‘赤霞珠’与‘北红’正反交群体抗寒旱性能遗传特点

选用抗寒性较弱的‘赤霞珠’与抗寒性强的‘北红’为亲本,构建了正反交群体,研究分析后代群体休眠季枝条中的含水量、冰点值(LTE)、萌芽率及春季萌芽后田间冻害情况。结果表明,亲本及正反交群体后代枝条含水量保持基本稳定,分布在39%～51%。正反交群体后代枝条LTE值均呈连续正态分布,两个群体中LTE值高于亲本值占比分别为43.47%和41.25%,说明正反交后代均存在抗寒性超亲遗传特点;两个群体后代LTE均值分别为﹣21.01℃和﹣21.52℃,差异不显著,即LTE值不存在明显的细胞质遗传特点;但以‘赤霞珠’做母本的群体后代中抗寒性弱于母本的株系比例高;试验正反交群体株系冬季枝条均能正常萌芽,但群体株系在春季田间表现出不同的萌芽状态。春季萌芽后正反交群体后代均具有较高比例的田间无冻害单株(分别为50.7%和68.4%),说明利用山葡萄F1代做亲本能选育出抗寒旱性能强的品种,且以抗寒旱性能强的品种做母本其后代获得抗寒旱株系的概率更大。试验还发现,两个群体的LTE值与束自比均具有相关性,但春季田间冻害等级与冬季枝条含水量无直接相关性,冻害等级与LTE值也无相关性,推测北方春季气温回升后枝条中水...     

The microbial content of raw and pasteurized cow milk as determined by molecular approaches

The microbial composition of raw and pasteurized milk is assessed on a daily basis. However, many such tests are culture-dependent, and, thus, bacteria that are present at subdominant levels, or that cannot be easily grown in the laboratory, may be overlooked. To address this potential bias, we have used several culture-independent techniques, including flow cytometry, real-time quantitative PCR, and high-throughput DNA sequencing, to assess the microbial population of milk from a selection of commercial milk producers, pre- and postpasteurization. The combination of techniques employed reveals the presence of a previously unrecognized and diverse bacterial population in unpasteurized cow milk. Most notably, the use of high-throughput DNA sequencing resulted in several bacterial genera being identified in milk samples for the first time. These included Bacteroides, Faecalibacterium, Prevotella, and Catenibacterium. Our culture-independent analyses also indicate that the bacterial population of pasteurized milk is more diverse than previously appreciated, and that nonthermoduric bacteria within these populations are likely to be in a damaged, nonculturable form. It is thus apparent that the application of state-of-the-art approaches can provide a detailed insight into the bacterial composition of milk and could potentially be employed in the future to investigate the factors that influence the composition of these populations.     

A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle

Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (?0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.     

北京市售生食果蔬中耐药基因赋存特征及迁移风险

目的 调研抗生素耐药基因(antibiotic resistance genes, ARGs)的赋存特征, 探讨其在食品安全领域的潜在风险。方法 采用高通量定量聚合酶链式反应(high-throughput quantitative polymerase chain reaction, HT-qPCR)技术, 对北京市售生食果蔬中ARGs及可移动遗传元件(mobile genetic elements, MGEs)的多样性和存在丰度进行描述, 并通过高风险筛查、相关性分析、冗余分析、方差分解分析, 探讨ARGs的迁移风险。结果 共检出9大类188个ARGs和9个MGEs, 丰度范围分别为6.18×103~1.24×108 copies/g、5.86×103~3.34×108 copies/g; 四环素类、氨基糖苷类、β-内酰胺类、大环内酯-林可霉素-链阳霉素类(macrolide-lincosamide-streptogramin B, MLSB) ARGs分布最广; 多重耐药类ARGs丰度最高; 涉及的主要耐药机制贡献大小为抗生素灭活>外排泵>细胞保护。其中, 苦菊的ARGs检出率最高, 青椒的ARGs丰度最高; 茄果类、叶菜类ARGs丰度普遍高于根茎类蔬菜; 水果类样品中ARGs的检出率和丰度最低。高风险ARGs普遍存在, 丰度最高可达7.85×107 copies/g, 且氨基糖苷类、磺胺类、四环素类、多重耐药类ARGs具有高迁移风险, 整合酶和转座酶两类转移机制共同构成主要驱动因素(46.44%)。结论 生食果蔬中ARGs赋存情况严重, 具有较高的迁移风险, 很可能导致耐药现象的大量产生及扩散, 危害人类健康, 应引起高度重视。     

Quantitative trait loci mapping of functional traits in the German Holstein cattle population

A whole-genome scan to detect quantitative trait loci (QTL) for functional traits was performed in the German Holstein cattle population. For this purpose, 263 genetic markers across all autosomes and the pseudoautosomal region of the sex chromosomes were genotyped in 16 granddaughter-design families with 872 sons. The traits investigated were deregressed breedingvalues for maternal and direct effects on dystocia (DYSm, DYSd) and stillbirth (STIm, STId) as well as maternal and paternal effects on nonreturn rates of 90 d (NR90m, NR90p). Furthermore, deregressed breeding values for functional herd life (FHL) and daughter yield deviation for somatic cell count (SCC) were investigated. Weighted multimarker regression analyses across families and permutation tests were applied for the detection of QTL and the calculation of statistical significance. A ten percent genomewise significant QTL was localized for DYSm on chromosome 8 and for SCC on chromosome 18. A further 24 putative QTL exceeding the 5% chromosomewise threshold were detected. On chromosomes 7, 8, 10, 18, and X/Yps, coincidence of QTL for several traits was observed. Our results suggest that loci with influence on udder health may also contribute to genetic variance of longevity. Prior to implementation of these QTL in marker assisted selection programs for functional traits, information about direct and correlated effects of these QTL as well as fine mapping of their chromosomal positions is required.     

K326全基因组分子模块库构建及其农艺、抗病性状评价

为构建烟草全基因组模块库,开展烟草分子模块育种,以K326为轮回亲本,烤烟OX2028和香料烟Samsun为供体亲本,经过杂交、连续多代回交、自交,最终获得两套以K326为背景,覆盖供体亲本整个基因组的分子模块库。分子模块的基因组大部分回复到了轮回亲本K326,其田间农艺及抗病性等重要性状偏向亲本K326,不同材料间存在较广泛的遗传变异;群体的基本农艺性状呈现连续的正态或近似正态分布,符合数量性状的表型分布特性;鉴定了7个重要性状显著改变的分子模块,其中较K326中上部叶片开片明显改善的材料32份,叶数明显提高的材料25份,TMV、CMV、PVY病毒病抗性明显提高的材料分别为111、25和5份,黑胫病、青枯病抗性显著提高的材料44和52份。构建的烟草分子模块库可用于后续开展烟草重要性状的QTL定位和基因功能研究。     

Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle

We report putative quantitative trait loci affecting female fertility and milk production traits using the merged data from two research groups that conducted independent genome scans in Dairy Bull DNA Repository grandsire families to identify quantitative trait loci (QTL) affecting economically important traits. Six families used by both groups had been genotyped for 367 microsatellite markers covering 2713.5 cM of the cattle genome (90%), with an average spacing of 7.4 cM. Phenotypic traits included PTA for pregnancy rate and daughter deviations for milk, protein and fat yields, protein and fat percentages, somatic cell score, and productive life. Analysis of the merged dataset identified putative quantitative trait loci that were not detected in the separate studies, and the pregnancy rate PTA estimates that recently became available allowed detection of pregnancy rate QTL for the first time. Sixty-one putative significant marker effects were identified within families, and 13 were identified across families. Highly significant effects were found on chromosome 3 affecting fat percentage and protein yield, on chromosome 6 affecting protein and fat percentages, on chromosome 14 affecting fat percentage, on chromosome 18 affecting pregnancy rate, and on chromosome 20 affecting protein percentage. Within-family analysis detected putative QTL associated with pregnancy rate on six chromosomes, with the effect on chromosome 18 being the most significant statistically. These findings may help identify the most useful markers available for QTL detection and, eventually, for marker-assisted selection for improvement of these economically important traits.     

Deriving Lactation Yields from Test-Day Yields Adjusted for Lactation Stage, Age, Pregnancy, and Herd Test Date

Lactation records for milk, fat, and protein yields were calculated from test-day data adjusted for the effects of lactation stage, age, previous days open, days pregnant, and test-day class (herd, test date, and milking frequency). Those lactation records reflect the improved accounting of environmental effects from a test-day model and can be combined with historical lactation records. Test-day data were adjusted with existing lactation multiplicative adjustments to maintain variance characteristics. Then additive adjustments for lactation stage, age, previous days open, and days pregnant were applied. The current multiplicative adjustments for previous days open were not applied because its effect was expected to differ by lactation stage. To remove genetic differences, the estimated breeding value from the previous evaluation divided by 305 was subtracted. Effects of test-day class, and permanent environment within and across parities were estimated within herd. The effect of test-day class was subtracted from adjusted test-day yield, and the breeding value restored. Those deviations then were combined with the best prediction procedure into a lactation measure. Heritabilities and repeatabilities of lactation records that were adjusted for test-day class were higher than for current lactation records. The adjusted records should improve the accuracy of evaluations and allow the use of test-day data as well as provide for the continued use of historical data when test-day data are not available.     

Short communication: Recursive model approach to traits defined as ratios: Genetic parameters and breeding values

A novel method for analysis of ratio traits (Y₂/Y₁) is proposed. Utilizing a recursive modeling approach, a proxy for Y₂/Y₁ can be postulated as Y₂ – λ × Y₁ (i.e., Y₂ adjusted for the effect of Y₁), where λ is a structural parameter describing an effect of change in phenotype of Y₂ caused by the phenotype of Y₁. Estimates of parameters (direct effect parameters) for the recursive model Y₁ → Y₂ can be derived from parameters of an equivalent 2-trait mixed effects model for Y₁ and Y₂, using linear (location) and quadratic (dispersion) transformations. The method is illustrated with an application for milk fat (protein) content, calculated as a ratio of fat (protein) and milk yields (kg), in the context of genetic parameters estimation and genetic evaluation via the Canadian test-day model for production traits. Results indicated the potential usefulness of the proposed approach for analysis of any Y₂/Y₁ (or Y₂ adjusted for the effect of Y₁) type of trait utilizing standard multiple-trait modeling techniques for Y₁ and Y₂.     

Estimating Effects of Permanent Environment, Lactation Stage, Age, and Pregnancy on Test-Day Yield

Test-day variances for permanent environmental effects within and across parities were estimated along with lactation stage, age, and pregnancy effects for use with a test-day model. Data were test-day records for calvings since 1990 for Jerseys and for Holsteins from California, Pennsylvania, Texas, and Wisconsin. Single-trait repeatability models were fitted for milk, fat, and protein test-day yields. Method R and a preconditioned conjugate gradient equation solver were used for variance component estimation because of large data sets. Test-day yields were adjusted for environmental effects of calving age, calving season, and milking frequency and for estimated breeding value (EBV) expressed on a daily basis. To assess the effect of adjustments, test-day yields also were analyzed without adjustment. For adjusted data, permanent environmental variances across parities relative to phenotypic variance ranged from 8.3 to 15.2% for milk, 4.4 to 8.3% for fat, and 6.9 to 11.0% for protein across regions and breeds; relative permanent environmental variances within parity ranged from 31.4 to 34.7% for milk, 18.2 to 22.3% for fat, and 28.3 to 29.1% for protein and were similar across regions and breeds. Adjustment for EBV reduced permanent environmental variance across parities and removed cow genetic variance. Relative permanent environmental variances within parity from unadjusted test-day yields were nearly identical to those from adjusted test-day yields. For unadjusted test-day yields, heritabilities ranged from 0.19 to 0.30 for milk, 0.13 to 0.15 for fat, and 0.17 to 0.23 for protein. Adjustments for lactation stage, age at milking, previous days open, and days pregnant were estimated from adjusted test-day yields using the same single-trait repeatability models and variance ratios estimated for permanent environment within and across parities. Those adjustments can be applied additively to test-day yields before evaluation analysis. Variance components and solutions for the various effects can be used to calculate test-day deviations in an analysis within herd that contributes to an analysis across herds.     

薄层色谱-薄层扫描法测定发酵液中 L-亮氨酸的研究

用层析硅胶G板进行薄层色谱分离发酵液中L-亮氨酸与L-缬氨酸,经茚三酮显色得到完整的L-亮氨酸色斑;用CS-9301薄层扫描仪测定L-亮氨酸色斑峰面积;根据公式C=(Y-97.286)×n/(2559.5×V)计算发酵液L-亮氨酸浓度.结果表明该方法平均回收率为97.3%,重现性试验的变异系数为0.045%,说明本方法的准确性和重现性良好,能满足大批量测定发酵液中L-亮氨酸含量的要求.     

Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides.     

Graduate Student Literature Review: Understanding the genetic mechanisms underlying mastitis

Mastitis is the costliest disease facing dairy producers today; consequently, it has been the subject of substantial research focus. Efforts have evolved from an initial focus on understanding the etiology of intramammary infections to the application of preventative measures, including attempts to breed cows that are resistant to infection. However, breeding for resistance to infection has proven difficult, given the complexity of the disease and the high expense associated with assembling high-quality genotypes and phenotypes. This review provides a brief background on mastitis; illustrates current understanding of the genetics influencing mastitis and the application of this knowledge; and discusses challenges and limitations in understanding these mechanisms and applying these findings to genetic improvement strategies.     

超高效液相色谱-四极杆-飞行时间质谱法快速筛查香橼中180种农药残留

为了解我国检验检测机构农药多残留高通量非靶向检测技术水平,提高食品安全风险监测能力,以有机苹果为原料制备空白和添加样品,采用F检验和S_S≤0.3σ准则评估样品的均匀性,t检验和|$overline{rm{x}}-overline{rm{y}} $|≤0.3σ准则评估样品的稳定性,并用于能力验证的考核。针对现有评价技术未考虑高通量非靶向残留筛查的问题,提出一种新的评价方法,通过建立二维（XY）分类,从农药检出数量（维度X：定性）和可接受z比分数数量（维度Y：定量）进行考核,并通过定性满意率和定量满意率引入c值作为一级评价指标,假阳性率和假阴性率作为二级评价指标,综合评估实验室的检测能力。结果显示,本次能力验证共有20家实验室参加,定性满意率70%,定量满意率45%,综合满意率40%,对于c值为0的实验室,进一步通过二级指标评价,发现87.5%的实验室未出现假阳性和假阴性,评价方式合理。本研究将定性分析和定量分析法联合应用于高通量非靶向农药多残留检测能力验证结果评价,对优化其他能力评价方式提供了新思路。

     

燕麦种质资源农艺性状的遗传多样性分析--测试文章

分析了国内外54份燕麦种质资源的遗传多样性,归纳其遗传亲缘关系,为合理利用燕麦品种(系)资源提供理论依据。结果表明：(1) 54份种质资源农艺性状间存在广泛的遗传多样性,5个质量性状(粒色、粒型、穗型、小穗型、抗病性)以抗病性的多样性指数最高,粒色的遗传多样性指数最低;8个数量性状(单株分蘖数、株高、穗长、小穗数、轮层数、单株粒数、单株粒重、千粒重)以单株粒重的遗传多样性指数最高,变异系数最大。(2)聚类分析将54份种质资源的8个数量性状分为4大类群,类群Ⅰ为高秆、大粒型育种目标亲本材料;类群Ⅱ为矮秆育种目标亲本材料,类群Ⅲ的有益性状不明显,为多目标性状育种的亲本材料,类群Ⅳ为增加轮层数、小穗数等育种目标的优良材料。     

Estimation of quantitative trait loci effects in dairy cattle populations

Standard animal model programs can be modified to include the effect of a quantitative gene, even if only a fraction of the population is genotyped. Five methods to estimate the effect of a diallelic quantitative gene affecting a quantitative trait were compared to a standard animal model (model I) on simulated populations, based on mean squared errors and bias. In models II, III, and IV complete linkage between a single genetic marker and the quantitative trait gene was assumed. In models II and III the elements of the incidence matrix for the gene effect were 0 or 1 for genotyped individuals, and the probabilities of the possible candidate gene genotypes for individuals that were not genotyped. In model III segregation analysis was used to compute these probabilities. If only some of the cows were genotyped, the model III estimates were nearly unbiased, while model II underestimated the simulated effects. When only sires were genotyped, model II overestimated the simulated effect. In models V and VI two markers bracketing the quantitative gene with recombination frequencies of 0.1 and 0.2 with the quantitative gene were simulated, and the algorithm of Whittaker et al. (1996) was used to derive estimates of gene effect and location. In model V marker allele effects were included in the animal model analysis. In model VI, the model I genetic evaluations were analyzed. Model V estimates for both effect and location of the quantitative gene were unbiased, while model VI estimates were only 0.25 of the simulated effect.     

Invited review: A perspective on the future of genomic selection in dairy cattle

Genomic evaluation has been successfully implemented in the United States, Canada, Great Britain, Ireland, New Zealand, Australia, France, the Netherlands, Germany, and the Scandinavian countries. Adoption of this technology in the major dairy producing countries has led to significant changes in the worldwide dairy industry. Gradual elimination of the progeny test system has led to a reduction in the number of sires with daughter records and fewer genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of “single-step” methodologies. Although genomic selection has been successful in increasing rates of genetic gain, we still know very little about the genetic architecture of quantitative variation. Apparently, a very large number of genes affect nearly all economic traits, in accordance with the infinitesimal model for quantitative traits. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and on environmentally friendly production with reduced waste and gas emission. Genetic variance for economic traits is maintained by the increase in frequency of rare alleles, new mutations, and changes in selection goals and management. Thus, it is unlikely that a selection plateau will be reached in the near future.     

Strategies for continual application of marker-assisted selection in an open nucleus population

The objectives of this study were to develop and simulate the implementation of several strategies for repeated application of quantitative trait loci (QTL) detection and marker-assisted selection (MAS) and to compare the short-term and continual genetic responses. A finite locus model was simulated with 20 QTL randomly distributed across 30 chromosome. Three hundred markers were evenly spaced across the genome. Allelic effects were sampled from a double exponential distribution. A daughter design was used every generation to determine the marker alleles favorably associated to QTL alleles. The MAS was applied within family to young bulls, before progeny testing, as part of an open nucleus. Young bulls were selected using strategies based on 1) the single marker with greatest contrast (BEST1), 2) the sum of n greatest contrasts (BESTn), 3) the best n contrasts, limited to one per chromosome (LIMn), 4) the sum of all contrasts exceeding a given threshold n (THRESn), and 5) the sum of contrasts exceeding a threshold, but limited to one per chromosome (LIMT). The maximum progress was achieved by strategies that selected upon several markers flanking multiple QTL in each generation. When THRES was applied, the mean true breeding value (TBV) of selected bulls was increased by 11.98% (over conventional selection) versus 6.73% for BEST1 in the first generation. Applying a full genome scan in each generation allowed selection for different QTL across time. By selecting for multiple QTL over time, MAS maintained superiority over conventional selection for many generations.     

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

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

Within-herd effects of age at test day and lactation stage on test-day yields

Variance ratios were estimated for random within-herd effects of age at test day and lactation stage, on test-day yield and somatic cell score to determine whether including these effects would improve the accuracy of estimation. Test-day data starting with 1990 calvings for the entire US Jersey population and Holsteins from California, Pennsylvania, Wisconsin, and Texas were analyzed. Test-day yields were adjusted for across-herd effects using solutions from a regional analysis. Estimates of the relative variance (fraction of total variance) due to within-herd age effects were small, indicating that regional adjustments for age were adequate. The relative variances for within-herd lactation stage were large enough to indicate that accuracy of genetic evaluations could be improved by including herd stage effects in the model for milk, fat, and protein, but not for somatic cell score. Because the within-herd lactation stage effect is assumed to be random, the effect is regressed toward the regional effects for small herds, but in large herds, lactation curves become herd specific. Model comparisons demonstrated the greater explanatory power of the model with a within-herd-stage effect as prediction error standard deviations were greater for the model without this effect. The benefit of the within-herd-stage effects was confirmed in a random regression model by comparing variance components from models with and without random within-herd regressions and through log-likelihood ratio tests.