Geno‐Diver: A combined coalescence and forward‐in‐time simulator for populations undergoing selection for complex traits

Howard, Jeremy; Tiezzi, Francesco; Pryce, J.E.; Maltecca, Christian

doi:10.1111/jbg.12277

Cited by 9 publications

(7 citation statements)

References 33 publications

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“…To investigate the long‐term impacts of continually removing data across generations on the genetic gain and the associated computational reductions, a simulation was conducted using the Geno‐Diver software (Howard, Tiezzi, Pryce, & Maltecca, ; V2). The simulation was generated to closely resemble the population structure of a swine nucleus population.…”

Section: Methodsmentioning

confidence: 99%

The impact of truncating data on the predictive ability for single‐step genomic best linear unbiased prediction

Howard

Rathje²,

Bruns³

et al. 2018

J Animal Breeding Genetics

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Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single-step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016-2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p-value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV.

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Section: Methodsmentioning

confidence: 99%

The impact of truncating data on the predictive ability for single‐step genomic best linear unbiased prediction

Howard

Rathje²,

Bruns³

et al. 2018

J Animal Breeding Genetics

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“…In this section, we present a case study in which we have investigated the use of alternative metrics of ancestry in OCS for simulated scenarios using genomic information. A production trait and a fitness trait were generated with GenoDiver (Howard et al, 2017c) software following typical genetic architectures of dairy populations. We simulated a polygenic yield trait (h 2 = 0.45; 1,000 QTL).…”

Section: Case Study 1: Simulation Study On the Optimal Contributionmentioning

confidence: 99%

Symposium review: Exploiting homozygosity in the era of genomics—Selection, inbreeding, and mating programs

Maltecca

Tiezzi

Cole

et al. 2020

Journal of Dairy Science

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The advent of genomic selection paved the way for an unprecedented acceleration in genetic progress. The increased ability to select superior individuals has been coupled with a drastic reduction in the generation interval for most dairy populations, representing both an opportunity and a challenge. Homozygosity is now rapidly accumulating in dairy populations. Currently, inbreeding depression is managed mostly by culling at the farm level and by controlling the overall accumulation of homozygosity at the population level. A better understanding of how homozygosity and recessive load are related will guarantee continued genetic improvement while curtailing the accumulation of harmful recessives and maintaining enough genetic variability to ensure the possibility of selection in the face of changing environmental conditions. In this review, we present a snapshot of the current dairy selection structure as it relates to response to selection and accumulation of homozygosity, briefly outline the main approaches currently used to manage inbreeding and overall variability, and present some approaches that can be used in the short term to control accumulation of harmful recessives while maintaining sustained selection pressure.

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“…A simulation was carried out using Geno-Diver (Howard et al, 2017) to mimic a purebred beef cattle population. Five replicates, each with a different founder genome, were simulated.…”

Section: Simulationmentioning

confidence: 99%

The impact of clustering methods for cross-validation, choice of phenotypes, and genotyping strategies on the accuracy of genomic predictions

Baller

Howard

Kachman

et al. 2019

Journal of Animal Science

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Generation 6: Progeny selected on highest EBV Randomly mate 100 sires and 2,000 dams (A ij < 0.125) 3 Parents cull based on EBV Generate founder population N M = 100, N F = 2000 Generation 1: Progeny selected randomly 1 Randomly mate 100 sires and 2,000 dams Parents culled randomly 1,2 Generation 5: Progeny selected randomly Randomly mate 100 sires and 2,000 dams Parents culled randomly Generation 15: Progeny selected on highest EBV Randomly mate 100 sires and 2,000 dams (A ij < 0.125) Parents cull based on EBV Establish pedigree Generations used for analysis

show abstract

Geno‐Diver: A combined coalescence and forward‐in‐time simulator for populations undergoing selection for complex traits

Cited by 9 publications

References 33 publications

The impact of truncating data on the predictive ability for single‐step genomic best linear unbiased prediction

The impact of truncating data on the predictive ability for single‐step genomic best linear unbiased prediction

Symposium review: Exploiting homozygosity in the era of genomics—Selection, inbreeding, and mating programs

The impact of clustering methods for cross-validation, choice of phenotypes, and genotyping strategies on the accuracy of genomic predictions

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