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Biases of incomplete linear models in forest genetic data analysis and optimal methods for estimating type B genetic correlations.

機(jī)譯：森林遺傳數(shù)據(jù)分析中不完全線(xiàn)性模型的偏差和估計(jì)B型遺傳相關(guān)性的最佳方法。

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摘要

Potential biases of incomplete mixed models in the estimation of variance component, heritability, and the prediction of breeding gains are theoretically formulated based on balanced data. For a given incomplete mixed model, the magnitudes of biases are functions of population genetic architecture, mating design, and field experimental designs, which can be precisely assessed using the derived formulae. It was found that most incomplete mixed models over-estimate additive genetic variance, resulting in upward-biased heritability and inflated genetic gains. The relative consequence of bias is severe for traits under weak additive genetic control with the strong influence of non-additive genetic effects. For incomplete mixed models ignoring additive genetic effects (GCA) x environment (E) interactions, the potential biases are linearly related to the number of environments included in the data. For incomplete mixed models ignoring dominance effects, biases are linearly proportional to the number of crosses that each parent is mated. For pure additive genetic models ignoring both dominance effects and GCA x E interaction, the biases are cumulative and can be as high as 60% of the true parameter. For unbalanced data, the formulae can be used to approximate the minimum biases for a given incomplete mixed model by substituting for the average number of design parameters of an experiment.;The search for optimal statistical methods in estimating type B genetic correlations is begun by developing a new univariate approach. The new method estimates type B genetic correlations using predicted parental GCA effects with the technique of best linear unbiased prediction (BLUP) in each individual environment. Numerical comparisons using simulated forest genetic data with various genetic architecture and data imbalance have demonstrated its unbiasedness, better match to underlying true population parameters, and suitability to various experimental designs and data imbalance.;The unbiasedness and precision of multivariate methods in estimating type B genetic correlations are also investigated with a simulation study. It was concluded that constrained multivariate methods produce empirically unbiased estimates of type B genetic correlations which have higher estimation precision, especially when heritabilities of traits are low in the concerned environments. The practical importance of keeping estimates within parameter space and other additional advantages makes the constrained multivariate method a desirable choice.
機(jī)譯：理論上基于平衡數(shù)據(jù)，提出了不完全混合模型在方差分量，遺傳力和育種收益預(yù)測(cè)中的潛在偏差。對(duì)于給定的不完全混合模型，偏差的大小取決于種群遺傳結(jié)構(gòu)，交配設(shè)計(jì)和田間實(shí)驗(yàn)設(shè)計(jì)的功能，可以使用派生公式精確評(píng)估。結(jié)果發(fā)現(xiàn)，大多數(shù)不完全的混合模型高估了加性遺傳變異，導(dǎo)致遺傳偏向性和遺傳增益增加。在弱的加性遺傳控制下，偏性的相對(duì)后果很?chē)?yán)重，而非加性遺傳效應(yīng)的影響很大。對(duì)于忽略加性遺傳效應(yīng)（GCA）x環(huán)境（E）相互作用的不完整混合模型，潛在偏差與數(shù)據(jù)中包含的環(huán)境數(shù)量線(xiàn)性相關(guān)。對(duì)于忽略?xún)?yōu)勢(shì)效應(yīng)的不完整混合模型，偏差與每個(gè)父級(jí)配對(duì)的雜交數(shù)目成線(xiàn)性比例。對(duì)于同時(shí)忽略?xún)?yōu)勢(shì)效應(yīng)和GCA x E相互作用的純加性遺傳模型，偏差是累積性的，可能高達(dá)真實(shí)參數(shù)的60％。對(duì)于不平衡數(shù)據(jù)，可通過(guò)公式來(lái)代替實(shí)驗(yàn)的設(shè)計(jì)參數(shù)的平均數(shù)，從而使用公式來(lái)近似給定不完全混合模型的最小偏差。;通過(guò)估算B型遺傳相關(guān)性來(lái)尋找最佳統(tǒng)計(jì)方法一種新的單變量方法。新方法使用預(yù)測(cè)的父母GCA效應(yīng)和每個(gè)個(gè)體環(huán)境中的最佳線(xiàn)性無(wú)偏預(yù)測(cè)（BLUP）技術(shù)來(lái)估計(jì)B型遺傳相關(guān)性。使用具有各種遺傳結(jié)構(gòu)和數(shù)據(jù)不平衡的模擬森林遺傳數(shù)據(jù)進(jìn)行的數(shù)值比較表明，該模型具有無(wú)偏性，與潛在的真實(shí)種群參數(shù)更好地匹配，并且適合各種實(shí)驗(yàn)設(shè)計(jì)和數(shù)據(jù)不平衡。；估計(jì)B型遺傳的多元方法的無(wú)偏性和準(zhǔn)確性。相關(guān)性也通過(guò)仿真研究進(jìn)行了研究。結(jié)論是，受約束的多元方法產(chǎn)生的B型遺傳相關(guān)性的經(jīng)驗(yàn)無(wú)偏估計(jì)具有較高的估計(jì)精度，尤其是在相關(guān)環(huán)境中性狀的遺傳力較低時(shí)。將估計(jì)保持在參數(shù)空間內(nèi)的實(shí)際重要性以及其他優(yōu)勢(shì)使得受約束的多元方法成為理想的選擇。

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  • 作者

    Lu, Pengxin.;

  • 作者單位

    University of Florida.;

  • 授予單位 University of Florida.;
  • 學(xué)科 Forestry.;Plant sciences.
  • 學(xué)位 Ph.D.
  • 年度 1998
  • 頁(yè)碼 128 p.
  • 總頁(yè)數(shù) 128
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類(lèi)
  • 關(guān)鍵詞

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