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Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins

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Abstract

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821–0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825–0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.

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Abbreviations

ANOVA:

Analysis of variance

DPR:

Daughter pregnancy rate

FY:

Fat yield

GEBV:

Genomic-estimated breeding value

GER:

Genotype (imputation) error rate

GPA:

Genomic prediction accuracy

GS:

Genomic selection

HD:

High-density

LD:

Low-density

LGPA:

Loss in genomic prediction accuracy

MAF:

Minor allele frequencies

MCMC:

Markov chain Monte Carlo

MD:

Moderate-density

MOLO:

Multiple-objective, local-optimization

MY:

Milk yield

PTAs:

Predicted transmitting abilities

RGPA:

Relative genomic prediction accuracy

RTMGL:

Relative total maximum gap length

TMGL:

Total maximum gap length

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Author notes

    Authors and Affiliations

    1. Department of Animal Science, University of Nebraska, Lincoln, NE, 68583, USA

      Jun He, Jungjae Lee, Gota Morota & Matthew L. Spangler

    2. College of Animal Science and Technology, Hunan Agricultural University, Changsha, 410128, Hunan, China

      Jun He

    3. Biostatistics and Bioinformatics, Neogen GeneSeek, Lincoln, NE, 68504, USA

      Jun He, Jiaqi Xu, Xiao-Lin Wu & Stewart Bauck

    4. Department of Statistics, University of Nebraska, Lincoln, NE, 68583, USA

      Jiaqi Xu & Stephen D. Kachman

    5. Department of Animal Sciences, University of Wisconsin, Madison, WI, 53706, USA

      Xiao-Lin Wu

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    1. Jun He
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    Contributions

    JH and JX analyzed the data. JH and XW drafted the manuscript. XW,JL,SB,GM,SK and MS participated in it’s the design and discussions of this research. All authors have proof-read and approved the final manuscript.

    Corresponding authors

    Correspondence to Xiao-Lin Wu or Matthew L. Spangler.

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    Conflict of interest

    The authors declare that they have no conflict of interests in this work.

    Funding

    JH, JX, and JL acknowledge the financial support by University of Nebraska–Lincoln, and GeneSeek (A Neogen company). HJ was also supported by the Bairen Plan of Hunan Province, China (XZ2016-08-07) and Hunan Co-Innovation center of Animal Production Safety, China.

    Additional information

    Jun He and Jiaqi Xu contributed equally.

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    He, J., Xu, J., Wu, XL. et al. Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins. Genetica 146, 137–149 (2018). https://doi.org/10.1007/s10709-017-0004-9

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    • DOI: https://doi.org/10.1007/s10709-017-0004-9

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