Evidence map›Paper›PMID 40234714›Full record

ArticleScientific reports2025

Uncovering genomic diversity and signatures of selection in red Angus × Chinese red steppe crossbred cattle population.

Elaheh Rostamzadeh Mahdabi, Rugang Tian, Jing Tian, Hojjat Asadollahpour Nanaie, Xiao Wang, Meng Zhao, Hui Li, Baolige Dalai, Yin Sai, Wenhua Guo and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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.

Elaheh Rostamzadeh MahdabiDepartment of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, PO BOX 76169-133, Kerman, Iran.
Rugang TianInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China. tiannky@163.com.
Jing TianInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Hojjat Asadollahpour NanaieInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Xiao WangInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Meng ZhaoInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Hui LiInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Baolige DalaiInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Yin SaiInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Wenhua GuoInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Yuan LiInner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot, 010031, China.
Hao ZhangForestry and Grassland Bureau of Siziwang Banner, Wulanchabu, China.
Ali EsmailizadehDepartment of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, PO BOX 76169-133, Kerman, Iran. aliesmaili@uk.ac.ir.

Funding

Foreign young talents program QN2023005001LInner Mongolia Agriculture and Animal Husbandry Innovation Fund 2023CXJJM01Inner Mongolia key technologies R&D And Achievement Transformation program 2022YFYZ0011Inner Mongolia major science and technology program 2021ZD0009National Natural Science Foundation of China 32060731Scientific and Technological Innovation 2030--Major Project 2023ZD0404803-01
6 · The paper itself

Abstract

Crossbreeding is a cornerstone of modern livestock improvement, combining desirable traits to enhance productivity and environmental resilience. This study conducts the first comprehensive genomic analysis of Red Angus × Chinese Red Steppe (RACS) crossbred cattle, evaluating their genetic architecture, diversity, and selection signatures relative to founder breeds (Red Angus and Chinese Red Steppe) and global populations. A total of 119 cattle, comprising 104 RACS crossbreds and 15 Chinese Red Steppes cattle, were genotyped using the GGP Bovine 100k SNP array. Additionally, the public available genotypic data generated using the BovineSNP50 chip from 550 animals across eight beef breeds (Angus, Hereford, Limousin, Charolais, Mongolian, Shorthorn, Red Angus, and Simmental) and one dairy breed (Holstein) were incorporated into the analysis. We aimed to (1) define the population structure of RACS cattle, (2) quantify their genomic diversity and inbreeding levels, and (3) pinpoint regions under selection linked to adaptive and economic traits. We employed runs of homozygosity (ROH) and population differentiation (Fst) analyses to detect selection signals. The results revealed that the crossbred (RACS), Angus, and Red Angus breeds exhibited similar clustering patterns in principal component analysis (PCA), but the crossbred population showed the highest nucleotide diversity and lowest inbreeding coefficients compared to other breeds. Notably, candidate regions associated with immune response, cold adaptation, and carcass traits were identified within the RACS population. These findings enhance our understanding of the genetic makeup of crossbred beef cattle and highlight their potential for genetic improvement, informing future selection and breeding strategies aimed at optimizing beef production in challenging environments.

Indexed as

Genetic VariationHybridization, GeneticSelection, GeneticAnimalsBreedingCattleChinaGenetics, PopulationGenomeGenomicsGenotypePolymorphism, Single NucleotideCandidate geneChinese beef cattleCrossbreedingPopulation structureSelection signatures

Identifiers

PMID40234714
PMCPMC12000499

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.