Data analysis - mixed models, variance components and breeding values.

Microarray experiments are multi-step processes. At each step—the growth of cultures, extraction of mRNA, reverse transcription, labelling, hybridization, scanning, and image analysis—variation and error cannot be completely avoided. Estimating the amount of such noise and variation is essential, not only to test for differential expression but also to suggest at which level replication is most effective.Replication and averaging are the key to the estimation as well as the reduction of variability. Here I discuss the use of ANOVA mixed models and of analysis of variance components as a rigorous way to calculate the number of replicates necessary to detect a given target fold-change in expression levels. Procedures are available in the package YASMA (http://www.cryst.bbk.ac.uk/wernisch/yasma.html) for the statistical data analysis system R (http://www.R-project.org).

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Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels

Genetics ◽

10.1093/genetics/iyaa048 ◽

2021 ◽

Vol 217 (2) ◽

Author(s):

L E Puhl ◽

J Crossa ◽

S Munilla ◽

P Pérez-Rodríguez ◽

R J C Cantet

Keyword(s):

Hexaploid Wheat ◽

Variance Components ◽

Bayesian Model ◽

Synthetic Hexaploid Wheat ◽

Data Sets ◽

Hierarchical Bayesian Model ◽

Additive Variance ◽

Ploidy Levels ◽

Breeding Values ◽

Synthetic Hexaploid

Abstract Cultivated bread wheat (Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii—T. aestivum and T. turgidum—T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

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A novel metric of reliability in pressure pain threshold measurement

Scientific Reports ◽

10.1038/s41598-021-86344-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bernard Liew ◽

Ho Yin Lee ◽

David Rügamer ◽

Alessandro Marco De Nunzio ◽

Nicola R. Heneghan ◽

...

Keyword(s):

Mixed Models ◽

Variance Components ◽

Pain Threshold ◽

Intraclass Correlation ◽

Pressure Pain Threshold ◽

Total Variance ◽

Lumbar Region ◽

Pressure Pain ◽

Pressure Algometer ◽

Testing Protocols

AbstractThe inter-session Intraclass Correlation Coefficient (ICC) is a commonly investigated and clinically important metric of reliability for pressure pain threshold (PPT) measurement. However, current investigations do not account for inter-repetition variability when calculating inter-session ICC, even though a PPT measurement taken at different sessions must also imply different repetitions. The primary aim was to evaluate and report a novel metric of reliability in PPT measurement: the inter-session-repetition ICC. One rater recorded ten repetitions of PPT measurement over the lumbar region bilaterally at two sessions in twenty healthy adults using a pressure algometer. Variance components were computed using linear mixed-models and used to construct ICCs; most notably inter-session ICC and inter-session-repetition ICC. At 70.1% of the total variance, the source of greatest variability was between subjects ($${\sigma }_{subj}^{2}$$ σ subj 2 = 222.28 N2), whereas the source of least variability (1.5% total variance) was between sessions ($${\sigma }_{sess}^{2}$$ σ sess 2 = 4.83 N2). Derived inter-session and inter-session-repetition ICCs were 0.88 (95%CI: 0.77 to 0.94) and 0.73 (95%CI: 0.53 to 0.84) respectively. Inter-session-repetition ICC provides a more conservative estimate of reliability than inter-session ICC, with the magnitude of difference being clinically meaningful. Quantifying individual sources of variability enables ICC construction to be reflective of individual testing protocols.

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130 Utilization of Nutrack to Assess Variance Components and Heritability of Activity Traits

Journal of Animal Science ◽

10.1093/jas/skab054.030 ◽

2021 ◽

Vol 99 (Supplement_1) ◽

pp. 17-17

Author(s):

Lexi M Ostrand ◽

Melanie D Trenhaile-Grannemann ◽

Garrett See ◽

Ty B Schmidt ◽

Eric Psota ◽

...

Keyword(s):

Animal Model ◽

Mixed Models ◽

Variance Components ◽

Average Speed ◽

Genetic Variances ◽

Sow Longevity ◽

Selection Decisions ◽

Recorded Data ◽

Residual Variances ◽

And Behavior

Abstract Overall activity and behavior are integral components of sows remaining productive in the herd. This investigation studied overall activity of group housed replacement gilts and the heritability of various activity traits. Beginning around 20 wk of age, video recorded data of approximately 75 gilts/group for a total of 2,378 gilts over 32 groups was collected for 7 consecutive d using the NUtrack System, which tracks distance travelled (m), avg speed (m/s), angle rotated (degrees), and time standing (s), sitting (s), eating (s), and laying (s). The recorded phenotypes were standardized to the distribution observed within a pen for each group. The final values used for analysis were the average daily standardized values. Data were analyzed using mixed models (RStudio V 1.2.5033) including effects of sire, dam, dam’s sire and dam, dam’s grandsire and granddam, farrowing group, barn, pen, and on-test date. Sire had an effect on every activity trait P < 0.001), and dam had an effect on average speed (P < 0.001). The dam’s sire had an effect on all activity traits (P < 0.001) and the dam’s grandsire had an effect on average speed (P < 0.001). Heritabilities and variance components of activity traits were estimated in ASReml 4 using an animal model with a two-generation pedigree. Genetic variances are 0.17 +/- 0.029, 0.19 +/- 0.034, and 0.11 +/- 0.024, residual variances are 0.37 +/- 0.023, 0.41 +/- 0.027, and 0.41 +/- 0.022, phenotypic variances are 0.54 +/- 0.018, 0.60 +/- 0.020, and 0.52 +/- 0.016, and heritabilities are 0.32 +/- 0.048, 0.32 +/- 0.049, and 0.21 +/- 0.044 for average speed, distance, and lie respectively. NUtrack offers potential to aid in selection decisions. Given the results presented herein, continued investigation into these activity traits and their association with sow longevity is warranted.

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Estimation of variance components in normal linear mixed models with additivity

10.1063/1.4912414 ◽

2015 ◽

Cited By ~ 1

Author(s):

Dário Ferreira ◽

Sandra S. Ferreira ◽

Célia Nunes ◽

João T. Mexia

Keyword(s):

Mixed Models ◽

Variance Components ◽

Linear Mixed Models ◽

Estimation Of Variance

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A note on genetic parameters and accuracy of estimated breeding values in honey bees

Genetics Selection Evolution ◽

10.1186/s12711-019-0510-6 ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 6

Author(s):

Evert W. Brascamp ◽

Piter Bijma

Keyword(s):

Honey Bees ◽

Variance Components ◽

Genetic Parameters ◽

Additive Genetic Variance ◽

Breeding Value ◽

Phenotypic Variance ◽

Breeding Values ◽

Estimated Breeding Values ◽

Additive Genetic Relationship ◽

The Relationship

Abstract Background In honey bees, observations are usually made on colonies. The phenotype of a colony is affected by the average breeding value for the worker effect of the thousands of workers in the colony (the worker group) and by the breeding value for the queen effect of the queen of the colony. Because the worker group consists of multiple individuals, interpretation of the variance components and heritabilities of phenotypes observed on the colony and of the accuracy of selection is not straightforward. The additive genetic variance among worker groups depends on the additive genetic relationship between the drone-producing queens (DPQ) that produce the drones that mate with the queen. Results Here, we clarify how the relatedness between DPQ affects phenotypic variance, heritability and accuracy of the estimated breeding values of replacement queens. Second, we use simulation to investigate the effect of assumptions about the relatedness between DPQ in the base population on estimates of genetic parameters. Relatedness between DPQ in the base generation may differ considerably between populations because of their history. Conclusions Our results show that estimates of (co)variance components and derived genetic parameters were seriously biased (25% too high or too low) when assumptions on the relationship between DPQ in the statistical analysis did not agree with reality.

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