Evidence map›Paper›PMID 35451781›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models.

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

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

5 authors at 4 institutions in 2 countries.

Shaolei ShiCollege of Animal Science and Technology, China Agricultural University, Beijing, China.
Zhe ZhangDepartment of Animal Breeding and genetics, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
Bingjie LiThe Roslin Institute Building, Scotland's Rural College, Edinburgh, UK.
Shengli ZhangCollege of Animal Science and Technology, China Agricultural University, Beijing, China.
Lingzhao FangMRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK. Lingzhao.fang@igmm.ed.ac.uk.
China Agricultural University · CNScotland's Rural College · GBSouth China Agricultural University · CNUniversity of Edinburgh · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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).

Indexed as

GenomicsModels, GeneticAnimalsCattleGenome-Wide Association StudyHumansLinkage DisequilibriumMalePhenotypePolymorphism, Single NucleotideQuantitative Trait LociFunctional annotationGenomic predictionOmics dataQTL

Identifiers

PMID35451781
OpenAlexW4226061096

What OpenQuestion holds

Textmetadata
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.