Evidence map›Paper›PMID 36809800›Full record

ArticleNucleic acids research2023

HIBLUP: an integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data.

Lilin Yin, Haohao Zhang, Zhenshuang Tang, Dong Yin, Yuhua Fu, Xiaohui Yuan, Xinyun Li, Xiaolei Liu, Shuhong Zhao

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 2 pooled it
34.4field-weighted citation impact, top 1% 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

62 citing papers in PubMed, 2 syntheses or guidelines pooled it, 110 citations in OpenAlex.

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  18. Impact of scale parameter for marker variance prior in some Bayesian whole-genome regression methods.Mammalian genome : official journal of the International Mammalian Genome Society · 2026
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2 more citing papers are in PubMed but not listed here.

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

9 authors at 2 institutions in 1 country.

Lilin YinKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China.ORCID 0000-0003-4413-7976
Haohao ZhangSchool of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, PR China.
Zhenshuang TangKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China.
Dong YinKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China.
Yuhua FuKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China.
Xiaohui YuanSchool of Computer Science and Technology, Wuhan University of Technology, Wuhan 430070, PR China.
Xinyun LiKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China.ORCID 0000-0002-7943-1713
Xiaolei LiuKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China.ORCID 0000-0002-9954-1426
Shuhong ZhaoKey Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education & College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, PR China.ORCID 0000-0002-3997-2320
Huazhong Agricultural University · CNWuhan University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human diseases and agricultural traits can be predicted by modeling a genetic random polygenic effect in linear mixed models. To estimate variance components and predict random effects of the model efficiently with limited computational resources has always been of primary concern, especially when it involves increasing the genotype data scale in the current genomic era. Here, we thoroughly reviewed the development history of statistical algorithms used in genetic evaluation and theoretically compared their computational complexity and applicability for different data scenarios. Most importantly, we presented a computationally efficient, functionally enriched, multi-platform and user-friendly software package named 'HIBLUP' to address the challenges that are faced currently using big genomic data. Powered by advanced algorithms, elaborate design and efficient programming, HIBLUP computed fastest while using the lowest memory in analyses, and the greater the number of individuals that are genotyped, the greater the computational benefits from HIBLUP. We also demonstrated that HIBLUP is the only tool which can accomplish the analyses for a UK Biobank-scale dataset within 1 h using the proposed efficient 'HE + PCG' strategy. It is foreseeable that HIBLUP will facilitate genetic research for human, plants and animals. The HIBLUP software and user manual can be accessed freely at https://www.hiblup.com.

Indexed as

GenomicsModels, GeneticAlgorithmsAnimalsGenomeGenotypeHumansLinear Models

Identifiers

PMID36809800
PMCPMC10164590
OpenAlexW4321434469

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.