Evidence map›Paper›PMID 34620094›Full record

ArticleGenetics, selection, evolution : GSE2021

Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs.

Cheng Bian, Dzianis Prakapenka, Cheng Tan, Ruifei Yang, Di Zhu, Xiaoli Guo, Dewu Liu, Gengyuan Cai, Yalan Li, Zuoxiang Liang and 3 more

Abstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
  2. Review
  3. Role ofZoological research · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Large-Sample Genomic Data Mining for Quantitative Traits in U.S. Holstein Cows.Journal of data mining in genomics & proteomics · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Genomic prediction with haplotype blocks in wheat.Frontiers in plant science · 2023
    Article
  15. Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Cheng BianState Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Dzianis PrakapenkaDepartment of Animal Science, University of Minnesota, Saint Paul, MN, 55108, USA.
Cheng TanCollege of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
Ruifei YangState Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Di ZhuState Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Xiaoli GuoState Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Dewu LiuCollege of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
Gengyuan CaiCollege of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China.
Yalan LiNational Engineering Research Center for Breeding Swine Industry, Wens Foodstuff Group Co., Ltd., Yunfu, 527400, China.
Zuoxiang LiangDepartment of Animal Science, University of Minnesota, Saint Paul, MN, 55108, USA.
Zhenfang WuCollege of Animal Science and National Engineering Research Center for Breeding Swine Industry, South China Agricultural University, Guangzhou, 510642, China. wzfemail@163.com.
Yang DaDepartment of Animal Science, University of Minnesota, Saint Paul, MN, 55108, USA. yda@umn.edu.
Xiaoxiang HuState Key Laboratory for Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China. huxx@cau.edu.cn.

Funding

Key-Area Research and Development Program of Guangdong Province 2018B020203002National Transgenic Breeding Project of China 2016ZX08009003-006Open Research Program of State Key Laboratory for Agro-Biotechnology 2020SKLAB6-25
6 · The paper itself

Abstract

backgroundGenomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction.

methodsWe analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries.

resultsCompared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR.

conclusionsHaplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.

Indexed as

GenomeGenomicsAnimalsChromosomesHaplotypesMalePhenotypePolymorphism, Single NucleotideSwine

Identifiers

PMID34620094
PMCPMC8496108

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.