Reducing the interval of a growth <scp>QTL</scp> on chromosome 4 in laying hens

Lyu, Shijie; Arends, Danny; Nassar, M. K.; Weigend, Annett; Weigend, Steffen; Preisinger, Rudolf; Brockmann, Gudrun A.

doi:10.1111/age.12685

Cited by 5 publications

(4 citation statements)

References 20 publications

(19 reference statements)

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“…Gu et al identified the same QTL interval, which spanned 8.6 Mb, by performing a GWAS on an F2 chicken resource population derived from a cross between Silky Fowl and White Plymouth Rock [33]. Other studies further narrowed the QTL region to a length of 1.5 Mb, harbouring 15 genes [34,35]. Although we were unable to further narrow the size of the QTL interval in our study due to a strong level of LD and limited SNP density, we identified two significant SNPs with the highest effects and contribution to heritability of growth traits.…”

Section: Comparison With Qtl From Previous Studies and Candidate Genesmentioning

confidence: 96%

Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing

Yang

Wang

et al. 2021

Genet Sel Evol

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Background Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features. Results We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model. Conclusions A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.

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Section: Comparison With Qtl From Previous Studies and Candidate Genesmentioning

confidence: 96%

Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing

Yang

Wang

et al. 2021

Genet Sel Evol

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“…Sewalem et al used an F 2 chicken population from a cross of a broiler sire-line and an egg laying (White Leghorn) line to identify QTLs on Chr1, -2, -4, -7, -8, and -13 that affected BW, and Chr4 had the largest single additive effect (Sewalem et al 2002). Researchers identified QTLs on Chr4 that affected growth performance in different varieties and populations (Brandt et al 2017;Jin et al 2015;Li et al 2018), such as a White Leghorn × Rhode Island Red cross (Sasaki et al 2004), a Silky Fowl × White Plymouth Rock cross (Gu et al 2011), Beijing-You chickens (Liu et al 2013), and a population of purebred white layers (WLA) with White Leghorn origin (Lyu et al 2018). In this study, 17 phenotypes, including BW8, BW12, SL12, CWe and the weight of several internal organs, were mapped on Chr4, indicating that genes on Chr4 may have large effects on BW.…”

Section: Figmentioning

confidence: 99%

Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population

Zhang

Wang

et al. 2020

Heredity

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Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F2 resource population were phenotyped for 68 growth and carcass traits, and 768 samples were genotyped based on the genotyping-by-sequencing (GBS) method. Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1–6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.

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“…2015 ; Lyu et al . 2017 , 2018 ) detected and progressively fine mapped a region containing 15 genes, including Ligand-dependent nuclear receptor corepressor like ( LCORL; ENSGALG00000014421 ) and Condensin complex subunit 3 ( NCAPG ; ENSGALG00000014425 ). This locus is also associated with body size traits in humans ( Weedon et al .…”

Section: Discussionmentioning

confidence: 99%

Genetics of tibia bone properties of crossbred commercial laying hens in different housing systems

Johnsson

Wall

Pinto

et al. 2022

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Osteoporosis and bone fractures are a severe problem for the welfare of laying hens, with genetics and environment, such as housing system, each making substantial contributions to bone strength. In this work, we performed genetic analyses of bone strength, bone mineral density and bone composition, as well as body weight, in 860 commercial crossbred laying hens from two different companies, kept in either furnished cages or floor pens. We compared bone traits between housing systems and crossbreds, and performed a genome-wide association study of bone properties and body weight. As expected, the two housing systems produced a large difference in bone strength, with layers housed in floor pens having stronger bones. These differences were accompanied by differences in bone geometry, mineralisation and chemical composition. Genome-scans either combining or independently analysing the two housing systems revealed no genome-wide significant loci for bone breaking strength. We detected three loci for body weight that were shared between the housing systems on chromosomes 4, 6 and 27 (either genome-wide significant or suggestive) and these coincide with associations for bone length. In summary, we found substantial differences in bone strength, content and composition between hens kept in floor pens and furnished cages that could be attributed to greater physical activity in pen housing. We found little evidence for large-effect loci for bone strength in commercial crossbred hens, consistent with a highly polygenic architecture for bone strength in the production environment. The lack of consistent genetic associations between housing systems in combination with the differences in bone phenotypes could be due to gene-by-environment interactions with housing system or a lack of power to detect shared associations for bone strength.

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Reducing the interval of a growth QTL on chromosome 4 in laying hens

Cited by 5 publications

References 20 publications

Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing

Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing

Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population

Genetics of tibia bone properties of crossbred commercial laying hens in different housing systems

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