Bivariate Random Regression Model Research Articles

Re-assessing the importance of linear type traits in predicting genetic merit for survival in an aging Holstein-Friesian dairy cow population.

The cumulative improvement achieved in the genetic merit for reproductive performance in dairy populations will likely improve dairy cow longevity; therefore, it is time to reassess whether linear type traits are still suitable predictors of survival in an aging dairy cow population. The objective of the present study was therefore to estimate the genetic correlations between linear type traits and survival from one parity to the next and, in doing so, evaluate if those genetic correlations change with advancing parity. After edits, 152,894 lactation survival records (first to ninth parity) were available from 52,447 Holstein-Friesian cows, along with linear type trait records from 52,121 Holstein-Friesian cows. A series of bivariate random regression models were used to estimate the genetic covariances between survival in different parities and each linear type trait. Heritability estimates for survival per parity ranged from 0.02 (SE = 0.004; first parity) to 0.05 (SE = 0.01; ninth parity). Pairwise genetic correlations between survival among different parities varied from 0.42 (first and ninth parity) to 1.00 (eighth to ninth parity), with the strength of these genetic correlations being inversely related to the interval between the compared parities. The genetic correlations between survival and the individual linear type traits varied across parities for 9 of the 20 linear type traits examined, but the correlations with only 3 of these linear type traits strengthened as the cows aged; these 3 traits were rear udder height, teat length, and udder depth. Given that linear type traits are frequently scored in first parity and are genetically correlated with survival in older parities, they may be suitable early predictors of survival, especially for later parity cows. Additionally, the direction of the genetic correlations between survival and rear udder height, teat length, and udder depth did not change between parities; hence, selection for survival in older parities using these linear type traits should not hinder genetic improvement for survival in younger parities.

Genetic parameters and genomic prediction for feed intake recorded at the group and individual level in different production systems for growing pigs

BackgroundIn breeding programs, recording large-scale feed intake (FI) data routinely at the individual level is costly and difficult compared with other production traits. An alternative approach could be to record FI at the group level since animals such as pigs are normally housed in groups and fed by a shared feeder. However, to date there have been few investigations about the difference between group- and individual-level FI recorded in different environments. We hypothesized that group- and individual-level FI are genetically correlated but different traits. This study, based on the experiment undertaken in purebred DanBred Landrace (L) boars, was set out to estimate the genetic variances and correlations between group- and individual-level FI using a bivariate random regression model, and to examine to what extent prediction accuracy can be improved by adding information of individual-level FI to group-level FI for animals recorded in groups. For both bivariate and univariate models, single-step genomic best linear unbiased prediction (ssGBLUP) and pedigree-based BLUP (PBLUP) were implemented and compared.ResultsThe variance components from group-level records and from individual-level records were similar. Heritabilities estimated from group-level FI were lower than those from individual-level FI over the test period. The estimated genetic correlations between group- and individual-level FI based on each test day were on average equal to 0.32 (SD = 0.07), and the estimated genetic correlation for the whole test period was equal to 0.23. Our results demonstrate that by adding information from individual-level FI records to group-level FI records, prediction accuracy increased by 0.018 and 0.032 compared with using group-level FI records only (bivariate vs. univariate model) for PBLUP and ssGBLUP, respectively.ConclusionsBased on the current dataset, our findings support the hypothesis that group- and individual-level FI are different traits. Thus, the differences in FI traits under these two feeding systems need to be taken into consideration in pig breeding programs. Overall, adding information from individual records can improve prediction accuracy for animals with group records.

Genetic parameters for efficiency of crude protein utilisation and its relationship with production traits across lactations in grazing dairy cows

ABSTRACT The objective of the study was to estimate genetic parameters of efficiency of crude protein utilisation (ECPU) and its genetic correlation (r G) with other production traits across lactation in grazing dairy cows. Data were originated from 467 (2016–17 season) and 460 cows (2017–18 season) representing Holstein-Friesian (F), Jersey (J) and their crosses (F×J), from two New Zealand farms. The ratio between crude protein yield in milk and crude protein intake is defined as ECPU. (Co)variance components between ECPU and other traits were estimated using bivariate random regression models (RRM) that included fixed effects of herd-test-date, parity, calving date, proportion of F, and F×J heterosis, with random effects of animal additive genetic, and within-lactation permanent environment. Estimates of heritability (h2 ) of daily ECPU ranged from 0.02 to 0.41. Estimates of r G between daily ECPU and milk yield traits were strong and positive throughout lactation suggesting that selection for milk production traits improves ECPU of dairy cows. Daily r G between ECPU and mid-infrared predictions of milk urea (MU) concentration were mostly negative during lactation confirming that cows with high ECPU had genetically lower MU. Estimates of genetic relationships between ECPU and fertility traits are required to assess implications of including ECPU in breeding objective.

The use of a Bivariate Random Regression Model for Genetic Analysis of Milk Yield in Iranian Native Buffalo

The use of a Bivariate Random Regression Model for Genetic Analysis of Milk Yield in Iranian Native Buffalo

Random regression analysis for growth traits in rainbow trout ( Oncorhynchus mykiss )

PDF HTML阅读 XML下载 导出引用 引用提醒 虹鳟生长性状的随机回归分析 DOI: 作者: 作者单位: 1. 上海海洋大学水产科学国家级实验教学示范中心, 上海 201306;2. 农业农村部水生动物基因组学重点实验室, 北京 100141;3. 中国水产科学研究院生物技术研究中心, 北京 100141;4. 中国水产科学研究院黑龙江水产研究所, 黑龙江 哈尔滨 150070 作者简介: 王悦玲(1994-),硕士研究生,研究方向为水产动物遗传育种.E-mail:850974565@qq.com 通讯作者: 中图分类号: S96 基金项目: 国家现代农业产业技术体系专项（CARS-46）；国家自然科学基金项目（31640087）. Random regression analysis for growth traits in rainbow trout (Oncorhynchus mykiss) Author: Affiliation: 1. National Demonstration Center for Experimental Fisheries Science Education, Shanghai Ocean University, Shanghai 201306, China;2. Key Laboratory of Aquatic Genomics, Ministry of Agriculture and Rural Affairs, Beijing 100141, China;3. Aquatic Genomics and Beijing Key Laboratory of Fishery Biotechnology, Chinese Academy of Fishery Sciences, Beijing, 100141, China;4. Heilongjiang River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Harbin 150070, China Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生长和抗逆是水产动物遗传育种工作中最重要的农艺性状，虹鳟的生长性状关乎虹鳟规模化养殖的生产经济效益，为了从遗传上精细解析虹鳟的生长性状，我们从渤海、丹麦、挪威、唐纳森和加利福尼亚5个虹鳟（）种系间的双列杂交开始，进行了连续4代的继代选育。本研究测量了第4代总共4368个实验个体在516日龄、608日龄、668日龄、883日龄和1036日龄5个时间点的生长数据。采用随机回归测定日模型，对虹鳟生长性状进行了动态遗传分析。根据贝叶斯信息准则，确定3阶勒让德多项式为拟合体重和体长的加性遗传效应和永久环境效应变化的最优子模型。利用双变量随机回归模型同时分析体重和体长两个生长性状。它们的遗传力在400~1000日龄之间呈现递减趋势，分别从0.288下降到0.164和从0.469下降到0.186，并且在该生长区间内体长的遗传力始终高于体重的遗传力。无论体重还是体长性状，在不同日龄之间的遗传相关都随着生长间隔的增大而降低，但是两个性状在生长初期和后期之间的遗传相关较高（遗传相关系数0.75以上），尤其是体重（遗传相关系数0.85以上），该研究结果为虹鳟早期的遗传选育提供了理论支撑。两个性状之间在相同日龄之间的遗传相关均在0.75以上，在不同日龄之间的遗传相关随着生长间隔的增大由0.83下降到0.63。以上的研究结论为虹鳟生长性状（主要是体长和体重）的遗传选育提供了理论基础，同时也为虹鳟的体长和体重两个性状的联合选育提供了精确的遗传分析结果，由于两性状在前期有较高的遗传相关，因此建议在虹鳟生长前期（400日龄）进行联合选择。 Abstract:Growth and resistance are the most important agricultural traits for genetic breeding in aquaculture animals. The growth trait of Rainbow trout is central to the economic development in scaled production. Launched from strains of Bohai, Denmark, Norway, Donaldson and California, family selection of the rainbow trout was performed for four consecutive generations based on biallele crossing design. Only data from the fourth generation was selected for this study. Dynamic genetic analysis was conducted using 19299 records repeated body weight (BW) and body length (BL) measurements, which were obtained to genetically evaluate the growth traits of 4368 samples for the fourth generation at their 516 days of age, 608 days of age, 668 days of age, 883 days of age and 1036 days of age. According to the Bayesian information criterion (BIC), Legendre polynomials of three orders were selected as the most optimized submodel to fit changes in additive genetic and permanent environmental effects on both BW and BL. With a bivariate random regression model (RRM), both traits were analysed simultaneously. The heritabilities were estimated to exhibit a downward tendency between 400 and 1000 days of age, from 0.288 to 0.164 and from 0.469 to 0.186 for BW and BL, respectively, while BL was inherited in a consistently higher manner than BW. The genetic correlation of BW and BL showed a tendency towards the decrease of heritabilities with the enlarged growth space. However, the traits in the initial and later days of age showed higher correlation for both traits which are all above 0.75, especially for BW, which is higher than 0.85. The genetic correlations in the same growth days of age for both traits are equal to or above 0.75, but decreased from 0.83 to 0.63 in different growth days of age. In summary, the genetic correlation of a single trait or both traits between paired day-ages decreased with the increasing age-interval. However, the consistent genetic correlation between paired day-ages makes the genetic selection for BW feasible at an early stage. These results provide the theoretical basis for breeding selection focused on BW and BL growth traits. At the same time, this research also provides accurate genetic analysis results which is most fitted for the combined selection. Due to the existing higher genetic correlations between early and later stage for BW and BL, the combined selection is suggested from early 400-growth-day. In addition, the big population with couple families will be artificially divided into several individual subpopulations and repeated random measurements for growth points series will be executed for every subpopulation. This technique will satisfy the requirements for an improved fitting with the growth curve and also save money, including from decreased labour costs. 参考文献 相似文献 引证文献

The effect of selection and sex on genetic parameters of body weight at different ages in a commercial broiler chicken population

Abstract Data from 54 selection rounds (SRs) for growth rate in a selection line from Cobb breeding company was used for analysis with the objective to investigate the extent of sex by genotype interaction for body weight (BW) in a commercial broiler chicken population, estimate genetic parameters of BW at three different ages, and study the development in genetic variance for BW over several generations of selection. BW was measured at three different ages (t, t-4 and t-7 days) in different birds of both sexes. For the first 39 SRs, BW was recorded at t days, however, as selection continued the birds reached desired BW earlier and the weighing age was changed to t-4 days for 7 SRs and then to t-7 days for the last 8 SRs. A bivariate random regression model regarding BW in each sex as different traits using segmented linear splines and heterogeneous residual variance was used to estimate genetic parameters of BW across the selection trajectory via Gibbs sampling. The genetic variance and the heritability estimates of BW in males and females were found to be different at all three ages. The genetic correlation between BW measured in males and females was less than unity and decreased further as age at weighing was increased. This illustrated that BW in the two sexes should be considered as different but correlated traits and models for BW should account for a decreasing genetic correlation as the distance between the weighing ages increased. An increased genetic variance of BW along the selection trajectory in the first period was observed and genetic correlations between BW measured in different SRs gradually decreased as the distance between SRs increased. Genetic correlations lower than unity between BW at early and late SRs in the first period indicated that the genetic background of BW has gradually changed along the selection trajectory. This change probably contributes significantly to maintaining large levels of genetic variance in highly selected broiler populations.

Genetic parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems

Lactose is a major component of milk (typically around 5% of composition) that is not usually directly considered in national genetic improvement programs of dairy cattle. Daily test-day lactose yields and percentage data from pasture-based seasonal calving herds in Australia were analyzed to assess if lactose content can be used for predicting fitness traits and if an additional benefit is achieved by including lactose yield in selecting for milk yield traits. Data on lactose percentage collected from 2007 to 2014, from about 600 herds, were used to estimated genetic parameters for lactose percentage and lactose yield and correlations with other milk yield traits, somatic cell count (SCC), calving interval (CIV), and survival. Daily test-day data were analyzed using bivariate random regression models. In addition, multi-trait models were also performed mainly to assess the value of lactose to predict fitness traits. The heritability of lactose percentage (0.25 to 0.37) was higher than lactose yield (0.11 to 0.20) in the first parity. Genetically, the correlation of lactose percentage with protein percentage varied from 0.3 at the beginning of lactation to -0.24 at the end of the lactation in the first parity. Similar patterns in genetic correlations were also observed in the second and third parity. At all levels (i.e., genetic, permanent environmental, and residual), the correlation between milk yield and lactose yield was close to 1. The genetic and permanent environmental correlations between lactose percentage and SCC were stronger in the second and third parity and toward the end of the lactation (-0.35 to -0.50) when SCC levels are at their maximum. The genetic correlation between lactose percentage in the first 120 d and CIV (-0.23) was similar to correlation of CIV with protein percentage (-0.28), another component trait with the potential to predict fertility. Furthermore, the correlations of estimated breeding values of lactose percentage and estimated breeding values of traits such as survival, fertility, SCC, and angularity suggest that the value of lactose percentage as a predictor of fitness traits is weak. The results also suggest that including lactose yield as a trait into the breeding objective is of limited value due to the high positive genetic correlation between lactose yield and protein yield, the trait highly emphasized in Australia. However, recording lactose percentage as part of the routine milk recording system will enable the Australian dairy industry to respond quickly to any future changes and market signals.

A Systematic Review of the Predictive Value of 18FDG-PET in Esophageal and Esophagogastric Junction Cancer After Neoadjuvant Chemoradiation on the Survival Outcome Stratification

PurposeWe studied the predictive value of [18 F]fluorodeoxyglucose-positron emission tomography (18FDG-PET) for assessing disease-free (DFS) and overall survival (OS) in esophageal and esophagogastric junction cancer. Materials and methodsA literature search (PUBMED/MEDLINE, EMBASE, Cochrane) was performed to identify full papers with 18FDG-PET and survival data, using indexing terms and free text words. Studies with >10 patients with locally advanced esophageal cancer, presenting sequential or at least one post-adjuvant treatment 18FDG-PET data and Kaplan–Meier survival curves with >6 months median follow-up period were included. We performed a meta-analysis for DFS and OS using the hazard ratio (HRs) as outcome measure. Sources of heterogeneity study were also explored. ResultsWe identified 26 eligible studies including a total of 1,544 patients (average age 62 years, 82 % males). The TNM distribution was as follows: stage I 7 %, II 24 %, III 53 % and IV 15 %. The pooled HRs for complete metabolic response versus no response were 0.51 for OS (95 % CI, 0.4–0.64; P < 0.00001) and 0.47 for DFS (95 % CI, 0.38–0.57; P < 0.00001), respectively. No statistical heterogeneity was present. To explore sources of clinical heterogeneity, we also realised subgroup and regression analyses. Taken into account the moderate correlation between OS and DFS (ρ = 0.54), we used joint bivariate random regression model. These analyses did not show a statistically significant impact of study characteristics and PET modalities on the pooled outcome estimates. ConclusionDespite methodological and clinical heterogeneity, metabolic response on 18FDG-PET is a significant predictor of long-term survival data.

Profile and genetic parameters of dairy cattle locomotion score and lameness across lactation

This study investigated the profile of locomotion score and lameness before the first calving and throughout the first (n=237) and second (n=66) lactation of 303 Holstein cows raised on a commercial farm. Weekly heritability estimates of locomotion score and lameness, and their genetic and phenotypic correlations with milk yield, body condition score, BW and reproduction traits were derived. Daughter future locomotion score and lameness predictions from their sires’ breeding values for conformation traits were also calculated. First-lactation cows were monitored weekly from 6 weeks before calving to the end of lactation. Second-lactation cows were monitored weekly throughout lactation. Cows were locomotion scored on a scale from one (sound) to five (severely lame); a score greater than or equal to two defined presence of lameness. Cows’ weekly body condition score and BW was also recorded. These records were matched to corresponding milk yield records, where the latter were 7-day averages on the week of inspection. The total number of repeated records amounted to 12 221. Data were also matched to the farm’s reproduction database, from which five traits were derived. Statistical analyses were based on uni- and bivariate random regression models. The profile analysis showed that locomotion and lameness problems in first lactation were fewer before and immediately after calving, and increased as lactation progressed. The profile of the two traits remained relatively constant across the second lactation. Highest heritability estimates were observed in the weeks before first calving (0.66 for locomotion score and 0.54 for lameness). Statistically significant genetic correlations were found for first lactation weekly locomotion score and lameness with body condition score, ranging from −0.31 to −0.65 and from −0.44 to −0.76, respectively, suggesting that cows genetically pre-disposed for high body condition score have fewer locomotion and lameness issues. Negative (favourable) phenotypic correlations between first lactation weekly locomotion score/lameness and milk yield averaged −0.27 and −0.17, respectively, and were attributed to management factors. Also a phenotypic correlation between lameness and conception rate of −0.19 indicated that lame cows were associated with lower success at conceiving. First-lactation daughter locomotion score and/or lameness predictions from sires’ estimated breeding values for conformation traits revealed a significant linear effect of rear leg side view, rear leg rear view, overall conformation, body condition score and locomotion, and a quadratic effect of foot angle.

Estimation of genetic parameters for the annual earnings at different race distances in young and adult Trotter Horses using a Random Regression Model

From a total of 231,609 records of individual earnings, registered from the racing performances of 4715 Spanish Trotter Horses which took place from 1991 to 2007, the annual earning (AE) was estimated for each horse. This produced a total of 99,419 records. The AE at different stages–both young horses (2 to 4years old) and adult horses (over 5years old) –and over different distances (from 1600 to 2700m) were considered as different traits and a Random Regression Model (RRM) was applied to estimate the (co)variance components throughout the trajectory of the age groups and distances. The following factors were considered as fixed effects: the combinations of hippodrome/year of the race; the sex of the animal; the type of start; its racing experience; the effect of the number of races recorded for each horse; and the fixed regression of Legendre polynomials of distance on the AE. The number of races in the year was included as a covariate on this model. The animal (out of a total of 10,089 animals in the pedigree) and the permanent environment effect of individual (4715 animals with data) were considered as random and were modeled using a bi-variate RRM with a first order Legendre polynomial. The heritability estimated by distance ranged from 0.08 to 0.10 for young horses and from 0.10 to 0.14 in adult animals. Within the same age, the genetic correlations (rg) between different distances ranged from 0.69 to 0.99. For the same distances but across the age ranges, the rg ranged from 0.30 to 0.59, which means that the AE results in young and adult horses do not represent exactly the same traits. The changes of the relative accumulated predicted earnings in different stages of the horses' sporting lives were analyzed. Differences were detected in age and distance. Therefore, the use of RRM is highly recommended as a very useful tool, because it allows us to estimate the animal's breeding value through all the stages of its trajectory in racing competitions. This contrasts with the classic procedures used to estimate breeding value, where all the results of each animal are considered as the same traits.

Genetic association between test-day milk fat to protein ratio and fertility traits in dairy cows: A random regression model analyses

In early lactating cows changes in energy balance (EB) and subsequent mobilisation of body reserves result changes in milk yield (MY) and milk composition. These variations could be used as indicators of changes or problems in feeding, health and fertility. Recently it is postulated that changes in milk fat to protein ratio (FPR) may be associated with a negative EB. A negative EB, typical of the early phase of lactation impairs cows fertility whereas a recovery in EB from its most negative state, signals the initiation of ovarian activity indicating a direct relationship. Therefore, since measuring EB in large populations is difficult and expensive, assessing the genetic association between ratios of milk components and fertility traits, especially at different stages of lactation may provide an inexpensive indicator of EB. Besides, in selection programs, such information could be used to identify sires and cow families that have chronic energy deficiency and poor fertility in early lactation. The objectives of this study were to estimate covariance components for test-day FPR, MY and fertility traits and to assess the genetic associations between these traits during lactation using random regression models (RRM). Genetic parameters of test-day FPR, MY and fertility were estimated using bivariate RRM that combine traits with different data structures employing a meta-model analyses. Fertility traits considered were days from calving to insemination (DFI), days open (DO), number of inseminations (NI), non-return rate to 56 days (NRR). Data was from a total of 22422 first lactation Finnish Ayrshire cows. The sire pedigree file had 638 males of which 509 sires had daughters with data. Heritability of test-day FPR during lactation ranged from 0.08 to 0.17 while the heritability of DFI, DO, NI and NRR were 0.06, 0.03, 0.01 and 0.02, respectively. Genetic correlations between test-day FPR and MY during early lactation ranged from 0.10-0.28. The positive genetic correlation between these traits indicates that genetically high producing cows tend to have high FPR during early lactation. Genetic correlations between test-day FPR and DFI, DO, NI, and NRR during early lactation were from 0.05-0.28, 0.03-0.24, 0.01-0.03, and -0.01-0.03, respectively. Of the fertility traits, the strongest genetic association was between test-day FPR and DFI or DO. The relatively low correlations between test-day FPR and the other fertility traits (NI and NRR) could be due to they are measures of fertility that are recorded after cows recovered from the most negative state of EB and started cycling. The positive and relatively higher genetic correlations between test-day FPR and DFI or DO indicate cows with high test-day FPR in early lactation tends to take longer from calving to first insemination and successful conception. The results from this study indicate that high FPR in early lactation could be used as an indicator of negative EB and cows of poor fertility, which take longer time from calving to first insemination and successful conception.

Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models

Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.

Genetic analysis of weight, fat and muscle depth in growing lambs using random regression models

AbstractGenetic parameters were estimated using uni- and bi-variate random regression models for weight, eye-muscle depth and fat depth measures between 60 and 360 days of age. Each trait was measured up to five times in 50-day intervals following weaning on approximately 4000 Australian Poll Dorset Sheep. The model accounted for rearing type, dam age, management group and age of recording. The model used for analysing weight included quadratic, orthogonal polynomials for direct genetic and environmental effects, a linear polynomial for maternal genetic effects and heterogeneous error variance across ages. The fat and muscle analysis used linear orthogonal polynomials for direct genetic and environmental effects and heterogeneous error variance. Throughout the 300-day trajectory heritability for weight traits ranged from 0·20 to 0·31, while heritability for fat depth ranged from 0·24 to 0·34 and heritability for eye-muscle depth ranged from 0·24 to 0·40. Genetic correlations between repeated measures of the same trait at different ages were positive and declined as the age interval increased, to minimum values of 0·60, 0·31 and 0·50 for weight, fat and muscle respectively between 60 and 360 days of age. Genetic correlations between weight and fat and weight and eye muscle were moderate to high (0·6 to 0·8) and positive but decreased slightly with age. The genetic correlations between fat and muscle were moderate to high (0·5 to 0·7) throughout the 300-day trajectory. In all cases, the estimates produced in this study were reasonably consistent with the limited number of studies that exist in the reported literature. This study demonstrated the relationships that exist between repeated measures of weight, fat and muscle measures over time, which is of interest to prime lamb producers looking to select for specific breeding objectives or market end points requiring precise weight, fat and muscle combinations at certain ages.