pedSimulate – An R package for simulating pedigree, genetic merit, phenotype, and genotype data

Nilforooshan, Mohammad Ali

doi:10.37496/rbz5120210131

Cited by 4 publications

(2 citation statements)

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“…Data were simulated to test the hypotheses on more than the wheat data. The R package pedSimulate ( Nilforooshan, 2022c ) was used for data simulation. The simulation began with a founder population (F0) of 100 males and 100 females randomly mated to each other to produce the next generation.…”

Section: Methodsmentioning

confidence: 99%

Understanding factors influencing the estimated genetic variance and the distribution of breeding values

Nilforooshan

Ruíz-Flores²

2022

Front. Genet.

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This study investigated the main factors influencing the genetic variance and the variance of breeding values (EBV). The first is the variance of genetic values in the base population, and the latter is the variance of genetic values in the population under evaluation. These variances are important as improper variances can lead to systematic bias. The inverse of the genetic relationship matrix (K−1) and the phenotypic variance are the main factors influencing the genetic variance and heritability (h2). These factors and h2 are also the main factors influencing the variance of EBVs. Pedigree- and genomic-based relationship matrices (A and G as K) and phenotypes on 599 wheat lines were used. Also, data were simulated, and a hybrid (genomic-pedigree) relationship matrix (H as K) and phenotypes were used. First, matrix K underwent a transformation (K* = wK + α11′ + βI), and the responses in the mean and variation of diag(K−1) and offdiag(K−1) elements, and genetic variance in the form of h2 were recorded. Then, the original K was inverted, and matrix K−1 underwent the same transformations as K, and the responses in the h2 estimate and the variance of EBVs in the forms of correlation and regression coefficients with the EBVs estimated based on the original K−1 were recorded. In response to weighting K by w, the estimated genetic variance changed by 1/w. We found that μ(diag(K)) − μ(offdiag(K)) influences the genetic variance. As such, α did not change the genetic variance, and increasing β increased the estimated genetic variance. Weighting K−1 by w was equivalent to weighting K by 1/w. Using the weighted K−1 together with its corresponding h2, EBVs remained unchanged, which shows the importance of using variance components that are compatible with the K−1. Increasing βI added to K−1 increased the estimated genetic variance, and the effect of α11′ was minor. We found that larger variation of diag(K−1) and higher concentration of offdiag(K−1) around the mean (0) are responsible for lower h2 estimate and variance of EBVs.

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Section: Methodsmentioning

confidence: 99%

Understanding factors influencing the estimated genetic variance and the distribution of breeding values

Nilforooshan

Ruíz-Flores²

2022

Front. Genet.

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“…Data were simulated for a species in a 1:1 sex ratio, litter size of 2, and generation overlap of 1. The pedigree, phenotypes, and genotypes were simulated using the R package pedSimulate [ 21 ]. Initially, ten generations were simulated, starting with a base generation (F0) of 100 animals (50 of each sex).…”

Section: Methodsmentioning

confidence: 99%

A Note on the Conditioning of the H−1 Matrix Used in Single-Step GBLUP

Nilforooshan

2022

Animals

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The single-step genomic BLUP (ssGBLUP) is used worldwide for the simultaneous genetic evaluation of genotyped and non-genotyped animals. It is easily extendible to all BLUP models by replacing the pedigree-based additive genetic relationship matrix (A) with an augmented pedigree–genomic relationship matrix (H). Theoretically, H does not introduce any artificially inflated variance. However, inflated genetic variances have been observed due to the incomparability between the genomic relationship matrix (G) and A used in H. Usually, G is blended and tuned with A22 (the block of A for genotyped animals) to improve its numerical condition and compatibility. If deflation/inflation is still needed, a common approach is weighting G−1−A22−1 in the form of τG−1−ωA22−1, added to A−1 to form H−1. In some situations, this can violate the conditional properties upon which H is built. Different ways of weighting the H−1 components (A−1, G−1, A22−1, and H−1 itself) were studied to avoid/minimise the violations of the conditional properties of H. Data were simulated on ten populations and twenty generations. Responses to weighting different components of H−1 were measured in terms of the regression of phenotypes on the estimated breeding values (the lower the slope, the higher the inflation) and the correlation between phenotypes and the estimated breeding values (predictive ability). Increasing the weight on H−1 increased the inflation. The responses to weighting G−1 were similar to those for H−1. Increasing the weight on A−1 (together with A22−1) was not influential and slightly increased the inflation. Predictive ability is a direct function of the slope of the regression line and followed similar trends. Responses to weighting G−1−A22−1 depend on the inflation/deflation of evaluations from A−1 to H−1 and the compatibility of the two matrices with the heritability used in the model. One possibility is a combination of weighting G−1−A22−1 and weighting H−1. Given recent advances in ssGBLUP, conditioning H−1 might become an interim solution from the past and then not be needed in the future.

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Short Communication: Reduced GBLUP equations to core animals in the algorithm for proven and young (APY)

Nilforooshan

2024

Veterinary and Animal Science

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pedSimulate – An R package for simulating pedigree, genetic merit, phenotype, and genotype data

Cited by 4 publications

References 10 publications

Understanding factors influencing the estimated genetic variance and the distribution of breeding values

Understanding factors influencing the estimated genetic variance and the distribution of breeding values

A Note on the Conditioning of the H−1 Matrix Used in Single-Step GBLUP

Short Communication: Reduced GBLUP equations to core animals in the algorithm for proven and young (APY)

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