Genetica

pp 1–13 | Cite as

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

  • Jun He
  • Jiaqi Xu
  • Xiao-Lin Wu
  • Stewart Bauck
  • Jungjae Lee
  • Gota Morota
  • Stephen D. Kachman
  • Matthew L. Spangler
Original Paper

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.

Keywords

Holstein Imputation Genomic prediction Low-density SNP chips 

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

Notes

Author 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.

Compliance with ethical standards

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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Animal ScienceUniversity of NebraskaLincolnUSA
  2. 2.College of Animal Science and TechnologyHunan Agricultural UniversityChangshaChina
  3. 3.Biostatistics and BioinformaticsNeogen GeneSeekLincolnUSA
  4. 4.Department of StatisticsUniversity of NebraskaLincolnUSA
  5. 5.Department of Animal SciencesUniversity of WisconsinMadisonUSA

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