interval. Finally, based on the analysis of haplotypes in 463 Montbéliarde sires, we estimated the frequency of the SHGC mutated allele in the population and could propose a strategy for the systematic eradication of this disorder in the near future.

Environmental effects on lactation curves included in a test-day model genetic evaluation.

A large number of environmental factors affect the daily milk production of a cow. Lactation curves included in the French test-day model are modelled as a function of days in milk with semi-parametric curves (splines). The proper modelling of environmental effects in the test-day analysis was investigated using test-day records collected from the first three lactations of French Montbéliarde cows from 1988 to 2005. Four lactation-curve effects describing calving month, length of dry period, age at calving and gestation defined within parity-class were fitted. The shape of lactation curves did not depend on year of calving, which can be modelled as a constant over the whole lactation. To reduce computational requirements and time, data were pre-adjusted in a first step for fixed effects with no year interaction, and then used for genetic evaluation. Correlations for each lactation between 305-day estimates of genetic and permanent environment effects computed using pre-adjustment factors obtained at a 4-year interval were virtually one. The use of a two-step procedure had a very limited impact on the estimates of genetic and permanent environment effects. The minimum correlations with values estimated with a one-step procedure were 0.9984 and 0.9974, respectively. The knowledge of systematic environmental effects affecting the cow daily yield through lactation curves offers interesting perspectives to predict future daily milk production.

Correlated responses of pre- and postweaning growth and backfat thickness to six generations of selection for ovulation rate or prenatal survival in French Large White pigs.

Correlated effects of selection for components of litter size on growth and backfat thickness were estimated using data from 3 pig lines derived from the same base population of Large White. Two lines were selected for 6 generations on either high ovulation rate at puberty (OR) or high prenatal survival corrected for ovulation rate in the first 2 parities (PS). The third line was an unselected control (C). Genetic parameters for individual piglet BW at birth (IWB); at 3 wk of age (IW3W); and at weaning (IWW); ADG from birth to weaning (ADGBW), from weaning to 10 wk of age (ADGPW), and from 25 to 90 kg of BW (ADGT); and age (AGET) and average backfat thickness (ABT) at 90 kg of BW were estimated using REML methodology applied to a multivariate animal model. In addition to fixed effects, the model included the common environment of birth litter, as well as direct and maternal additive genetic effects as random effects. Genetic trends were estimated by computing differences between OR or PS and C lines at each generation using both least squares (LS) and mixed model (MM) methodology. Average genetic trends for direct and maternal effects were computed by regressing line differences on generation number. Estimates of direct and maternal heritabilities were, respectively, 0.10, 0.12, 0.20, 0.24, and 0.41, and 0.17, 0.33, 0.32, 0.41, and 0.21 (SE = 0.03 to 0.04) for IWB, IW3W, IWW, ADGBW, and ADGPW. Genetic correlations between direct and maternal effects were moderately negative for IWB (-0.21 +/- 0.18), but larger for the 4 other traits (-0.59 to -0.74). Maternal effects were nonsignificant and were removed from the final analyses of ADGT, AGET, and ABT. Direct heritability estimates were 0.34, 0.46, and 0.21 (SE = 0.03 to 0.05) for ADGT, AGET, and ABT, respectively. Direct and maternal genetic correlations of OR with performance traits were nonsignificant, with the exception of maternal correlations with IWB (-0.28 +/- 0.13) and ADGPW (0.23 +/- 0.11) and direct correlation with AGET (-0.23 +/- 0.09). Prenatal survival also had low direct but moderate to strong maternal genetic correlations (-0.34 to -0.65) with performance traits. The only significant genetic trends were a negative maternal trend for IBW in the OR line and favorable direct trends for postweaning growth (ADGT and AGET) in both lines. Selection for components of litter size has limited effects on growth and backfat thickness, although it slightly reduces birth weight and improves postweaning growth.

Characterization of the DGAT1 K232A and variable number of tandem repeat polymorphisms in French dairy cattle.

A quantitative trait locus (QTL) underlying different milk production traits has been identified with a high significance threshold value in the genomic region containing the acylCoA:diacylglycerol acyltransferase (DGAT1) gene, in the 3 main French dairy cattle breeds: French Holstein, Normande, and Montbéliarde. Previous studies have confirmed that the K232A polymorphism in DGAT1 is responsible for a major QTL underlying several milk production traits in Holstein dairy cattle and several other bovine breeds. In this study, we estimate the frequency of the 2 alternative alleles, K and A, of the K232A polymorphism in French Holstein, Normande, and Montbéliarde breeds. Although the K allele segregates in French Holstein and Normande breeds with a similar effect on production traits, the existence of additional mutations contributing to the observed QTL effect is strongly suggested in both breeds by the existence of sires heterozygous at the QTL but homozygous at the K232A polymorphism. One allele at a variable number of tandem repeats (VNTR) locus in the 5' noncoding region of DGAT1 has been recently proposed as a putative causative variant. In our study, this marker was found to present a high mutation rate of 0.8% per gamete and per generation, making the allele diversity observed compatible with that expected under neutrality. Moreover, among the sires homozygous at the K232A polymorphism, no allele at the VNTR can fully explain their QTL status. Finally, no allele at the VNTR was found to be significantly associated with the fat percentage variation in the 3 breeds simultaneously after correction for the effect of the K232A polymorphism. Therefore, our results suggest the existence of at least one other causative polymorphism not yet described. Because the A allele is nearly fixed in the Montbéliarde breed, this breed represents an interesting model to identify and confirm other mutations that have a strong effect on milk production traits.