Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle

Zhu, Bo; Zhang, Jingjing; Niu, Hong; Guan, Long; Guo, Ping; Xu, Lingyang; Chen, Yan; Zhang, Lupei; Gao, Huijiang; Gao, Xue; Li, Junya

doi:10.1016/s2095-3119(16)61474-0

Cited by 19 publications

(9 citation statements)

References 42 publications

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“…However, increasing marker density above 50k has provided limited improvement in the accuracy of genomic predictions in some cases e.g. dairy and beef cattle [ 49 – 52 ], which is in consistent with our results. Proper estimation of relationships among individuals accounting for Mendelian sampling can be the main factor of genetic improvement caused by genomic evaluation [ 6 , 53 , 54 ].…”

Section: Resultssupporting

confidence: 91%

Opportunities for genomic selection in American mink: A simulation study

Karimi¹,

Sargolzaei²,

Plastow³

et al. 2019

PLoS ONE

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Genomic selection can be considered as an effective tool for developing breeding programs in American mink. However, the genetic gains for economically important traits can be influenced by the accuracy of genomic predictions. The objective of this study was to investigate the prediction accuracies of traditional best linear unbiased prediction (BLUP), multi-step genomic BLUP (GBLUP) and single-step GBLUP (ssGBLUP) methods in American mink using simulated data with different levels of heritability, marker density, training set (TS) sizes and selection designs based on either phenotypic performance or estimated breeding values (EBVs). Under EBV selection design, the accuracy of BLUP predictions was increased by 38% and 44% for h2 = 0.10, 27% and 29% for h2 = 0.20, and 5.8% and 6% for h2 = 0.50 using GBLUP and ssGBLUP methods, respectively. Under phenotypic selection design, the accuracies of prediction by ssGBLUP method were 11.8% and 15.4% higher than those obtained by GBLUP for heritability of 0.10 and 0.20, respectively. However, the efficiency of ssGBLUP and GBLUP was not influenced by selection design at higher level of heritability (h2 = 0.50). Furthermore, higher selection intensity increased the bias of predictions in both pedigree-based and genomic evaluations. Regardless of selection design, TS sizes for GBLUP and ssGBLUP methods should be at least 3000 to achieve more accuracy than using BLUP for heritability of 0.50 and marker density of 10k and 50k. Overall, more accurate predictions were obtained using ssGBLUP method particularly for lowly heritable traits and low density of markers. Our results indicated that TS sizes should be optimized in accordance with heritability level, marker density, selection design and prediction method for genomic selection in American mink. The results provided an initial framework for designing genomic selection in mink breeding programs.

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Section: Resultssupporting

confidence: 91%

Opportunities for genomic selection in American mink: A simulation study

Karimi¹,

Sargolzaei²,

Plastow³

et al. 2019

PLoS ONE

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show abstract

“…With increases in marker density of 50K and 777K, the accuracies of the genomic evaluations did not improve. Zhu et al [ 31 ] reported limited prediction accuracy of a genomic evaluation with an increase in marker density from 0.5K to 20K in live weight, carcass weight and average daily gain. However, an increase in the marker density had a conflicting effect on prediction accuracy due to co-linearity between the effects of the markers in a simulated population [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population

Nwogwugwu¹,

Kim²,

Choi³

et al. 2020

Asian-Australas J Anim Sci

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Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h2) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population.Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h2 of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined.Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h2 was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios.Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

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“…Genetic data may be compressed efficiently by selecting for each bi-allelic marker depending on the minor allele frequency (MAF) of the respective marker[ 22 ]. Before compressed by using TRCMGene algorithm, genetic data had to be processed numerically according to the related MAF.…”

Section: Methodsmentioning

confidence: 99%

TRCMGene: A two-step referential compression method for the efficient storage of genetic data

You

Sun

et al. 2018

PLoS ONE

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BackgroundThe massive quantities of genetic data generated by high-throughput sequencing pose challenges to data storage, transmission and analyses. These problems are effectively solved through data compression, in which the size of data storage is reduced and the speed of data transmission is improved. Several options are available for compressing and storing genetic data. However, most of these options either do not provide sufficient compression rates or require a considerable length of time for decompression and loading.ResultsHere, we propose TRCMGene, a lossless genetic data compression method that uses a referential compression scheme. The novel concept of two-step compression method, which builds an index structure using K-means and k-nearest neighbours, is introduced to TRCMGene. Evaluation with several real datasets revealed that the compression factor of TRCMGene ranges from 9 to 21. TRCMGene presents a good balance between compression factor and reading time. On average, the reading time of compressed data is 60% of that of uncompressed data. Thus, TRCMGene not only saves disc space but also saves file access time and speeds up data loading. These effects collectively improve genetic data storage and transmission in the current hardware environment and render system upgrades unnecessary. TRCMGene, user manual and demos could be accessed freely from https://github.com/tangyou79/TRCM. The data mentioned in this manuscript could be downloaded from: https://github.com/tangyou79/TRCM/wiki.

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Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle

Cited by 19 publications

References 42 publications

Opportunities for genomic selection in American mink: A simulation study

Opportunities for genomic selection in American mink: A simulation study

Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population

TRCMGene: A two-step referential compression method for the efficient storage of genetic data

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