Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models

Shaolei Shi, Zhe Zhang, Bingjie Li, Shengli Zhang, Lingzhao Fang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)


Due to the rapid development of high-throughput sequencing technology, we can easily obtain not only the genetic variants at the whole-genome sequence level (e.g., from 1000 Genomes project and 1000 Bull Genomes project), but also a wide range of functional annotations (e.g., enhancers and promoters from ENCODE, FAANG, and FarmGTEx projects) across a wide range of tissues, cell types, developmental stages, and environmental conditions. This huge amount of information leads to a revolution in studying genetics and genomics of complex traits in humans, livestock, and plant species. In this chapter, we focused on and reviewed the genomic prediction methods that incorporate external biological information into genomic prediction, such as sequence ontology, linkage disequilibrium (LD) of SNPs, quantitative trait loci (QTL), and multi-layer omics data (e.g., transcriptome, epigenome, and microbiome).

Original languageEnglish
Title of host publicationComplex Trait Prediction
Number of pages12
Publication statusFirst published - 22 Apr 2022

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Humans
  • Linkage Disequilibrium
  • Cattle
  • Genome-Wide Association Study
  • Polymorphism, Single Nucleotide
  • Male
  • QTL
  • Models, Genetic
  • Omics data
  • Genomics - methods
  • Phenotype
  • Animals
  • Genomic prediction
  • Quantitative Trait Loci
  • Functional annotation


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