混合家系遗传参数估计方法研究

针对混合家系遗传参数估计,本文在假定公畜方差组分和母畜方差组分相等这一理论基础上,通过对方差分析的期望均方组成分析,提出了新的遗传力估计方法,以及某些特殊情况下的近似估计方法。通过一个估测实例比较了几种遗传力估计方法,结果表明,本文方法与全同胞组分估计最为接近,而且遗传力标准误最小,本文近似估计方法的效果也较好。对各种方法而言,资料越不平衡其差异越大。本文方法可以在一定程度上弥补全同胞分析时,因实际资料的公母畜方差组分差异过大的缺陷,具有实际可行性。此外,由于本文方法是用单因方差分析解决二因方差分析问题,计算更为简便,并可免于计算混合家系平均亲缘相关系数。     

单元内混合家系相关法和阈性状法估计遗传力的比较研究

本文提出了一种估计遗传力的方法即单元内混合家系相关法,并用这种方法及阈性状分析法进行了猪肢蹄结实度的遗传力估计,结果表明,对于多阈性状,用单元内混合家系相关法估计的结果准确性好。     

用单元内混合家系相关法计算遗传力时的抽样误差估计

对应用单元内混合家系相关法计算遗传力时的抽样误差估计问题进行了探讨,推导出的抽样误差估计公式可用于遗传力的显著性检验．     

R法遗传参数估值置信区间的计算方法和重复估计次数的研究

探讨R法遗传参数估值置信区间的计算方法和重复估计次数(NORE)对参数估值的影响,利用4种模型通过模拟产生数据集。基础群中公、母畜数分别为200和2000头,BLUP育种值选择5个世代。利用多变量乘法迭代(MMI)法,结合先决条件的共扼梯度(PCG)法求解混合模型方程组估计方差组分。用经典方法、Box-Cox变换后的经典方法和自助法计算参数估值的均数、标准误和置信区间。结果表明,重复估计次数较多时,3种方法均可;重复估计次数较少时,建议使用自助法。简单模型下需要较少的重复估计,但对于复杂模型则需要较多的重复估计。随模型中随机效应数的增加,直接遗传力高估。随着PCG和MMI轮次的增大,参数估值表现出低估的趋势。     

成都白鸡某些性状遗传力的几种估测方法的比较

性状遗传力是进行选种、育种工作不可缺 少的遗传参数。有关鸡的经济行状遗传力,国 外已有许多研究,而国内这方面的报道较少。由 于不同品种同一性状的遗传力不尽相同,因而 国外估测的有关性状遗传力只能供我们参考。 为使成都白鸡今后的选育工作更为科学可靠, 有必要正确估测成都白鸡某些主要经济性状的 遗传力。关于估测性状遗传力的方法,许多学 者作了介绍。这些方法大体可归纳为相关分析 和方差分析两类。相关分析是度量亲缘间的相 似性。方差分析是估测遗传方差在总的表型方 差中所占的比分。度量同胞间的组内相关分析 方法实际上也是一种方差分析。本研究的目的 在于估测成都白鸡某些经济性状的遗传力,比 较使用子亲相关、子亲回归、半同胞相关、全同 胞相关和实际选择反应估测这些性状遗传力的 差别,讨论估测这些性状遗传力的可行方法。     

性状遗传力与QTL方差对标记辅助选择效果的影响

在采用动物模型标记辅助最佳线性无偏预测方法对个体育种值进行估计的基础上,模拟了在一个闭锁群体内连续对单个性状选择10个世代的情形,并系统地比较了性状遗传力和QTL方差对标记辅助选择所获得的遗传进展、QTL增效基因频率和群体近交系数变化的影响。结果表明：在对高遗传力和QTL方差较小的性状实施标记辅助选择时,可望获得更大的遗传进展;遗传力越高,QTL方差越大,则QTL增效基因频率的上升速度越快;遗传力较高时,群体近交系数上升的速度较为缓慢,而QTL方差对群体近交系数上升速度的影响则不甚明显。结合前人关于标记辅助选择相对效率的研究结果,可以认为：当选择性状的遗传力和QTL方差为中等水平时,标记辅助选择可望获得理想的效果。     

“三黄”鸡采精量和受精率的方差组分估计

用4种方法(Henderson方法Ⅰ、 MIVQUE、ML、REML)估计了两个“三黄”慢羽系的公鸡采精量和种蛋受精率的方差组分,并计算了它们与产蛋性状间的遗传相关。结果表明,采精量为中等到高遗传力性状(h2=0.29-0.3 9);受精率的遗传方差在品系之间存在着较大的差异,L系受精率性状的遗传力低,而Ⅰ系受精率为中等到高遗传力性状。采精量和受精率与开产日龄、 43周龄蛋重以及64周龄产蛋量间遗传相关不显著。此外,还对4种方差组分估计方法和两种数值转换方法的实际效果作了比较。     

相关遗传力抽样方差的估计

本文给出了单因子组和系统模型下有关相关传力及相关环境力抽样方差的估计方法。有关该参数在育种中的潜在应用其它与遗传相关之产间的相互关系也给予了一定的讨论。概括地来讲,相关遗传力具有遗传力和遗传相关的双重特征。遗传力是相关遗传力的一个特例而相关遗传力则是遗传力概念本身的扩展。用相关遗传力来表达性状间的相关遗传变异要比通常所用的遗传相关更准确一些。     

影响动物模型MBLUP评定准确性的主要因素

标记辅助最佳线性无偏预测（marker-assisted best linear unbiased prediction,MBLUP）是对动物实施标记辅助选择（marker-assisted selection,MAS）的一种重要方法。通过计算机随机模拟研究了所选性状的遗传力、QTL方差和相邻两个标记间图距3个因素对动物模型MBLUP评定准确性的影响。结果表明,性状的遗传力越高、QTL方差和相邻两个标记间图距越小时,动物模型MBLUP评定的准确性越高;相反,当性状的遗传力较低、QTL方差和相邻两个标记间图距较大时,动物模型MBLUP评定的准确性则较低。     

长期人工选择对群体遗传结构的影响——一项计算机模拟研究 Ⅱ.长期选择对群体遗传方差的作用

本文给出了显性与超显性模型下加性方差的分剖公式,为研究选择作用下基因间关系的变化提供了有力的方法。并模拟研究了群体大小、连锁强度与遗传力水平对遗传方差变化的影响。小群体中遗传方差在世代间波动很大;大群体中则稳定下降、波动较小。选择作用下平衡加性方差下降很快,特别是高遗传力性状。紧密连锁在小群体中一方面降低选择反应,一方面维持了更多的加性方差,从而使得预测长期选择反应甚为困难。     

Genotype — hatch interactions as a source of bias in estimating genetic components of variance for eight-week weight in poultry

Summary Data on 16,557 chicks from 198 sire groups were analyzed to determine the importance of genotype-hatch interactions as sources of bias in the estimation of genetic parameters for eight-week weight in poultry. Estimates of heritability for eight-week weight obtained from a paternal half-sib correlation were .27±.04 and .29±.04 for males and females respectively when the sire component of variance was unconfounded with the sire-hatch interaction variance. If the sire-hatch interaction effects were confounded with the sire effects in an intra-hatch analysis the corresponding estimates of heritability would be .30 and .35 for males and females respectively. No attempt was made to artificially create differences in the hatch environments.The ratio of the dam-hatch interaction variance to the dam component of variance as compared with the sire-hatch interaction variance to the sire component gave a slight indication of a greater degree of importance of the interaction of non-additive genetic-environment effects than additive x environmental effects in the male progeny but not in the female progeny.The authors gratefully acknowledge Nichols, Inc., Brunswick, Maine for providing the data for this study.     

Effects of data imbalance on estimation of heritability

Summary Effects of data imbalance on bias, sampling variance and mean square error of heritability estimated with variance components were examined using a random two-way nested classification. Four designs, ranging from zero imbalance (balanced data) to low, medium and high imbalance, were considered for each of four combinations of heritability (h²=0.2 and 0.4) and sample size (N=120 and 600). Observations were simulated for each design by drawing independent pseudo-random deviates from normal distributions with zero means, and variances determined by heritability. There were 100 replicates of each simulation; the same design matrix was used in all replications. Variance components were estimated by analysis of variance (Henderson's Method 1) and by maximum likelihood (ML). For the design and model used in this study, bias in heritability based on Method 1 and ML estimates of variance components was negligible. Effect of imbalance on variance of heritability was smaller for ML than for Method 1 estimation, and was smaller for heritability based on estimates of sire-plus-dam variance components than for heritability based on estimates of sire or dam variance components. Mean square error for heritability based on estimates of sire-plus-dam variance components appears to be less sensitive to data imbalance than heritability based on estimates of sire or dam variance components, especially when using Method 1 estimation. Estimation of heritability from sire-plus-dam components was insensitive to differences in data imbalance, especially for the larger sample size.Supported by grants from the Illinois Agricultural Experiment Station and the University of Illinois Research Board. Charles Smith, H. W. Norton and D. Gianola contributed valuable suggestions     

Responses of alloplasmic (cytoplasm=Triticum timopheevii) and euplasmic wheats (Triticum aestivum) to photoperiod and vernalization

Summary Heritability estimated from sire family variance components, ignoring dams, pools conventional paternal and maternal half sib estimates, in a way which is biased upward, and sub-optimal for minimizing the sampling variance. Standard error of a sire family estimate will be smaller than that of the equivalent paternal half sib estimate, but not as small as that of an estimate obtained by optimal pooling of paternal and maternal half sib estimates. If only additive genetic variance components are significant, the bias may be removed by use of a computed average genetic relationship for sire families, in place of a nominal R = 0.25. Average genetic relationship may be computed from mean and variance of dam family size within sire families. If dominance, epistatic, or maternal components are significant, this simple correction is not appropriate. In situations likely to be encountered in large domestic species such as sheep and cattle (dam family size small and uniform) bias will be negligible. The method could be useful where cost of dam identification is a limiting factor.     

Probability of Inadmissible Estimates of Heritability from Full-Sib Analysis Under a General Model of Gene Action

Evaluation of probabilities of the heritability (h²) estimates in full-sib analysis falling outside the permissible limits [0,1] was undertaken for several combinations of sire/dam numbers, h² and proportions of dominance and additive × additive epistatic variances, assuming two full-sibs per mating and the dominance related epistasis as negligible. In the light of the results use of sire component and sire-dam combination has been recommended in the estimation of heritability of lowly and highly heritable traits respectively.     

Simulation study for analysis of binary responses in the presence of extreme case problems

Background

Estimates of variance components for binary responses in presence of extreme case problems tend to be biased due to an under-identified likelihood. The bias persists even when a normal prior is used for the fixed effects.

Methods

A simulation study was carried out to investigate methods for the analysis of binary responses with extreme case problems. A linear mixed model that included a fixed effect and random effects of sire and residual on the liability scale was used to generate binary data. Five simulation scenarios were conducted based on varying percentages of extreme case problems, with true values of heritability equal to 0.07 and 0.17. Five replicates of each dataset were generated and analyzed with a generalized prior (g-prior) of varying weight.

Results

Point estimates of sire variance using a normal prior were severely biased when the percentage of extreme case problems was greater than 30%. Depending on the percentage of extreme case problems, the sire variance was overestimated when a normal prior was used by 36 to 102% and 25 to 105% for a heritability of 0.17 and 0.07, respectively. When a g-prior was used, the bias was reduced and even eliminated, depending on the percentage of extreme case problems and the weight assigned to the g-prior. The lowest Pearson correlations between true and estimated fixed effects were obtained when a normal prior was used. When a 15% g-prior was used instead of a normal prior with a heritability equal to 0.17, Pearson correlations between true and fixed effects increased by 11, 20, 23, 27, and 60% for 5, 10, 20, 30 and 75% of extreme case problems, respectively. Conversely, Pearson correlations between true and estimated fixed effects were similar, within datasets of varying percentages of extreme case problems, when a 5, 10, or 15% g-prior was included. Therefore this indicates that a model with a g-prior provides a more adequate estimation of fixed effects.

Conclusions

The results suggest that when analyzing binary data with extreme case problems, bias in the estimation of variance components could be eliminated, or at least significantly reduced by using a g-prior.     

Comparing sire and dam estimates of heritability: jackknife and likelihood approaches

Three estimates of heritability are available from the half-sib pedigree design: the sire, dam and genotypic estimates. Because of its significantly smaller standard error, the genotypic estimate is preferred provided that there are no non-additive effects that inflate the estimate. I present two methods to test for such effects: these are a t-test of the paired sire and dam pseudovalues from the jackknife procedure and the likelihood ratio test from the animal model. Both methods are shown to be valid tests for significant dominance and/or maternal effects. SPLUS coding for the implementation of the jackknife method is provided. Unless sample sizes are very large, the power of the tests is low and hence caution is advised in the use of the genotypic estimate following a nonsignificant test. An approximate power analysis can be done using the data from the jackknife method but the estimated power is typically a substantial underestimate of the true power and its use is not recommended.     

Estimation of quantitative genetic parameters using marker-inferred relatedness in Japanese flounder: a case study of upward bias

Marker-based methods for estimating heritability have been proposed as an effective means to study quantitative traits in long-lived organisms and natural populations. However, practical examinations to evaluate the usefulness and robustness of a regression method are limited. Using several quantitative traits of Japanese flounder Paralichthys olivaceus, the present study examined the influence of relatedness estimator and population structure on the estimation of heritability and genetic correlation under a regression method with 7 microsatellite loci. Significant heritability and genetic correlation were detected for several quantitative traits in 2 laboratory populations but not in a natural population. In the laboratory populations, upward bias in heritability appeared depending on the relatedness estimators and the populations. Upward bias in heritability increased with decreasing the actual variance of relatedness, suggesting that the estimates of heritability under the regression method tend to be overestimated due to the underestimation of the actual variance of relatedness. Therefore, relationship structure and precise estimation of relatedness are critical for applying this method.     

Estimation of Additive Genetic Variance Components in Aquaculture Populations Selectively Pedigreed by DNA Fingerprinting

A method to estimate genetic variance components in populations partially pedigreed by DNA fingerprinting is presented. The focus is on aquaculture, where breeding procedures may produce thousands of individuals. In aquaculture populations the individuals available for measurement will often be selected, i.e. will come from the upper tail of a size‐at‐age distribution, or the lower tail of an age‐at‐maturity distribution etc. Selection typically occurs by size grading during grow‐out and/or choice of superior fish as broodstock. The method presented in this paper enables us to estimate genetic variance components when only a small proportion of individuals, those with extreme phenotypes, have been identified by DNA fingerprinting. We replace the usual normal density by appropriate robust least favourable densities to ensure the robustness of our estimates. Standard analysis of variance or maximum likelihood estimation cannot be used when only the extreme progeny have been pedigreed because of the biased nature of the estimates. In our model‐based procedure a full robust likelihood function is defined, in which the missing information about non‐extreme progeny has been taken into account. This robust likelihood function is transformed into a computable function which is maximized to get the estimates. The estimates of sire and dam additive variance components are significantly and uniformly more accurate than those obtained by any of the standard methods when tested on simulated population data and have desirable robustness properties.     

Heritability and Y-chromosome influence in the jack male life history of chinook salmon (Oncorhynchus tshawytscha)

Jacking in chinook salmon (Oncorhynchus tshawytscha) is an alternative reproductive strategy in which males sexually mature at least 1 year before other members of their year class. We characterize the genetic component of this reproductive strategy using two approaches; hormonal phenotypic sex manipulation, and a half-sib breeding experiment. We 'masculinized' chinook salmon larvae with testosterone, reared them to first maturation, identified jacks and immature males based on phenotype, and genotyped all fish as male ('XY') or female ('XX') using PCR-based Y-chromosome markers. The XY males had a much higher incidence of jacking than the XX males (30.8% vs 9.9%). There was no difference in body weight, gonad weight, and plasma concentrations of testosterone and 17beta-estradiol between the two jack genotypes, although XY jacks did have a higher gonadosomatic index (GSI) than XX jacks. In the second experiment, we bred chinook salmon in two modified half-sib mating designs, and scored the number of jacks and immature fish at first maturation. Heritability of jacking was estimated using two ANOVA models: dams nested within sires, and sires nested within dams with one-half of the half-sib families common to the two models. The sire component of the additive genetic variance yielded a high heritability estimate and was significantly higher than the dam component (h(2)(sire) = 0.62 +/- 0.21; h(2)(dam) = -0.14 +/- 0.12). Our experiments both indicated a strong sex-linked component (Y-chromosome) to jacking in chinook salmon, although evidence for at least some autosomal contribution was also observed.