Relationship between reproductive and productive traits in Holstein cattle using multivariate analysis

Multivariate procedures are used for the extraction of variables from the correlation matrix of phenotypes in order to identify those traits that explain the largest proportion of phenotypic variation and to evaluate the relationship structure between these traits. The reproductive traits (days from calving to first estrus [CFE], days from calving to last service [CLS], calving interval [CI] and gestation length [GL]) and milk production traits (milk yield at 305 days of lactation [MY305], peak yield [PY] and milk yield per day of calving interval [MYCI]) of 5,217 Holstein females (primiparous and multiparous) were measured. Principal component analysis (PCA) and factor analysis of the correlation matrix were used to estimate the correlation between traits. Analysis grouped the seven traits into three principal components and four latent factors that retained approximately 81.5% and 88.9% of the total variation of the data, respectively. The production variables exhibited positive phenotypic correlation coefficients of high magnitude (>.67). The phenotypic correlation estimates between the productive and reproductive traits were low, ranging from .13 to .22. A strong association (.99) was observed between CLS and CI. Our results indicate that multivariate analysis was effective in generating correlations between the traits studied, grouping the seven traits in a smaller number of variables that retained approximately 81% of the total variation of the data.     

Genetic evaluation in the presence of uncertain additive relationships. I. Use of phenotypic information to ascertain paternity

A simulation was carried out to investigate the implementation of a genetic evaluation when the additive relationship matrix is not completely known due to the presence of uncertain paternity in the pedigree. Data were simulated and analyzed using a linear mixed model that included a fixed contemporary group effect plus random additive and residual effects. For the univariate scenario, either 1 or 2 records of a single trait with heritabilities of 33, 50, and 67% were used to compute the probability of being the true sire (PTS) of each candidate sire for a given offspring. One record of 3 correlated traits was used to compute PTS in a 3-trait scenario. A Bayesian procedure via Markov Chain Monte Carlo was used to carry out the implementation, in which the PTS was computed without the need to invert the relationship matrix. The average probability of the true sire being identified as such (PSA), as well as the percentage difference (PD) between PSA and an equal prior probability assigned to each candidate sire, were computed for the single and 3-trait scenarios. Using 1 trait, PSA increased with an increase in heritability. When repeated records were considered, the PD was increased by 50 to 386% compared with using just 1 record per animal for the varying heritabilities and number of candidate sires, suggesting that phenotypic information was better able to discriminate among candidate sires when more than 1 record was used to determine PSA. Using 3 correlated traits increased PD by 77 to 98% when compared with using 1 record of a trait with 67% heritability. Similarly, the PD was increased by 105 to 1,021%, when compared with using 1 record of a trait with 33% heritability. These results indicate that the probability of identifying the true sire increased when 3 correlated traits were used to compute PSA. The correlations between true and predicted breeding values of 3 traits were increased by 6 to 7% for all animals and 64 to 89% for animals with unknown paternity in the pedigree when estimated probability of paternity was used as compared with equal prior probability assigned to each candidate sire. For traits such as birth weight and weaning weight, in which only 1 measurement is taken, the 3-trait scenario could result in more animals being assigned the true sire than if birth or weaning weight was used separately. Further research is needed to determine the performance of this methodology in field data as well as the potential implementation of this methodology in conjunction with molecular information.     

Estimation of direct and correlated responses to selection using univariate animal models.

Analytic results obtained using simple models show that estimates of selection response of univariate experiments using animal models are completely dependent on the heritability used as prior when fixed effects are nested within generations, and both on the prior and on the true heritability parameter when fixed effects overlap across generations. Univariate animal model estimators of correlated changes of a trait not selected directly are usually biased. The absolute value of the estimate of the correlated response is smaller than the true value when the traits are only genetically correlated and larger than the expected value of zero when they are only environmentally correlated. The validity of the results derived from the analysis of simple models is confirmed using computer simulations, which illustrate the magnitude of the bias. It is emphasized that use of univariate animal models to estimate response in breeding programs whose breeding objectives include several correlated traits may lead to erroneous conclusions.     

狗牙根种质资源表型遗传多样性分析

为提高狗牙根(Cynodon dactylon)种质资源的利用效率,利用11个数量性状和4个质量性状对831份狗牙根种质资源的遗传变异、性状相关性及主成分进行了分析。结果表明:狗牙根不同种源各性状具有广泛的变异,质量性状的变异系数显著高于数量性状。不同性状的多样性指数差异较大,数量性状多样性指数高于质量性状。国外群体的变异系数最高,而国内群体中北方群体的平均变异系数高于南方群体。诱变后代群体的多样性指数最高,国内群体的多样性指数高于国外群体,国内南方群体高于北方群体。各性状间不是相互独立的,而是存在着较广泛的联系。15个性状中前6个特征根的累积贡献率达75.86%,表明各性状的贡献率分散,累积贡献率增长不明显。     

天津长白猪简化综合育种值的研究

研究了天津长白猪各种简化综合育种值中目标性状的经济权重及其与完全综合育种值的相关。结果表明，简化综合育种值中只包含一人目标性状时，最好的性状避料转化效率，与完全综合育种值的相关系数（ｒ）等于０．８７；包含２个目标性状时，最佳的性状组合是饲料转化效率与瘦肉率；包括３个目标性状时，最好的目标性状组合是窝活仔数、日增竽圾瘦肉主，它已经达到很高的精确度，与完全综合育种值的相关为１．００。     

辽宁省野古草野生居群表型性状多样性研究

为揭示野古草野生居群的表型变异规律,采用变异系数、主成分分析和聚类分析方法对采自辽宁省的8个野古草居群的10项形态性状进行多样性分析。结果表明,这10个表型性状在居群间的差异均达到极显著水平,各性状特征在居群间存在广泛的变异,变异系数由大到小依次为小穗总数(42.01%)旗叶厚度(27.94%)旗叶长度(26.83%)旗叶宽度(20.98%)叶数(19.64%)茎基粗(19.70%)花序长(19.30%)单枝干重(18.62%)株高(10.19%)花序重(8.16%)。10个形态特征可归成为3个主成分因子,累计贡献率达到79.44%,最大程度上反映了所有材料的表型性状特征,不同种群之间的差异主要来源于花序长、花序干重、单枝干重、小穗总数。采用欧氏距离系统聚类法将8个野古草居群依据10个表型性状分为3类:株型高大,旗叶长而宽,生物量大;株高中等,茎基粗壮,旗叶长而窄,小穗数量多,生物量较大;植株矮,叶片短、窄,生物量最小。表型性状与地理因子的相关性分析表明,部分表型性状的变化与地理因子呈显著或极显著相关性。     

Heterozygosity for genes influencing a quantitative trait

Choosing families to sample for a quantitative trait locus mapping experiment is a critical component of experimental design because only heterozygous families contribute information to the analysis. Additive genetic variance of a paternal half-sib family can be partitioned into two parts: a variance component of maternal source that is constant across different families and a variance component of paternal source that is defined as an index of heterozygosity of a sire. This index is shown to be an upper limit of variance among marker genotypes of a half-sib family and can be used to identify highly heterozygous sires, thus improving the power of detecting QTL in detection studies. Simulated progeny phenotypic data were used to estimate sire's heterozygosity index via an ANOVA method, and accuracy of the estimation was evaluated with the correlation coefficient between the true and estimated index summarized both as the correlation and by the correct ranking of results as measured by the ratio of the true average heterozygosity index of experimentally selected parents to average heterozygosity of all sires. Positive but small correlation can be achieved in the estimation of a sire's heterozygosity when based on the daughters' phenotypic data, and accuracy was improved when progeny-tested sons were used to estimate their grandsire's heterozygosity index, depending on the genetic model of a trait and the size and structure of families.     

Economic weights of production and functional traits for Holstein‐Friesian cattle in Hungary

A bio‐economic model was used to estimate economic values of 15 milk production, functional, growth and carcass traits for Hungarian Holstein‐Friesian cattle. The calculations were carried out for the situation in Hungary from 2000 to 2007, assuming no production quotas. The marginal economic values were defined as partial derivatives of the profit function with respect to each trait in a production system with dairy cow herds and with sales of surplus male calves. The economic weights for maternal and direct components of traits were calculated multiplying the marginal economic values by the number of discounted expression summed over a 25‐year investment period for 2‐year‐old bulls (candidates for selection). The standardized economic weight (economic weight × genetic standard deviation) of the trait or trait component expressed as percentage of the sum of the standardized economic weights for all traits and trait components represented the relative economic importance of this trait or trait component. The highest relative economic importance was obtained for milk yield (25%), followed by productive lifetime of cows (23%), protein yield and the direct component of a cow’s total conception rate (9% each), the maternal effect of the total conception rate of cows and the somatic cell score (approximately 7% each), fat yield (5%) and mature weight of cows and daily gain in rearing of calves (approximately 4% each). Other functional traits (clinical mastitis incidence, calving difficulty score, total conception rate of heifers and calf mortality) reached a relative economic importance between 0.5% and 2%. Birth weight and dressing percentage were least important (<0.5%). Based on these results, the inclusion of productive lifetime and cow fertility in the breeding programme for Holstein‐Friesian cattle in Hungary is advisable.     

湖北白猪活体测定性状与屠宰性状间的典型相关分析

通过对湖北白猪新系90头样本的话体测定性状的9个变量与屠宰测定性状的10个变量之间的典型相关分析,得出了决定两组性状间相关关系的三对典型变量.湖北白猪活体测定性状与屠宰测定性状之间的相关主要是由:腹围、臀宽及活体膘厚与板油率密切相关,胸围、臀宽、臀长、臀围与屠宰率及后腿比例密切相关,及体长、胸围与瘦肉率和肥肉率间的较强相关所决定的.分析表明在湖北白猪新品系选育中,要加强胸围选择,注意保持适宜的体长.可以依据三个活体性状典型变量的信息利用达到改良屠宰性状的目的.     

Comparison of heritabilities of dairy traits in Australian Holstein‐Friesian cattle from genomic and pedigree data and implications for genomic evaluations

The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single‐nucleotide poly‐ morphism (SNP) markers for 29 traits in Australian Holstein‐Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The propor‐ tion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.     

The efficiency of genome-wide selection for genetic improvement of net merit

Four methods of selection for net merit comprising 2 correlated traits were compared in this study: 1) EBV-only index (I?), which consists of the EBV of both traits (i.e., traditional 2-trait BLUP selection); 2) GEBV-only index (I?), which comprises the genomic EBV (GEBV) of both traits; 3) GEBV-assisted index (I?), which combines both the EBV and the GEBV of both traits; and 4) GBV-assisted index (I?), which combines both the EBV and the true genomic breeding value (GBV) of both traits. Comparisons of these indices were based on 3 evaluation criteria [selection accuracy, genetic response (ΔH), and relative efficiency] under 64 scenarios that arise from combining 2 levels of genetic correlation (r(G)), 2 ratios of genetic variances between traits, 2 ratios of the genomic variance to total genetic variances for trait 1, 4 accuracies of EBV, and 2 proportions of r(G) explained by the GBV. Both selection accuracy and genetic responses of the indices I?, I?, and I? increased as the accuracy of EBV increased, but the efficiency of the indices I? and I? relative to I? decreased as the accuracy of EBV increased. The relative efficiency of both I? and I? was generally greater when the accuracy of EBV was 0.6 than when it was 0.9, suggesting that the genomic markers are most useful to assist selection when the accuracy of EBV is low. The GBV-assisted index I? was superior to the GEBV-assisted I? in all 64 cases examined, indicating the importance of improving the accuracy of prediction of genomic breeding values. Other parameters being identical, increasing the genetic variance of a high heritability trait would increase the genetic response of the genomic indices (I?, I?, and I?). The genetic responses to I?, I?, and I(4) was greater when the genetic correlation between traits was positive (r(G) = 0.5) than when it was negative (r(G) = -0.5). The results of this study indicate that the effectiveness of the GEBV-assisted index I? is affected by heritability of and genetic correlation between traits, the ratio of genetic variances between traits, the genomic-genetic variance ratio of each index trait, the proportion of genetic correlation accounted for by the genomic markers, and the accuracy of predictions of both EBV and GBV. However, most of these affecting factors are genetic characteristics of a population that is beyond the control of the breeders. The key factor subject to manipulation is to maximize both the proportion of the genetic variance explained by GEBV and the accuracy of both GEBV and EBV. The developed procedures provide means to investigate the efficiency of various genomic indices for any given combination of the genetic factors studied.     

Genetic parameters for within-litter variation in piglet birth weight and change in within-litter variation during suckling

The objective of this study was to ascertain whether maternal additive genetic variance exists for within-litter variation in birth weight and for change in within-litter variation in piglet weight during suckling. A further objective was to estimate maternal genetic correlations of these two traits with mortality, birth weight, growth, and number of piglets born alive. Data were obtained from L?vsta research station, Swedish University of Agricultural Sciences, and included 22,521 piglets born in 2,003 litters by 1,074 Swedish Yorkshire sows. No cross fostering was used in the herd. The following seven traits were analysed in a multivariate animal (sow) model: number of piglets born alive, within-litter SD in birth weight, within-litter SD in piglet weight at 3 wk of age, mean weight at birth, mean weight at 3 wk of age, proportion of stillborn piglets, and proportion of dead piglets during suckling. Maternal genetic variance for the change in within-litter SD in piglet weight during suckling was assessed from the estimated additive genetic covariance components by conditioning on within-litter SD in birth weight. Similarly, mean growth of piglets during suckling was assessed from the additive genetic covariance components by conditioning on mean weight at birth. The heritability for within-litter SD in birth weight was 0.08 and 0.06 for within-litter SD in piglet weight at 3 wk. The genetic correlation between these two traits was 0.71. Little maternal genetic variance was found for the change in within-litter SD in piglet weight during suckling, and opportunity for genetic improvement of this trait by selective breeding seems limited. The genetic correlation of within-litter SD in birth weight with proportion of dead piglets during suckling was 0.25 and of within-litter SD in birth weight with mean growth of piglets was -0.31. The maternal genetic variance and heritability found for within-litter SD in birth weight indicates that genetic improvement of this trait by selective breeding is possible. In addition, selection for sows' capacity to give birth to homogeneous litters may be advantageous for piglet survival, piglet growth, and litter homogeneity at weaning.     

扁穗雀麦种质资源形态和农艺性状变异的初步分析

检测种质资源的多样性对于利用和有效管理种质非常重要。本研究对87份世界来源的扁穗雀麦种质利用常用的9个形态-农艺性状进行了统一田间评价。结果表明,所有采用的性状变异系数均表现出高度的变异,尤其以单株干物质产量、分蘖数和叶宽的变异幅度最大。反映株型大小的各性状之间呈显著相关,茎粗和第一节间长与其余8个性状之间存在显著相关(P<0.01),旗叶宽与倒二叶宽的正相关性最强(r=0.912,P<0.01)。主成分分析(PCA)表明前2个主成分可以解释总变异的74%。反映株型大小的性状以及分蘖数可能是扁穗雀麦种质形态变异的主要来源。基于欧氏距离的UPGMA聚类分析将供试材料分成3个主要的类群,第一类群具有最大的株型表现,茎干粗壮,旗叶和倒二叶宽大,分蘖数中等;第二类群具有中等的株型;第三类群具有较小的株型,叶片短而窄,但分蘖数较多。聚类分析很好地支持了前2个主成分分值的二维散点图的结果。总之,本研究表明扁穗雀麦是一种在西南平原和丘陵地区适应性极强的禾草,其表型多样性对于品种选育和资源收集具有重要的参考价值。     

川西北高原50份燕麦种质农艺性状遗传多样性分析及综合评价

为客观评价燕麦种质资源农艺性状的遗传多样性,本试验对50份国外引进燕麦种质资源的27个农艺性状进行了形态多样性指数分析,并对其中的16个数量性状进行相关性分析、聚类分析和主成分分析,结果表明:遗传多样性指数最高的是穗长 (H'=2.04),变异系数最大的是营养枝数(63.36%);鲜草产量与干草产量、株高、分蘖数等呈极显著正相关(P<0.01);聚类分析将50份燕麦种质资源分为4大类群,第Ⅰ类群为早熟种质,具有植株高大、单株鲜草产量高、茎节数和小穗多、茎秆粗壮等特点,可作为选育多目标性状的优良种质或材料;第Ⅱ类群为中晚熟种质,但有益性状不明显;第Ⅲ类群属于中熟种质,植株分蘖能力强,种子千粒重高,可作为选育高产分蘖能力强的优良亲本;第Ⅳ类群属于晚熟种质,植株茎秆粗壮,可作为选育抗倒品种的优良亲本。主成分分析将16个数量性状指标集中在累计贡献率达77.00%的5个主成分中:第一主成分与燕麦牧草产量密切相关;第二主成分载荷最高的是单株营养枝数;第三主成分载荷最高的是生殖枝数;第四和五主成分主要反映茎节数和株高。综上所述,川西北地区引进的50份国内外燕麦种质资源的遗传多样性丰富,综合评价表明,种质Golden Yellow、Lightning、Golden Rain Ⅱ、Bambull Ⅱ可以作为亲本以改良当地燕麦品种。     

Selective genotyping with a main trait and a correlated trait

In some instances of quantitative trait loci (QTL) mapping, a correlated trait may be available for which measurement is less costly or more convenient than direct measurement of the trait of main interest. In this paper we consider ways of combining phenotyping for a main trait and a correlated trait to maximize power within the overall framework of a selective genotyping design. Four schemes are compared: (1) Selection for the main trait only (2) Selection for the correlated trait only (3) Two-stage selection (4) Three-stage selection. Optimum proportion selected for the two traits were obtained for the various schemes, and power parameters compared, according to the correlation r, between the two traits, and the proportion of the population, Q, phenotyped for the main trait. The schemes were compared under two sets of assumptions: (1) Total population size and proportion that can be phenotyped for the main trait are fixed, and (2) Total costs are fixed, with relative cost, c', of rearing and phenotyping for the correlated trait as compared to the main trait. Under (1), the multi-stage schemes are very advantageous when r is high, and Q is low. Under (2), the single-stage schemes were always superior to the multi-stage schemes; Scheme 1 being preferred when r²/c' < 1.0, and Scheme 2 when r²/c' > 1.0.     

Genome‐wide association study for egg production and quality in layer chickens

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome‐wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13 000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. ( 2012 ). The Bayesian whole genome prediction model – BayesB (Meuwissen et al. 2001 ) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non‐zero effect with posterior probability > 0.9 for one or more traits.     

拉萨18个引进燕麦品种主要农艺性状和营养成分的遗传多样性分析

为了解在拉萨地区引种的18个燕麦(Avena satica)品种的遗传多样性,对18个农艺性状指标进行了多样性指数分析、聚类分析和主成分分析。结果表明:18个性状的遗传多样性指数范围在1.458~2.054,平均为1.87,遗传多样性丰富,同一性状间差异化大,各性状变异系数大,其中较高的是干草产量(52.354%)、旗叶长(51.946%)。聚类分析将18个燕麦品种分为4类,类群Ⅰ包括11个品种,属于高秆类群。类群Ⅱ包括1个品种,属于高干草产量类群。类型Ⅲ包括2个品种,属高营养、多籽粒型类群。类群Ⅳ包括4个品种,此类群在各个指标中均有较好的表现,是适宜在拉萨地区种植的燕麦品种。主成分分析共提取5个主成分,累计方差贡献率为79.592%,第一因子反映叶产量和种子产量,第二因子反映酸性洗涤纤维和中性洗涤纤维,第三因子反映粗灰分,第四因子反映茎叶比和粗蛋白。第五因子反映株高。通过构建5个主成分为参数的综合评价模型,计算综合得分,可以筛选出贝勒Ⅱ、牧王、海威为适宜在拉萨地区种植的燕麦品种。     

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers

The aim of this study was to separate marked additive genetic variability for three quantitative traits in chickens into components associated with classes of minor allele frequency (MAF), individual chromosomes and marker density using the genomewide complex trait analysis (GCTA) approach. Data were from 1351 chickens measured for body weight (BW), ultrasound of breast muscle (BM) and hen house egg production (HHP), each bird with 354 364 SNP genotypes. Estimates of variance components show that SNPs on commercially available genotyping chips marked a large amount of genetic variability for all three traits. The estimated proportion of total variation tagged by all autosomal SNPs was 0.30 (SE 0.04) for BW, 0.33 (SE 0.04) for BM, and 0.19 (SE 0.05) for HHP. We found that a substantial proportion of this variation was explained by low frequency variants (MAF <0.20) for BW and BM, and variants with MAF 0.10–0.30 for HHP. The marked genetic variance explained by each chromosome was linearly related to its length (R² = 0.60) for BW and BM. However, for HHP, there was no linear relationship between estimates of variance and length of the chromosome (R² = 0.01). Our results suggest that the contribution of SNPs to marked additive genetic variability is dependent on the allele frequency spectrum. For the sample of birds analysed, it was found that increasing marker density beyond 100K SNPs did not capture additional additive genetic variance.