Gerinnungshemmende Therapie

Dempfle, Carl-Erik

doi:10.1055/a-0593-3513

Cited by 1 publication

(2 citation statements)

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“…By doing so, we take on the above mentioned critique that GCTA-GREML neglects the contribution of LD due to the diagonal covariance structure of the marginal β (Kumar et al 2015(Kumar et al , 2016. We show that the conditional genomic variance explicitly accounts for LD which has special practical relevance in animal breeding (Dempfle 2018), and remarkably reduces the missing heritability. In addition to that, the difference of the novel predictor and the estimator of the marginal genomic variance in REM can be used as an indicator for the contribution of LD to the genomic variance.…”

Section: Introductionmentioning

confidence: 99%

“…In more detail, epistasis is only important on the gene-level but not for genetic variances (Hill et al 2008), and Zhu et al (2015) show that for human complex traits dominance variation contributes little. Nevertheless, linkage disequilibrium (LD) is an important factor especially when departing from random mating and Hardy-Weinberg equilibrium, which is often the case in animal breeding (Hill et al 2008;Dempfle 2018). The genomic variance, the genomic equivalent to the genetic variance, is defined as the variance of a trait that can be explained by a linear regression on a set of markers (de los Campos et al 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Best Prediction of the Additive Genomic Variance in Random-Effects Models

Schreck

Piepho

Schlather

2018

Preprint

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The additive genomic variance, the chief ingredient for the heritability, is often underestimated in phenotypegenotype regression models. Various remedies, including different models and estimators, have been proposed in order to improve on what has been coined the "missing heritability". Recently, debates have been conducted whether estimators for the genomic variance include linkage disequilibrium (LD) and how to explicitly account for LD in estimation procedures.Up-to-now, the genomic variance in random effect models (REM) has been estimated as a parameter of the marginal, i.e. unconditional model. We propose that the genomic variance in REM should be predicted as a conditional random quantity based on the conditional distribution of β. This signifies a paradigm shift from the estimation to the prediction of the genomic variance. This approach is structurally in perfect accordance to the Bayesian regression model (BRM), where the posterior expectation of the genomic variance is estimated based on the posterior of β. We introduce a novel, mathematically rigorously founded predictor for the conditional genomic variance in (g)BLUP, which is structurally close to the Bayesian estimator. The conditioning of the novel predictor on the data is intrinsically tied to the inclusion of the contribution of LD and the predicted effects. In addition to that, the predictor shows much weaker dependence on distribution assumptions than estimators of other approaches, e.g. GCTA-GREML. Last but not least, the predictor, contrasted with the estimator in the unconditional model, enables an innovative approximation of the influence of LD on the genomic variance in the dataset.An exemplary simulation study based on the commonly used dataset of 1814 mice genotyped for 10346 polymorphic markers substantiates that the bias of the novel predictor is small in all standard situations, i.e. that the predictor for the conditional genomic variance remarkably reduces the "missing heritability".

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Section: Introductionmentioning

confidence: 99%