Genetic heteroscedasticity of teat count in pigs

Felleki, Majbritt; Lundeheim, N.

doi:10.1111/jbg.12134

Cited by 11 publications

(6 citation statements)

References 20 publications

(35 reference statements)

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“…When comparing estimates of r mv based on untransformed data and transformed data, it becomes apparent that transformations such as Box-Cox and log have a high impact on the sign and magnitude of r mv (Felleki & Lundeheim, 2015;Iung, Neves, Mulder, & Carvalheiro, 2017;Sae-Lim et al, 2015;Sonesson et al, 2013;Yang, Christensen, & Sorensen, 2011). Data transformation is used to satisfy the normality assumption and to account for scale effects, that is higher means are associated with higher variances, and then, transformation can reduce the mean-variance relationship (Box & Cox, 1964).…”

Section: Associated With Residual Variancementioning

confidence: 99%

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Iung

Carvalheiro

Neves³

et al. 2019

J Animal Breeding Genetics

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Genetic control of residual variance offers opportunities to increase uniformity and resilience of livestock and aquaculture species. Improving uniformity and resilience of animals will improve health and welfare of animals and lead to more homogenous products. Our aims in this review were to summarize the current models and methods to study genetic control of residual variance, genetic parameters and genomic results for residual variance and discuss future research directions. Typically, the genetic coefficient of variation is high (median = 0.27; range 0-0.86) and the heritability of residual variance is low (median = 0.01; range 0-0.10). Higher heritabilities can be achieved when increasing the number of records per animal. Divergent selection experiments have supported the feasibility of selecting for high or low residual variance. Genomic studies have revealed associations in regions related to stress, including those from the heat shock protein family. Although the number of studies is growing, genetic control of residual variance is still poorly understood, but big data and genomics offer great opportunities. K E Y W O R D Sgenetic control of residual variance, genetic heterogeneity of residual variance, micro-environmental sensitivity, resilience, uniformity 264 | IUNG et al.

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Section: Associated With Residual Variancementioning

confidence: 99%

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Iung

Carvalheiro

Neves³

et al. 2019

J Animal Breeding Genetics

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“…The medium/high level of heritability of this parameter can facilitate selection to improve overall sow reproductive performances (e.g. Willham & Whatley 1962 ; Toro et al 1986 ; McKay & Rahnefeld 1990 ; Borchers et al 2002 ; Chalkias et al 2013 ; Felleki & Lundeheim 2015 ; Balzani et al 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Single‐marker and haplotype‐based genome‐wide association studies for the number of teats in two heavy pig breeds

et al. 2021

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Summary The number of teats is a reproductive‐related trait of great economic relevance as it affects the mothering ability of the sows and thus the number of properly weaned piglets. Moreover, genetic improvement of this trait is fundamental to parallelly help the selection for increased litter size. We present the results of single‐marker and haplotypes‐based genome‐wide association studies for the number of teats in two large cohorts of heavy pig breeds (Italian Large White and Italian Landrace) including 3990 animals genotyped with the 70K GGP Porcine BeadChip and other 1927 animals genotyped with the Illumina PorcineSNP60 BeadChip. In the Italian Large White population, genome scans identified three genome regions (SSC7, SSC10, and SSC12) that confirmed the involvement of the VRTN gene (as we previously reported) and highlighted additional loci known to affect teat counts, including the FRMD4A and HOXB1 gene regions. A different picture emerged in the Italian Landrace population, with a total of 12 genome regions in eight chromosomes (SSC3, SSC6, SSC8, SSC11, SSC13, SSC14, SSC15, and SSC16) mainly detected via the haplotype‐based genome scan. The most relevant QTL was close to the ARL4C gene on SSC15. Markers in the VRTN gene region were not significant in the Italian Landrace breed. The use of both single‐marker and haplotype‐based genome‐wide association analyses can be helpful to exploit and dissect the genome of the pigs of different populations. Overall, the obtained results supported the polygenic nature of the investigated trait and better elucidated its genetic architecture in Italian heavy pigs.

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“…The CV values ranged from 0.0050 ( r = 0.05 and CV = 0.05), increasing with the value of the simulated parameters up to 1.4854 ( r = 1.00 and CV = 0.50). Compared with the values reviewed by Hill and Mulder (2010), the estimations of the posterior revision reported above (Neves et al 2011; Felleki et al 2012; Janhunen et al 2012; Rönnegård et al 2013; Fina et al 2013; Sae-Lim et al 2015; Felleki and Lundeheim 2015; Sell-Kubiak et al 2015a; Sell-Kubiak et al 2015b; Mulder et al 2016; Marjanovic et al 2016) and with the exception of the anomalous unreliable estimation for birth weight in mice by Gutiérrez et al (2006) as justified by Pun et al (2013), GCV values greater than 0.69 were never reported and might even be considered meaningless (Hill and Mulder 2010). High-scale effect strength is not typical and would never be possible in the context of high CV values.…”

Section: Discussionmentioning

confidence: 81%

“…A more recent review suggests that this parameter tends to be more frequently positive, i.e. , birth weight, 0.42 and 0.44 in cattle (Neves et al 2011; Fina et al 2013), 0.55 and 0.62 in pigs (Sell-Kubiak et al 2015b), also positive for adult weight, 0.30 and 0.79 in rainbow trout (Sae-Lim et al 2015) and 0.58 in tilapia (Marjanovic et al 2016); milk yield, 0.60 in dairy cattle (Rönnegård et al 2013); teat count, 0.80 in pigs (Felleki and Lundeheim 2015); litter size, 0.49 in pigs (Sell-Kubiak et al 2015a). Lower positive values were observed for other traits such as morphology traits in tilapia (0.11-0.37 (Marjanovic et al 2016) or 0.06 for conformation scores in cattle (Neves et al 2011).…”

Section: Discussionmentioning

confidence: 99%

The Statistical Scale Effect as a Source of Positive Genetic Correlation Between Mean and Variability: A Simulation Study

Tatliyer

Cervantes

García-Rebollar

et al. 2019

G3 Genes|Genomes|Genetics

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The selection objective for animal production is the highest income with the lowest production cost, while ensuring the highest animal welfare. A selection experiment for environmental variability of birth weight in mice showed a correlated response in the mean after 20 generations starting from a crossed panmictic population. The relationship between the birth weight and its environmental variability explained the correlated response. The scale effect represents a potential cause of this correlation. The relationship between the mean and the variability implies: the higher the mean, the higher the variability. The study was to quantify by simulation the genetic correlation between a trait and its environmental variability. This can be attributable to the scale effect in a range of coefficients of variation and heritabilities between 0.05 and 0.50. The resulting genetic correlation ranged from 0.1335 to 0.7021 being the highest for the highest heritability and the lowest CV. The scale effect for a trait with heritability between 0.25 and 0.35 and CV between 0.15 and 0.25 generated a genetic correlation between 0.43 and 0.57. The genetic coefficient of variation (GCV) affecting residual variability was modulated by the strength reducing the impact of the scale effect. GCV ranged from 0.0050 to 1.4984. The strength of the scale effect might be in the range between 0 and 1. The scale effect would explain many reported genetic correlation and the additive genetic variance for the variability. This is relevant when increasing the mean of a trait jointly with the reduction of its variability.

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Genetic heteroscedasticity of teat count in pigs

Cited by 11 publications

References 20 publications

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Genetics and genomics of uniformity and resilience in livestock and aquaculture species: A review

Single‐marker and haplotype‐based genome‐wide association studies for the number of teats in two heavy pig breeds

The Statistical Scale Effect as a Source of Positive Genetic Correlation Between Mean and Variability: A Simulation Study

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