A deterministic multi-tier model of  assortative mating following selection

Shepherd, Robert K.; Kinghorn, BP

doi:10.1051/gse:19940603

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…However, if there are a small number of loci, Fernando and Gianola found a larger effect, which appears to have arisen from faster allele frequency change under assortative mating (as opposed to generation of positive disequilibrium). Likewise, Shepherd and Kinghorn (1994) found a much larger effect gain with assortative mating when selection (and mating) are based on estimating breeding values (BLUP Selection) as opposed to simple individual selection. As Example 16.5 demonstrates, under the infinitesimal model the per-generation increase in response of a population subjected to multiple rounds of assortative mating prior to selection rapidly decays to zero once selection starts.…”

Section: Assortative Mating and Enhanced Responsementioning

confidence: 97%

“…The efficiency of assortative mating under truncation selection has been examined in detail by Baker (1973), DeLange (1974), Fernando and Gianola (1986), Smith and Hammond (1987), Tallis and Leppard (1988a), and Shepherd and Kinghorn (1994). The general conclusion is that the relative increase in response is greatest when h 2 is large and selection is weak.…”

Section: Assortative Mating and Enhanced Responsementioning

confidence: 99%

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Short-term Changes in the Variance: 1. Changes in the Additive Variance

Walsh¹,

Lynch²

2018

Oxford Scholarship Online

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Selection changes the additive-genetic variance (and hence the response in the mean) by both changing allele frequencies and by generating correlations among alleles at different loci (linkage disequilibrium). Such selection-induced correlations can be generated even between unlinked loci, and (generally) are negative, such that alleles increasing trait values tend to become increasingly negative correlated under direction or stabilizing selection, and positively correlated under disruptive selection. Such changes in the additive-genetic variance from disequilibrium is called the Bulmer effects. For a large number of loci, the amount of change can be predicted from the Bulmer equation, the analog of the breeder's equation, but now for the change in the variance. Upon cessation of selection, any disequilibrium decays away, and the variances revert back to their additive-genic variances (the additive variance in the absence of disequilibrium). Assortative mating also generates such disequilibrium.

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Section: Assortative Mating and Enhanced Responsementioning

confidence: 97%

Section: Assortative Mating and Enhanced Responsementioning

confidence: 99%

Short-term Changes in the Variance: 1. Changes in the Additive Variance

Walsh¹,

Lynch²

2018

Oxford Scholarship Online

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“…However, to improve quantitative traits, animals are classified into subgroups based on identifiable and stable phenotypic characteristics. Some authors indicated that there was an increase in some genetic parameters with assortative mating system, which is mating of similar phenotypes animals (Shepherd and Kinghorn, 1994).…”

Section: Introductionmentioning

confidence: 99%

Modeling with Gaussian mixture regression for lactation milk yield in Anatolian buffaloes

Yeşilova

Yılmaz

Ser

et al. 2016

IJAR

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The purpose of this study was to classify Anatolian buffalo using Gaussian mixture regression model according to discrete and continuous environmental effects. Gaussian mixture model performs separately regression analysis both within and between groups. This is an important property of Gaussian mixture models which makes it different from other multivariate statistical methods. The data were obtained from 1455 Anatolian buffalo lactation milk yield records reared in seven different locations in Bitlis province, Turkey. Age of dam, lactation duration and locations were considered as environmental effects on lactation milk yield. Data set was divided into three homogenous subgroups with respect to AIC and BIC in the Gaussian mixture regression, based on environmental effects on lactation milk yield. Estimated mean for lactation milk yields and mixing probabilities for the first, second and third subgroups were determined as 1494.33 kg (16.9%), 540.33 kg (45.2%) and 847.61 (37.9%), respectively. The numbers of buffalo in each subgroup according to mixing probability were obtained as 159, 756, and 540 for the first, second, and third groups, respectively. The effects of lactation period, age of dam and villages were found statistically significant on lactation milk yield in subgroup 1 that was highest mean for lactation milk yield (p<0.01). In conclusion, results showed that Gaussian mixture regression was an important tool for classifying quantitative traits considering environmental effects in animal breeding.

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A deterministic multi-tier model of assortative mating following selection

Cited by 2 publications

References 9 publications

Short-term Changes in the Variance: 1. Changes in the Additive Variance

Short-term Changes in the Variance: 1. Changes in the Additive Variance

Modeling with Gaussian mixture regression for lactation milk yield in Anatolian buffaloes

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