Identification of candidate markers on bovine chromosome 14 (BTA14) under milk production trait quantitative trait loci in Holstein

The objective of this study was to identify single-nucleotide polymorphisms using a bovine chromosome 14 high-density SNP panel after accounting for the effect of DGAT1. Linkage disequilibrium information and sire heterozygosity were used to select markers for linkage analysis on bovine chromosome 14 for milk production traits in 321 Holstein animals. Results show putative milk peaks at 42 and 61 cM, both at p<0.10, a fat yield peak at 42 and 63 cM, both at p<0.05; a protein yield peak at 42 (p<0.01) and 84 cM (p<0.05); fat per cent peaks at 3 (p<0.01) and 29 cM (p<0.05), and a protein per cent peak at 4 cM (p<0.05). Once quantitative trait loci positions were established, allele substitution effects for all markers were evaluated using the same statistical model. Overlaying information between quantitative trait loci (QTL) and allele effect analysis enabled the identification (p<0.01) of 20 SNPs under the milk yield QTL, 2 under both of the fat yield peaks, 8 and 9 under the protein yield peaks, 2 and 6 for the fat per cent peaks and 5 for the protein per cent peak. One SNP in particular, ss61514555:A>C, showed association with 3 of the 5 traits: milk (p=1.59E-04), fat (p=6.88E-05) and protein yields (p=5.76E-05). Overall, combining information from linkage disequilibrium, sire heterozygosity and genetic knowledge of traits enabled the characterization of additional markers with significant associations with milk production traits.     

Investigations in the character of QTL affecting negatively correlated milk traits

The primary aim of this study was to investigate the quantitative trait loci (QTL) on BTA6 that affect negatively correlated milk traits, using bivariate covariance component analysis of milk yield and fat (or protein) content, protein yield and fat content, and fat yield and protein content. A set of five different genetic models was adapted to differentiate trait‐specific QTL in close linkage from pleiotropy. Using a grand‐daughter design consisting of five half‐sib families from the German Holstein population and 298 sons genotyped for 16 microsatellite markers on BTA6, we found significant trait‐specific QTL for fat content and protein yield, 24 cM apart. Markers BM1329 and FBN12 bracketed the QTL for fat content, and the region between TGLA37 and FBN13 most likely harbours a QTL for protein yield. The analysis based on the close linkage model fully confirmed this result. Despite the pure QTL findings confirming results from the literature, distinguishing pleiotropic and closely linked QTL for competitive traits is a new aspect. Our multivariate analysis results did not suggest a pleiotropic QTL for the investigated negatively correlated traits. The QTL‐based trait correlations were discussed as an important aspect of modelling that needs to be considered in the future.     

A whole genome scan to map QTL for milk production traits and somatic cell score in Canadian Holstein bulls

The detection and mapping of genetic markers linked to quantitative trait loci (QTL) can be utilized to enhance genetic improvement of livestock populations. With the completion of the bovine genome sequence assembly, single nucleotide polymorphisms (SNP) assays spanning the whole bovine genome and research work on large scale identification, validation and analysis of genotypic variation in cattle has become possible. The objective of the present study was to perform a whole genome scan to identify and map QTL affecting milk production traits and somatic cell scores using linkage disequilibrium (LD) regression and 1536 SNP markers. Three and 18 SNP were found to be associated with only milk yield (MY) at a genome and chromosome wise significance (p < 0.05) level respectively. Among the 21 significant SNP, 16 were in a region reported to have QTL for MY in other dairy cattle populations and while the rest five were new QTL finding. Four SNP out of 21 are significant for the milk production traits (MY, fat yield, protein yield (PY), and milk contents) in the present study. Six and nine SNP were associated with PY at a genome and chromosome wise significant (p < 0.05) level respectively. Three and 17 SNP were found to be associated with FY at a genome and chromosome wise significant (p < 0.05) level. Five and seven SNP were mapped with somatic cell score at a genome and chromosome wise significant (p < 0.05) level respectively. The results of this study have revealed QTL for MY, PY, protein percentage, FY, fat percentage, somatic cell score and persistency of milk in the Canadian dairy cattle population. The chromosome regions identified in this study should be further investigated to potentially identify the causative mutations underlying the QTL.     

An independent confirmation of a quantitative trait locus for milk yield and composition traits on bovine chromosome 26

Several reports have demonstrated that bovine chromosome 26 (BTA26) harbours significant or suggestive quantitative trait loci (QTL) for milk production and composition traits in dairy cattle. Our previous study showed that a C/T substitution in the bovine TCF7L2 gene on BTA26 was significantly linked to QTL for protein yield (PY) in a Canadian dairy cattle population. Actually, this polymorphism was one of the markers derived from a genome‐wide screening of QTL for milk PY using an amplified fragment length polymorphism technique combined with a DNA pooling strategy. In the present study, 990 Holstein bulls with complete genotype and phenotype data from 14 sire families were analysed to confirm, if the QTL effects exist in other populations. Statistical analysis revealed that this marker was significantly associated with PY, protein percentage, milk yield and fat yield (FY) (p < 0.001) in the US Holstein population. These results indicate that this QTL region has a pleiotrophic effect on different milk traits and is portable in different populations.     

Mapping genes for fatness and growth on pig chromosome 13: a search in the region close to the pig PIT1 gene

Previous research has shown that PIT1 polymorphisms in several resource populations and the chromosomal region near PIT1 in some populations have been significantly associated with fatness and growth QTLs on pig chromosome 13. To confirm these previous results and to clarify the role of the PIT1 gene in the putative QTL region, this research project was enlarged to include two microsatellite markers flanking each side of the PIT1 gene ( Swr1008 , S0068 , Sw398 and Sw1056 ). The ISU Chinese × US resource families were used and the traits analysed were birth weight, 21 day weight, 42 day weight, longissimus muscle area, back-fat thickness at several locations, meat colour, marbling and firmness on the carcass, and growth rate for selected time periods. The total number of F₂ pigs used ranged from 241 to 330. The data were analysed using interval mapping for each breed-cross separately as well as with the pooled data, and single marker least squares analyses for the pooled data. Significant evidence of a QTL for first rib back-fat thickness was detected approximately 20 cM from the PIT1 gene by using both single marker (p < 0.01) and interval mapping analyses in the pooled data (p < 0.0001) as well as in one family (p < 0.01). Evidence of a QTL for birth weight was detected at the estimated PIT1 position in the interval mapping analysis by using the pooled data (p < 0.014) and verified by the single marker analyses. These results confirmed the previously published QTL work on pig chromosome 13 for the birth weight QTL but suggest that other genes in the region may be partly responsible for the earlier results on the back-fat thickness QTL in our resource families.     

QTL detection for milk production traits in goats using a longitudinal model.

Eight paternal half-sib families were used to identify chromosomal regions associated with variation in the lactation curves of dairy goats. DNA samples from 162 animals were amplified by PCR for 37 microsatellite markers, from Capra hircus autosomes CHI3, CHI6, CHI14 and CHI20. Milk samples were collected during 6 years, and there were 897 records for milk yield (MY) and 814 for fat (FP) and protein percentage (PP). The analysis was conducted in two stages. First, a random regression model with several fixed effects was fitted to describe the lactation function, using a scale (alpha) plus four shape parameters: beta and gamma, both associated with a decrease in the slope of the curve, and delta and phi that are related to the increase in slope. Predictions of alpha, beta, gamma, delta and phi were regressed using an interval mapping model, and F-tests were used to test for quantitative trait loci (QTL) effects. Significant (p < 0.05) QTLs were found for: (i) MY: CHI6 at 70-80 cM for all parameters; CHI14 at 14 cM for delta and phi; (ii) FP: CHI14, at 63 cM was associated with beta; CHI20, at 72 cM, showed association with alpha; (iii) PP: chromosomal regions associated with beta were found at 59 cM in CHI3 and at 55 cM in CHI20 with alpha and gamma. Analyses using more families and more animals will be useful to confirm or to reject these findings.     

A comprehensive search for quantitative trait loci affecting growth and carcass composition of cattle segregating alternative forms of the myostatin gene

The objective of this study was to identify quantitative trait loci for economically important traits in two families segregating an inactive copy of the myostatin gene. Two half-sib families were developed from a Belgian Blue x MARC III (n = 246) and a Piedmontese x Angus (n = 209) sire. Traits analyzed were birth, weaning, and yearling weight (kg); preweaning average daily gain (kg/d); postweaning average daily gain (kg/d); hot carcass weight (kg); fat depth (cm); marbling score; longissimus muscle area (cm2); estimated kidney, pelvic, and heart fat (%); USDA yield grade; retail product yield (%); fat yield (%); and wholesale rib-fat yield (%). Meat tenderness was measured as Warner-Bratzler shear force at 3 and 14 d postmortem. The effect of the myostatin gene was removed using phase information from six microsatellite markers flanking the locus. Interactions of the myostatin gene with other loci throughout the genome were also evaluated: The objective was to use markers in each family, scanning the genome approximately every 25 to 30 centimorgans (cM) on 18 autosomal chromosomes, excluding 11 autosomal chromosomes previously analyzed. A total of 89 markers, informative in both families, were used to identify genomic regions potentially associated with each trait. In the family of Belgian Blue inheritance, a significant QTL (expected number of false-positives = 0.025) was identified for marbling score on chromosome 3. Suggestive QTL for the same family (expected number of false-positives = 0.5) were identified for retail product yield on chromosome 3, for hot carcass weight and postweaning average daily gain on chromosome 4, for fat depth and marbling score on chromosome 8, for 14-d Warner-Bratzler shear force on chromosome 9, and for marbling score on chromosome 10. Evidence suggesting the presence of an interaction for 3-d Warner-Bratzler shear force between the myostatin gene and a QTL on chromosome 4 was detected. In the family of Piedmontese and Angus inheritance, evidence indicates the presence of an interaction for fat depth between the myostatin gene and chromosome 8, in a similar position where the evidence suggests the presence of a QTL for fat depth in the family with Belgian Blue inheritance. Regions identified underlying QTL need to be assessed in other populations. Although the myostatin gene has a considerable effect, other loci with more subtle effects are involved in the expression of the phenotype.     

Evaluation of association between polymorphism within the thyroglobulin gene and milk production traits in dairy cattle

In dairy cattle, many studies have reported quantitative trait loci (QTL) on the centromeric end of chromosome 14 that affect milk production traits. One of the candidate genes in this QTL region – thyroglobulin (TG) – was previously found to be significantly associated with marbling in beef cattle. Thus, based on QTL studies in dairy cattle and because of possible effects of this gene on fat metabolism, we investigated the association of TG with milk yield and composition in Holstein dairy cattle. A total of 1279 bulls from the Cooperative Dairy DNA Repository Holstein population were genotyped for a single nucleotide polymorphism in TG used previously in beef cattle studies. Analysis of 29 sire families showed no significant association between TG variants and milk production traits. Within‐sire family analysis suggests that TG is neither the responsible gene nor a genetic marker in association with milk production traits.     

Quantitative trait loci affecting growth and carcass composition of cattle segregating alternate forms of myostatin

The effects of the bovine myostatin gene on chromosome 2 on birth and carcass traits have been previously assessed. The objective of this study was to identify additional quantitative trait loci (QTL) for economically important traits in two families segregating an inactive copy of myostatin. Two half-sib families were developed from Belgian Blue x MARC III (n = 246) and Piedmontese x Angus (n = 209) sires. Traits analyzed were birth (kg) and yearling weight (kg); hot carcass weight (kg); fat depth (cm); marbling score; longissimus muscle area (cm2); estimated kidney, pelvic, and heart fat (%); USDA yield grade; retail product yield (%); fat yield (%); and wholesale rib-fat yield (%). Meat tenderness was measured as Warner-Bratzler shear force at 3 and 14 d postmortem. The effect of myostatin on these traits was removed by using phase information obtained from the previous study with six microsatellite markers flanking the locus. Selective genotyping was done on 92 animals from both families to identify genomic regions potentially associated with retail product yield and fat depth, using a total of 150 informative markers in each family. Regions in which selective genotyping indicated the presence of QTL were evaluated further by genotyping the entire population and additional markers. For the family with Belgian Blue inheritance (n = 246), a significant QTL for birth and yearling weight was identified on chromosome 6. Suggestive QTL were identified for longissimus muscle area and hot carcass weight on chromosome 6 and for marbling on chromosomes 17 and 27. For the family with Piedmontese inheritance (n = 209), suggestive QTL on chromosome 5 were identified for fat depth, retail product yield, and USDA yield grade and on chromosome 29 for Warner-Bratzler shear force at 3 and 14 d postmortem. Interactions suggesting the presence of QTL were observed between myostatin and chromosome 5 for Warner-Bratzler shear force at 14 d postmortem and between myostatin and chromosome 14 for fat depth. Thus, in families segregating an inactive copy of myostatin in cattle, other loci influencing quantitative traits can be detected. These results are the initial effort to identify and characterize QTL affecting carcass and growth traits in families segregating myostatin.     

Mapping of quantitative trait loci for growth and carcass traits in commercial sheep populations

Quantitative trait loci analyses were applied to data from Suffolk and Texel commercial sheep flocks in the United Kingdom. The populations comprised 489 Suffolk animals in three half-sib families and 903 Texel animals in nine half-sib families. Phenotypic data comprised measurements of live weight at 8 and 20 wk of age and ultrasonically measured fat and muscle depth at 20 wk. Lambs and their sires were genotyped across candidate regions on chromosomes 1, 2, 3, 4, 5, 6, 11, 18, and 20. Data were analyzed at the breed level, at the family level, and across extended families when families were genetically related. The breed-level analyses revealed a suggestive QTL on chromosome 1 in the Suffolk breed, between markers BM8246 and McM130, affecting muscle depth, although the effect was only significant in one of the three Suffolk families. A two-QTL analysis suggested that this effect may be due to two adjacent QTL acting in coupling. In total, 24 suggestive QTL were identified from individual family analyses. The most significant QTL affected fat depth and was segregating in a Texel family on chromosome 2, with an effect of 0.62 mm. The QTL was located around marker ILSTS030, 26 cM distal to myostatin. Two of the Suffolk and two of the Texel sires were related, and a three-generation analysis was applied across these two extended families. Seven suggestive QTL were identified in this analysis, including one that had not been detected in the individual family analysis. The most significant QTL, which affected muscle depth, was located on chromosome 18 near the callipyge and Carwell loci. Based on the phenotypic effect and location of the QTL, the data suggest that a locus similar to the Carwell locus may be segregating in the United Kingdom Texel population.     

A quantitative trait locus for faecal worm egg and blood eosinophil counts on chromosome 23 in Australian goats

Three microsatellite markers on goat chromosome 23 adjacent to the MHC were used to test for quantitative trait loci (QTL) affecting faecal worm egg count (WEC) and leukocyte traits in ten Australian Angora and twelve Australian Cashmere half‐sib families (n = 16–57 per family). Data were collected from 280 Angora and 347 Cashmere kids over a 3‐ and 4‐year period. A putative QTL affecting trichostrongyle WEC was found in two small families at the 5% chromosome‐wise threshold level. The biggest QTL effect for WEC of 1.65 standard deviations (σ_p) was found within the region of OarCP73‐BM1258. A significant QTL affecting blood eosinophil counts at the 1% chromosome–wise threshold level was detected at marker BM1258 (at 26 cM) in two Angora and Cashmere families. The magnitude of the putative QTL was 0.69 and 0.85 σ_p in Angora and Cashmere families, respectively. Due to the comparatively low power of the study these findings should be viewed as indicative rather than definitive.     

Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle

Feed intake and feed efficiency are economically important traits in beef cattle because feed is the greatest variable cost in production. Feed efficiency can be measured as feed conversion ratio (FCR, intake per unit gain) or residual feed intake (RFI, measured as DMI corrected for BW and growth rate, and sometimes a measure of body composition, usually carcass fatness, RFI(bf)). The goal of this study was to fine map QTL for these traits in beef cattle using 2,194 markers on 24 autosomes. The animals used were from 20 half-sib families originating from Angus, Charolais, and University of Alberta Hybrid bulls. A mixed model with random sire and fixed QTL effect nested within sire was used to test each location (cM) along the chromosomes. Threshold levels were determined at the chromosome and genome levels using 20,000 permutations. In total, 4 QTL exceeded the genome-wise threshold of P < 0.001, 3 exceeded at P < 0.01, 17 at P < 0.05, and 30 achieved significance at the chromosome-wise threshold level (at least P < 0.05). No QTL were detected on BTA 8, 16, and 27 above the 5% chromosome-wise significance threshold for any of the traits. Nineteen chromosomes contained RFI QTL significant at the chromosome-wise level. The RFI(bf) QTL results were generally similar to those of RFI, the positions being similar, but occasionally differing in the level of significance. Compared with RFI, fewer QTL were detected for both FCR and DMI, 12 and 4 QTL, respectively, at the genome-wise thresholds. Some chromosomes contained FCR QTL, but not RFI QTL, but all DMI QTL were on chromosomes where RFI QTL were detected. The most significant QTL for RFI was located on BTA 3 at 82 cM (P = 7.60 x 10(-5)), for FCR on BTA 24 at 59 cM (P = 0.0002), and for DMI on BTA 7 at 54 cM (P = 1.38 x 10(-5)). The RFI QTL that showed the most consistent results with previous RFI QTL mapping studies were on BTA 1, 7, 18, and 19. The identification of these QTL provides a starting point to identify genes affecting feed intake and efficiency for use in marker-assisted selection and management.     

Effects of DGAT1 variants on milk production traits in German cattle breeds

Various QTL mapping experiments led to the detection of a QTL in the centromeric region of cattle chromosome 14 that had a major effect on the fat content of milk. Recently, the gene encoding diacylglycerol O-acyltransferase (DGAT1) was proposed to be a positional and functional candidate for this trait. This study investigated the effects of a nonconservative lysine to alanine (K232A) substitution in DGAT1, which very likely represents the causal mutation, on milk production traits. Existing granddaughter designs for Fleckvieh and German Holstein, the two major dairy/dual-purpose breeds in Germany, were used to estimate allele frequencies and gene substitution effects for milk, fat, and protein yield, as well as fat and protein content. A restriction fragment length polymorphism assay was applied to diagnose the K232A substitution in DGAT1. Estimates of the allele frequencies for the lysine-encoding variant were based on maternally inherited alleles in sons and amounted to 0.072 for Fleckvieh and 0.548 for German Holstein. Effects of DGAT1 variants on content traits were pronounced; estimates of the gene substitution effect for the lysine-encoding variant were 0.35 and 0.28% for fat content and 0.10 and 0.06% for protein content in Fleckvieh and German Holstein, respectively. Conversely, negative effects of the lysine variant of -242 to -180 kg for Fleckvieh and -260 to -320 kg for German Holstein were revealed for milk yield from first to third lactation, resulting in enhanced fat yield of 7.5 to 14.8 kg in Fleckvieh and 7.6 to 10.7 kg in German Holstein. For protein yield, however, mainly negative effects of -3.6 to 0.2 kg in Fleckvieh and -4.8 to -5.2 kg in German Holstein were observed. Pearson correlations between residuals of milk yield and content traits were decreased when omitting DGAT1 effects in the analysis, thereby indicating that DGAT1 contributes to negative correlations between these traits. Molecular tests allow for the direct selection among variants; however, the benefits of the alternative alleles depend on economic weights given to the different milk production traits in the breeding goal.     

Primary genome scan to identify putative quantitative trait loci for feedlot growth rate, feed intake, and feed efficiency of beef cattle

Feed intake and feed efficiency of beef cattle are economically relevant traits. The study was conducted to identify QTL for feed intake and feed efficiency of beef cattle by using genotype information from 100 microsatellite markers and 355 SNP genotyped across 400 progeny of 20 Angus, Charolais, or Alberta Hybrid bulls. Traits analyzed include feedlot ADG, daily DMI, feed-to-gain ratio [F:G, which is the reciprocal of the efficiency of gain (G:F)], and residual feed intake (RFI). A mixed model with sire as random and QTL effects as fixed was used to generate an F-statistic profile across and within families for each trait along each chromosome, followed by empirical permutation tests to determine significance thresholds for QTL detection. Putative QTL for ADG (chromosome-wise P < 0.05) were detected across families on chromosomes 5 (130 cM), 6 (42 cM), 7 (84 cM), 11 (20 cM), 14 (74 cM), 16 (22 cM), 17 (9 cM), 18 (46 cM), 19 (53 cM), and 28 (23 cM). For DMI, putative QTL that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 1 (93 cM), 3 (123 cM), 15 (31 cM), 17 (81 cM), 18 (49 cM), 20 (56 cM), and 26 (69 cM) in the across-family analyses. Putative across-family QTL influencing F:G that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 3 (62 cM), 5 (129 cM), 7 (27 cM), 11 (16 cM), 16 (30 cM), 17 (81 cM), 22 (72 cM), 24 (55 cM), and 28 (24 cM). Putative QTL influencing RFI that exceeded the chromosome-wise P < 0.05 threshold were detected on chromosomes 1 (90 cM), 5 (129 cM), 7 (22 cM), 8 (80 cM), 12 (89 cM), 16 (41 cM), 17 (19 cM), and 26 (48 cM) in the across-family analyses. In addition, a total of 4, 6, 1, and 8 chromosomes showed suggestive evidence (chromosome-wise, P < 0.10) for putative ADG, DMI, F:G, and RFI QTL, respectively. Most of the QTL detected across families were also detected within families, although the locations across families were not necessarily the locations within families, which is likely because of differences among families in marker informativeness for the different linkage groups. The locations and direction of some of the QTL effects reported in this study suggest potentially favorable pleiotropic effects for the underlying genes. Further studies will be required to confirm these QTL in other populations so that they can be fine-mapped for potential applications in marker-assisted selection and management of beef cattle.     

Comparison of estimated breeding values, daughter yield deviations and de-regressed proofs within a whole genome scan for QTL

An important issue in quantitative trait loci (QTL) detection is the use of phenotypic measurement as a dependent variable. Daughter yield deviations (DYDs) as the unit of choice are not available for all traits of interest. The use of de-regressed proofs (DRPFs) of estimated breeding values (EBVs) is an alternative to using daughter yield deviations. The objective of this study was to examine possible differences between DYDs and DRPFs within the use of QTL detection. The pedigree used was part of the granddaughter design of the German QTL effort. Consisting marker maps for livestock species were derived from all available data of 16 German Holstein paternal half-sib families with a total of 872 sires. The number of progeny ranged from 19 to 127. A whole genome scan was performed using weighted and unweighted multimarker regression with DYDs, DRPFs and EBVs as dependent variables for the traits milk, fat and protein yields. Results were compared with respect to the number of QTL detected. A similar number of QTL was detected with DRPFs and DYDs. Also, when dependent variables were weighted according to the variance of the trait, a higher number of QTL was detected at the desired level of significance as compared to using unweighted variables.     

Joint analysis of the influence of CYP11B1 and DGAT1 genetic variation on milk production, somatic cell score, conformation, reproduction, and productive lifespan in German Holstein cattle

Recent publications indicate genetic variation in milk production traits on proximal BTA14, which cannot be explained solely with genetic variation in the DGAT1 gene. To elucidate these QTL effects, animals from a German Holstein granddaughter design (18 families, 1,291 sons) were genotyped for CYP11B1 (V30A) and DGAT1 (K232A) polymorphisms. Frequencies of alleles of maternal descent were estimated for CYP11B1(V) (0.776) and DGAT1(K) (0.549). Allele substitution effects (alpha/2) were first calculated for both alleles in separate models and then in a joint model. From the joint analysis, CYP11B1(V) effects on fat content (+0.04%) and protein content (+0.01%) were positive. Effects on milk yield (-82 kg), fat yield (-0.5 kg), and protein yield (-1.9 kg) were negative. Compared with the individual analysis, DGAT1(K) effects on fat content (+0.28%), protein content (+0.06%), and milk yield (-258 kg) were reduced; fat yield (+10.8 kg) was enhanced; and protein yield (-3.8 kg) was reduced. In the joint analysis, allele substitution effects of CYP11B1(V) and DGAT1(K) together explained more of the variation in milk production traits than DGAT1(K) alone. Further significant effects were found for CYP11B1(V) and DGAT1(K) among 6 reproduction traits and 14 conformational traits. These observations indicate a possible negative influence of DGAT1(K) on maternal nonreturn rate, and thus, on length of productive life.     

Mapping of quantitative trait loci for lactation persistency traits in German Holstein dairy cattle

A whole genome scan to map quantitative trait loci (QTL) for persistency of milk yield (PMY), persistency of fat yield (PFY), persistency of protein yield (PPY) and persistency of milk energy yield (PEY) was performed in a granddaughter design in the German Holstein dairy cattle population. The analysis included 16 paternal half‐sib families with a total of 872 bulls. The analysis was carried out for the first lactation and for the first three lactations combined using univariate weighted multimarker regression. Controlling the false discovery rate across traits and data sets at a level of 0.15 and treating the four persistency traits as different traits revealed 27 significant QTL. A total of 12 chromosomes showed significant QTL effects on a chromosomewise basis. The DGAT1 effect was highly significant for PPY and protein yield. A haplotype analysis using results of previous studies of the same design revealed a co‐segregation of various persistency QTL and QTL affecting health traits like dystocia and stillbirth and functional traits like non‐return rate 90 and somatic cell score.     

Predicting milk yield and composition in lactating sows: a Bayesian approach

The objective of this study was to develop a framework describing the milk production curve in sows as affected by parity, method of milk yield (MY) determination, litter size (LS), and litter gain (LG). A database containing data on LS, LG, dietary protein and fat content, MY, and composition measured on more than 1 d during lactation and method for determining MY from peer reviewed publications and individual sow data from 3 studies was constructed. A Bayesian hierarchical model was developed to analyze milk production data. The classical Wood curve was used to model time trends in MY during lactation, and it was re-parameterized expressing the natural logarithm of MY values at d 5, 20, and 30 as functional parameters. The model incorporated random effects of experiment, sow nested within experiment, and fixed effects of LS, LG, parity, and method through the functional parameters of the Wood curve. A second set of models were constructed to analyze milk composition data, including day in milk, LS, dietary protein, and fat contents. Four scenarios with different LG and LS were constructed using the framework to estimate the energy output in milk at different days during lactation. The estimated energy output was compared with energy output values calculated using the 1998 NRC method. Milk yield was underestimated by approximately 20% with the weigh-suckle-weigh technique compared with the deuterium oxide dilution technique (P < 0.001). The mean LG and LS for the dataset were 2.05 kg/d (1.0; 3.3) and 9.5 piglets (5; 14), respectively. The MY was affected by LS on d 5 and 20 (P < 0.001) and by LG on d 20 (P < 0.001) and d 30 (P = 0.004). The mean time to peak lactation was 18.7 d (SD = 1.06) postpartum and mean MY at peak lactation was 9.23 kg (SD = 0.14). The average protein, lactose, and fat content of milk was 5.22 (SD = 0.06), 5.41 (SD = 0.08), and 7.32% (SD = 0.17%), respectively. The NE requirement for lactation increased from d 5 to 20 because of increased MY. Requirements also increased with increasing LG and LS. The framework could be used to predict energy and protein requirements for lactation under different production expectations and can be incorporated into a whole animal model for determination of energy and nutrient requirements for lactating sows, which can optimize sow performance and longevity.     

中国荷斯坦牛RPL23A和ACACB基因对产奶性状的遗传效应分析

作者所在团队前期通过奶牛乳腺上皮组织转录组测序及荷斯坦公牛全基因组重测序研究发现RPL23A和ACACB基因是奶牛乳蛋白和乳脂性状的候选功能基因,本研究旨在探究这两个基因是否对奶牛产奶性状具有显著遗传效应。以北京地区7个牧场的1059头中国荷斯坦母牛为试验群体,采集尾根静脉血并提取基因组DNA,通过飞行时间质谱方法检测SNP位点基因型,利用SAS9.4软件的MIXED过程进行关联分析。结果表明,RPL23A基因的SNP位点g.20146771C>T与第1泌乳期5个产奶性状达到显著或极显著关联(P=0.0001~0.0416),其优势等位基因为T;ACACB基因的g.63878254T>C位点与第1泌乳期产奶量、乳脂量和乳蛋白量呈极显著关联(P<0.01),其优势等位基因为C;g.63962768G>A位点与第1泌乳期产奶量、乳脂量、乳脂率和乳蛋白率关联显著或极显著(P=0.0001~0.0391),其优势等位基因为A。综上,RPL23A基因主要影响中国荷斯坦牛产奶量和乳蛋白,ACACB基因对产奶量和乳脂具有显著遗传效应,3个SNP位点可考虑作为遗传标记用于标记辅助选择培育奶牛高乳蛋白乳脂新品系和选育提高。     

Searching for DNA markers for milk production and composition on chromosome 6 in sheep

Several milk protein polymorphisms are potential tools for selection in dairy ruminants. However, research results for dairy sheep are not as conclusive as those for goats or cattle and are often controversial. The main objective of this study was to find and later use molecular genetic markers in selection to improve milk production and milk composition in Awassi ewes. Chromosome 6 was chosen because several studies have reported the presence of significant quantitative trait loci (QTL) affecting milk production traits on ovine and bovine chromosome 6. Altogether, genotypes for 13 microsatellite loci were determined for 258 ewes, which were purebred Awassi or Awassi-Merino crosses. Phenotypic data were lactation yield of milk, milk fat, protein and lactose (kg), average milk protein and fat percentage and average somatic cell count. Five out of the 13 microsatellites showed significant association with at least one of the examined traits.