↓ Skip to main content

Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations

Overview of attention for article published in Journal of Animal Science and Biotechnology, January 2020
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
30 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Comparisons of improved genomic predictions generated by different imputation methods for genotyping by sequencing data in livestock populations
Published in
Journal of Animal Science and Biotechnology, January 2020
DOI 10.1186/s40104-019-0407-9
Pubmed ID
Authors

Xiao Wang, Guosheng Su, Dan Hao, Mogens Sandø Lund, Haja N. Kadarmideen

Abstract

Genotyping by sequencing (GBS) still has problems with missing genotypes. Imputation is important for using GBS for genomic predictions, especially for low depths, due to the large number of missing genotypes. Minor allele frequency (MAF) is widely used as a marker data editing criteria for genomic predictions. In this study, three imputation methods (Beagle, IMPUTE2 and FImpute software) based on four MAF editing criteria were investigated with regard to imputation accuracy of missing genotypes and accuracy of genomic predictions, based on simulated data of livestock population. Four MAFs (no MAF limit, MAF ≥ 0.001, MAF ≥ 0.01 and MAF ≥ 0.03) were used for editing marker data before imputation. Beagle, IMPUTE2 and FImpute software were applied to impute the original GBS. Additionally, IMPUTE2 also imputed the expected genotype dosage after genotype correction (GcIM). The reliability of genomic predictions was calculated using GBS and imputed GBS data. The results showed that imputation accuracies were the same for the three imputation methods, except for the data of sequencing read depth (depth) = 2, where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2. GcIM was observed to be the best for all of the imputations at depth = 4, 5 and 10, but the worst for depth = 2. For genomic prediction, retaining more SNPs with no MAF limit resulted in higher reliability. As the depth increased to 10, the prediction reliabilities approached those using true genotypes in the GBS loci. Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points, and FImpute gained 3 percentage points at depth = 2. The best prediction was observed at depth = 4, 5 and 10 using GcIM, but the worst prediction was also observed using GcIM at depth = 2. The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths. Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths. These results suggest that the application of IMPUTE2, based on a corrected GBS (GcIM) to improve genomic predictions for higher depths, and FImpute software could be a good alternative for routine imputation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 13%
Researcher 4 13%
Student > Ph. D. Student 4 13%
Student > Master 3 10%
Professor > Associate Professor 2 7%
Other 2 7%
Unknown 11 37%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 30%
Biochemistry, Genetics and Molecular Biology 5 17%
Computer Science 2 7%
Unknown 14 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 January 2020.
All research outputs
#16,733,516
of 25,387,668 outputs
Outputs from Journal of Animal Science and Biotechnology
#403
of 905 outputs
Outputs of similar age
#282,802
of 473,221 outputs
Outputs of similar age from Journal of Animal Science and Biotechnology
#6
of 24 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 905 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 473,221 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.