Next Article in Journal
Peculiar k-mer Spectra Are Correlated with 3D Contact Frequencies and Breakpoint Regions in the Human Genome
Previous Article in Journal
Molecular Characterization of Peroxidase (PRX) Gene Family in Cucumber
Previous Article in Special Issue
Identification and Characterization of the miRNA Transcriptome Controlling Green Pigmentation of Chicken Eggshells
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 15
Issue 10
10.3390/genes15101246
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genome-Wide Association Study Reveals the Genetic Architecture of Growth and Meat Production Traits in a Chicken F2 Resource Population

by
Natalia A. Volkova
1,†,
Michael N. Romanov
1,2,3,*,†,
Anastasia N. Vetokh
1,
Polina V. Larionova
1,
Ludmila A. Volkova
1,
Alexandra S. Abdelmanova
1,
Alexander A. Sermyagin
4,
Darren K. Griffin
2,3 and
Natalia A. Zinovieva
1,*
1
L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia
2
School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK
3
Animal Genomics and Bioresource Research Unit (AGB Research Unit), Faculty of Science, Kasetsart University, Bangkok 10900, Thailand
4
Russian Research Institute of Farm Animal Genetics and Breeding—Branch of the L. K. Ernst Federal Research Centre for Animal Husbandry, Pushkin, St. Petersburg 196601, Russia
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2024, 15(10), 1246; https://doi.org/10.3390/genes15101246
Submission received: 31 August 2024 / Revised: 22 September 2024 / Accepted: 24 September 2024 / Published: 25 September 2024
(This article belongs to the Special Issue Poultry Genetics and Genomics—2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: For genomic selection to enhance the efficiency of broiler production, finding SNPs and candidate genes that define the manifestation of main selected traits is essential. We conducted a genome-wide association study (GWAS) for growth and meat productivity traits of roosters from a chicken F2 resource population (n = 152). Methods: The population was obtained by crossing two breeds with contrasting phenotypes for performance indicators, i.e., Russian White (slow-growing) and Cornish White (fast-growing). The birds were genotyped using the Illumina Chicken 60K SNP iSelect BeadChip. After LD filtering of the data, 54,188 SNPs were employed for the GWAS analysis that allowed us to reveal significant specific associations for phenotypic traits of interest and economic importance. Results: At the threshold value of p < 9.2 × 10−7, 83 SNPs associated with body weight at the age of 28, 42, and 63 days were identified, as well as 171 SNPs associated with meat qualities (average daily gain, slaughter yield, and dressed carcass weight and its components). Moreover, 34 SNPs were associated with a group of three or more traits, including 15 SNPs significant for a group of growth traits and 5 SNPs for a group of meat productivity indicators. Relevant to these detected SNPs, nine prioritized candidate genes associated with the studied traits were revealed, including WNT2, DEPTOR, PPA2, UNC80, DDX51, PAPPA, SSC4D, PTPRU, and TLK2. Conclusions: The found SNPs and candidate genes can serve as genetic markers for growth and meat performance characteristics in chicken breeding in order to achieve genetic improvement in broiler production.

1. Introduction

Over recent decades, there has been a trend towards an increase in the production and consumption of poultry meat relative to other meat products, with health, low fat, high protein, and a high concentration of polyunsaturated fatty acids typically cited [1,2]. Growth and production traits are hugely important to the poultry industry, with meat quality depending on a number of genetically determined factors [3,4,5]. Commercial crossbred broiler chickens are earlier in maturing and are characterized by a higher percentage of breast muscle compared to purebred chickens, especially local breeds [6,7,8]. At the same time, the meat of broilers and meat-type breeds may contain a greater amount of subcutaneous and abdominal fat compared to meat obtained from slow-growing breed chickens [9,10]. In order to maximize the potential of poultry breeding, a deeper understanding of the genetic factors that control growth and meat quality [11,12,13], and how they interact with environmental conditions involving rearing, keeping [14,15], and feeding [16,17] is essential. In other words, research progress on individual traits influencing environmental factors and the genetic mechanisms that govern them is of great value to the poultry industry and its worldwide consumers [18,19]. For commercial production of chicken meat [20], highly productive broiler crosses that are characterized by a high growth rate and good meat qualities are usually used [21,22]. As a result of extensive functional genomic research, birds of this type are now distinguished by effective feed conversion and high slaughter yield (SY) of both the carcass and its individual components, including breast weight (BrW) [23,24,25]. Directed selection for body weight (BW) contributed to an increase in the efficiency of meat production [26] by reducing the time it takes to grow birds while increasing the marketable weight and meat yield, including pectoral muscle weight [23,27].
A number of studies have demonstrated high heritability of growth traits in early-age broiler chickens [28,29,30]. Phenotypic selection for these traits can contribute to significant progress in broiler breeding and the creation of highly productive commercial crosses. A correlation between BW and carcass characteristics has been shown in commercial broiler lines [31]. Along with traditional selection methods, studies aimed at finding and identifying genetic markers associated with growth and other performance indicators in chickens are in demand [32,33]. Research in this area is crucial for understanding the genetic basis of growth traits in broiler chickens toward the subsequent implementation of effective breeding programs aimed at increasing genetic potential of commercial poultry. To date, significant progress has been made in the genetic study of indicators characterizing the growth rate, meat qualities, and other phenotypic traits of chickens [34,35,36].
With the development of high-density single nucleotide polymorphism (SNP) arrays, genome-wide association studies (GWAS) have been instrumental in identifying hitherto undiscovered genetic associations of SNPs with phenotypic traits in livestock [37,38,39]. This approach was broadly applied to seeking associations (especially with BW and BrW) and, thereafter, identifying related candidate genes [40,41,42]. In our earlier study [43], we analyzed potential genes and selective signatures in grandparent lines undergoing strong selection pressure for broiler productivity.
The purpose of this study was to extend this prior work to focus on the search for, and identification of, SNPs associated with growth and meat productivity parameters in chickens, such as BW, average daily BW gain (ADBWG), SY, dressed carcass weight (DCW), and weight of its components, including BrW and weights of thighs (TW), drumsticks (DW), and wings (WW). Of special interest was the search for significant SNPs and prime candidate genes common to several traits taken into account. In accordance with this goal, the GWAS analysis for growth parameters and meat qualities in roosters of a chicken F2 resource population was carried out based on genome-wide genotyping data. The F2 resource population was obtained by interbreeding the meat-type Cornish White (CW) breed characterized by fast growth [44] and the egg-layer Russian White (RW) breed of slow growth [45,46].

2. Materials and Methods

2.1. Birds Involved in the Experiment

Chickens of the original breeds were hatched from eggs obtained from Genofond LLC (All-Russian Poultry Research and Technological Institute, Sergiev Posad, Russia) and the Russian Research Institute of Farm Animal Genetics and Breeding (Pushkin, Russia), raised at the L. K. Ernst Federal Research Centre for Animal Husbandry (LKEFRCAH), and sampled for DNA. The F2 chickens of the resource population were produced and reared at the LKEFRCAH.
To obtain the F2 resource population, two breeds with contrasting growth rates and meat qualities were used: RW, of slow growth [47,48,49], and CW, of fast growth [43,44,50]. At the first stage, based on the data of genome-wide genotyping (to exclude close relationships), two families (F0_1 and F0_2) were formed from individuals of the original parental breeds, each of which contained one RW rooster and five CW females. Through interbreed crosses, F1 hybrids (n = 36) were produced from each family and chosen for further research. These interbred F1 hybrids were used to obtain F2 individuals. For this purpose, nine families, F1_1 to F1_9, were established, each of which included one F1 male and three F1 females that were not close relatives. The resultant F2 offspring (n = 152, males of groups F2_1 to F2_9) were utilized as a model resource population for further molecular genetic studies to search for SNPs associated with growth and meat productivity indicators of chickens.
F2 chickens were raised in brooders up to 3 weeks of age with a gradual temperature decrease from 34 °C (in the first hours post hatch) to 23 °C and then transferred to floor maintenance. Keeping the birds according to their age implied permanent access to complete commercial compound feed and fresh water, good supply ventilation (ensuring the absence of dampness, drafts, and gas pollution), and normal lighting [51,52].

2.2. Phenotypic Characteristics

F2 males of the resource population were phenotyped for the following growth and meat productivity parameters (in g): BW at the age of 14 (BW14), 28 (BW28), 42 (BW42), and 63 (BW63) days, ADBWG, SY, DCW, BrW, TW, DW, and WW. ADBWG was calculated for the growing period from 1 to 63 days. At the age of 63 days, the birds were experimentally slaughtered to evaluate the weight parameters of the carcass and its components using a laboratory scale. The carcass was cut into parts for further determining DCW, BrW, TW, DW, and WW. When measuring such traits as TW, DW, and WW, the mean value of these indicators established for each of the two thighs, drumsticks, or wings was calculated.

2.3. Sampling and DNA Extraction

Feather pulp was used to extract DNA. DNA isolation was executed using the DNA Extran kit for DNA isolation from animal tissues (Syntol, Moscow, Russia). The concentration of DNA solutions was determined using a Qubit 3.0 Fluorimeter (Thermo Fisher Scientific, Wilmington, DE, USA). The OD260/280 ratio was measured using the NanoDrop-2000 device (Thermo Fisher Scientific) to verify the isolated DNA’s purity.

2.4. SNP Genotyping and Quality Control

Whole-genome genotyping of chickens was performed using the Illumina Chicken 60K SNP iSelect BeadChip (Illumina, San Diego, CA, USA) containing 60 thousand SNPs. Quality control and filtering of genotyping data for each sample and each SNP were performed in the R-4.0 software environment [53] using the PLINK 1.9 software package [54,55], applying the following filters in the program: –mind 0.10, –geno 0.10, –maf 0.01, –hwe 1e-6. After pruning, 54,188 SNPs were retained for further analysis.

2.5. Principal Component Analysis

Principal component analysis (PCA; [56]) was performed and visualized in the R package ggplot2 [57,58]. Data files were prepared in the R-4.0 software environment [59].

2.6. GWAS Analysis

To identify SNP associations with growth and meat productivity indicators in the F2 resource population chickens, the respective regression analysis in PLINK 1.9 was used. Significance of the SNP effects and the identification of significant regions in the chicken genome were assessed using the Bonferroni null hypothesis test at a threshold of p < 9.2 × 10−7. The data were visualized in the qqman package (version 0.1.9) [60] using the R-4.0 programming language [61].
Search for candidate genes localized in the region of the identified SNPs (including 0.2-Mb flanks on both sides) was performed according to the chicken (Gallus gallus; GGA) reference genome assembly GRCg6a [62] and using the Genome Data Viewer in the NCBI chicken databases [63]. The web-based Ensembl Genes release 106 database and Ensembl BioMart data mining tool [64] were utilized to get detailed information for SNPs located within or near the candidate genes identified. To perform functional annotation and gene ontology (GO) term enrichment analysis for prime candidate genes, the Ensembl BioMart data mining tool and Database for Annotation, Visualization, and Integrated Discovery (DAVID Knowledgebase; version DAVID 2021 (December 2021), v2023q4, updated quarterly) [65,66] were exploited.

3. Results

3.1. Population Stratification

PCA showed the distribution of the studied F2 resource population into several clusters. The first component (PC1) accounted for 16.57% of the genetic variability, the second component (PC3) for 7.84%, and the third component (PC3) for 6.20%. In the PC1–PC2 projection, the population under study was differentiated into five groups: the first group included F2_7, F2_9, F2_8, and F2_4 progenies, the second group F2_5, the third group F2_3, the fourth group F2_1, and the fifth group F2_2. In the PC1–PC3 projection, three groups were distinguished: the first group consisted of F2_3, F2_2, and F2_1 progenies, the second group of F2_7, F2_8, and F2_9 progenies, and the third group was evenly spaced from the previous two and included F2_5. This information is visually presented in Figure 1.
Given the observed population stratification, i.e., its revealed structure, we performed the GWAS using the first three PCs as covariates.

3.2. GWAS Results

Table 1 summarizes the data on the studied growth and meat productivity indices in F2 males of the resource population. In particular, descriptive statistics are presented that characterize the distribution of values established for the measured characteristics. Herein, the coefficient of variation of the values of the studied traits varied from 5.8 to 26.1%.
The obtained phenotypic data for growth and meat productivity in F2 males of the resource population were used for the GWAS. The GWAS results are presented in Figure 2.
The conducted analysis revealed 83 SNPs associated with the BW of chickens in the studied population at the age of 28, 42, and 63 days and 171 SNPs associated with the meat productivity parameters at the threshold level of the established significance value p < 9.2 × 10−7 (Supplementary Table S1). These SNPs were observed on 27 chromosomes. Herein, the maximum number of identified SNPs was localized on chromosomes GGA1, GGA2, and GGA13 (18, 37, and 15 SNPs, respectively), while the minimum SNP number (1–2 SNPs) on GGA8, GGA14, GGA15, GGA17, GGA19–GGA23, GGA25, and GGA27. On GGA16, no significant SNPs were found for any of the examined parameters. Data on the number of identified significant SNPs and their distribution on chromosomes, taking into account each specifically growth and meat productivity indicator studied in the chicken F2 resource population, are presented in Table 2.
The GWAS for BW parameters in F2 males of the studied population returned the result of 2, 34, and 69 SNPs associated with this trait at the age of 28, 42, and 63 days, respectively. The maximum number of SNPs was established on GGA1 and GGA2 (11 and 20, respectively), and the minimum (1 SNP) on GGA6, GGA8, GGA14, GGA15, GGA17, GGA21, GGA23, and GGA27. Analysis for ADBWG in the period from 1 to 63 days of age revealed 148 significant SNPs associated with this parameter. Similar to the GWAS results for the BW trait, the maximum number of these SNPs was detected on GGA1 and GGA2 (15 and 33 SNPs, respectively).
The number of significant SNPs associated with the examined weight parameters of the carcass and its components varied from 16 to 30, with the exception of TW for which only one SNP was determined on GGA10. The maximum number of SNPs localized in the specific chromosomes was found for the following traits: DCW on GGA13 (5 SNPs); BrW on GGA4, GGA7, GGA9, and GGA27 (2 SNPs); and DW on GGA7 (5 SNPs). The minimum SNP number (1–2 SNPs) was identified for the following traits: DCW on GGA2, GGA3, GGA6, GGA8, GGA15, GGA17–GGA19, GGA23, and GGA27; BrW on GGA1, GGA2, GGA3, GGA8, GGA15, GGA18, GGA19, and GGA23; and DW on GGA1, GGA2, GGA13, GGA15, GGA18, GGA19, GGA23, and GGA26. For the two studied parameters—SY and WW—no significant SNPs were observed at the established significance threshold.
Comparative analysis of the defined genomic associations with growth and meat productivity indicators in F2 roosters of the resource population demonstrated the presence of SNPs common to the group of traits assessed in this investigation (Table 2). In particular, 22 SNPs associated with any three traits were identified. Herein, we found 15 common SNPs significantly associated with growth indicators (BW42, BW63, and ADBWG) and five SNPs associated with meat qualities (DCW, BrW, and TW). The number of SNPs common to four, five, and six traits was five, three, and four SNPs, respectively. These SNPs were significantly associated with a group of traits including both growth indicators and meat productivity. For one of the traits studied in this study, TW, no SNPs were found in common with the other traits studied. For one of the traits investigated in this study, i.e., TW, no SNPs were detected in common with the other traits studied.

3.3. Candidate Genes

SNPs established jointly for a group of studied traits (3–6 traits) were used to annotate prime candidate genes associated with growth and meat productivity in broiler chickens. Structural annotation in the area of identified SNPs (i.e., SNP position ± 0.2 Mb) resulted in 239 genes described in the NCBI databases. These candidate genes are listed in Supplementary Table S1, with their locations indicated in the flanking regions relative to the respective SNPs or at exact SNP position. Supplementary Table S2 also shows that most genes overlapping the SNP positions contained polymorphic variants of these SNPs in introns, plus one gene with a synonymous (exonic) variant and one gene with a 5′ UTR variant. Herein, there were the following nine prioritized candidate genes (PCGs) localized at the positions of the SNPs identified for three and more traits: WNT2 (Wnt family member 2), DEPTOR (DEP domain containing MTOR-interacting protein), PPA2 (inorganic pyrophosphatase 2), UNC80 (unc-80 homolog, NALCN activator), DDX51 (DEAD-box helicase 51), PAPPA (pappalysin 1), SSC4D (scavenger receptor cysteine rich family member with 4 domains), PTPRU (protein tyrosine phosphatase, receptor type U), and TLK2 (tousled-like kinase 2). These PCGs are located on the following nine chromosomes: GGA1, GGA2, GGA4, GGA7, GGA15, GGA17, GGA19, GGA23, and GGA27. Candidate genes, including PCGs, and significant SNPs (p < 9.2 × 10−7) associated with growth and meat productivity indicators in F2 roosters of the resource population are shown in Table 3.
Based on the GO term enrichment assessment, the annotated genes were grouped into six functional clusters. However, three clusters had Enrichment Scores below one, so we considered them insignificant. The other three clusters (with Enrichment Scores > 1.2) included genes associated with peptidyl-serine phosphorylation, kinase, and lipoprotein. The list of annotated genes and their functions are presented in Supplementary Table S2.

4. Discussion

Identification and mapping of genes determining the manifestation of economically important traits in poultry is one of the key tasks of genomic selection aimed at increasing the efficiency of poultry production [67,68,69]. The GWAS approach manifested in this study is pivotal in elucidating the genetic mechanisms determining BW and muscle production traits in chickens [70], thereby having the potential to increase the efficiency of poultry production [71,72,73,74,75]. Here, the resource population was obtained by interbreeding two breeds with contrasting productivity indicators, i.e., RW (slow-growing) and CW (fast-growing). Although these traits, to a certain extent, depend on the nature of the feed given to the animals as well as their housing conditions, they are also genetically determined by multiple QTLs [76]. We identified significant SNPs (p < 9.2 × 10−7) associated with growth and meat performance in F2 roosters of the resource population. In particular, BW28, BW42, and BW63 (2, 34, and 69 SNPs, respectively); ADBWG (148 SNPs); DCW (30 SNPs); BrW (16 SNPs); and DW (21 SNPs) were implicated, with the greatest number of identified SNPs localized on the largest two chromosomes (GGA1 and GGA2).
For practical use in genomic selection, it is essential to identify SNPs and prime candidate genes associated with a small number of selected traits [77,78,79]. Here, we identified such SNPs significant for specific growth and meat productivity indicators. In particular, associations with 15 SNPs characterizing growth (BW42, BW63, and ADBWG) were identified, alongside five SNPs for meat performance traits (DCW, BrW, and TW). Moreover, 14 SNPs were identified associated with traits characterizing both growth indicators and meat productivity.
Regarding the positions of the identified SNPs and likely causative alleles for three or more traits, nine PCGs were located, including WNT2, DEPTOR, PPA2, UNC80, DDX51, PAPPA, SSC4D, PTPRU, and TLK2. As shown in other investigations, of these nine, four (UNC80, TLK2, PTPRU, and DDX51) were associated with growth indicators in farm animals, including poultry. Three of these genes (TLK2, PTPRU, and DDX51) were associated with BW and BrW in chickens. These results tally with Zhang et al.’s [80], who explored growth indicators in Jinghai Yellow chickens at the age of 2, 4, 6, 8, 12, 14, and 16 weeks based on genomic data using the same Illumina Chicken 60K SNP array. In accordance with their GWAS analysis, a significant association of the PTPRU gene (p < 1.80 × 10−6) with BW of chickens at the age of 12 weeks was established. Walugembe et al. [81] investigated the growth indicators of chickens infected with lentogenic Newcastle disease virus. An association of the DDX51 gene with the BW of chickens before infection was revealed. Kang et al. [82] established an association of the TLK2 gene with BrW in broiler chickens at the age of 126 days. A number of other studies have shown associations of the TLK2 gene with BW in cattle [83] and that of the UNC80 gene with BW in sheep at the age of 180 days [84].
The effects of the genes PPA2, SSC4D, and PAPPA on the growth rates and meat productivity established in F2 roosters of the resource population in this study has also confirmed other studies. In particular, it was shown that there was an influence of the PPA2 gene on feed consumption in quails [85] and pigs [86,87], that of the SSC4D gene on the hip height in cattle at the age of 18 and 24 months [88], and that of the PAPPA gene on the body length and depth in pigs [89]. Feed consumption and body measurements are associated with the growth rates in several animals, including poultry, as the feed consumption indicator affects the growth, while linear body measurements correlate with BW and body size.
Along with feed consumption and linear measurements, BW and meat qualities of animals, including poultry [90,91,92], are also associated with the fatty acid metabolism index that determines the fat content in the carcass as well as the taste of the meat [93,94,95]. A number of studies have demonstrated a relationship between the PTPRU gene and the abdominal fat weight in broilers [96], the content of flavor-presenting aldehydes related to the meat taste in chickens [97], and intramuscular fatty acid composition in pigs [98].
The growth and development of animals, including poultry, can be affected, to a certain extent, by immunity that governs resistance to infectious diseases, as well as adaptation to environmental conditions [37,71,81,99]. Of the prime candidate genes identified in the present study, some other investigations have shown a connection between the PPA2 gene and the ability of sheep to adapt to high-altitude conditions [100] and a relationship of the WNT2 and TLK2 genes with resistance to infectious diseases in cattle [101] and chickens [81].
Thus, the available findings from other studies are largely concordant with the data we obtained on the direct effects of the genes UNC80, TLK2, PTPRU, and DDX51 on the growth and meat productivity in chickens. For other PCGs identified in our work, a number of observations have also shown their connection with selected traits in other farm animals, including poultry. We also analyzed all genes overlapping significant SNP regions revealed in the GWAS for functional enrichment (Supplementary Table S2), based on the idea that genes interacting within similar biological networks may collaboratively influence the growth/meat performance phenotype [102]. GO analysis illustrated that prime candidates were enriched in relation to peptidyl−serine phosphorylation, kinase activity, and membrane lipoprotein component that have broad biological/metabolic roles [64,65,66]. Further research using GWAS and whole-genome sequencing approaches [102] is required to confirm the association of these PCGs with the growth and meat performance in chickens.

5. Conclusions

In this work, we performed a GWAS for parameters related to growth and meat productivity in F2 roosters of the resource population using the Illumina Chicken 60K SNP iSelect BeadChip. SNPs, and the respective prime candidate genes, showing significant association with BW at the age of 28, 42, and 63 days, and meat qualities of the studied birds at the age of 63 days were identified using the characterized genetic variants. The maximum number of identified SNPs was observed on GGA1, GGA2, and GGA13 (15–37 SNPs), while their minimum number was revealed on chromosomes GGA8, GGA14, GGA15, GGA17, GGA19–GGA23, GGA25, and GGA27 (1–2 SNPs). Herein, 34 SNPs were found that were common to three or more traits examined in this work. Nine PCGs that have biological functions potentially relevant for growth and meat performance were identified at these SNP positions: WNT2, DEPTOR, PPA2, UNC80, DDX51, PAPPA, SSC4D, PTPRU, and TLK2. These data are of great importance for understanding the genetic basis for the formation and manifestation of growth and meat qualities in chickens. The identified SNPs and PCGs warrant further investigation and can be used as genetic markers in breeding programs aimed at increasing growth rates and improving meat performance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes15101246/s1, Table S1: List of SNPs associated with growth and meat productivity indicators in F2 roosters of the resource population; Table S2: Gene ontology (GO) term enrichment analysis at the positions of the determined SNPs in F2 roosters of the resource population.

Author Contributions

Conceptualization, N.A.V. and N.A.Z.; methodology, A.S.A., P.V.L. and A.A.S.; software, A.S.A., P.V.L. and A.A.S.; validation, N.A.V., A.N.V. and L.A.V.; formal analysis, M.N.R., A.S.A., P.V.L. and A.A.S.; investigation, A.N.V., L.A.V. and P.V.L.; data curation, N.A.V., A.N.V. and P.V.L.; writing—original draft preparation, N.A.V. and M.N.R.; writing—review and editing, N.A.V., M.N.R., D.K.G. and N.A.Z.; visualization, N.A.V., M.N.R., A.S.A. and P.V.L.; supervision, N.A.V., D.K.G. and N.A.Z.; project administration, N.A.V. and N.A.Z.; funding acquisition, N.A.V. and N.A.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Russian Science Foundation, Grant No. 21-66-00007.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and the LKEFRCAH ethical guidelines. Protocol No. 3/1 was approved by the LKEFRCAH Commission on the Ethics of Animal Experiments on 4 December 2019.

Informed Consent Statement

Not applicable.

Data Availability Statement

The genotyping data presented in this study can be shared with the third parties upon approval with the GWMAS Consortium. Other original contributions presented in the study are included in the article and Supplementary Materials; further inquiries can be directed to the corresponding authors with the permission provided by the chickens’ owners.

Acknowledgments

We thank the USDA Chicken GWMAS Consortium, Cobb Vantress, and Hendrix Genetics for access to the developed 50K_CobbCons chicken array produced by Illumina Inc. for the GWMAS Consortium.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. OECD/FAO. OECD-FAO Agricultural Outlook 2019–2028; OECD Publishing: Paris, France; Food and Agriculture Organization of the United Nations: Rome, Italy, 2019; Available online: https://doi.org/10.1787/agr_outlook-2019-en (accessed on 30 August 2024).
  2. Pereira, P.M.D.C.C.; Vicente, A.F.D.R.B. Meat nutritional composition and nutritive role in the human diet. Meat Sci. 2013, 93, 586–592. [Google Scholar] [CrossRef] [PubMed]
  3. Mir, N.A.; Rafiq, A.; Kumar, F.; Singh, V.; Shukla, V. Determinants of broiler chicken meat quality and factors affecting them: A review. J. Food Sci. Technol. 2017, 54, 2997–3009. [Google Scholar] [CrossRef] [PubMed]
  4. Marchewka, J.; Sztandarski, P.; Solka, M.; Louton, H.; Rath, K.; Vogt, L.; Rauch, E.; Ruijter, D.; de Jong, I.C.; Horbańczuk, J.O. Linking key husbandry factors to the intrinsic quality of broiler meat. Poult. Sci. 2023, 102, 102384. [Google Scholar] [CrossRef] [PubMed]
  5. Deng, S.; Liu, R.; Li, C.; Xu, X.; Zhou, G. Meat quality and flavor compounds of soft-boiled chickens: Effect of Chinese yellow-feathered chicken breed and slaughter age. Poult. Sci. 2022, 101, 102168. [Google Scholar] [CrossRef]
  6. Dalle Zotte, A.; Gleeson, E.; Franco, D.; Cullere, M.; Lorenzo, J.M. Proximate composition, amino acid profile, and oxidative stability of slow-growing indigenous chickens compared with commercial broiler chickens. Foods 2020, 9, 546. [Google Scholar] [CrossRef]
  7. Jung, Y.-K.; Jeon, H.-J.; Jung, S.; Choe, J.-H.; Lee, J.-H.; Heo, K.-N.; Kang, B.-S.; Jo, C.-R. Comparison of quality traits of thigh meat from Korean native chickens and broilers. Food Sci. Anim. 2011, 31, 684–692. [Google Scholar] [CrossRef]
  8. Berri, C.; Wacrenier, N.; Millet, N.; Le Bihan-Duval, E. Effect of selection for improved body composition on muscle and meat characteristics of broilers from experimental and commercial lines. Poult. Sci. 2001, 80, 833–838. [Google Scholar] [CrossRef]
  9. Tůmová, E.; Chodová, D.; Skřivanová, E.; Laloučková, K.; Šubrtová-Salmonová, H.; Ketta, M.; Machander, V.; Cotozzolo, E. Research Note: The effects of genotype, sex, and feeding regime on performance, carcasses characteristic, and microbiota in chickens. Poult. Sci. 2021, 100, 760–764. [Google Scholar] [CrossRef]
  10. Suzuki, S.; Kobayashi, M.; Murai, A.; Tsudzuki, M.; Ishikawa, A. Characterization of growth, fat deposition, and lipid metabolism-related gene expression in lean and obese meat-type chickens. J. Poult. Sci. 2019, 56, 101–111. [Google Scholar] [CrossRef]
  11. Petracci, M.; Cavani, C. Muscle growth and poultry meat quality issues. Nutrients 2012, 4, 1–12. [Google Scholar] [CrossRef]
  12. Mueller, S.; Taddei, L.; Albiker, D.; Kreuzer, M.; Siegrist, M.; Messikommer, R.E.; Gangnat, I.D.M. Growth, carcass, and meat quality of 2 dual-purpose chickens and a layer hybrid grown for 67 or 84 D compared with slow-growing broilers. J. Appl. Poult. Res. 2020, 29, 185–196. [Google Scholar] [CrossRef]
  13. Xiong, X.; Liu, X.; Zhu, X.; Tan, Y.; Wang, Z.; Xu, J.; Tu, X.; Rao, Y.; Duan, J.; Zhao, W.; et al. A mutation in PHKG1 causes high drip loss and low meat quality in Chinese Ningdu yellow chickens. Poult. Sci. 2022, 101, 101556. [Google Scholar] [CrossRef]
  14. Elkhazen, A.; Larbi, M.; M’hamdi, N.; Haddad, B. Comparison of meat quality of local poultry and Arbors acres reared in two farming systems in Tunisia. J. New Sci. 2016, 34, 1922–1929. [Google Scholar]
  15. Jin, S.; Yang, L.; Zang, H.; Xu, Y.; Chen, X.; Chen, X.; Liu, P.; Geng, Z. Influence of free-range days on growth performance, carcass traits, meat quality, lymphoid organ indices, and blood biochemistry of Wannan Yellow chickens. Poult. Sci. 2019, 98, 6602–6610. [Google Scholar] [CrossRef]
  16. Yang, L.; Wang, X.; He, T.; Xiong, F.; Chen, X.; Chen, X.; Jin, S.; Geng, Z. Association of residual feed intake with growth performance, carcass traits, meat quality, and blood variables in native chickens. J. Anim. Sci. 2020, 98, skaa121. [Google Scholar] [CrossRef] [PubMed]
  17. Bughio, E.; Hussain, J.; Mahmud, A.; Khalique, A. Effects of production system and feeding regimen on carcass and meat quality traits of Naked Neck chicken. S. Afr. J. Anim. Sci. 2021, 51, 250–261. [Google Scholar] [CrossRef]
  18. Arthur, J.A.; Albers, G.A. Industrial perspective on problems and issues associated with poultry breeding. In Poultry Genetics, Breeding and Biotechnology; Muir, W.M., Aggrey, S.E., Eds.; CAB International: Wallingford, UK; Cambridge, UK, 2003; pp. 1–12. [Google Scholar] [CrossRef]
  19. da Silva, D.C.F.; de Arruda, A.M.V.; Gonçalves, A.A. Quality characteristics of broiler chicken meat from free-range and industrial poultry system for the consumers. J. Food Sci. Technol. 2017, 54, 1818–1826. [Google Scholar] [CrossRef]
  20. Santhi, D.; Kalaikannan, A. Enrichment of chicken meat with dietary fibre sources as functional ingredients. Worlds Poult. Sci. J. 2023, 79, 783–806. [Google Scholar] [CrossRef]
  21. Bessei, W. Welfare of broilers: A review. Worlds Poult. Sci. J. 2006, 62, 455–466. [Google Scholar] [CrossRef]
  22. Petracci, M.; Mudalal, S.; Soglia, F.; Cavani, C. Meat quality in fast-growing broiler chickens. Worlds Poult. Sci. J. 2015, 71, 363–374. [Google Scholar] [CrossRef]
  23. Scheuermann, G.N.; Bilgili, S.F.; Hess, J.B.; Mulvaney, D.R. Breast muscle development in commercial broiler chickens. Poult. Sci. 2003, 82, 1648–1658. [Google Scholar] [CrossRef] [PubMed]
  24. Devatkal, S.K.; Naveena, B.M.; Kotaiah, T. Quality, composition, and consumer evaluation of meat from slow-growing broilers relative to commercial broilers. Poult. Sci. 2019, 98, 6177–6186. [Google Scholar] [CrossRef]
  25. Reyer, H.; Hawken, R.; Murani, E.; Ponsuksili, S.; Wimmers, K. The genetics of feed conversion efficiency traits in a commercial broiler line. Sci. Rep. 2015, 5, 16387. [Google Scholar] [CrossRef] [PubMed]
  26. Patreva, L.S.; Kovalenko, V.P.; Tereshchenko, O.V.; Katerynych, O.O. Miasne Ptakhivnytstvo [Poultry Meat Production]; Mykolaivskyi DAU: Mykolaiv, Ukraine, 2010; ISBN 978-966-8205-60-6. (In Ukrainian) [Google Scholar]
  27. Vakhrameev, A.B.; Narushin, V.G.; Larkina, T.A.; Barkova, O.Y.; Peglivanyan, G.K.; Dysin, A.P.; Dementieva, N.V.; Shcherbakov, Y.S.; Pozovnikova, M.V.; Griffin, D.K.; et al. Pectoral angle: A glance at a traditional phenotypic trait in chickens from a new perspective. J. Agric. Sci. 2023, 161, 606–615. [Google Scholar] [CrossRef]
  28. Dou, D.; Shen, L.; Zhou, J.; Cao, Z.; Luan, P.; Li, Y.; Xiao, F.; Guo, H.; Li, H.; Zhang, H. Genome-wide association studies for growth traits in broilers. BMC Genom. Data 2022, 23, 1. [Google Scholar] [CrossRef] [PubMed]
  29. Mebratie, W.; Madsen, P.; Hawken, R.; Romé, H.; Marois, D.; Henshall, J.; Bovenhuis, H.; Jensen, J. Genetic parameters for body weight and different definitions of residual feed intake in broiler chickens. Genet. Sel. Evol. 2019, 51, 53. [Google Scholar] [CrossRef]
  30. Chu, T.T.; Madsen, P.; Norberg, E.; Wang, L.; Marois, D.; Henshall, J.; Jensen, J. Genetic analysis on body weight at different ages in broiler chicken raised in commercial environment. J. Anim. Breed. Genet. 2020, 137, 245–259. [Google Scholar] [CrossRef]
  31. Dou, T.; Li, Z.; Wang, K.; Liu, L.; Rong, H.; Xu, Z.; Huang, Y.; Gu, D.; Chen, X.; Hu, W.; et al. Regulation of myostatin expression is associated with growth and muscle development in commercial broiler and DMC muscle. Mol. Biol. Rep. 2018, 45, 511–522. [Google Scholar] [CrossRef]
  32. Guo, X.; Zhang, H.; Wang, H.; He, X.-X.; Wang, J.-X.; Wei, W.; Liu, M.; Xu, J.-M.; Liu, Y.-N.; Jiang, R.-S. Identification of key modules and hub genes involved in regulating the color of chicken breast meat using WGCNA. Animals 2023, 13, 2356. [Google Scholar] [CrossRef]
  33. Romanov, M.N.; Miao, Y.W.; Wilson, P.W.; Morris, A.; Sharp, P.J.; Dunn, I.C. Detection and Assay of Polymorphism in Reproductive Gene Loci in a Commercial Broiler Breeder Population for Use in Association Studies. In Proceedings of the Conference “From Jay Lush to Genomics: Visions for Animal Breeding and Genetics”, Ames, IA, USA, 16–18 May 1999; Dekkers, J.C.M., Lamont, S.J., Rothschild, M.F., Eds.; Department of Animal Science, Iowa State University: Ames, IA, USA, 1999; p. 155. Available online: https://web.archive.org/web/20050314091227/http:/www.agbiotechnet.com/proceedings/jaylush.asp#15 (accessed on 14 March 2005).
  34. Dunn, I.C.; Miao, Y.-W.; Morris, A.; Romanov, M.N.; Wilson, P.W.; Waddington, D.; Sharp, P.J. Candidate genes and reproductive traits in a commercial broiler breeder population, an association study. J. Anim. Sci. 2001, 79 (Suppl. S1), 43. [Google Scholar]
  35. Dunn, I.C.; Miao, Y.-W.; Morris, A.; Romanov, M.N.; Waddington, D.W.; Wilson, P.W.; Sharp, P.J. Association between candidate genes and reproductive traits in a commercial broiler breeder population. Br. Poult. Sci. 2001, 42 (Suppl. S1), S113–S114. [Google Scholar] [CrossRef]
  36. Dementieva, N.V.; Fedorova, E.S.; Krutikova, A.A.; Mitrofanova, O.V.; Stanishevskaya, O.I.; Pleshanov, N.V.; Smaragdov, M.G.; Kudinov, A.A.; Terletsky, V.P.; Romanov, M.N. Genetic variability of indels in the prolactin and dopamine receptor D2 genes and their association with the yield of allanto-amniotic fluid in Russian White laying hens. Tarım Bilim. Derg. J. Agric. Sci. 2020, 26, 373–379. [Google Scholar] [CrossRef]
  37. Pickering, N.K.; Auvray, B.; Dodds, K.G.; McEwan, J.C. Genomic prediction and genome-wide association study for dagginess and host internal parasite resistance in New Zealand sheep. BMC Genom. 2015, 16, 958. [Google Scholar] [CrossRef] [PubMed]
  38. Nikitkina, E.V.; Dementieva, N.V.; Shcherbakov, Y.S.; Atroshchenko, M.M.; Kudinov, A.A.; Samoylov, O.I.; Pozovnikova, M.V.; Dysin, A.P.; Krutikova, A.A.; Musidray, A.A.; et al. Genome-wide association study for frozen-thawed sperm motility in stallions across various horse breeds. Anim. Biosci. 2022, 35, 1827. [Google Scholar] [CrossRef]
  39. Dementieva, N.V.; Dysin, A.P.; Shcherbakov, Y.S.; Nikitkina, E.V.; Musidray, A.A.; Petrova, A.V.; Mitrofanova, O.V.; Plemyashov, K.V.; Azovtseva, A.I.; Griffin, D.K.; et al. Risk of sperm disorders and impaired fertility in frozen–thawed bull semen: A genome-wide association study. Animals 2024, 14, 251. [Google Scholar] [CrossRef] [PubMed]
  40. Dementeva, N.V.; Kudinov, A.A.; Mitrofanova, O.V.; Stanishevskaya, O.I.; Fedorova, E.S.; Romanov, M.N. Genome-wide association study of reproductive traits in a gene pool breed of the Russian White chickens. Reprod. Domest. Anim. 2018, 53 (Suppl. S2), 123–124. [Google Scholar] [CrossRef]
  41. Guo, J.; Qu, L.; Dou, T.C.; Shen, M.M.; Hu, Y.P.; Ma, M.; Wang, K.H. Genome-wide association study provides insights into the genetic architecture of bone size and mass in chickens. Genome 2020, 63, 133–143. [Google Scholar] [CrossRef]
  42. Zhou, G.; Liu, T.; Wang, Y.; Qu, H.; Shu, D.; Jia, X.; Luo, C. Genome-wide association studies provide insight into the genetic determination for hyperpigmentation of the visceral peritoneum in broilers. Front. Genet. 2022, 13, 820297. [Google Scholar] [CrossRef]
  43. Romanov, M.N.; Shakhin, A.V.; Abdelmanova, A.S.; Volkova, N.A.; Efimov, D.N.; Fisinin, V.I.; Korshunova, L.G.; Anshakov, D.V.; Dotsev, A.V.; Griffin, D.K.; et al. Dissecting selective signatures and candidate genes in grandparent lines subject to high selection pressure for broiler production and in a local Russian chicken breed of Ushanka. Genes 2024, 15, 524. [Google Scholar] [CrossRef]
  44. Abdelmanova, A.S.; Dotsev, A.V.; Romanov, M.N.; Stanishevskaya, O.I.; Gladyr, E.A.; Rodionov, A.N.; Vetokh, A.N.; Volkova, N.A.; Fedorova, E.S.; Gusev, I.V.; et al. Unveiling comparative genomic trajectories of selection and key candidate genes in egg-type Russian White and meat-type White Cornish chickens. Biology 2021, 10, 876. [Google Scholar] [CrossRef]
  45. Moiseeva, I.G. Variability and heritability of some features of egg quality in Russkaya Belaya chickens. Tr. Akad. Nauk SSSR Inst. Genet. 1964, 31, 302–308. (In Russian) [Google Scholar]
  46. Moiseeva, I.G. Soderzhanie lipidov i kholesterina v iatsakh kur russkoĭ beloĭ porody v sviazi s produktivnost’iu [The lipid and cholesterin contents of hen’s eggs of the Russian white breed in relation to productiveness]. Tr. Akad. Nauk SSSR Inst. Genet. 1965, 33, 119–128. Available online: https://www.cabidigitallibrary.org/doi/full/10.5555/19660103382 (accessed on 24 April 2024). (In Russian). [PubMed]
  47. Dementeva, N.V.; Romanov, M.N.; Kudinov, A.A.; Mitrofanova, O.V.; Stanishevskaya, O.I.; Terletsky, V.P.; Fedorova, E.S.; Nikitkina, E.V.; Plemyashov, K.V. Studying the structure of a gene pool population of the Russian White chicken breed by genome-wide SNP scan. Sel’skokhozyaistvennaya Biol. Agric. Biol. 2017, 52, 1166–1174. [Google Scholar] [CrossRef]
  48. Kudinov, A.A.; Dementieva, N.V.; Mitrofanova, O.V.; Stanishevskaya, O.I.; Fedorova, E.S.; Larkina, T.A.; Mishina, A.I.; Plemyashov, K.V.; Griffin, D.K.; Romanov, M.N. Genome-wide association studies targeting the yield of extraembryonic fluid and production traits in Russian White chickens. BMC Genom. 2019, 20, 270. [Google Scholar] [CrossRef] [PubMed]
  49. Moiseeva, I.G. Content of lipids and cholesterol in eggs of Russian White chickens. Collect. Works Young Sci. All-Union Res. Tech. Poult. Inst. 1966, 8, 225–235. (In Russian) [Google Scholar]
  50. Bondarenko, Y.V.; Sergheyeva, V.D.; Kuranova, E.N.; Krasnozhon, S.A.; Romanov, M.N. Autosexing maternal form of meat-type chickens. Ptitsevodstvo Poult. Farm. 1987, 40, 6–11. (In Russian) [Google Scholar]
  51. Romanov, M.N.; Yakubovskaya, S.N.; Nachalnaya, Z.P. Problems of Cage Keeping of Meat-Type Chickens and Broilers; Information Bulletin; Ukrainian Poultry Research Institute: Kharkov, Russia, 1990; Issue 4. (In Russian) [Google Scholar]
  52. Tereshchenko, A.V.; Artemenko, A.B.; Pudov, V.Y. A hidden source of increasing the production of broiler chickens. Eksklyuziv Agro [Exclus. Agro] 2007, 4, 64–65. (In Russian) [Google Scholar]
  53. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2018; Available online: https://www.r-project.org/ (accessed on 30 August 2024).
  54. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; De Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef]
  55. Purcell, S. PLINK 1.9; Center for Human Genetic Research: Boston, MA, USA; Massachusetts General Hospital: Boston, MA, USA; Broad Institute of Harvard & MIT: Cambridge, MA, USA, 2017; Available online: https://zzz.bwh.harvard.edu/plink/index.shtml (accessed on 30 August 2024).
  56. DataCamp. Principal Component Analysis in R Tutorial; Tutorials, R Programming; DataCamp, Inc.: New York, NY, USA, 2023; Available online: https://www.datacamp.com/tutorial/pca-analysis-r (accessed on 30 August 2024).
  57. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2009; ISBN 978-0-387-98141-3. [Google Scholar] [CrossRef]
  58. GitHub. ggplot2. Tidyverse; GitHub, Inc.: San Francisco, CA, USA, 2023; Available online: https://github.com/tidyverse/ggplot2 (accessed on 30 August 2024).
  59. R Core Team. R-4; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: https://cran.r-project.org/src/base/R-4/ (accessed on 30 August 2024).
  60. GitHub. qqman; GitHub, Inc.: San Francisco, CA, USA, 2024; Available online: https://github.com/qqman (accessed on 30 August 2024).
  61. Turner, S.D. qqman: An R package for visualizing GWAS results using Q-Q and Manhattan plots. J. Open Source Softw. 2018, 3, 731. [Google Scholar] [CrossRef]
  62. NCBI. Gallus gallus Genome Assembly GRCg6a; Genome; National Library of Medicine: Bethesda, MD, USA, 2018. Available online: https://ncbi.nlm.nih.gov/datasets/genome/GCF_000002315.6/ (accessed on 30 August 2024).
  63. NCBI. Gallus gallus (Chicken); Genome; National Library of Medicine: Bethesda, MD, USA, 2024. Available online: https://www.ncbi.nlm.nih.gov/datasets/genome/?taxon=9031 (accessed on 30 August 2024).
  64. Kinsella, R.J.; Kähäri, A.; Haider, S.; Zamora, J.; Proctor, G.; Spudich, G.; Almeida-King, J.; Staines, D.; Derwent, P.; Kerhornou, A.; et al. Ensembl BioMarts: A hub for data retrieval across taxonomic space. Database 2011, 2011, bar030. [Google Scholar] [CrossRef]
  65. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 2009, 4, 44–57. [Google Scholar] [CrossRef] [PubMed]
  66. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009, 37, 1–13. [Google Scholar] [CrossRef] [PubMed]
  67. Tang, H.; Gong, Y.; Wu, C.; Jiang, J.; Wang, Y.; Li, K. Variation of meat quality traits among five genotypes of chicken. Poult. Sci. 2009, 88, 2212–2218. [Google Scholar] [CrossRef]
  68. Sun, Y.; Zhao, G.; Liu, R.; Zheng, M.; Hu, Y.; Wu, D.; Zhang, L.; Li, P.; Wen, J. The identification of 14 new genes for meat quality traits in chicken using a genome-wide association study. BMC Genom. 2013, 14, 458. [Google Scholar] [CrossRef] [PubMed]
  69. Devatkal, S.K.; Vishnuraj, M.R.; Kulkarni, V.V.; Kotaiah, T. Carcass and meat quality characterization of indigenous and improved variety of chicken genotypes. Poult. Sci. 2018, 97, 2947–2956. [Google Scholar] [CrossRef]
  70. Tarsani, E.; Kranis, A.; Maniatis, G.; Avendano, S.; Hager-Theodorides, A.L.; Kominakis, A. Discovery and characterization of functional modules associated with body weight in broilers. Sci. Rep. 2019, 9, 9125. [Google Scholar] [CrossRef]
  71. Habimana, R.; Ngeno, K.; Okeno, T.O.; Hirwa, C.D.A.; Keambou Tiambo, C.; Yao, N.K. Genome-wide association study of growth performance and immune response to Newcastle disease virus of indigenous chicken in Rwanda. Front. Genet. 2021, 12, 723980. [Google Scholar] [CrossRef]
  72. Li, Y.D.; Bai, X.; Liu, X.; Wang, W.J.; Li, Z.W.; Wang, N.; Xiao, F.; Gao, H.H.; Guo, H.S.; Li, H.; et al. Integration of genome-wide association study and selection signatures reveals genetic determinants for skeletal muscle production traits in an F2 chicken population. J. Integr. Agric. 2022, 21, 2065–2075. [Google Scholar] [CrossRef]
  73. Wang, S.; Wang, Y.; Li, Y.; Xiao, F.; Guo, H.; Gao, H.; Wang, N.; Zhang, H.; Li, H. Genome-wide association study and selective sweep analysis reveal the genetic architecture of body weights in a chicken F2 resource population. Front. Vet. Sci. 2022, 9, 875454. [Google Scholar] [CrossRef]
  74. Yang, R.; Xu, Z.; Wang, Q.; Zhu, D.; Bian, C.; Ren, J.; Huang, Z.; Zhu, X.; Tian, Z.; Wang, Y.; et al. Genome-wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing. Genet. Sel. Evol. 2021, 53, 82. [Google Scholar] [CrossRef]
  75. Zhang, Y.; Wang, Y.; Li, Y.; Wu, J.; Wang, X.; Bian, C.; Tian, Y.; Sun, G.; Han, R.; Liu, X.; et al. Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population. Heredity 2021, 126, 293–307. [Google Scholar] [CrossRef]
  76. Pampouille, E.; Berri, C.; Boitard, S.; Hennequet-Antier, C.; Beauclercq, S.A.; Godet, E.; Praud, C.; Jégo, Y.; Le Bihan-Duval, E. Mapping QTL for white striping in relation to breast muscle yield and meat quality traits in broiler chickens. BMC Genom. 2018, 19, 202. [Google Scholar] [CrossRef]
  77. Paredes, M.; Vásquez, B. Growth, carcass characteristics, weight of internal organs and meat proximate composition of six genotypes in chickens reared in Andean region of northern Peruvian. Sci. Agropecu. 2020, 11, 365–374. [Google Scholar] [CrossRef]
  78. Sun, J.; Tan, X.; Yang, X.; Bai, L.; Kong, F.; Zhao, G.; Wen, J.; Liu, R. Identification of candidate genes for meat color of chicken by combing selection signature analyses and differentially expressed genes. Genes 2022, 13, 307. [Google Scholar] [CrossRef] [PubMed]
  79. Dunn, I.C.; Miao, Y.-W.; Morris, A.; Romanov, M.; Wilson, P.W.; Sharp, P.J. The Detection and Assay of Polymorphism in Candidate Reproductive Gene Loci in a Commercial Broiler Breeder Population for Association Studies. In Proceedings of the Poultry Genetics Symposium, Mariensee, Germany, 6–8 October 1999; Preisinger, R., Ed.; Working Group 3 of WPSA, Lohmann Tierzucht: Cuxhaven, Germany, 1999; p. 113. [Google Scholar]
  80. Zhang, G.X.; Fan, Q.C.; Zhang, T.; Wang, J.Y.; Wang, W.H.; Xue, Q.; Wang, Y.J. Genome-wide association study of growth traits in the Jinghai Yellow chicken. Genet. Mol. Res. 2015, 14, 15331–15338. [Google Scholar] [CrossRef] [PubMed]
  81. Walugembe, M.; Amuzu-Aweh, E.N.; Botchway, P.K.; Naazie, A.; Aning, G.; Wang, Y.; Saelao, P.; Kelly, T.; Gallardo, R.A.; Zhou, H.; et al. Genetic basis of response of Ghanaian local chickens to infection with a lentogenic new castle disease virus. Front. Genet. 2020, 11, 739. [Google Scholar] [CrossRef]
  82. Kang, H.; Zhao, D.; Xiang, H.; Li, J.; Zhao, G.; Li, H. Large-scale transcriptome sequencing in broiler chickens to identify candidate genes for breast muscle weight and intramuscular fat content. Genet. Sel. Evol. 2021, 53, 66. [Google Scholar] [CrossRef] [PubMed]
  83. Du, L.; Duan, X.; An, B.; Chang, T.; Liang, M.; Xu, L.; Zhang, L.; Li, J.; E, G.; Gao, H. Genome-wide association study based on random regression model reveals candidate genes associated with longitudinal data in Chinese Simmental beef cattle. Animals 2021, 11, 2524. [Google Scholar] [CrossRef]
  84. Wang, W.; Zhang, Y.; Zhang, X.; Li, C.; Yuan, L.; Zhang, D.; Zhao, Y.; Li, X.; Cheng, J.; Lin, C.; et al. Heritability and recursive influence of host genetics on the rumen microbiota drive body weight variance in male Hu sheep lambs. Microbiome 2023, 11, 197. [Google Scholar] [CrossRef]
  85. Mohammadi, H.; Khalababdi Farahani, A.H.; Moradi, M.H. The genome-wide study in Japanese quail for traits related to feed efficiency using a single step approach. Anim. Prod. 2022, 24, 117–126. [Google Scholar] [CrossRef]
  86. Tiezzi, F.; Brito, L.F.; Howard, J.; Huang, Y.J.; Gray, K.; Schwab, C.; Fix, J.; Maltecca, C. Genomics of heat tolerance in reproductive performance investigated in four independent maternal lines of pigs. Front. Genet. 2020, 11, 629. [Google Scholar] [CrossRef] [PubMed]
  87. Do, D.N.; Strathe, A.B.; Ostersen, T.; Jensen, J.; Mark, T.; Kadarmideen, H.N. Genome-wide association study reveals genetic architecture of eating behavior in pigs and its implications for humans obesity by comparative mapping. PLoS ONE 2013, 8, e71509. [Google Scholar] [CrossRef] [PubMed]
  88. Zhang, X.; Chu, Q.; Guo, G.; Dong, G.; Li, X.; Zhang, Q.; Zhang, S.; Zhang, Z.; Wang, Y. Genome-wide association studies identified multiple genetic loci for body size at four growth stages in Chinese Holstein cattle. PLoS ONE 2017, 12, e0175971. [Google Scholar] [CrossRef] [PubMed]
  89. Fan, B.; Onteru, S.K.; Du, Z.Q.; Garrick, D.J.; Stalder, K.J.; Rothschild, M.F. Genome-wide association study identifies Loci for body composition and structural soundness traits in pigs. PLoS ONE 2011, 6, e14726. [Google Scholar] [CrossRef]
  90. Liu, R.; Sun, Y.; Zhao, G.; Wang, F.; Wu, D.; Zheng, M.; Chen, J.; Zhang, L.; Hu, Y.; Wen, J. Genome-wide association study identifies loci and candidate genes for body composition and meat quality traits in Beijing-You chickens. PLoS ONE 2013, 8, e61172. [Google Scholar] [CrossRef]
  91. Wang, W.; Zhang, T.; Wang, J.; Zhang, G.; Wang, Y.; Zhang, Y.; Zhang, J.; Li, G.; Xue, Q.; Han, K.; et al. Genome-wide association study of 8 carcass traits in Jinghai Yellow chickens using specific-locus amplified fragment sequencing technology. Poult. Sci. 2016, 95, 500–506. [Google Scholar] [CrossRef]
  92. Volkova, N.A.; Romanov, M.N.; Abdelmanova, A.S.; Larionova, P.V.; German, N.Y.; Vetokh, A.N.; Shakhin, A.V.; Volkova, L.A.; Sermyagin, A.A.; Anshakov, D.V.; et al. Genome-wide association study revealed putative SNPs and candidate genes associated with growth and meat traits in Japanese quail. Genes 2024, 15, 294. [Google Scholar] [CrossRef]
  93. Abdalla, B.A.; Chen, J.; Nie, Q.; Zhang, X. Genomic insights into the multiple factors controlling abdominal fat deposition in a chicken model. Front. Genet. 2018, 9, 262. [Google Scholar] [CrossRef] [PubMed]
  94. Moreira, G.C.M.; Boschiero, C.; Cesar, A.S.M.; Reecy, J.M.; Godoy, T.F.; Pértille, F.; Ledur, M.C.; Moura, A.S.A.M.T.; Garrick, D.J.; Coutinho, L.L. Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken. Sci. Rep. 2018, 8, 16222. [Google Scholar] [CrossRef]
  95. Volkova, N.A.; German, N.Y.; Larionova, P.V.; Vetokh, A.N.; Romanov, M.N.; Zinovieva, N.A. Identification of SNPs and candidate genes associated with abdominal fat deposition in quails (Coturnix japonica). Sel’skokhozyaistvennaya Biol. Agric. Biol. 2023, 58, 1079–1087. [Google Scholar] [CrossRef]
  96. Li, F.; Hu, G.; Zhang, H.; Wang, S.; Wang, Z.; Li, H. Epistatic effects on abdominal fat content in chickens: Results from a genome-wide SNP-SNP interaction analysis. PLoS ONE 2013, 8, e81520. [Google Scholar] [CrossRef] [PubMed]
  97. Yuan, X.; Cui, H.; Jin, Y.; Zhao, W.; Liu, X.; Wang, Y.; Ding, J.; Liu, L.; Wen, J.; Zhao, G. Fatty acid metabolism-related genes are associated with flavor-presenting aldehydes in Chinese local chicken. Front. Genet. 2022, 13, 902180. [Google Scholar] [CrossRef] [PubMed]
  98. Valdés-Hernández, J.; Folch, J.M.; Crespo-Piazuelo, D.; Passols, M.; Sebastià, C.; Criado-Mesas, L.; Castelló, A.; Sánchez, A.; Ramayo-Caldas, Y. Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis. Genet. Sel. Evol. 2024, 56, 12. [Google Scholar] [CrossRef]
  99. Lawal, R.A.; Hanotte, O. Domestic chicken diversity: Origin, distribution, and adaptation. Anim. Genet. 2021, 52, 385–394. [Google Scholar] [CrossRef] [PubMed]
  100. Zhang, Y.; Xue, X.; Liu, Y.; Abied, A.; Ding, Y.; Zhao, S.; Wang, W.; Ma, L.; Guo, J.; Guan, W.; et al. Genome-wide comparative analyses reveal selection signatures underlying adaptation and production in Tibetan and Poll Dorset sheep. Sci. Rep. 2021, 11, 2466. [Google Scholar] [CrossRef]
  101. Pauciullo, A.; Küpper, J.; Brandt, H.; Donat, K.; Iannuzzi, L.; Erhardt, G. Wingless-type MMTV integration site family member 2 (WNT2) gene is associated with resistance to MAP in faecal culture and antibody response in Holstein cattle. Anim. Genet. 2015, 46, 122–132. [Google Scholar] [CrossRef]
  102. Drobik-Czwarno, W.; Wolc, A.; Petal, C.R.; Miedzinska, K.; Dekkers, J.; Fulton, J.E.; Smith, J. Candidate genes associated with survival following highly pathogenic avian influenza infection in chickens. Int. J. Mol. Sci. 2024, 25, 10056. [Google Scholar] [CrossRef]
Genes 15 01246 g001aGenes 15 01246 g001bGenes 15 01246 g001c
Figure 1. Principal component analysis for the chicken F2 resource population: (A) in the plane of the first (PC1; X-axis) and second (PC2; Y-axis) components; and (B) in the plane of the first (PC1; X-axis) and third (PC3; Y-axis) components; (C) in the plane of the second (PC2; X-axis) and third (PC3; Y-axis) components; (D); in a 3D chart with three components (PC1–PC2–PC3). Individuals from different groups are indicated by different colors.
Figure 1. Principal component analysis for the chicken F2 resource population: (A) in the plane of the first (PC1; X-axis) and second (PC2; Y-axis) components; and (B) in the plane of the first (PC1; X-axis) and third (PC3; Y-axis) components; (C) in the plane of the second (PC2; X-axis) and third (PC3; Y-axis) components; (D); in a 3D chart with three components (PC1–PC2–PC3). Individuals from different groups are indicated by different colors.
Genes 15 01246 g001aGenes 15 01246 g001bGenes 15 01246 g001c
Genes 15 01246 g002aGenes 15 01246 g002b
Figure 2. Manhattan plots for the studied growth and meat productivity parameters in the chicken F2 resource population: (A) body weight (BW) at 14 days of age, (B) BW at 28 days of age, (C) BW at 42 days of age, (D) BW at 63 days of age, (E) average daily BW gain, (F) carcass weight, (G) breast weight, (H) thigh weight, and (I) drumstick weight. Manhattan plots show the distribution of single nucleotide mutations in chicken chromosomes to the significance level (−log10 (p)) according to the expected probability value of p < 1.05 × 10−6 (blue line) and p < 1.05 × 10−10 (red line) for the studied traits. Dots are color-coded only to visualize chromosome segregation.
Figure 2. Manhattan plots for the studied growth and meat productivity parameters in the chicken F2 resource population: (A) body weight (BW) at 14 days of age, (B) BW at 28 days of age, (C) BW at 42 days of age, (D) BW at 63 days of age, (E) average daily BW gain, (F) carcass weight, (G) breast weight, (H) thigh weight, and (I) drumstick weight. Manhattan plots show the distribution of single nucleotide mutations in chicken chromosomes to the significance level (−log10 (p)) according to the expected probability value of p < 1.05 × 10−6 (blue line) and p < 1.05 × 10−10 (red line) for the studied traits. Dots are color-coded only to visualize chromosome segregation.
Genes 15 01246 g002aGenes 15 01246 g002b
Table 1. Descriptive statistics 1 for growth and meat performance indicators (in g) in F2 roosters of the resource population.
Table 1. Descriptive statistics 1 for growth and meat performance indicators (in g) in F2 roosters of the resource population.
TraitMeanSDMin–MaxCV, %
BW at 14-day age, g215.745.792.8–396.121.2
BW at 28-day age, g611.6111.7341.6–902.418.3
BW at 42-day age, g1132.9207.4644.2–1690.118.3
BW at 63-day age, g1829.1377.9963.9–2747.720.7
Average daily BW gain, g28.76.114.6–43.021.4
Slaughter weight, %71.14.155.4–80.15.8
Dressed carcass weight, g1346.4309.1665.3–2032.123.0
Breast weight, g385.3100.5144.8–632.626.1
Thigh weight, g104.125.849.4–163.224.8
Drumstick weight, g88.618.342.7–133.320.6
Wing weight, g77.815.833.4–119.120.3
1 BW, body weight; SD, standard deviation; min, minimum; max, maximum; CV, coefficient of variation.
Table 2. Distribution of significant SNPs (p < 9.2 × 10−7) across chromosomes there were associated with body weight (BW) and meat productivity in the chicken F2 resource population.
Table 2. Distribution of significant SNPs (p < 9.2 × 10−7) across chromosomes there were associated with body weight (BW) and meat productivity in the chicken F2 resource population.
TraitNo. of SNPsChromosomes
BW at 14-day age--
BW at 28-day age22, 6
BW at 42-day age341, 2, 6–8, 11, 13–14, 21, 23, 28
BW at 63-day age691–5, 7, 8, 10, 11, 13–15, 17, 21, 23, 27, 28
Average daily BW gain1481–15, 17, 18, 20–28
Slaughter weight--
Dressed carcass weight301–9, 13, 15, 17–19, 23, 24, 27
Breast weight161-4, 7-9, 15, 18, 19, 23, 27
Thigh weight110
Drumstick weight211–2, 4–5, 7, 9, 13, 15, 17–19, 23, 26
Wing weight--
Table 3. SNPs and prime candidate genes (p < 9.2 × 10−7) associated with growth and meat productivity in the chicken F2 resource population.
Table 3. SNPs and prime candidate genes (p < 9.2 × 10−7) associated with growth and meat productivity in the chicken F2 resource population.
GGA 1SNPPosition, bpTraits 2Genes
1Gga_rs1480086224,842,665DCW, BrW, DWCTTNBP2, CFTR, ASZ1, WNT2, ST7, CAPZA2
1Gga_rs14902811152,430,990BW42, BW63, ADBWG-
1Gga_rs14902833152,488,231BW42, BW63, ADBWGSLC2A13
1GGaluGA050529152,453,938BW42, BW63, ADBWGSLC2A13
1GGaluGA034658102,412,092BW42, BW63, ADBWG-
2Gga_rs1416000531,441,781BW42, BW63, ADBWG, DCW, BrW, DWIGF2BP3, TRA2A, CCDC126, FAM221A, STK31, NPY, PALS2, DFNA5
2Gga_rs14248546125,490,179BW42, BW63, ADBWGTRIQK
2Gga_rs15168561136,710,388BW28, BW42, BW63, ADBWGENPP2, TAF2, DSCC1, DEPTOR, COL14A1
2Gga_rs16088599103,517,528BW42, BW63, ADBWGOSBPL1A, IMPACT, ZNF521
3Gga_rs1435673648,921,434BW63, ADBWG, DCW, BrWPLEKHG1, MTHFD1L, AKAP12, ZBTB2, RMND1, ARMT1, CCDC170, ESR1
4Gga_rs1351646738,746,248BW63, ADBWG, DCW, BrW, DWNPNT, GSTCD, INTS12, ARHGEF38, PPA2, TET2
4GGaluGA24648012,518,793DCW, BrW, DWSLC16A2, RLIM, NEXMIF, gga-mir-1573, ABCB7, UPRT, ZDHHC15
7Gga_rs1373765714,269,161BW42, BW63, ADBWG, DCW, BrW, DWU4, PDE1A, PPP1R1C, ITPRID2, NEUROD1, ITGA4
7Gga_rs1462227228,057,143BW42, BW63, ADBWGKALRN, ACADL, UMPS, ITGB5, HEG1, MYL1, ZNF148, SNX4, OSBPL11, LMLN, DTX3L
7Gga_rs1462261128,327,789BW42, BW63, ADBWGMYL1, OSBPL11, LMLN, DTX3L, PARP9, LANCL1, FAIM, CEP70, ESYT3, CFAP221, SCTR, TMEM37, DBI, C7H2ORF76, STEAP3, CPS1, C1QL2, MARCO, EN1
7Gga_rs1584886014,393,379BW42, BW63, ADBWG, DCW, BrW, DWU4, PDE1A, PPP1R1C, ITPRID2, NEUROD1, ITGA4
7GGaluGA3085862,639,082BW42, BW63, ADBWG, DCW, DWCNTNAP5, MAP2, MRAS, gga-mir-3530, TMEM177, PTPN4, EPB41L5, RALB, INHBB, GLI2, UNC80, TFCP2L1, CLASP1, NIFK, TSN, IQCB1, EAF2, SLC15A2, HSPBAP1, SLC49A4, SEMA5B, PDIA5, SEC22A, ADCY6, KANSL1L, HACD2, MYLK, CCDC14, KALRN, ACADL, UMPS, ITGB5, HEG1, MYL1, ZNF148, SNX4, OSBPL11, LMLN, DTX3L, PARP9, LANCL1, FAIM, CEP70, ESYT3, CFAP221, SCTR, TMEM37, DBI, C7H2ORF76
8Gga_rs1664078522,847,287BW63, ADBWG, DCW, BrWTRABD2B, SLC5A9, SPATA6, gga-mir-1809
8GGaluGA33015222,760,396BW42, BW63, ADBWGTRABD2B
9Gga_rs1594755911,450,206DCW, BrW, DWPLOD2
13Gga_rs156773778,879,549BW63, ADBWG, DCW, DWTTC1, ADRA1B, IL12B, FBXO38, HTR4, gga-mir-458a, SLC26A2
13Gga_rs156792618,271,910BW42, BW63, ADBWGGABRB2, ATP10B
13Gga_rs156802697,909,523BW42, BW63, ADBWG-
13GGaluGA0936269,139,110BW63, ADBWG, DCWgga-mir-458a, HTR4, SLC26A2, CSNK1A1, gga-mir-145, gga-mir-143, IL17B, PCYOX1L, GRPEL2, AFAP1L1, ABLIM3
14Gga_rs150037672,062,529BW42, BW63, ADBWGFAM20C, FOXL3
15GGaluGA1095238,381,798BW63, ADBWG, DCW, BrW, DWDGCR2, VPS29L, VPREB3, CHCHD10, MMP11, SMARCB1, DERL3, SLC2A11, SLC2A11L1, MIF, DDX51, GSTT1, DDTL, CABIN1, TBX6, CRKL
17Gga_rs141024543,408,140BW63, ADBWG, DCW, DWPAPPA, ASTN2
18Gga_rs163474959,967,210DCW, BrW, DWTIMP2, USP36, CYTH1, PGS1, SOCS3, AFMID, TK1, SYNGR2, TMC6, ARL16, HGS, MRPL12, GCGR, MCRIP1, PPP1R27, P4HB, ARHGDIA, ALYREF, NPB, PCYT2, SIRT7, MAFG, PYCR1, NME1, SPAG9, PITPNM3, FBXO39, TEKT1, SMTNL2
19GGaluGA1261884,370,123DCW, BrW, DWCUX1, PRKRIP1, ORAI3, ALKBH4, LRWD1, RASA4B, UPK3B, DTX2, SSC4D, YWHAG, HSPB1, SRRM3, MDH2, TMEM120A, POR, TAF15, MMP28, RASL10B, AP2B1
21Gga_rs151822252,760,476BW42, BW63, ADBWGTNFRSF18, gga-mir-429, gga-mir-200a, gga-mir-200b, gga-mir-6680, C1orf159
23GGaluGA1885092,994,311BW42, BW63, ADBWG, DCW, BrW, DWEPB41, TMEM200B, SRSF4, MECR, PTPRU, gga-mir-1724, PTPRU
27Gga_rs136203244,812,782BW63, ADBWG, BrWCRHR1, ITGB3, METTL2B, TLK2, MRC2, TANC2
28Gga_rs143064441,714,462BW42, BW63, ADBWGZBTB7A, PIAS4, EEF2, gga-mir-1434, NMRK2, ATCAY, NRTN, DUS3L, LARP6L, RFX2, ACSBG2, MLLT1, ACER1, ANP32B, ZNF414, MYO1F, ADAMTS10, gga-mir-6615, ZAP70
28Gga_rs143065811,592,968BW42, BW63, ADBWGNCLN, CELF5, HSD11B1L, MICOS13, gga-mir-1774, FSD1, YJU2, gga-mir-6593, ZBTB7A, PIAS4, EEF2, gga-mir-1434, NMRK2, ATCAY, NRTN, DUS3L, LARP6L, RFX2, ACSBG2, MLLT1
1 GGA, Gallus gallus chromosomes. 2 Traits: BW42, body weight (BW) at 42 days; BW63, BW at 63 days; ADBWG, average daily body weight gain; DCW, dressed carcass weight; BrW, breast weight; DW, drumstick weight. Prioritized candidate genes localized at the positions of SNPs identified for three and more traits are highlighted in bold.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Volkova, N.A.; Romanov, M.N.; Vetokh, A.N.; Larionova, P.V.; Volkova, L.A.; Abdelmanova, A.S.; Sermyagin, A.A.; Griffin, D.K.; Zinovieva, N.A. Genome-Wide Association Study Reveals the Genetic Architecture of Growth and Meat Production Traits in a Chicken F2 Resource Population. Genes 2024, 15, 1246. https://doi.org/10.3390/genes15101246

AMA Style

Volkova NA, Romanov MN, Vetokh AN, Larionova PV, Volkova LA, Abdelmanova AS, Sermyagin AA, Griffin DK, Zinovieva NA. Genome-Wide Association Study Reveals the Genetic Architecture of Growth and Meat Production Traits in a Chicken F2 Resource Population. Genes. 2024; 15(10):1246. https://doi.org/10.3390/genes15101246

Chicago/Turabian Style

Volkova, Natalia A., Michael N. Romanov, Anastasia N. Vetokh, Polina V. Larionova, Ludmila A. Volkova, Alexandra S. Abdelmanova, Alexander A. Sermyagin, Darren K. Griffin, and Natalia A. Zinovieva. 2024. "Genome-Wide Association Study Reveals the Genetic Architecture of Growth and Meat Production Traits in a Chicken F2 Resource Population" Genes 15, no. 10: 1246. https://doi.org/10.3390/genes15101246

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop