1. Introduction
Broiler production is both one of the leading and fastest-growing parts of the worldwide food production industry [1,2,3,4]. By 2031, 153.85 metric kilotons of poultry meat are anticipated to be consumed globally and 41% of all world meat consumption will be chicken (as reviewed by [2]). The progress in broiler production relies upon advances in selective breeding [5] and the genetics of quantitative traits [6,7]. The respective influence of the latter on reproductive fitness has been, and continues to be, a major subject of study in artificial selection experiments [8,9]. It is well recognized that long-term artificial selection of animals affects the genomic architecture of breeds and yields genetic signatures for breeding traits [10]. More about the dynamics of advantageous (and disadvantageous) alleles arising from the founder population, or appearing as novel mutations under continuing selection, can be learnt by tracking genomic changes through time in populations that have been subjected to intense artificial directional selection [11,12,13,14,15]. Such studies are a key source of knowledge in exploring how selection affects the genome and quantitative trait loci (QTLs). In this vein, domesticated chicken breeds can be considered as long-term artificial selection experiments. One such example is the famous bidirectional selection experiment for body weight in the Virginia chicken lines that started in 1957 [16,17,18,19,20]. Continual monitoring of the existing poultry genetic stocks from these breeds and lines is crucial for their sustainability and use in commercial breeding programs (e.g., [21,22,23,24,25,26,27]).
A representative proportion of segregating variation in breeding experimental data available from designs of commercial broiler crosses using parent strains of divergent artificial selection experiments is higher than that produced by crossing two random inbred lines [28,29,30]. This principle is used in these cross designs and consists of four grandparent lines: two (paternal and maternal) for producing a male parent (breeder) stock and two (paternal and maternal) for producing a female parent (breeder) stock (e.g., [31,32,33]). Usually, two transboundary meat-type breeds, Cornish White (CRW; Figure 1a) and Plymouth Rock White (PRW; Figure 1b), are used in commercial broiler production as male and female breeder stocks, respectively [34,35,36,37].
By selecting for characteristics such as rapid growth/development, feed efficiency, and high yield/quality of breast meat, it is possible to enhance the genetic potential of broiler chickens further [31,38]. This can be achieved through studies of association between candidate genes and phenotypic traits in commercial broiler (breeder) populations [39,40,41,42,43,44,45,46]. Quantitative genetics, computer science, and DNA chip technologies are used in broiler breeding operations to select breeding birds. In pedigree selection systems, significant genetic markers such as single nucleotide polymorphisms (SNPs) can be identified through the use of DNA chip technology, as well as more novel marker-assisted and genomic selection strategies [31,47,48,49]. Previously, genome-wide SNP scans have been used efficiently for studying the genetic architecture, diversity, selective footprints, and evolutionary implications in various Russian meat-type and other chicken breeds and lines [50,51,52,53,54,55,56,57,58,59,60,61,62,63]. Whole-genome resequencing approaches can produce even more SNPs that are usable for in-depth analyses of genomic architecture and candidate genes [20,64,65].
The purpose of the present study was to perform whole-genome resequencing and examine in more detail the selection trajectories in the genomes of the pure grandparent lines of two transboundary meat-type breeds. These were CRW (Figure 1a) and PRW (Figure 1b) that are used in a four-way broiler cross developed in Russia. As a comparison group, we chose the primitive dual-purpose and fancy breed named Ushanka (USH; Figure 1c) that has been bred in Russia for a few centuries in a closed population without any strict selection pressure [59,62,63].
2. Materials and Methods
2.1. Experimental Animals, Sample Collection, and DNA Extraction
The sampling of animals examined included 20 CRW, 20 PRW, and 17 USH male birds. The transboundary CRW and PRW breeds were represented by birds of the four-way broiler cross Smena 8 [66,67] developed at the Breeding and Genetic Center “Smena” (BGCS; Bereznyaki, Moscow Oblast, Russia), Branch of the Federal Scientific Center “All-Russian Research and Technological Poultry Institute” (FSC ARRTPI; Sergiev Posad, Moscow Oblast, Russia). Within the CRW breed there were two lines: B5 (the paternal line of the male parent stock of this cross) and B6 (the maternal line of the male parent stock). Similarly, for PRW, there were two lines: B7 (female parent stock’s paternal line) and B9 (female parent stock’s maternal line). For each of the above four grandparent lines, 10 male birds were sampled. USH is known as an archaic indigenous breed that manifests cold adaptation and is bred in a confined population with little or no selection pressure [62,63]; it was used as a comparative group in the present investigation.
The CRW and PRW chickens were provided by BGCS and those of the USH breed by the Breeding and Genetic Center “Zagorsk Experimental Breeding Farm” (Sergiev Posad, Moscow Oblast, Russia), FSC ARRTPI Branch. The breed flocks were housed in the bioresource Gene Pool Collection of Farm and Wild Animals and Birds at the L. K. Ernst Federal Research Center for Animal Husbandry (LKEFRCAH, Dubrovitsy, Moscow Oblast, Russia). All of the experimental birds had a basic feed and maintenance conditions that complied with zootechnic and zoohygienic norms stated elsewhere (e.g., [68,69]).
Samples of pulp-containing feathers were taken from 57 chickens of all three breeds and lines studied. The Syntol kit for DNA isolation from animal tissues (Syntol LLC, Moscow, Russia) was used to extract DNA. The concentration of the DNA solution was measured with a Qubit 3.0 fluorimeter (Thermo Fisher Scientific, Wilmington, DE, USA). Using a NanoDrop-2000 device (Thermo Fisher Scientific), the OD260/280 ratio was measured to verify the extracted DNA’s purity.
2.2. Sequencing, SNP Genotyping and Quality Control
The samples collected were sequenced using paired-end sequencing (2 × 150 bp) and an Illumina NextSeq instrument (San Diego, CA, USA), with a mean coverage of 20×.
Filtering of whole-genome resequencing raw data was carried out using the Fastp program [70], with the recommended launch parameters. During the filtering process, nucleotide sequences of Illumina adapters and sequences with low-quality reads were trimmed. Quality control before and after using the Fastp program was carried out using the FastQC program [71]. Mapping of short reads to the reference genome was carried out using the BWA-MEM2 software package [72] based on the bwa-mem algorithm of the original BWA program [73]. The chicken (Gallus gallus; GGA) assembly bGalGal1.mat.broiler.GRCg7b (Ensembl release 108) [74,75] was used as a reference genome. Sorting, removing duplicates, and indexing the resultant files in BAM format were carried out using the samtools set of utilities [76]. Determination of SNP positions, insertions and deletions, and manipulation of VCF files were performed using the bcftools package [77]. The Tabix program [78] was also used to index VCF files. The GNU Parallel program [79] was used to execute tasks in parallel in order to reduce calculation time.
The generated number of reads per breed was 308.24 ± 10.85 million, totaling 44.40 ± 0.88 GB. An average sequence coverage was 21.07 ± 0.42 X. A total of 12,563,892 polymorphic SNPs were selected for further analysis. Hereby, sex chromosome (GGAZ and GGAW) SNPs were excluded from the analysis.
2.3. Genetic Diversity and Population Structure
Analysis of genetic diversity and interbreed relationships was performed as described in [58]. In brief, to assess genetic diversity within populations, PLINK v1.9 software was employed [80,81]. Principal component analysis (PCA) based on the variance-standardized relationship matrix was performed using PLINK [80], and the results were visualized using the R package ggplot2 [82]. R package diveRsity [83] was used to calculation observed heterozygosity (HO), unbiased expected heterozygosity (UHE) [84], rarefied allelic richness (AR) [85], and inbreeding coefficient (UFIS) based on the unbiased expected heterozygosity.
The genetic admixture analysis of the populations studied was performed using Admixture v1.3 software [86,87], and the results were plotted using the R package BITE [88]. The number of ancestral populations (K) was determined using a conventional admixture cross-validation (CV) approach [89]. When compared to different K numbers, the assumed number of K conformed to the CV error value that was lowest (Supplementary Figure S1).
2.4. Genetic Diversity and Population Structure
2.4.1. ZFST Estimation
We analyzed population differentiation based on mean FST values over a 50 kB sliding window with 10 kB steps, assuming that changes during selection pressure affect not only the target region but also its associated sites. The window size was chosen based on the degree of attenuation of linkage disequilibrium (LD) toward the genome-wide background in order to set the window to roughly the size where LD decays to the genome-wide background (Supplementary Figure S2).
LD decay was calculated with PLINK using the original script (as described in [90]). To limit false-positive outliers, the mean FST values were Z-transformed to generate ZFST values as follows: ZFST = (FST − μFST)/σFST, where μFST and σFST are the mean and standard deviation of FST values in all windows [64,91]. In fact, ZFST values indicate the number of standard deviations of the nth value from the mean. That is, they are suitable to search for outliers in a data array because they relate to the conventional values of ±3σ that include 99.7% of values with a normal distribution. In our case, we specifically looked for variants of FST values that deviate greatly from the mean. Regions containing SNPs for which ZFST values were included in the 0.1% of maximum values were considered to be the areas most subject to selection pressure.
2.4.2. HapFLK Procedure
To detect the signatures of selection through haplotype differentiation among the studied breeds, we also employed the hapFLK 1.4 program [92,93]. In fastPHASE, the number of haplotype clusters per chromosome was established at 35 by the use of cross-validation-based estimation [94,95,96,97,98]. We chose the hapFLK areas with at least one SNP and a p-value cutoff of 0.00001 (−log10(p) > 5) for in-depth analysis.
2.4.3. ROH and Inbreeding Estimation
We used a consecutiveRUNS.run function [99,100,101,102] implemented in the R package detectRUNS [103,104,105] for estimation of runs of homozygosity (ROH) [106,107]. To avoid the inclusion of the most common short fragments in the results, we set the minimum length for ROH to 0.5 MB. Considering that the density of genomic data is significantly higher than that of SNP-chip data, the values of a possible missing genotype and a possible heterozygous genotype (maxMissRun and maxOppRun) [108,109,110] were taken as 21. The latter value was obtained as the ratio of the density of our data (12.5 M SNPs) to the densest SNP chip for chickens (600 K SNPs). We determined the minimal number of SNPs (l) as was first assessed by Lencz et al. [111] and later modified by Purfield et al. [112] in order to minimize false-positive outcomes. In our study, the minimum number of SNPs was equal to 50. The respective genomic inbreeding coefficient (FROH) was computed using data regarding the homozygous region count and length in the examined breed genomes [113]. This FROH estimate was represented by the proportion of each individual’s total length of ROH to the length of the autosomal SNP-covered reference genome [62,114,115].
2.5. Detection of Candidate Genes and QTLs in Selective Sweep Regions
The web-based Ensembl Genes release 103 database and Ensembl BioMart data mining tool [116,117,118,119] were utilized to retrieve chicken genes and their human orthologs based on the boundaries of these regions as located in the GRCg7b reference assembly chromosomes. To find primary candidate genes and other genes of interest, results for each genomic area of selection signature that were obtained from the Ensembl BioMart browser were manually sifted and compared to pertinent published studies. The genes from the regions supported by at least two different techniques were considered as prioritized candidate genes (PCGs).
QTLs that are localized in the genomic regions of interest and can contain candidate genes were searched using an in-house R script. Herewith, we identified the boundaries of the region of interest matching to QTL genome location using a downloaded copy of the Chicken QTLdb database [120,121].
3. Results
3.1. Between- and Within-Breed Genetic Diversity
PCA revealed that the three studied breeds formed the appropriate breed-specific clusters (Figure 2a,b). Moreover, the USH chickens, being separated from the two meat-type breeds, demonstrated the lowest genetic variability based on their scattering on both PCA plots. The individuals that made up the PRW sample were more diverse than CRW and USH. The admixture analysis resulted in the optimal number of ancestral populations at K = 3 (Supplementary Figure S1), suggesting also that a few CRW and PRW individuals reflected admixtures from the other breeds in this dataset, while such admixtures were absent in USH (Figure 2c).
As follows from the data in Table 1, values of AR, UHE, and HO in the aboriginal USH breed were significantly lower (1.6218 ± 0.0004, 0.2068 ± 0.0001, and 0.2103 ± 0.0002, respectively; p < 0.001) than in both transboundary breeds CRW and PRW, for which these indicators differed slightly. The UFIS inbreeding coefficients of CRW and PRW were, however, four times higher than that of USH. PRW was superior in AR to both USH and CRW, but in terms of HO, significant differences were found only for USH. A greater AR-based heterogeneity identified for PRW was confirmed by the above PCA results.
3.2. Signatures of Selection
3.2.1. ZFST Statistic at Pairwise Comparison of Breeds
We searched for genomic regions that were established in the studied breeds under the influence of natural or artificial selection. These regions were identified by estimating the largest average FST for a sliding window. The analysis was carried out for each pair of breeds separately (Supplementary Figure S3, Supplementary Table S1). The analysis did not include the GGAZ and GGAW sex chromosomes.
As can be seen from Table 2, a pairwise comparison of CRW with two other breeds revealed six genomic regions containing three genes. For PRW and USH, there were nine and 11 identified regions containing two and four genes, respectively.
3.2.2. HapFLK Statistic
The hapFLK analysis was carried out for the combined sample of the three breeds. The results are visualized as a Manhattan plot with two threshold values (Figure 3).
The results of the hapFLK analysis for the three-breed dataset included four genomic regions on chromosomes GGA1, GGA6, GGA16, and GGA31, within or near which a total of 68 genes were localized (Table 3, Supplementary Figure S4, Supplementary Table S2).
3.2.3. ROH Islands Detection
Within each breed, we established that over 50% of samples had overlapping ROH islands (Supplementary Table S3, Supplementary Figure S5, Table 4). A total of 261 homozygosity islands were discovered, which were localized on 19 chromosomes. The majority of ROH islands (95.40%) was of the USH breed.
At the same time, the distribution analysis of the average length (Figure 4a) and the number of homozygosity segments by length class (Figure 4b) showed that USH, like two other studied breeds, are distinguished mainly by shorter segments, suggesting events of longstanding inbreeding. Herewith, the values of these indicators for USH significantly exceeded similar values for other breeds in the shortest length class; however, as the length of the ROH fragments increases, USH was inferior to CRW and PRW. Thus, the longest (4–8 Mb) fragments were not identified for USH at all, while the average length of fragments of this class for CRW and PRW was 4.94 and 4.26 Mb, respectively (Figure 4a,b, Supplementary Table S4a,b).
Analysis of overlapping ROH islands in the three breeds revealed common homozygous regions on chromosomes GGA4 and GGA33 (Table 5). Automated analysis using the Biomart tool on Ensembl found no characterized candidate genes in these regions. However, a manual search on the NCBI resource identified long noncoding RNA (lncRNA) regions on chromosome GGA4. On chromosome GGA33, the genomic area identified as the homozygous region shows the absence of a nucleotide reference sequence, which was likely identified as a common ROH segment for all the breeds studied.
3.3. Candidate Genes Affected by Selection and QTLs
We accepted the regions identified by at least two methods or in two pairs of breeds as the areas most strongly subjected to selection pressure in different breeds. Accordingly, the 12 partially overlapping genomic regions on eight chromosomes were established that contained 134 genes, including 27 PCGs (Table 6, Supplementary Tables S1–S3).
Using the Chicken QTLdb database [122,123], we searched for QTLs in the identified genomic regions (Table 7, Supplementary Table S5). A total of 524 QTLs associated with conformation, health, productivity, reproductive, and other phenotypic traits were identified.
As follows from Table 7, the largest number of QTLs was identified in USH (507), whereas PRW and CRW had 12 and 5 QTLs, respectively. Moreover, a significant portion of the QTLs were associated with production traits and reproductive characters (475 and 22, respectively). Notably, QTLs associated with indicators of health (2) and physiological processes (7) were identified in USH selected for cold tolerance [62].
4. Discussion
4.1. Genetic Diversity among the Breeds Studied
The meat industry in general and the poultry sector specifically essentially rely on the evaluation, characterization, and utilization of genetic diversity inherent in various breeds and lines (e.g., [124,125,126,127,128,129]). In this whole-genome resequencing study, we examined the genetic diversity features in the two divergently selected transboundary meat-type breeds, CRW and PRW, used for producing the Smena 8 broiler cross and compared to the aboriginal cold-tolerant Russian breed of USH. The latter was genetically most distant from the two other breeds, as identified by PCA (Figure 2a,b) and confirmed by the admixture/ancestry analysis (Figure 2c). At K = 2, CRW and PRW demonstrated a common ancestry that was different from that of USH, and most likely corresponds to the meat (Asiatic) type in accordance with the evolutionary model of chicken breed origin and formation [67,130]. At K = 3, the genotyped CRW and PRW chickens split into two obvious clusters (Figure 2c). These can be attributed to the meat (CRW) and dual-purpose (PRW) types according to the traditional classification and phenotypic clustering models, as described in Larkina et al. [59] and Kochish et al. [67].
On the other hand, the PRW and, to a lesser degree, CRW chickens showed signs of admixture from the other breeds in the explored dataset. Overall, USH turned out to be more consolidated genetically and less diverse (Table 1) than PRW and CRW. Also, the allelic diversity in PRW was slightly, but significantly, greater than that in CRW. This is likely due to the history of the creation of this breed and may be a consequence of the use of a larger number of ancestral breeds and lines in developing PRW [131] compared to CRW. Originally, CRW descended from the English local game chickens and Asiatic game (Asil, White Malay, and Indian Game) and meat-type (Cochin) breeds. The initial stock of breeds for developing PRW was somewhat more diverse and included chickens of Asiatic (Java Black, Brahma, Cochin White and Cochin Buff), North American (Dominique), and European (White-faced Black Spanish) origins [67]. The unique genetic makeup and diversity peculiarities of USH we established here were well in line with our previous genome-wide surveys of this old Russian breed [62,63] relative to the genomes of CRW, as well as the Orloff Mille Fleur [59,62,132,133,134,135,136], Russian White [50,51,57,58,137,138,139,140], and other chicken breeds [132,141,142,143].
4.2. Inbreeding and ROH Characterization
In a long-term breeding experiment in PRW chickens selected for body weight, Harrison et al. [131] established that, even when inbreeding gradually accumulates and reduces genetic diversity, heterozygosity persists to enable additional responses to selection. Judging from the UFIS inbreeding coefficients in our study, their significant and much larger values in CRW and PRW perhaps resulted from greater selection pressure in these two transboundary meat-type breeds compared to the local primitive USH fowls that have not been subject to strong artificial selection. On the other hand, the significantly higher number of ROH islands found in USH, as compared to the two other breeds, was likely due to a higher level of inbreeding assessed via FROH in this breed (Table 4), which may also be a consequence of the small size of the existing USH population.
In our previous publication [62], where USH and CRW were also studied, USH was superior to three other breeds, including CRW, in terms of UFIS inbreeding coefficient. A similar overall pattern was observed for ROH-based inbreeding. In this work, UFIS turned out to be smaller for USH (about four times less than for CRW and PRW). It was nominally equal to 0.0082, i.e., almost seven times lower than in the previous study (0.055; [62]).
To interpret these apparent inconsistencies in inbreeding estimates, we can assume that this may be due to the chosen genome-wide assessment tool. In the previous article [62], we had an SNP array containing markers that were polymorphic for the breeds on the basis of which it was created. In USH, these same loci could be monomorphic, hence the increased homozygosity. The density of the SNP chip and the size of the examined USH sample can also be important when comparing the two experiments. In the present study, there were about 12.5 M SNPs vs. 50 K on the SNP chip, and the USH sample was smaller (17 vs. 40). We think this may have shifted the FROH inbreeding coefficients in the current survey compared with the data in Romanov et al. [62].
In addition, we would also approach the interpretation of UFIS and FROH results differently. In the first case, we can talk about selection pressure for a limited number of traits, which, in turn, leads to positive selection in favor of polygenic loci involved in the formation of selected traits, increasing homozygosity at these loci [144,145,146,147]. Since the loci are polygenic, an increase in homozygosity, in most cases, will not be associated with the formation of homozygous haplotypes (only for SNPs located at close distances, that is, due to the hitchhiking effect). However, we introduced a minimum ROH length filter (0.5 Mb) just to exclude short segments resulting from concatenation. In the case of USH, there is no selection and there is virtually no selection pressure. This means that almost all USH individuals produced during the population propagation are left in the next generation, with the exception of the very weak. Moreover, FROH is a more realistic measure of inbreeding. That is, the greater number of longer ROHs in USH (Figure 4b) suggests that this breed has been subject to a more recent inbreeding. This is not surprising, as the USH population has been maintained at 100–200 hens and about 25 roosters for many years, suggesting a higher likelihood of inbreeding. The two commercial breeds surveyed, CRW and PRW, are significantly more numerous and are maintained with over 1000 birds in each line, i.e., over 2000 per breed, hence their lower inbreeding degree. Thus, because FROH is calculated directly from the genome homozygosity of individuals, it provides a more accurate estimate of the inbreeding status within a breed.
4.3. Prioritized Candidate Genes within Selective Sweeps
The 12 genomic regions containing the identified selection footprints harbored a total of 27 PCGs that will be described below by chromosome and in terms of their relevance to economically and physiologically important traits in the breeds and lines studied. Notably, this study was consistent with our previous findings presented by Abdelmanova et al. [58] and Romanov et al. [62] for eight selective sweep regions on GGA1, GGA4, GGA5, GGA7, GGA10, and GGA28 in the genomes of CRW, USH, RUW, and OMF chickens (Table 6). On the other hand, we discovered four new genomic regions under selection pressures on GGA2 and GGA14.
4.3.1. GGA1
On this chromosome, we found the NUAK1 (NUAK family kinase 1) gene known as a potential regulator for chicken plumage pigmentation that overlapped with the respective QTLs [148]. In humans, it is broadly expressed in various tissues, with the highest upregulation in brain [149]. To the best of our knowledge, another nearby gene, RFX4 (regulatory factor X4), has not been functionally described yet in chickens or other birds. However, its human homolog encodes a testis-specific DNA-binding protein [150] and has a restricted expression toward the brain and, especially, the testis [149], suggesting it as a good reproductive and behavioral candidate gene in chickens.
The CHST11 (carbohydrate sulfotransferase 11) gene is a reported candidate for plumage color in the chicken that is associated with aggressive behavior, and it is overlapped with the corresponding QTLs [151]. It is also a strong candidate gene for body weight at 35 days in broiler chickens [152], which is also relevant for our study. This gene has a ubiquitous expression in human tissues [149]. The CHST11 enzyme is responsible for catalyzing the chondroitin sulfate that is found on the surface of many cells and extracellular matrix and is the main proteoglycan in cartilage, which might also be important for broiler growth and development. On GGA1, we also identified another important growth and development candidate factor, IGF1 (insulin like growth factor 1). Previously, it was linked to a signal of selective sweeps, being associated with abdominal fat weight/deposition, body weight, and other traits in chickens [153,154,155,156]. Its human homolog was recognized by a broad expression in various tissues [149].
4.3.2. GGA2
Among the PCGs found on this chromosome, there was C18orf63, which encodes an uncharacterized protein in chickens. However, its human homolog (chromosome 18 open reading frame 63) has a restricted, but very high, expression exclusively toward testis tissue [149], suggesting that C18orf63 may also play a certain role in chicken reproduction. The CYB5A (cytochrome b5 type A, or epididymis secretory sperm binding protein type 1 cyt-b5) gene is related to heme binding. In humans, it demonstrates a broad expression in various tissues, especially in liver and kidney [149]. Additionally, it is also described as a rheumatoid arthritis susceptibility gene and is also involved in androgen synthesis [157], thus being supposedly important for functioning of skeletal and reproductive systems. The third PCG revealed on GGA2 was CCDC102B (coiled-coil domain containing 102B). It enables protein binding and shows a broad expression in placenta, lung, and other human tissues [149].
4.3.3. GGA4
This chromosome also encapsulates several significant PCGs. One of them, PCDH7 (protocadherin 7), is a positional candidate gene associated with internal organ traits in chickens and located within a QTL for intestine length and gizzard weight; it is differentially expressed in the epidermis of the feather bud [158,159]. In human tissues, it is relevant to calcium ion binding and cell adhesion and shows a broad expression, especially in the brain [149].
LCORL (ligand dependent nuclear receptor corepressor like) is a candidate gene associated with slaughter traits, being positionally associated with internal organ traits in chickens and located within a QTL for intestine length and gizzard weight. It is also a possible candidate responsible for growth and body weight and a reported candidate gene for carcass and eviscerated weight and egg quality traits [57,158,160,161,162,163]. Its human homolog is involved in spermatogenesis, skeletal frame size, and adult height, with a ubiquitous expression in different tissues and the most upregulation in the testis [149]. The NCAPG (non-SMC condensin I complex subunit G) gene is involved in mitotic chromosome condensation and may regulate chicken bone growth and development. It is known as a candidate gene for bone size/mass and slaughter traits, with its SNP being also associated with egg albumen quality and other egg traits [160,163,164,165,166]. NCAPG has a broad expression in bone marrow, lymph node, testis, and other human tissues [149]. Because of the high importance of the NCAPG-LCORL locus due to its association with performance and other phenotypic traits, its genetic variation was previously thoroughly explored in chickens of commercial (selected for egg and meat production), local, and imported fancy breeds [59,60]. This investigation suggested prevailed genotypes and specific LD structure at this locus across the studied breeds depending on their utility type and origin.
4.3.4. GGA7
One PCG, ANKRD44 (ankyrin repeat domain 44), enabling protein binding was found within a selective sweep on this chromosome. This is a candidate gene for dermatological diseases/conditions and is associated with amino acid changes [167]. In humans, its broad expression was reported in various tissues, especially in the lymph node, appendix, and spleen [149].
4.3.5. GGA10
The FAM189A1 (family with sequence similarity 189 member A1) gene for an uncharacterized protein located in membrane is still understudied in the chicken. However, its human homolog, ENTREP2 (endosomal transmembrane epsin interactor 2), is expressed in various tissues, with a biased upregulation in the brain [149]. Another PCG, TJP1 (tight junction protein 1), related to cell adhesion molecule binding was also shown to be associated with decreased fertility in aged laying breeders [168]. It has a ubiquitous expression in human tissues, especially in the testis, placenta, and brain [149].
4.3.6. GGA14
This chromosome harbors the KDELR2 (KDEL endoplasmic reticulum protein retention receptor 2) gene. The respective protein enables endoplasmic reticulum retention sequence binding. Human KDELR2 is associated with osteogenesis disorder [169] and has demonstrated a ubiquitous expression in the placenta, stomach, and other tissues [149]. NUBP1 (nucleotide binding protein 1), involved in ATP-dependent iron–sulfur cluster assembly, is known as a host protein that interacts with duck enteritis virus [170]. The NUBP1 gene has also a ubiquitous expression in adrenal, heart, lymph node, and other human tissues [149].
TEKT5 (tektin 5) is a nondescribed gene in chickens; its human homolog, however, is involved in cilium assembly and movement, with a restricted expression toward the testis [149]. One more PCG, EMP2 (epithelial membrane protein 2), is responsible for the corresponding plasma membrane component. It is slightly expressed in various human tissues, with a biased upregulation in the lung, skin, and esophagus [149].
4.3.7. GGA28
A number of PCGs were revealed within one genomic region affected by putative selection on this chromosome and can represent a relevance and significance for describing economically and physiologically important traits in the breeds and lines studied. Of note, five of them, i.e., CHERP (calcium homeostasis endoplasmic reticulum protein), CALR3 (calreticulin 3), PTPRS (protein tyrosine phosphatase, receptor type S), KLF2 (Kruppel like factor 2), and RAB8A (RAB8A, member of the RAS oncogene family), have been established as candidates for plasma very-low-density lipoprotein concentration in the chicken [171]. In addition, the CHERP gene is known for enabling transmembrane transporter binding activity and RNA binding. It is also typified by ubiquitous expression in human tissues, especially in the testis, spleen, and ovary [149]. CALR3 is responsible for the respective protein that participates in calcium ion binding and may be associated with obesity in chickens [172]. Its human homolog is marked by a restricted expression exclusively toward the testis [149]. The PTPRS gene involved in protein binding and dephosphorylation also has a broad expression in human tissues, especially in fat, the brain, and the prostate [149]. KLF2 involved in regulation of transcription by RNA polymerase II is additionally related to angiogenesis at tibial lesions in broilers, is considered as a chick connective-tissue-associated transcription factor, and may partly inhibit chicken adipogenesis [173,174,175]. Its human homolog plays roles in many processes during development and disease and is recognized by a broad expression in various tissues, especially in fat and the ovary [149]. The RAB8A gene facilitates GTP binding and is distinguished by ubiquitous expression in human tissues, with a higher activity in digestive and immune systems [149].
We also discovered some other PCGs on GGA28. In particular, C19orf44, which encodes an uncharacterized chicken protein, is a homolog to the human C19orf44 (chromosome 19 open reading frame 44) gene, with the latter being defined by ubiquitous expression in different human tissues, especially in the testis and ovary [149]. The FAM32A (family with sequence similarity 32 member A) gene product is localized in the nucleolus, being involved in RNA binding activity and, presumably, the apoptotic process. Its human homolog is characterized by ubiquitous expression in digestive and excretory systems and in other tissues [149]. The protein encoded by the CIB3 (calcium and integrin binding family member 3) gene enables calcium ion binding and has a low expression in human tissues, with a slightly higher expression in the testis [149]. The TPM4 (tropomyosin 4) gene is related to actin filament binding and muscle contraction, with ubiquitous expression in human tissues and a higher synthesis level in gall and urinary bladders [149]. The TINCR (TINCR ubiquitin domain containing) gene is a part of the protein binding pathway and is expressed in several human tissues, with a biased upregulation in the skin, placenta, and esophagus [149].
In summation, the above description of the identified chicken PCGs and the respective functional projection from their human homologs suggest their relevance for artificial selection signatures in the genomes of the transboundary broiler breeds, CRW and PRW, and the primitive native breed of USH. In terms of functionality and association, the 27 PCGs can be attributed to such key economically and physiologically important trait clusters as body weight (CHST11, IGF1, LCORL), growth and development (IGF1, CYB5A, PCDH7, LCORL, NCAPG, KDELR2, KLF2), muscles (TPM4), fat metabolism and deposition (IGF1, CHERP, CALR3, PTPRS, KLF2, RAB8A), exterior (NUAK1, CHST11, PCDH7, ANKRD44, EMP2, TINCR), behavior (NUAK1, RFX4, CHST11, PCDH7, FAM189A1, TJP1, TEKT5, PTPRS), immunity (NCAPG, ANKRD44, KDELR2, NUBP1, CHERP, KLF2, RAB8A), reproduction (RFX4, C18orf63, CYB5A, CCDC102B, LCORL, NCAPG, TJP1, KDELR2, TEKT5, CHERP, CALR3, PTPRS, C19orf44, CIB3, TINCR), circulatory (NUBP1, TEKT5), digestive (KDELR2, EMP2, RAB8A, FAM32A, TPM4, TINCR), excretory (CYB5A, NUBP1, FAM32A, TPM4), and respiratory (CCDC102B, EMP2) systems (Table 6). These results largely overlap with the QTLs found within the determined genomic regions (Table 7, Supplementary Table S5).
Most sweep regions and the conforming 15 PCGs were shared between two or three breeds, suggesting possible similar selective pressure trajectories in their selection history. Partially, this sharing pattern might also be due to common ancestral breeds used for the formation of the three chicken composite breeds studied and occasional gene introgression [176,177], as can be seen from their peculiar admixture-based plots in our investigation (Figure 2c). On the other hand, there were 12 PCGs mostly specific for one breed, PRW, and especially USH, that may reflect certain differences in their distinct genomic architecture. Further in-depth studies will be required to validate shared and breed-specific PCGs that can be linked to the traits under selection pressure.
5. Conclusions
In this study, we examined the genomic architecture and diversity of the grandparent lines subject to high selection pressure for meat production and, contrastingly, in an aboriginal Russian chicken breed of USH using whole-genome resequencing data. Probably because of a small population size and peculiar breed history, USH was less heterozygous and diverse and showed a higher genetic distinctiveness relative to two commercial broiler breeds, CRW and PRW. We also dissected 12 regions of selective signatures and the respective candidate genes in these three breeds. To reveal regions under selective pressure, we employed three techniques based on ZFST estimation, hapFLK procedure, and ROH assessment.
The description of the found PCGs in chickens and the corresponding functional projection from human homologs point out that these genes may be relevant for signals of artificial selection seen in the genomes of the transboundary broiler breeds, CRW and PRW, as well as the old local USH breed. Functionally, the 27 PCGs can be associated with important trait clusters that are both physiologically and economically significant, including body weight (CHST11, IGF1, LCORL), growth and development (CYB5A, PCDH7, NCAPG, KDELR2, KLF2, etc.), muscles (TPM4), fat metabolism and deposition (CHERP, CALR3, PTPRS, RAB8A, etc.), exterior (NUAK1, ANKRD44, EMP2, TINCR, etc.), behavior (RFX4, FAM189A1, TJP1, TEKT5, etc.), immunity (NUBP1, etc.), reproduction (C18orf63, etc.), and digestion, as well as circulatory, excretory, and respiratory systems. The majority of these findings coincide with the QTLs present in the identified chromosomal areas. The information reported here will serve as the basis for detailing the genomic architecture and selection footprints in these breeds and lines and further implementing genomic selection aimed at improving productive and other phenotypic traits in chickens [178,179,180].
Conceptualization, N.A.Z. and M.N.R.; methodology, A.S.A., A.V.S., A.V.D., M.N.R. and N.A.Z.; software, A.S.A., A.V.S. and A.V.D.; validation, A.S.A., A.V.D., N.A.V., M.N.R. and N.A.Z.; formal analysis, A.S.A., A.V.S., N.A.V. and M.N.R.; investigation, A.S.A. and A.V.S.; resources, A.V.D., D.V.A., V.I.F., D.N.E., L.G.K. and N.A.Z.; data curation, N.A.V. and A.V.D.; writing—original draft preparation, A.S.A., A.V.S., N.A.V., M.N.R. and N.A.Z.; writing—review and editing, A.S.A., A.V.D., N.A.V., M.N.R., A.V.S., L.G.K., D.K.G. and N.A.Z.; visualization, A.S.A., A.V.D., D.V.A., A.V.S., L.G.K. and M.N.R.; supervision, N.A.V., V.I.F., D.K.G. and N.A.Z.; project administration, N.A.Z.; funding acquisition, A.V.D. All authors have read and agreed to the published version of the manuscript.
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.
Not applicable.
The original contributions presented in the study are included in the article and
The skilled technical assistance of Olga M. Romanova in preparing chicken images (
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.
Footnotes
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.
Enlarge this image.Figure 1. The three chicken breeds examined in this study. (a) Cornish White (female, left; male, right); (b) Plymouth Rock White (male, front; females, back); and (c) Ushanka (female, left; male, right).
Enlarge this image.Figure 2. Genetic relationships among the three chicken breeds studied using genome-wide SNP genotyping. (a,b) PCA plots showing the distribution of breeds and individuals in the dimensions of two coordinates, i.e., the first (PC1; X-axis) and second (PC2; Y-axis; (a) or third (PC3; Y-axis; (b) principal components; (c) admixture-based bar plots illustrating the proportions of individual ancestry in the breeds under study at K = 2 (top) and K = 3 (bottom). Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka.
Enlarge this image.Figure 3. Search for signatures of selection in genomes of the studied breeds as revealed by the hapFLK analysis. Chicken autosomes are the values for the X-axis, and statistical significance values (−log10 p-values) are the values for the Y-axis. The red line that indicates the threshold of significance at p < 2.8 × 10−8 (i.e., −log10(p) > 7.55) was determined using the Bonferroni correction and defines the strongest hapFLK regions, while the blue line indicates the threshold of significance at p < 1 × 10−5 (i.e., −log10(p) > 5) and defines the putative hapFLK regions.
Enlarge this image.Figure 4. Descriptive statistics of the runs of homozygosity (ROHs) by ROH length class in the studied chicken breeds: (a) Overall mean length of ROHs (Y-axis) by ROH length class (X-axis; 0.5–2, 2–4 and 4–8 Mb). (b) Mean number of ROHs (Y-axis) by ROH length class (X-axis; 0.5–2, 2–4, and 4–8 Mb). Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka.
Genetic diversity in the three studied breeds using the basic descriptive statistics 1.
| Breed 2 | n | HO (M ± SE) | UHE (M ± SE) | AR (M ± SE) | UFIS [CI 95%] |
|---|---|---|---|---|---|
| CRW | 20 | 0.2958 ± 0.0001 | 0.3034 ± 0.0001 | 1.9101 ± 0.0002 a | 0.0363 [0.0358; 0.0368] |
| PRW | 20 | 0.2958 ± 0.0001 | 0.3022 ± 0.0001 | 1.9187 ± 0.0002 b | 0.0321 [0.0316; 0.0326] |
| USH | 17 | 0.2103 ± 0.0002 c | 0.2068 ± 0.0001 c | 1.6218 ± 0.0004 c | 0.0082 [0.0075; 0.0089] |
1 n, number of individuals; HO, observed heterozygosity; M, mean value; SE, standard error; UHE, unbiased expected heterozygosity; AR, rarefied allelic richness; UFIS, unbiased inbreeding coefficient (CI 95%, range variation of coefficient at a confidence interval of 95%). 2 Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka. The significance of pairwise values within a column is indicated by different superscripts: a CRW vs. PRW or USH, p < 0.001; b PRW vs. CRW or USH, p < 0.001; c USH vs. CRW or PRW, p < 0.001.
Mean ZFST values and blocks of SNPs joined by two or more top 0.1% neighbored SNPs at pairwise comparison of the three breeds studied 1.
| Chromosome | Bin Start 2 | Bin End 3 | N 4 | ZFST | Breed Pairs | Genes |
|---|---|---|---|---|---|---|
| GGA1 | 54,420,001 | 54,470,000 | 729 | 0.404483 | CRW/PRW | CHST11 |
| GGA1 | 54,530,001 | 54,580,000 | 1040 | 0.602406 | CRW/USH | CHST11 |
| GGA1 | 55,310,001 | 55,360,000 | 190 | 0.884104 | CRW/USH | IGF1 |
| GGA1 | 55,310,001 | 55,360,000 | 291 | 0.597753 | PRW/USH | IGF1 |
| GGA1 | 55,320,001 | 55,370,000 | 196 | 0.752325 | CRW/USH | IGF1 |
| GGA1 | 55,320,001 | 55,370,000 | 276 | 0.568095 | PRW/USH | IGF1 |
| GGA1 | 75,490,001 | 75,540,000 | 148 | 0.622485 | CRW/USH | TEAD4 |
| GGA1 | 75,490,001 | 75,540,000 | 157 | 0.565816 | PRW/USH | TEAD4 |
| GGA1 | 75,500,001 | 75,550,000 | 132 | 0.663862 | CRW/USH | TEAD4 |
| GGA1 | 75,500,001 | 75,550,000 | 149 | 0.563412 | PRW/USH | TEAD4 |
| GGA1 | 75,510,001 | 75,560,000 | 67 | 0.386978 | CRW/PRW | TEAD4 |
| GGA1 | 75,510,001 | 75,560,000 | 125 | 0.714440 | CRW/USH | TEAD4 |
| GGA1 | 75,510,001 | 75,560,000 | 138 | 0.579394 | PRW/USH | TEAD4 |
| GGA1 | 75,520,001 | 75,570,000 | 77 | 0.372578 | CRW/PRW | TEAD4 |
| GGA1 | 75,520,001 | 75,570,000 | 130 | 0.637783 | CRW/USH | TEAD4 |
| GGA1 | 188,000,001 | 188,050,000 | 477 | 0.775700 | PRW/USH | GRM5 |
| GGA1 | 188,010,001 | 188,060,000 | 872 | 0.366252 | CRW/PRW | GRM5 |
| GGA1 | 188,010,001 | 188,060,000 | 548 | 0.697367 | PRW/USH | GRM5 |
| GGA1 | 188,020,001 | 188,070,000 | 865 | 0.371221 | CRW/PRW | GRM5 |
| GGA1 | 188,020,001 | 188,070,000 | 596 | 0.649595 | PRW/USH | GRM5 |
| GGA1 | 188,030,001 | 188,080,000 | 866 | 0.35713 | CRW/PRW | GRM5 |
| GGA1 | 188,030,001 | 188,080,000 | 649 | 0.592529 | PRW/USH | GRM5 |
| GGA2 | 93,720,001 | 93,770,000 | 344 | 0.619266 | CRW/USH | CCDC102B |
| GGA2 | 93,720,001 | 93,770,000 | 360 | 0.599763 | PRW/USH | CCDC102B |
| GGA11 | 140,001 | 190,000 | 362 | 0.432514 | CRW/PRW | SMPD3 |
| GGA11 | 140,001 | 190,000 | 358 | 0.603890 | CRW/USH | SMPD3 |
1 Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka. 2 Bin start, start position of sliding window; 3 Bin end, end position of sliding window; 4 N, number of SNP variants in a window. The breed for which a region was determined by comparison with each of the other two breeds is given in bold. Regions identified by pairwise comparison of two breeds are highlighted in color as follows: CRW vs. PRW (red), CRW vs. USH (green), PRW vs. USH (blue).
HapFLK blocks revealed in the genomes of the studied chicken breeds 1.
| Chromosome | Breed | Position | Length, Mb | No. of SNPs | Most Significant SNP | Genes | |
|---|---|---|---|---|---|---|---|
| Start | End | ||||||
| GGA1 | CRW | 53,119,864 | 53,212,505 | 0.093 | 82 | rs15269046 | SYN3, TIMP3 |
| PRW | 53,637,245 | 54,504,503 | 0.867 | 569 | rs314634881 | NUAK1, C12orf75, MTERF2, TMEM263, RIC8B, RFX4, POLR3B, CRY1, APPL2, WASHC4, ALDH1L2, SLC41A2, CHST11, TCP11 × 2, CKAP4, gga-mir-12210 | |
| GGA6 | USH | 8,693,825 | 8,814,126 | 0.120 | 108 | rs315872719 | KROX20, ADO |
| GGA16 | USH | 2,090,051 | 2,170,380 | 0.080 | 190 | rs737045576 | IL4I1, TRIM7.1, SLURP1, TRIM39.2, TRIM27.2, TRIM39.1, TRIM27.1, TRIM41, RACK1, BG1 |
| USH | 2,230,563 | 2,248,418 | 0.018 | 95 | rs740720869 | CENPA, CYP21A1 | |
| GGA31 | PRW | 626,104 | 665,706 | 0.040 | 421 | 31:6,534,09 | – |
1 Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka. SNP significance level: p < 1 × 10−5. Genes in or near which the most significant SNP in the region is located are highlighted in bold. Genes identified at a significance level of p < 2.8 × 10−8 are underlined.
The descriptive statistics summary of the homozygosity runs (ROH) 1.
| Breed 2 | n | ROH Length, Mb | ROH No. | F ROH | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| M ± SE | Min | Max | M ± SE | Min | Max | M ± SE | Min | Max | ||
| CRW | 20 | 102.59 ± 5.71 | 52.06 | 147.61 | 123 ± 6.75 | 62 | 164 | 0.108 ± 0.006 | 0.06 | 0.16 |
| PRW | 20 | 95.25 ± 7.54 | 36.81 | 155.47 | 116.45 ± 8.91 | 50 | 190 | 0.101 ± 0.008 | 0.04 | 0.16 |
| USH | 17 | 338.75 ± 9.31 | 262.60 | 394.09 | 390.94 ± 9.47 | 306 | 435 | 0.358 ± 0.010 | 0.28 | 0.42 |
1 n, number of individuals; ROH Length, the overall length of ROHs in a genome; ROH No., the number of ROHs in a genome; FROH, inbreeding coefficient calculated based on ROHs; M, mean value; SE, standard error; min, minimal value; and max, maximal value. 2 Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka.
The ROH islands overlapping in two or more breeds.
| Chromosome | Position | Length, Mb | Breed 1 | Genes | |
|---|---|---|---|---|---|
| Start | End | ||||
| GGA4 | 70,462,265 | 70,739,807 | 0.278 | CRW | ENSGALG00010011849, ENSGALG00010011854 |
| 70,701,554 | 70,970,711 | 0.269 | USH | ENSGALG00010011854, ENSGALG00010011667, ENSGALG00010011863, ENSGALG00010011687 | |
| 70,740,151 | 71,008,491 | 0.268 | CRW | ENSGALG00010011667, ENSGALG00010011863, ENSGALG00010011687 | |
| 70,740,151 | 70,753,263 | 0.013 | PRW | ENSGALG00010011667 | |
| GGA33 | 245,471 | 1,033,316 | 0.788 | PRW | – |
| 245,535 | 1,033,647 | 0.788 | CRW | – | |
| 245,535 | 1,033,347 | 0.788 | USH | – | |
1 Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka.
Genes within the overlapped genomic regions affected by putative selection in the studied chicken breeds and identified by at least two methods.
| Chromosome | Sequential Region No. | Position | Length, Mb | Breeds 1 | Method | Genes | ||
|---|---|---|---|---|---|---|---|---|
| Start | End | This Study | Previous Studies [ | |||||
| GGA1 | 1 | 53,637,245 | 54,504,503 | 0.867 | PRW | CRW, USH | hapFLK | NUAK1, C12orf75, MTERF2, TMEM263, RIC8B, RFX4, POLR3B, CRY1, APPL2, WASHC4, ALDH1L2, SLC41A2, CHST11, TCP11X2, CKAP4, gga-mir-12210 |
| 53,740,001 | 53,790,000 | 0.050 | CRW, PRW | ZFST | RFX4 | |||
| 2 | 55,266,291 | 55,354,497 | 0.088 | PRW | CRW, USH | ROH | IGF1 | |
| 55,280,001 | 55,330,000 | 0.050 | CRW, USH | ZFST | IGF1 | |||
| GGA2 | 3 | 91,027,506 | 92,075,494 | 1.048 | USH | – | ROH | FAM69C, C18orf63, CYB5A, TIMM21, ZNF407, CNDP1, CNDP2U1, FBXO15 |
| 91,520,001 | 91,570,000 | 0.050 | CRW, USH | ZFST | C18orf63, CYB5A | |||
| 4 | 92,075,780 | 93,852,482 | 1.777 | USH | – | ROH | RTTNDOK6, TMX3, SOCS6, CCDC102B, NETO1, CBLN2, gga-mir-1803, gga-mir-1681, gga-mir-6584 | |
| 93,720,001 | 93,770,000 | 0.050 | CRW, USH | ZFST | CCDC102B | |||
| GGA4 | 5 | 70,754,254 | 71,145,478 | 0.391 | PRW | RUW, CRW | ROH | PCDH7 |
| 70,971,231 | 71,354,713 | 0.383 | USH | ROH | PCDH7 | |||
| 71,140,001 | 71,190,000 | 0.050 | PRW, USH | ZFST | PCDH7 | |||
| 6 | 74,938,839 | 75,922,825 | 0.984 | USH | USH, RUW, CRW | ROH | LCORL, NCAPG, MED28, LAP3, CLRN2, QDPR, LDB2 | |
| 75,380,001 | 75,430,000 | 0.050 | PRW, USH | ZFST | LCORL, NCAPG | |||
| GGA5 | 7 | 30,830,001 | 30,880,000 | 0.050 | CRW, PRW | CRW, USH, RUW, OMF | ZFST | MEIS2 |
| 30,830,467 | 31,703,025 | 0.873 | CRW | ROH | CDIN1, DPH6, ZNF770, AQR, gga-mir-1718 | |||
| GGA7 | 8 | 9,270,001 | 9,320,000 | 0.050 | CRW, USH | CRW | ZFST | DNAH7 |
| 9,281,277 | 10,029,387 | 0.748 | USH | ROH | SF3B1, STK17B, HECW2, GTF3C3, C7H2ORF66, PGAP1, ANKRD44, COQ10B, HSPD1, HSPE1, MOB4, RFTN2, BOLL, PLCL1 | |||
| 9,670,001 | 9,720,000 | 0.050 | CRW, USH | ZFST | ANKRD44 | |||
| GGA10 | 9 | 5,355,392 | 6,359,502 | 1.004 | USH | USH, OMF | ROH | LRRC49, THSD4, BNIP2, GTF2A2, GCNT3, OTUD7A, KLF13, TRPM1, MTMR10, FAN1, MPHOSPH10, MCEE, APBA2, FAM189A1, TJP1, TARSL2, TM2D3, ADAL, LARP6, gga-mir-204-2, gga-mir-1574 |
| 5,920,001 | 5,970,000 | 0.050 | CRW, PRW | ZFST | FAM189A1 | |||
| GGA14 | 10 | 8,062,881 | 8,813,937 | 0.751 | USH | – | ROH | C14H16ORF52, VWA3A, SDR42E2, EEF2K, POLR3E, CDR2, METTL9, IGSF6, OTOA, KDELR2, RPS15A, ARL6IP1, SMG1, CLEC19A, SYT17, COQ7, TMC7, TMC5, GDE1, CCP110, ITPRIPL2, gga-mir-1644 |
| 8,790,001 | 8,840,000 | 0.050 | PRW, USH | ZFST | KDELR2, DAGLB, RAC1 | |||
| 11 | 9,118,484 | 10,172,206 | 1.054 | USH | – | ROH | CARHSP1, PMM2, TMEM186, ABAT, METTL22, TMEM114, C16orf72, USP7, NUBP1, TEKT5, EMP2, GRIN2A | |
| 9,120,001 | 9,170,000 | 0.050 | PRW, USH | ZFST | NUBP1, TEKT5 | |||
| GGA28 | 12 | 4,740,302 | 5,396,354 | 0.656 | USH | RUW, CRW | ROH | CHERP, C19orf44, CALR3, PTPRS, KDM4B, KLF2, AP1M1, FAM32A, CIB3, RAB8A, TPM4, TINCR, DPP9, TNFAIP8L1, MED26, SLC35E1, UHRF1, TICAM1, FEM1A, PLIN3, gga-mir-7-3, gga-mir-6666, MYDGF |
| 4,760,001 | 4,810,000 | 0.050 | CRW, USH | ZFST | CHERP, C19orf44, CALR3 | |||
1 Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka; RUW, Russian White; OMF, Orloff Mille Fleur. Genes identified by more than one method are highlighted in bold.
Number of QTLs associated with phenotypic traits identified in the most significant regions presumably subject to selection pressure.
| Traits | Breeds 1 | Total | |||||
|---|---|---|---|---|---|---|---|
| CRW | CRW/PRW | CRW/USH | PRW | PRW/USH | USH | ||
| Exterior | 2 | 4 | 1 | 6 | 4 | 6 | 23 |
| Aggressive behavior | 2 | 3 | 5 | ||||
| Feather density | 4 | 2 | 6 | ||||
| Feather pecking | 2 | 1 | 1 | 2 | 6 | ||
| Feather pigmentation | 2 | 2 | 4 | ||||
| Receiving feather pecking | 2 | 2 | |||||
| Health | 2 | 2 | |||||
| Campylobacter intestinal colonization | 2 | 2 | |||||
| Physiology | 11 | 4 | 15 | ||||
| Blood carbon dioxide level | 1 | 1 | |||||
| CO2 partial pressure | 3 | 1 | 4 | ||||
| VLDL cholesterol level | 8 | 2 | 10 | ||||
| Production | 1 | 3 | 9 | 2 | 265 | 368 | 648 |
| Abdominal fat percentage | 2 | 2 | |||||
| Abdominal fat weight | 2 | 2 | 1 | 5 | |||
| Albumen height | 6 | 2 | 8 | ||||
| Average daily gain | 16 | 69 | 85 | ||||
| Body slope length | 1 | 1 | |||||
| Body weight | 1 | 1 | 27 | 107 | 136 | ||
| Body weight gain | 1 | 1 | |||||
| Bursa of Fabricius weight | 3 | 3 | |||||
| Carcass fat content | 3 | 1 | 4 | ||||
| Carcass weight | 12 | 12 | |||||
| Chest width | 4 | 1 | 5 | ||||
| Claw percentage | 2 | 2 | |||||
| Claw weight | 8 | 8 | |||||
| Drumstick and thigh muscle percentage | 1 | 1 | |||||
| Drumstick and thigh muscle weight | 1 | 1 | |||||
| Drumstick and thigh percentage | 1 | 1 | |||||
| Drumstick and thigh weight | 1 | 1 | |||||
| Egg number | 1 | 2 | 3 | ||||
| Egg production rate | 3 | 1 | 4 | ||||
| Egg weight | 128 | 63 | 191 | ||||
| Eggshell weight | 1 | 1 | |||||
| Feed conversion ratio | 2 | 12 | 46 | 60 | |||
| Feed intake | 3 | 3 | |||||
| Feet weight | 5 | 5 | |||||
| Femur area | 3 | 1 | 4 | ||||
| Femur length | 3 | 1 | 4 | ||||
| Gizzard weight | 12 | 5 | 17 | ||||
| Head weight | 1 | 1 | |||||
| Heart weight | 12 | 5 | 17 | ||||
| Liver weight | 12 | 5 | 17 | ||||
| Muscle dry matter content | 1 | 1 | |||||
| Proventriculus weight | 12 | 5 | 17 | ||||
| Shank diameter | 1 | 1 | |||||
| Shank length | 3 | 3 | 6 | ||||
| Spleen weight | 1 | 1 | |||||
| Tibia length | 3 | 2 | 5 | ||||
| Tibia weight | 3 | 2 | 5 | ||||
| Wing weight | 1 | 1 | |||||
| Yolk weight | 6 | 2 | 8 | ||||
| Reproduction | 28 | 13 | 41 | ||||
| Oviduct length | 12 | 5 | 17 | ||||
| Oviduct weight | 16 | 8 | 24 | ||||
| Total | 3 | 7 | 21 | 8 | 297 | 393 | 729 |
1 Breeds: CRW, Cornish White; PRW, Plymouth Rock White; USH, Ushanka.
Supplementary Materials
The following supporting information can be downloaded at:
References
1. Arthur, J.A.; Albers, G.A. Industrial perspective on problems and issues associated with poultry breeding. Poultry Genetics, Breeding and Biotechnology; Muir, W.M.; Aggrey, S.E. CAB International: Wallingford, UK, Cambridge, UK, 2003; pp. 1-12. [DOI: https://dx.doi.org/10.1079/9780851996608.0001]
2. Santhi, D.; Kalaikannan, A. Enrichment of chicken meat with dietary fibre sources as functional ingredients. Worlds Poult. Sci. J.; 2023; 79, pp. 783-806. [DOI: https://dx.doi.org/10.1080/00439339.2023.2239767]
3. 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)
4. 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, pp. 64-65. (In Russian)
5. Iqbal, M.A.; Hadlich, F.; Reyer, H.; Oster, M.; Trakooljul, N.; Murani, E.; Perdomo-Sabogal, A.; Wimmers, K.; Ponsuksili, S. RNA-Seq-based discovery of genetic variants and allele-specific expression of two layer lines and broiler chicken. Evol. Appl.; 2023; 16, pp. 1135-1153. [DOI: https://dx.doi.org/10.1111/eva.13557] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37360029]
6. Neeteson, A.M.; Avendaño, S.; Koerhuis, A.; Duggan, B.; Souza, E.; Mason, J.; Ralph, J.; Rohlf, P.; Burnside, T.; Kranis, A. et al. Evolutions in commercial meat poultry breeding. Animals; 2023; 13, 3150. [DOI: https://dx.doi.org/10.3390/ani13193150] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37835756]
7. Katerinich, O.O.; Tkachik, T.E.; Bondarenko, Y.V. Methodical approaches to the genetic-informational profile of the immunogenetic structure of meat and egg chickens. Sučasne Ptahìvnictvo [Mod. Poult. Farm.]; 2014; 3, pp. 12-13. (In Ukrainian with English summary)
8. Hill, W.G.; Caballero, A. Artificial selection experiments. Annu. Rev. Ecol. Syst.; 1992; 23, pp. 287-310. [DOI: https://dx.doi.org/10.1146/annurev.es.23.110192.001443]
9. Bondarenko, Y.V.; Ivanova, T.V.; Kutnyuk, P.I. Efficiency of Improving of Hens Reproductive Traits by Elimination Method of Lethal Genes. The Poultry Industry towards the 21st Century, Proceedings and Abstracts of the 10th European Poultry Conference, Jerusalem, Israel, 21–26 June 1998; World’s Poultry Science Association: Jerusalem, Israel, 1998; 65.
10. Seo, D.; Lee, D.H.; Jin, S.; Won, J.I.; Lim, D.; Park, M.; Kim, T.H.; Lee, H.K.; Kim, S.; Choi, I. et al. Long-term artificial selection of Hanwoo (Korean) cattle left genetic signatures for the breeding traits and has altered the genomic structure. Sci. Rep.; 2022; 12, 6438. [DOI: https://dx.doi.org/10.1038/s41598-022-09425-0]
11. Pettersson, M.E.; Johansson, A.M.; Siegel, P.B.; Carlborg, Ö. Dynamics of adaptive alleles in divergently selected body weight lines of chickens. G3 Genes Genomes Genet.; 2013; 3, pp. 2305-2312. [DOI: https://dx.doi.org/10.1534/g3.113.008375]
12. Altukhov, Y.P.; Zakharov, I.A.; Stolpovskiy, Y.A.; Salmenkova, E.A.; Evsyukov, A.N.; Moiseyeva, I.G. Dynamics of animal population gene pools. Dinamika Populyatsionnykh Genofondov pri Antropogennykh Vozdeistviyakh [Dynamics of Population Gene Pools under Anthropogenic Pressures]; Altukhov, Y.P. Nauka: Moscow, Russia, 2004; Chapter 3 pp. 110-294. Available online: https://search.rsl.ru/en/record/01002561306 (accessed on 26 March 2024). (In Russian)
13. Tkachik, T.E.; Kutnyuk, P.I.; Bondarenko, Y.V. Genetic load in land poultry populations. Ptakhivnytstvo [Poultry Farm.]; 2005; 57, pp. 94-98. (In Russian with English summary)
14. Khvostik, V.P.; Bondarenko, Y.V. The level of genetic load in meat and egg chickens of different origin. Vìsn. Sumsʹkogo Nac. Agrar. Univ. Ser. Tvarynnytstvo [Bull. Sumy Natl. Agrar. Univ. Ser. Livest.]; 2011; 7, pp. 38-39. (In Ukrainian)
15. Khvostik, V.P.; Bondarenko, Y.V. Hereditary loads in the chicken population domestic gene pool. Vìsn. Sumsʹkogo Nac. Agrar. Univ. Ser. Tvarynnytstvo [Bull. Sumy Natl. Agrar. Univ. Ser. Livest.]; 2016; 7, pp. 112-114. (In Ukrainian with English summary)
16. Siegel, P.B.; Honaker, C.F.; Andersson, L. Research Note: Phenotypic trends for the multigenerational advanced intercross of the Virginia body weight lines of chickens. Poult. Sci.; 2024; 103, 103480. [DOI: https://dx.doi.org/10.1016/j.psj.2024.103480] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38330887]
17. Le Rouzic, A.; Siegel, P.B.; Carlborg, Ö. Phenotypic evolution from genetic polymorphisms in a radial network architecture. BMC Biol.; 2007; 5, 50. [DOI: https://dx.doi.org/10.1186/1741-7007-5-50] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18001473]
18. Sheng, Z.; Pettersson, M.E.; Honaker, C.F.; Siegel, P.B.; Carlborg, Ö. Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment. Genome Biol.; 2015; 16, 219. [DOI: https://dx.doi.org/10.1186/s13059-015-0785-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26438066]
19. Lillie, M.; Honaker, C.F.; Siegel, P.B.; Carlborg, Ö. Bidirectional selection for body weight on standing genetic variation in a chicken model. G3 Genes Genomes Genet.; 2019; 9, pp. 1165-1173. [DOI: https://dx.doi.org/10.1534/g3.119.400038] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30737239]
20. Rönneburg, T.; Zan, Y.; Honaker, C.F.; Siegel, P.B.; Carlborg, Ö. Low-coverage sequencing in a deep intercross of the Virginia body weight lines provides insight to the polygenic genetic architecture of growth: Novel loci revealed by increased power and improved genome-coverage. Poult. Sci.; 2023; 102, 102203. [DOI: https://dx.doi.org/10.1016/j.psj.2022.102203] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36907123]
21. Moiseyeva, I.G.; Bannikova, L.V.; Altukhov, Y.P. State of poultry breeding in Russia: Genetic monitoring. Mezhdunarodnyi Sel’skokhozyaystvennyi Zhurnal [Int. Agron. J.]; 1993; 5–6, pp. 66-69. (In Russian)
22. Bondarenko, Y.V.; Kutnyuk, P.I. Some Results of Genetic Monitoring of Embryonic Defects in Poultry Populations. Proceedings of the Gene Pool of Animal Breeds and Methods of Its Use, Materials of the International Scientific and Practical Conference Dedicated to the 110th Anniversary of the Birth of Academician N.D. Potemkin; Kharkov, Ukraine, 5–6 December 1995; Ministry of Agriculture and Food of Ukraine, Kharkov Zooveterinary Institute, RIO KhZVI: Kharkov, Ukraine, 1995; pp. 63-64. (In Russian)
23. Bondarenko, Y.V.; Podstreshny, A.P. Genetic Monitoring of Chicken Populations. Proceedings of the Abstracts of the 2nd International Conference on Molecular Genetic Markers of Animals; Kiev, Ukraine, 15–17 May 1996; Agrarna Nauka: Kiev, Ukraine, 1996; pp. 47-48. (In Russian)
24. Ryabokon, Y.O.; Mykytyuk, D.M.; Frolov, V.V.; Katerynych, O.O.; Bondarenko, Y.V.; Mosyakina, T.V.; Gadyuchko, O.T.; Kovalenko, G.T.; Bogatyr, V.P.; Lyuty, Y.S. Catalog of Poultry Breeding Resources of Ukraine; Ryabokon, Y.O. Poultry Research Institute: Kharkiv, Ukraine, 2005; (In Ukrainian)
25. Zakharov-Gesekhus, I.A.; Stolpovsky, Y.A.; Ukhanov, S.V.; Moiseyeva, I.G.; Sulimova, G.E. Monitoring the gene pools of animal populations in connection with selection tasks and the study of phylogeny. Farm Animals; Russian Academy of Sciences: Moscow, Russia, 2007; pp. 122-124. (In Russian)
26. Khvostyk, V.P.; Bondarenko, Y.V. Information and statistical parameters of live weight the chickens of domestic a gene pool. Vìsn. Sumsʹkogo Nac. Agrar. Univ. Ser. Tvarynnytstvo [Bull. Sumy Natl. Agrar. Univ. Ser. Livest.]; 2017; 5/1, pp. 161-165. (In Ukrainian with English summary)
27. Prituzhalova, A.O.; Volkova, N.A.; Kuzmina, T.I.; Vetokh, A.N.; Dzhagaev, A.Y. Monitoring of indicators of chromatin status in quails ovarian follicles granulosa cells of different directions of productivity. Agrar. Nauka [Agric. Sci.]; 2023; 368, pp. 53-57. (In Russian with English summary) [DOI: https://dx.doi.org/10.32634/0869-8155-2023-368-3-53-57]
28. van Arendonk, J.A.M.; Bovenhuis, H. Designs and methods to detect QTL for production traits based on mapped genetic markers. Poultry Genetics, Breeding and Biotechnology; Muir, W.M.; Aggrey, S.E. CAB International: Wallingford, UK, Cambridge, UK, 2003; pp. 439-464. [DOI: https://dx.doi.org/10.1079/9780851996608.0439]
29. Bondarenko, Y.V. Genetic principles for the creation and use of autosexing crosses of chickens. Naučno-Tehničeskij Bûlletenʹ—Ukrainskij Naučno-Issledovatelʹskij Institut Pticevodstva; 1982; 12, pp. 8-10. (In Russian)
30. Khvostyk, V.; Tereshchenko, O.; Zakharchenko, O.; Bondarenko, Y. Influence of «adding blood» of cocks of foreign crosses upon economically beneficial attributes of meat-egg hens of domestic selection. Vìsnik Agrar. Nauki [Bull. Agric. Sci.]; 2017; 95, pp. 44-48. [DOI: https://dx.doi.org/10.31073/agrovisnyk201709-08]
31. FAD PreP. Poultry Industry Manual. Foreign Animal Disease Preparedness & Response Plan (FAD PreP). Center for Food Security and Public Health; Iowa State University of Science and Technology: Ames, IA, USA, 2013; Available online: https://www.cfsph.iastate.edu/pdf/fad-prep-nahems-poultry-industry-manual (accessed on 26 March 2024).
32. de Jong, I.C.; van Emous, R.A. Broiler breeding flocks: Management and animal welfare. Achieving Sustainable Production of Poultry Meat, Volume 3: Health and Welfare; Applegate, T. Burleigh Dodds Science Publishing: London, UK, 2017; pp. 1-19. [DOI: https://dx.doi.org/10.19103/AS.2016.0011.26]
33. Bondarenko, Y.V.; Sergheyeva, V.D.; Kuranova, E.N.; Krasnozhon, S.A.; Romanov, M.N. Autosexing maternal form of meat-type chickens. Ptitsevodstvo [Poultry Farm.]; 1987; 40, pp. 6-11. (In Russian)
34. Romanov, M.N.; Sazanov, A.A.; Moiseyeva, I.G.; Smirnov, A.F. Poultry. Genome Mapping and Genomics in Animals, Volume 3: Genome Mapping and Genomics in Domestic Animals; Cockett, N.E.; Kole, C. Springer: Berlin/Heidelberg, Germany, 2009; pp. 75-141. [DOI: https://dx.doi.org/10.1007/978-3-540-73835-0_5]
35. Bondarenko, Y.V.; Ostapenko, V.I.; Ali, O.H.; Bulchenko, I.A.; Shubin, P.I. Sexual dimorphism and sex determination of broilers of the Ross 308 cross. Ptakhivnytstvo [Poultry Farm.]; 2013; 69, pp. 51-54. (In Ukrainian with English summary)
36. Khvostyk, V.P.; Bondarenko, Y.V. Genetic features of meat and egg chickens of the improved population. Vìsn. Sumsʹkogo Nac. Agrar. Univ. Ser. Tvarynnytstvo [Bull. Sumy Natl. Agrar. Univ. Ser. Livest.]; 2013; 7, pp. 200-203. (In Ukrainian with English summary)
37. Katerynych, O.O.; Bondarenko, Y.V.; Pankova, S.M.; Ruda, S.V.; Khvostyk, V.P.; Zakharchenko, O.P. Production line G2 of meat and egg chickens of the breed of Plymutrok white. Ahrarna Nauka—Vyrobnytstvu [Agrar. Sci. Prod.]; 2016; 4, 32.(In Ukrainian with English summary)
38. Khvostik, V.P.; Bondarenko, Y.V. Growth intensity of the meat and egg chickens of different genetic origin. Vìsn. Sumsʹkogo Nac. Agrar. Univ. Ser. Tvarynnytstvo [Bull. Sumy Natl. Agrar. Univ. Ser. Livest.]; 2021; 3, pp. 91-94. (In Ukrainian with English summary)
39. 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. 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. Iowa State University, Department of Animal Science: Ames, IA, USA, 1999; 155.Available online: https://web.archive.org/web/20050314091227/http://www.agbiotechnet.com/proceedings/jaylush.asp#15 (accessed on 14 March 2005).
40. 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. Proceedings of the Poultry Genetics Symposium; Mariensee, Germany, 6–8 October 1999; Preisinger, R. Working Group 3 of WPSA, Lohmann Tierzucht: Cuxhaven, Germany, 1999; 113.
41. 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.Available online: http://www.jtmtg.org/JAM/2001/jointabs/iaafsc18.pdf (accessed on 26 March 2024).
42. 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), pp. S113-S114. [DOI: https://dx.doi.org/10.1080/00071660152681692]
43. Zhang, H.; Hunt, H.D.; Cheng, H.H.; Dodgson, J.B.; Romanov, M.N.; Bacon, L.D. Identification and Evaluation of SNPs at the 3’ End of the Tva Gene Segregating Among ALSV Resistance and Susceptible Lines of Chickens. Proceedings of the International Plant and Animal Genome XIII Conference; San Diego, CA, USA, 15–19 January 2005; Scherago International: San Diego, CA, USA, 2005; 123.Available online: https://www.ars.usda.gov/research/publications/publication/?seqNo115=170524 (accessed on 26 March 2024).
44. Yang, X.; Sun, J.; Zhao, G.; Li, W.; Tan, X.; Zheng, M.; Feng, F.; Liu, D.; Wen, J.; Liu, R. Identification of major loci and candidate genes for meat production-related traits in broilers. Front. Genet.; 2021; 12, 645107. [DOI: https://dx.doi.org/10.3389/fgene.2021.645107] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33859671]
45. Fang, X.; Ye, H.; Zhang, S.; Guo, L.; Xu, Y.; Zhang, D.; Nie, Q. Investigation of potential genetic factors for growth traits in yellow-feather broilers using weighted single-step genome-wide association study. Poult. Sci.; 2023; 102, 103034. [DOI: https://dx.doi.org/10.1016/j.psj.2023.103034]
46. Kim, M.; Munyaneza, J.P.; Cho, E.; Jang, A.; Jo, C.; Nam, K.C.; Choo, H.J.; Lee, J.H. Genome-wide association study on the content of nucleotide-related compounds in Korean native chicken breast meat. Animals; 2023; 13, 2966. [DOI: https://dx.doi.org/10.3390/ani13182966]
47. Plemyashov, K.V.; Smaragdov, M.G.; Romanov, M.N. Genomic Assessment of Breeding Bulls. Proceedings of the Molecular Genetic Technologies for Analysis of Gene Expression Related to Animal Productivity and Disease Resistance, Materials of the 3rd International Scientific and Practical Conference; Moscow, Russia, 30 September 2021; Pozyabin, S.V.; Kochish, I.I.; Romanov, M.N. Ministry of Agriculture of the Russian Federation; Federal State Budgetary Educational Institution of Higher Education “Moscow State Academy of Veterinary Medicine and Biotechnology—MVA Named after K.I. Scriabin”; Sel’skokhozyaistvennye Tekhnologii: Moscow, Russia, 2021; pp. 363-367. (In Russian with English summary) [DOI: https://dx.doi.org/10.18720/SPBPU/2/z21-43]
48. Plemyashov, K.V.; Smaragdov, M.G.; Romanov, M.N. Molecular Genetic Polymorphism in Animal Populations and Its Application in Intensive Breeding of Dairy Cattle—A Review. Proceedings of the Molecular Genetic Technologies for Analysis of Gene Expression Related to Animal Productivity and Disease Resistance, Materials of the 3rd International Scientific and Practical Conference; Moscow, Russia, 30 September 2021; Pozyabin, S.V.; Kochish, I.I.; Romanov, M.N. Ministry of Agriculture of the Russian Federation; Federal State Budgetary Educational Institution of Higher Education “Moscow State Academy of Veterinary Medicine and Biotechnology—MVA Named after K.I. Scriabin”; Sel’skokhozyaistvennye Tekhnologii: Moscow, Russia, 2021; pp. 368-378. (In Russian with English summary) [DOI: https://dx.doi.org/10.18720/SPBPU/2/z21-43]
49. 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. [DOI: https://dx.doi.org/10.1186/s12863-021-01017-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34979907]
50. 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, pp. 1166-1174. [DOI: https://dx.doi.org/10.15389/agrobiology.2017.6.1166eng]
51. 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), pp. 123-124. [DOI: https://dx.doi.org/10.1111/rda.13300]
52. Dementieva, N.V.; Mitrofanova, O.V.; Kudinov, A.A.; Smaragdov, M.G.; Yakovlev, A.F.; Romanov, M.N. Opportunities of SNP-genotyping for Studying the Features of the Genetic Architecture of Chicken Populations with Different Histories. World and Russian Trends in Development of Poultry Industry: Realities and Challenges of Future, Proceedings of the 19th International Conference; Sergiyev Posad, Russia, 15–17 May 2018; Fisinin, V.I. World’s Poultry Science Association, Russian Branch; All-Russian Research and Technological Poultry Institute: Sergiyev Posad, Russia, 2018; pp. 80-81. Available online: https://www.researchgate.net/publication/371280603_Opportunities_of_SNP-genotyping_for_studying_the_features_of_the_genetic_architecture_of_chicken_populations_with_different_histories_Vozmoznosti_SNP-genotipirovania_dla_izucenia_osobennostej_genetice (accessed on 26 March 2024). (In Russian)
53. Dementieva, N.V.; Kudinov, A.A.; Larkina, T.A.; Mitrofanova, O.V.; Dysin, A.P.; Terletsky, V.P.; Tyshchenko, V.I.; Griffin, D.K.; Romanov, M.N. Genetic variability in local and imported germplasm chicken populations as revealed by analyzing runs of homozygosity. Animals; 2020; 10, 1887. [DOI: https://dx.doi.org/10.3390/ani10101887] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33076516]
54. Dementieva, N.V.; Mitrofanova, O.V.; Dysin, A.P.; Kudinov, A.A.; Stanishevskaya, O.I.; Larkina, T.A.; Plemyashov, K.V.; Griffin, D.K.; Romanov, M.N.; Smaragdov, M.G. Assessing the effects of rare alleles and linkage disequilibrium on estimates of genetic diversity in the chicken populations. Animal; 2021; 15, 100171. [DOI: https://dx.doi.org/10.1016/j.animal.2021.100171] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33563558]
55. Dementieva, N.V.; Shcherbakov, Y.S.; Tyshchenko, V.I.; Terletsky, V.P.; Vakhrameev, A.B.; Nikolaeva, O.A.; Ryabova, A.E.; Azovtseva, A.I.; Mitrofanova, O.V.; Peglivanyan, G.K. et al. Comparative analysis of molecular RFLP and SNP markers in assessing and understanding the genetic diversity of various chicken breeds. Genes; 2022; 13, 1876. [DOI: https://dx.doi.org/10.3390/genes13101876] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36292761]
56. Dementieva, N.V.; Shcherbakov, Y.S.; Stanishevskaya, O.I.; Vakhrameev, A.B.; Larkina, T.A.; Dysin, A.P.; Nikolaeva, O.A.; Ryabova, A.E.; Azovtseva, A.I.; Mitrofanova, O.V. et al. Large-scale genome-wide SNP analysis reveals the rugged (and ragged) landscape of global ancestry, phylogeny and demographic history in chicken breeds. J. Zhejiang Univ. Sci. B; 2024; 25, pp. 324-340. [DOI: https://dx.doi.org/10.1631/jzus.B2300443] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38584094]
57. 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. [DOI: https://dx.doi.org/10.1186/s12864-019-5605-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30947682]
58. 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. [DOI: https://dx.doi.org/10.3390/biology10090876] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34571753]
59. Larkina, T.A.; Barkova, O.Y.; Peglivanyan, G.K.; Mitrofanova, O.V.; Dementieva, N.V.; Stanishevskaya, O.I.; Vakhrameev, A.B.; Makarova, A.V.; Shcherbakov, Y.S.; Pozovnikova, M.V. et al. Evolutionary subdivision of domestic chickens: Implications for local breeds as assessed by phenotype and genotype in comparison to commercial and fancy breeds. Agriculture; 2021; 11, 914. [DOI: https://dx.doi.org/10.3390/agriculture11100914]
60. Larkina, T.A.; Romanov, M.N.; Barkova, O.Y.; Peglivanyan, G.K.; Mitrofanova, O.V.; Dementieva, N.V.; Stanishevskaya, O.I.; Vakhrameev, A.B.; Makarova, A.V.; Shcherbakov, Y.S. et al. Genetic Variation of the NCAPG-LCORL Locus in Chickens of Local Breeds Based on SNP Genotyping Data. Proceedings of the Molecular Genetic Technologies for Analysis of Gene Expression Related to Animal Productivity and Disease Resistance, Materials of the 3rd International Scientific and Practical Conference; Moscow, Russia, 30 September 2021; Pozyabin, S.V.; Kochish, I.I.; Romanov, M.N. Ministry of Agriculture of the Russian Federation; Federal State Budgetary Educational Institution of Higher Education “Moscow State Academy of Veterinary Medicine and Biotechnology—MVA Named after K.I. Scriabin”; Sel’skokhozyaistvennye tekhnologii: Moscow, Russia, 2021; pp. 133-146. (In Russian with English summary) [DOI: https://dx.doi.org/10.18720/SPBPU/2/z21-43]
61. Romanov, M.N.; Dementyeva, N.V.; Terletsky, V.P.; Plemyashov, K.V.; Stanishevskaya, O.I.; Kudinov, A.A.; Perinek, O.Y.; Fedorova, E.S.; Larkina, T.A.; Pleshanov, N.V. Applying SNP Array Technology to Assess Genetic Diversity in Russian Gene Pool of Chickens. Proceedings of the International Plant and Animal Genome XXV Conference; San Diego, CA, USA, 14–18 January 2017; Scherago International: San Diego, CA, USA, 2017; Abstract P0115 Available online: https://pag.confex.com/pag/xxv/webprogram/Paper23948.html (accessed on 26 March 2024).
62. Romanov, M.N.; Abdelmanova, A.S.; Fisinin, V.I.; Gladyr, E.A.; Volkova, N.A.; Koshkina, O.A.; Rodionov, A.N.; Vetokh, A.N.; Gusev, I.V.; Anshakov, D.V. et al. Selective footprints and genes relevant to cold adaptation and other phenotypic traits are unscrambled in the genomes of divergently selected chicken breeds. J. Anim. Sci. Biotechnol.; 2023; 14, 35. [DOI: https://dx.doi.org/10.1186/s40104-022-00813-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36829208]
63. Romanov, M.N.; Abdelmanova, A.S.; Fisinin, V.I.; Gladyr, E.A.; Volkova, N.A.; Anshakov, D.V.; Stanishevskaya, O.I.; Vakhrameev, A.B.; Dotsev, A.V.; Griffin, D.K. et al. Whole genome screening procures a holistic hold of the Russian chicken gene pool heritage and demographic history. Biology; 2023; 12, 979. [DOI: https://dx.doi.org/10.3390/biology12070979] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37508409]
64. Khalkhali-Evrigh, R.; Hedayat, N.; Ming, L.; Jirimutu,. Identification of selection signatures in Iranian dromedary and Bactrian camels using whole genome sequencing data. Sci. Rep.; 2022; 12, 9653. [DOI: https://dx.doi.org/10.1038/s41598-022-14376-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35688969]
65. Yang, K.; Zhang, J.; Zhao, Y.; Shao, Y.; Zhai, M.; Liu, H.; Zhang, L. Whole genome resequencing revealed the genetic relationship and selected regions among Baicheng-You, Beijing-You, and European-origin broilers. Biology; 2023; 12, 1397. [DOI: https://dx.doi.org/10.3390/biology12111397] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37997996]
66. Vodyanov, A.D. Poultry Farming; SGC Smena—Branch of the Federal Research Centre “All-Russian Research and Technological Poultry Institute” of the Russian Academy of Sciences: Bereznyaki, Moscow Oblast, Russia, 2022; Available online: https://spsmena.ru/ptitsievodstvo (accessed on 26 March 2024). (In Russian)
67. Kochish, I.I.; Titov, V.Y.; Nikonov, I.N.; Brazhnik, E.A.; Vorobyov, N.I.; Korenyuga, M.V.; Myasnikova, O.V.; Dolgorukova, A.M.; Griffin, D.K.; Romanov, M.N. Unraveling signatures of chicken genetic diversity and divergent selection in breed specific patterns of early myogenesis, nitric oxide metabolism and post-hatch growth. Front. Genet.; 2023; 13, 1092242. [DOI: https://dx.doi.org/10.3389/fgene.2022.1092242] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36712856]
68. Bratyshko, N.I.; Prytulenko, O.V.; Lemesheva, M.M.; Tereshchenko, O.V. Recommendations on Rationing of Feeding of Agricultural Poultry; 3rd ed. Tereshchenko, O.V. Poultry Research Institute, NAAS, NTMT: Birky, Ukraine, 2010; (In Ukrainian)
69. Katerynych, O.O.; Pankova, S.M.; Tereshchenko, O.V.; Ruda, S.V.; Havilei, O.V.; Riabinina, O.V.; Muzyka, N.M.; Ionov, I.A. Rearing, Maintenance and Feeding of Egg and Meat-Egg Hens: Scientific and Practical Guide; Poultry Research Institute, DDSP NAAS: Birky, Ukraine, 2017; (In Ukrainian)
70. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics; 2018; 34, pp. i884-i890. [DOI: https://dx.doi.org/10.1093/bioinformatics/bty560]
71. Andrews, S. FastQC: A Quality Control Tool for High throughput Sequence Data, Version 0.10.1; Bioinformatics Group, Babraham Institute: Cambridge, UK, 2012; Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (accessed on 26 March 2024).
72. Vasimuddin, M.; Misra, S.; Li, H.; Aluru, S. Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems. Proceedings of the 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS); Rio de Janeiro, Brazil, 20–24 May 2019; IEEE: Rio de Janeiro, Brazil, 2019; pp. 314-324. [DOI: https://dx.doi.org/10.1109/IPDPS.2019.00041]
73. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv; 2013; [DOI: https://dx.doi.org/10.48550/arXiv.1303.3997] arXiv: 1303.3997v2 [q-bio.GN]
74. GRCg7b. Genome Assembly bGalGal1.mat.broiler.GRCg7b (Reference); National Library of Medicine, National Center for Biotechnology Information: Bethesda, MD, USA, 2021; Available online: https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_016699485.2/ (accessed on 26 March 2024).
75. Huang, Z.; Xu, Z.; Bai, H.; Huang, Y.; Kang, N.; Ding, X.; Liu, J.; Luo, H.; Yang, C.; Chen, W. et al. Evolutionary analysis of a complete chicken genome. Proc. Natl. Acad. Sci. USA; 2023; 120, e2216641120. [DOI: https://dx.doi.org/10.1073/pnas.2216641120]
76. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R. 1000 Genome Project Data Processing Subgroup. The sequence alignment/map format and SAMtools. Bioinformatics; 2009; 25, pp. 2078-2079. [DOI: https://dx.doi.org/10.1093/bioinformatics/btp352] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19505943]
77. Danecek, P.; Bonfield, J.K.; Liddle, J.; Marshall, J.; Ohan, V.; Pollard, M.O.; Whitwham, A.; Keane, T.; McCarthy, S.A.; Davies, R.M. et al. Twelve years of SAMtools and BCFtools. GigaScience; 2021; 10, giab008. [DOI: https://dx.doi.org/10.1093/gigascience/giab008]
78. Li, H. Tabix: Fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics; 2011; 27, pp. 718-719. [DOI: https://dx.doi.org/10.1093/bioinformatics/btq671] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21208982]
79. Tange, O. GNU Parallel 20210822 (‘Kabul’); Zenodo, CERN (European Organization for Nuclear Research), IT Department, Digital Repositories Section: Genève, Switzerland, 2021; [DOI: https://dx.doi.org/10.5281/zenodo.5233953]
80. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.R.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.W.; Daly, M.J. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet.; 2007; 81, pp. 559-575. [DOI: https://dx.doi.org/10.1086/519795] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17701901]
81. Chang, C.C.; Chow, C.C.; Tellier, L.C.; Vattikuti, S.; Purcell, S.M.; Lee, J.J. Second-generation PLINK: Rising to the challenge of larger and richer datasets. Gigascience; 2015; 4, 7. [DOI: https://dx.doi.org/10.1186/s13742-015-0047-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25722852]
82. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2009; [DOI: https://dx.doi.org/10.1007/978-0-387-98141-3] ISBN 978-0-387-98141-3
83. Keenan, K.; McGinnity, P.; Cross, T.F.; Crozier, W.W.; Prodohl, P.A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol.; 2013; 4, pp. 782-788. [DOI: https://dx.doi.org/10.1111/2041-210X.12067]
84. Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics; 1978; 89, pp. 583-590. [DOI: https://dx.doi.org/10.1093/genetics/89.3.583] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17248844]
85. Kalinowski, S.T. Counting alleles with rarefaction: Private alleles and hierarchical sampling designs. Conserv. Genet.; 2004; 5, pp. 539-543. [DOI: https://dx.doi.org/10.1023/B:COGE.0000041021.91777.1a]
86. Alexander, D.H.; Novembre, J.; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res.; 2009; 19, pp. 1655-1664. [DOI: https://dx.doi.org/10.1101/gr.094052.109]
87. Alexander, D.H.; Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinform.; 2011; 12, 246. [DOI: https://dx.doi.org/10.1186/1471-2105-12-246] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21682921]
88. Milanesi, M.; Capomaccio, S.; Vajana, E.; Bomba, L.; Garcia, J.F.; Ajmone-Marsan, P.; Colli, L. BITE: An R package for biodiversity analyses. bioRxiv; 2017; 181610. [DOI: https://dx.doi.org/10.1101/181610]
89. Weir, B.S.; Cockerham, C.C. Estimating F-statistics for the analysis of population structure. Evolution; 1984; 38, pp. 1358-1370. [DOI: https://dx.doi.org/10.2307/2408641]
90. Ravinet, M.; Meier, J. Calculating LD decay. Speciation & Population Genomics: A How-to-Guide; Wellcome Sanger Institute, University of Cambridge, University of Nottingham: Nottingham, UK, 2024; Available online: https://speciationgenomics.github.io/ld_decay/ (accessed on 26 March 2024).
91. Yin, X.; Martinez, A.S.; Sepúlveda, M.S.; Christie, M.R. Rapid genetic adaptation to recently colonized environments is driven by genes underlying life history traits. BMC Genom.; 2021; 22, 269. [DOI: https://dx.doi.org/10.1186/s12864-021-07553-x]
92. Fariello, M.I.; Boitard, S.; Naya, H.; SanCristobal, M.; Servin, B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics; 2013; 193, pp. 929-941. [DOI: https://dx.doi.org/10.1534/genetics.112.147231]
93. Bonhomme, M.; Chevalet, C.; Servin, B.; Boitard, S.; Abdallah, J.; Blott, S.; Sancristobal, M. Detecting selection in population trees: The Lewontin and Krakauer test extended. Genetics; 2010; 186, pp. 241-262. [DOI: https://dx.doi.org/10.1534/genetics.110.117275] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20855576]
94. Scheet, P.; Stephens, M. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet.; 2006; 78, pp. 629-644. [DOI: https://dx.doi.org/10.1086/502802]
95. Pei, Y.F.; Li, J.; Zhang, L.; Papasian, C.J.; Deng, H.W. Analyses and comparison of accuracy of different genotype imputation methods. PLoS ONE; 2008; 3, e3551. [DOI: https://dx.doi.org/10.1371/journal.pone.0003551] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18958166]
96. Johnson, N. Genotype Prediction with SVMs. CS 229 Machine Learning Final Projects, Autumn 2008; Stanford University: Stanford, CA, USA, 2008; Available online: http://cs229.stanford.edu/proj2008/Johnson-GenotypePredictionWithSVMs.pdf (accessed on 26 March 2024).
97. Moser, G.; Khatkar, M.S.; Raadsma, H.W. Imputation of Missing Genotypes in High Density SNP Data. Proceedings of the 18th Conference of the Association for the Advancement of Animal Breeding and Genetics; Brossa Valley, Australia, 28 September –1 October 2009; Association for the Advancement of Animal Breeding and Genetics: Brossa Valley, Australia, 2009; pp. 612-615.
98. Yu, X.; Li, X.; Guo, T.; Zhu, C.; Wu, Y.; Mitchell, S.E.; Roozeboom, K.L.; Wang, D.; Wang, M.L.; Pederson, G.A. et al. Genomic prediction contributing to a promising global strategy to turbocharge gene banks. Nat. Plants; 2016; 2, 16150. [DOI: https://dx.doi.org/10.1038/nplants.2016.150]
99. Marras, G.; Gaspa, G.; Sorbolini, S.; Dimauro, C.; Ajmone-Marsan, P.; Valentini, A.; Williams, J.L.; Macciotta, N.P.P. Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy. Anim. Genet.; 2014; 46, pp. 110-121. [DOI: https://dx.doi.org/10.1111/age.12259] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25530322]
100. Dixit, S.P.; Singh, S.; Ganguly, I.; Bhatia, A.K.; Sharma, A.; Kumar, N.A.; Dang, A.K.; Jayakumar, S. Genome-wide runs of homozygosity revealed selection signatures in Bos indicus. Front. Genet.; 2020; 11, 92. [DOI: https://dx.doi.org/10.3389/fgene.2020.00092]
101. Cesarani, A.; Gaspa, G.; Pauciullo, A.; Degano, L.; Vicario, D.; Macciotta, N.P.P. Genome-wide analysis of homozygosity regions in european simmental bulls. J. Anim. Breed. Genet.; 2021; 138, pp. 69-79. [DOI: https://dx.doi.org/10.1111/jbg.12502]
102. Li, D.; Huang, M.; Zhuang, Z.; Ding, R.; Gu, T.; Hong, L.; Zheng, E.; Li, Z.; Cai, G.; Wu, Z. et al. Genomic analyses revealed the genetic difference and potential selection genes of growth traits in two Duroc lines. Front. Vet. Sci.; 2021; 8, 725367. [DOI: https://dx.doi.org/10.3389/fvets.2021.725367]
103. Biscarini, F.; Paolo Cozzi, P.; Gaspa, G.; Marras, G. detectRUNS: Detect Runs of Homozygosity and Runs of Heterozygosity in Diploid Genomes. R Package Version 0.9.6. The Comprehensive R Archive Network (CRAN); Institute for Statistics and Mathematics, Vienna University of Economics and Business: Vienna, Austria, 2019; Available online: https://cran.r-project.org/web/packages/detectRUNS/index.html (accessed on 26 March 2024).
104. Dzomba, E.F.; Chimonyo, M.; Pierneef, R.; Muchadeyi, F.C. Runs of homozygosity analysis of South African sheep breeds from various production systems investigated using OvineSNP50k data. BMC Genom.; 2021; 22, 7. [DOI: https://dx.doi.org/10.1186/s12864-020-07314-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33407115]
105. Fedorova, E.S.; Dementieva, N.V.; Shcherbakov, Y.S.; Stanishevskaya, O.I. Identification of key candidate genes in runs of homozygosity of the genome of two chicken breeds, associated with cold adaptation. Biology; 2022; 11, 547. [DOI: https://dx.doi.org/10.3390/biology11040547]
106. Howrigan, D.P.; Simonson, M.A.; Keller, M.C. Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms. BMC Genom.; 2011; 12, 460. [DOI: https://dx.doi.org/10.1186/1471-2164-12-460]
107. Ferenčaković, M.; Hamzić, E.; Gredler, B.; Solberg, T.R.; Klemetsdal, G.; Curik, I.; Sölkner, J. Estimates of autozygosity derived from runs of homozygosity: Empirical evidence from selected cattle populations. J. Anim. Breed. Genet.; 2013; 130, pp. 286-293. [DOI: https://dx.doi.org/10.1111/jbg.12012] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23855630]
108. Bizarria dos Santos, W.; Pimenta Schettini, G.; Fonseca, M.G.; Pereira, G.L.; Loyola Chardulo, L.A.; Rodrigues Machado Neto, O.; Baldassini, W.A.; Nunes de Oliveira, H.; Abdallah Curi, R. Fine-scale estimation of inbreeding rates, runs of homozygosity and genome-wide heterozygosity levels in the Mangalarga Marchador horse breed. J. Anim. Breed. Genet.; 2021; 138, pp. 161-173. [DOI: https://dx.doi.org/10.1111/jbg.12508]
109. Abdoli, R.; Mirhoseini, S.Z.; Ghavi Hossein-Zadeh, N.; Zamani, P.; Moradi, M.H.; Ferdosi, M.H.; Sargolzaei, M.; Gondro, C. Runs of homozygosity and cross-generational inbreeding of Iranian fat-tailed sheep. Heredity; 2023; 130, pp. 358-367. [DOI: https://dx.doi.org/10.1038/s41437-023-00611-y]
110. Chessari, G.; Criscione, A.; Marletta, D.; Crepaldi, P.; Portolano, B.; Manunza, A.; Cesarani, A.; Biscarini, F.; Mastrangelo, S. Characterization of heterozygosity-rich regions in Italian and worldwide goat breeds. Sci. Rep.; 2024; 14, 3. [DOI: https://dx.doi.org/10.1038/s41598-023-49125-x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38168531]
111. Lencz, T.; Lambert, C.; DeRosse, P.; Burdick, K.E.; Morgan, T.V.; Kane, J.M.; Kucherlapati, R.; Malhotra, A.K. Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. Proc. Natl. Acad. Sci. USA; 2007; 104, pp. 19942-19947. [DOI: https://dx.doi.org/10.1073/pnas.0710021104] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18077426]
112. Purfield, D.C.; Berry, D.P.; McParland, S.; Bradley, D.G. Runs of homozygosity and population history in cattle. BMC Genet.; 2012; 13, 70. [DOI: https://dx.doi.org/10.1186/1471-2156-13-70]
113. Keller, M.C.; Visscher, P.M.; Goddard, M.E. Quantification of inbreeding due to distant ancestors and its detection using dense single nucleotide polymorphism data. Genetics; 2011; 189, pp. 237-249. [DOI: https://dx.doi.org/10.1534/genetics.111.130922]
114. Bjelland, D.W.; Weigel, K.A.; Vukasinovic, N.; Nkrumah, J.D. Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J. Dairy Sci.; 2013; 96, pp. 4697-4706. [DOI: https://dx.doi.org/10.3168/jds.2012-6435] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23684028]
115. Toro-Ospina, A.M.; Herrera Rios, A.C.; Pimenta Schettini, G.; Vallejo Aristizabal, V.H.; Bizarria dos Santos, W.; Zapata, C.A.; Ortiz Morea, E.G. Identification of runs of homozygosity islands and genomic estimated inbreeding values in Caqueteño Creole Cattle (Colombia). Genes; 2022; 13, 1232. [DOI: https://dx.doi.org/10.3390/genes13071232]
116. 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. [DOI: https://dx.doi.org/10.1093/database/bar030] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21785142]
117. Vitt, U.; Gietzen, D.; Stevens, K.; Wingrove, J.; Becha, S.; Bulloch, S.; Burrill, J.; Chawla, N.; Chien, J.; Crawford, M. et al. Identification of candidate disease genes by EST alignments, synteny, and expression and verification of Ensembl genes on rat chromosome 1q43-54. Genome Res.; 2004; 14, pp. 640-650. [DOI: https://dx.doi.org/10.1101/gr.1932304] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15060005]
118. Martin, F.J.; Amode, M.R.; Aneja, A.; Austine-Orimoloye, O.; Azov, A.G.; Barnes, I.; Becker, A.; Bennett, R.; Berry, A.; Bhai, J. et al. Ensembl 2023. Nucleic Acids Res.; 2023; 51, pp. D933-D941. [DOI: https://dx.doi.org/10.1093/nar/gkac958] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36318249]
119. Harrison, P.W.; Amode, M.R.; Austine-Orimoloye, O.; Azov, A.G.; Barba, M.; Barnes, I.; Becker, A.; Bennett, R.; Berry, A.; Bhai, J. et al. Ensembl 2024. Nucleic Acids Res.; 2024; 52, pp. D891-D899. [DOI: https://dx.doi.org/10.1093/nar/gkad1049] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37953337]
120. Hu, Z.L.; Park, C.A.; Reecy, J.M. Bringing the Animal QTLdb and CorrDB into the future: Meeting new challenges and providing updated services. Nucleic Acids Res.; 2022; 50, pp. D956-D961. [DOI: https://dx.doi.org/10.1093/nar/gkab1116]
121. Hu, Z.L.; Reecy, J.M. Animal QTLdb: Beyond a repository. A public platform for QTL comparisons and integration with diverse types of structural genomic information. Mamm. Genome; 2007; 18, pp. 1-4. [DOI: https://dx.doi.org/10.1007/s00335-006-0105-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17245610]
122. Hu, Z.L.; Fritz, E.R.; Reecy, J.M. AnimalQTLdb: A livestock QTL database tool set for positional QTL information mining and beyond. Nucleic Acids Res.; 2007; 35, pp. D604-D609. [DOI: https://dx.doi.org/10.1093/nar/gkl946]
123. Hu, Z.L.; Park, C.A.; Wu, X.L.; Reecy, J.M. Animal QTLdb: An improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res.; 2013; 41, pp. D871-D879. [DOI: https://dx.doi.org/10.1093/nar/gks1150]
124. Romanov, M.N.; Weigend, S.; Bondarenko, Y.V.; Podstreshny, A.P.; Kutnyuk, P.I.; Sakhatsky, N.I. Studies on Poultry Germplasm Diversity and Conservation in Ukraine. Proceedings of the Poultry Genetics Symposium; Mariensee, Germany, 6–8 October 1999; Preisinger, R. Working Group 3 of WPSA, Lohmann Tierzucht: Cuxhaven, Germany, 1999; 140.
125. Weigend, S.; Romanov, M.N.; Rath, D. Methodologies to Identify, Evaluate and Conserve Poultry Genetic Resources. Proceedings of the XXII World’s Poultry Congress & Exhibition: Participant List & Full Text CD + Book of Abstracts; Istanbul, Turkey, 8–13 June 2004; WPSA—Turkish Branch: Istanbul, Turkey, 2004; 84.Available online: https://www.researchgate.net/profile/Michael-Romanov-4/publication/250917228_Methodologies_to_identify_evaluate_and_conserve_poultry_genetic_resources/links/02e7e51edb9d4a883e000000/Methodologies-to-identify-evaluate-and-conserve-poultry-genetic-resources.pdf (accessed on 26 March 2024).
126. Moiseyeva, I.G. The state of poultry genetic resources in Russia. Anim. Genet. Resour.; 1996; 17, pp. 73-86. [DOI: https://dx.doi.org/10.1017/S1014233900000596]
127. Sulimova, G.E.; Stolpovsky, Y.A.; Kashtanov, S.N.; Moiseeva, I.G.; Zakharov, I.A. Methods of managing the genetic resources of domesticated animals. Fundamentals of Biological Resource Management: Collection of Scientific Articles; Rysin, L.P. Partnership of Scientific Publications KMK LLC: Moscow, Russia, 2005; pp. 331-342. ISBN 5-87317-254-4 Available online: https://elibrary.ru/item.asp?id=50435256 (accessed on 26 March 2024). (In Russian)
128. Moiseyeva, I.G.; Romanov, M.N.; Kovalenko, A.T.; Mosyakina, T.V.; Bondarenko, Y.V.; Kutnyuk, P.I.; Podstreshny, A.P.; Nikiforov, A.A. The Poltava chicken breed of Ukraine: Its history, characterization and conservation. Anim. Genet. Resour. Inf.; 2007; 40, pp. 71-78. [DOI: https://dx.doi.org/10.1017/S1014233900002212]
129. Tereshchenko, O.V.; Pankova, S.M.; Katerynych, O.O. Napriamy rozvytku haluzi ptakhivnytstva [Directions of development of poultry industry]. Vìsnik agrar. Nauki [Bull. Agric. Sci.]; 2015; 93, pp. 27-30. [DOI: https://dx.doi.org/10.31073/agrovisnyk201505-06] Available online: https://agrovisnyk.com/index.php/agrovisnyk/article/view/75 (accessed on 26 March 2024).
130. Moiseyeva, I.G.; Romanov, M.N.; Nikiforov, A.A.; Sevastyanova, A.A.; Semyenova, S.K. Evolutionary relationships of Red Jungle Fowl and chicken breeds. Genet. Sel. Evol.; 2003; 35, pp. 403-423. [DOI: https://dx.doi.org/10.1186/1297-9686-35-5-403] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12927074]
131. Harrison, S.J.; Siegel, P.B.; Honaker, C.F.; Lewis, R.M. Population dynamics of a long-term selection experiment in White Plymouth Rock chickens selected for low or high body weight. Poult. Sci.; 2023; 102, 102575. [DOI: https://dx.doi.org/10.1016/j.psj.2023.102575] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36907125]
132. Moiseyeva, I.G. Native breeds of domestic fowl. Geneticheskie nesursy sel’skokhozyajstvennykh zhivothykh: Redkie i ischezayushchie otechestvennye porody [Farm Animal Genetic Resources: Rare and Endangered Native Breeds]; Moiseyeva, I.G.; Zakharov, I.A.; Mitichashvili, R.S. Nauka: Moscow, Russia, 1992; pp. 11-112. Available online: https://agris.fao.org/search/en/providers/122621/records/647396783ed73003714cc15a (accessed on 26 March 2024). (In Russian)
133. Moiseyeva, I.; Romanov, M.; Ovsyannikova, H.; Alimov, A. Orloff chicken breed [De Orloff]. Avicult. Eur.; 2013; 9, 3.Available online: http://www.aviculture-europe.nl/nummers/13E04A03.pdf (accessed on 26 March 2024).
134. Moiseyeva, I.G.; Sevastyanova, A.A.; Aleksandrov, A.V.; Vakhrameev, A.B.; Romanov, M.N.; Dmitriev, Y.I.; Semenova, S.K.; Sulimova, G.E. Orloff chicken breed: History, current status and studies. Izv. Timiryazev. S-Kh. Akad. [Izv. Timiryazev Agric. Acad.]; 2016; 1, pp. 78-96. Available online: https://www.elibrary.ru/item.asp?id=25664565 (accessed on 26 March 2024). (In Russian with English summary)
135. Oyun, N.Y.; Moiseyeva, I.G.; Sevastianova, A.A.; Vakhrameev, A.B.; Alexandrov, A.V.; Kuzevanova, A.Y.; Alimov, A.A.; Sulimova, G.E. Mitochondrial DNA polymorphism in different populations of Spangled Orloff chickens. Genetika; 2015; 51, pp. 1057-1065. (In Russian with English summary) [DOI: https://dx.doi.org/10.1134/S1022795415090094] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26606802]
136. Oyun, N.Y.; Moiseyeva, I.G.; Sevastianova, A.A.; Vakhrameev, A.B.; Alexandrov, A.V.; Kuzevanova, A.Y.; Alimov, A.A.; Sulimova, G.E. Mitochondrial DNA polymorphism in different populations of Orloff Spangled chicken breed. Russ. J. Genet.; 2015; 51, pp. 908-915. [DOI: https://dx.doi.org/10.1134/S1022795415090094]
137. Moiseeva, I.G. Variability and heritability of some features of egg quality in Russkaya Belaya chickens. Tr. Akad. Nauk SSSR Inst. Genet.; 1964; 31, pp. 302-308. (In Russian)
138. 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, pp. 119-128. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/5896310]Available online: https://www.cabidigitallibrary.org/doi/full/10.5555/19660103382 (accessed on 26 March 2024). (In Russian)
139. 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, pp. 225-235. (In Russian)
140. 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, pp. 373-379. [DOI: https://dx.doi.org/10.15832/ankutbd.483561]
141. Tixier-Boichard, M.; Coquerelle, G.; Vilela-Lamego, C.; Weigend, S.; Barre-Dirrie, A.; Groenen, M.; Crooijmans, R.; Vignal, A.; Hillel, J.; Freidlin, P. et al. Contribution of Data on History, Management and Phenotype to the Description of the Diversity between Chicken Populations Sampled within the AVIANDIV Project. Proceedings of the Poultry Genetics Symposium; Mariensee, Germany, 6–8 October 1999; Preisinger, R. Working Group 3 of WPSA, Lohmann Tierzucht: Cuxhaven, Germany, 1999; pp. 15-21. Available online: https://jukuri.luke.fi/handle/10024/446389 (accessed on 26 March 2024).
142. Moiseeva, I. Fowl genetic resources in Russia. Ptitsevodstvo [Poultry Farm.]; 1995; 5, pp. 12-15. Available online: https://www.cabidigitallibrary.org/doi/full/10.5555/19950110592 (accessed on 26 March 2024). (In Russian)
143. Nikiforov, A.A.; Moiseyeva, I.G.; Zakharov, I.A. Position of Russian chicken breeds in the diversity of Eurasian fowl. Russ. J. Genet.; 1998; 34, pp. 702-703.
144. Moiseeva, I.G. The effect of inbreeding on the quality of fowl eggs. Genetika; 1970; 6, pp. 99-107. Available online: https://www.cabidigitallibrary.org/doi/full/10.5555/19700104311 (accessed on 26 March 2024). (In Russian)
145. Doekes, H.P.; Bijma, P.; Windig, J.J. How depressing is inbreeding? A meta-analysis of 30 years of research on the effects of inbreeding in livestock. Genes; 2021; 12, 926. [DOI: https://dx.doi.org/10.3390/genes12060926]
146. Wang, Q.; Zhang, J.; Wang, H.; Wang, Z.; Li, Q.; Zhao, G.; Zheng, M.; Wen, J. Estimates of genomic inbreeding and identification of candidate regions in Beijing-You chicken populations. Anim. Genet.; 2023; 54, pp. 155-165. [DOI: https://dx.doi.org/10.1111/age.13286]
147. Wang, H.; Wang, Q.; Tan, X.; Wang, J.; Zhang, J.; Zheng, M.; Zhao, G.; Wen, J. Estimation of genetic variability and identification of regions under selection based on runs of homozygosity in Beijing-You Chickens. Poult. Sci.; 2023; 102, 102342. [DOI: https://dx.doi.org/10.1016/j.psj.2022.102342] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36470032]
148. Yang, L.; Du, X.; Wei, S.; Gu, L.; Li, N.; Gong, Y.; Li, S. Genome-wide association analysis identifies potential regulatory genes for eumelanin pigmentation in chicken plumage. Anim. Genet.; 2017; 48, pp. 611-614. [DOI: https://dx.doi.org/10.1111/age.12573]
149. Fagerberg, L.; Hallström, B.M.; Oksvold, P.; Kampf, C.; Djureinovic, D.; Odeberg, J.; Habuka, M.; Tahmasebpoor, S.; Danielsson, A.; Edlund, K. et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Mol. Cell. Proteom.; 2014; 13, pp. 397-406. [DOI: https://dx.doi.org/10.1074/mcp.M113.035600] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24309898]
150. Morotomi-Yano, K.; Yano, K.I.; Saito, H.; Sun, Z.; Iwama, A.; Miki, Y. Human regulatory factor X 4 (RFX4) is a testis-specific dimeric DNA-binding protein that cooperates with other human RFX members. J. Biol. Chem.; 2002; 277, pp. 836-842. [DOI: https://dx.doi.org/10.1074/jbc.M108638200]
151. Huang, T.; Pu, Y.; Song, C.; Sheng, Z.; Hu, X. A quantitative trait locus on chromosome 2 was identified that accounts for a substantial proportion of phenotypic variance of the yellow plumage color in chicken. Poult. Sci.; 2020; 99, pp. 2902-2910. [DOI: https://dx.doi.org/10.1016/j.psj.2020.01.030] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32475423]
152. Dadousis, C.; Somavilla, A.; Ilska, J.J.; Johnsson, M.; Batista, L.; Mellanby, R.J.; Headon, D.; Gottardo, P.; Whalen, A.; Wilson, D. et al. A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens. Genet. Sel. Evol.; 2021; 53, 70. [DOI: https://dx.doi.org/10.1186/s12711-021-00663-w]
153. Wang, W.J.; Guo, Y.Q.; Xie, K.J.; Li, Y.D.; Li, Z.W.; Wang, N.; Xiao, F.; Guo, H.S.; Li, H.; Wang, S.Z. A functional variant in the promoter region of IGF1 gene is associated with chicken abdominal fat deposition. Domest. Anim. Endocrinol.; 2021; 75, 106584. [DOI: https://dx.doi.org/10.1016/j.domaniend.2020.106584] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33276215]
154. Qanbari, S.; Strom, T.M.; Haberer, G.; Weigend, S.; Gheyas, A.A.; Turner, F.; Burt, D.W.; Preisinger, R.; Gianola, D.; Simianer, H. A high resolution genome-wide scan for significant selective sweeps: An application to pooled sequence data in laying chickens. PLoS ONE; 2012; 7, e49525. [DOI: https://dx.doi.org/10.1371/journal.pone.0049525]
155. Qanbari, S.; Rubin, C.J.; Maqbool, K.; Weigend, S.; Weigend, A.; Geibel, J.; Kerje, S.; Wurmser, C.; Peterson, A.T.; Brisbin, I.L., Jr. et al. Genetics of adaptation in modern chicken. PLoS Genet.; 2019; 15, e1007989. [DOI: https://dx.doi.org/10.1371/journal.pgen.1007989]
156. Kulibaba, R.A.; Tereshchenko, A.V. Transforming growth factor β1, pituitary-specific transcriptional factor 1 and insulin-like growth factor I gene polymorphisms in the population of the Poltava clay chicken breed: Association with productive traits. Agric. Sci. Pract.; 2015; 2, pp. 67-72. [DOI: https://dx.doi.org/10.15407/agrisp2.01.067]
157. Stark, K.; Straub, R.H.; Rovenský, J.; Blažičková, S.; Eiselt, G.; Schmidt, M. CYB5A polymorphism increases androgens and reduces risk of rheumatoid arthritis in women. Arthritis Res. Ther.; 2015; 17, 56. [DOI: https://dx.doi.org/10.1186/s13075-015-0574-9]
158. Moreira, G.C.M.; Salvian, M.; Boschiero, C.; Cesar, A.S.M.; Reecy, J.M.; Godoy, T.F.; Ledur, M.C.; Garrick, D.; Mourão, G.B.; Coutinho, L.L. Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens. BMC Genom.; 2019; 20, 669. [DOI: https://dx.doi.org/10.1186/s12864-019-6040-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31438838]
159. Lin, J.; Wang, C.; Redies, C. Expression of multiple delta-protocadherins during feather bud formation. Gene Expr. Patterns; 2013; 13, pp. 57-65. [DOI: https://dx.doi.org/10.1016/j.gep.2013.01.001]
160. Liu, J.; Zhou, J.; Li, J.; Bao, H. Identification of candidate genes associated with slaughter traits in F2 chicken population using genome-wide association study. Anim. Genet.; 2021; 52, pp. 532-535. [DOI: https://dx.doi.org/10.1111/age.13079]
161. 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. [DOI: https://dx.doi.org/10.1371/journal.pone.0061172] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23637794]
162. Pozovnikova, M.V.; Larkina, T.A.; Vakhrameev, A.B.; Fedorova, Z.L.; Reynbakh, N.R.; Ryabovan, A.E.; Dement’eva, N.V. Analysis of the biophysical qualities of eggs in the Pushkinsk and Tzarskosel’skaya hens with various allelic variants of the LCORL gene. Izv. Timiryazev. S-Kh. Akad. [Izv. Timiryazev Agric. Acad.]; 2023; 2, pp. 135-143. (In Russian with English summary) [DOI: https://dx.doi.org/10.26897/0021-342X-2023-2-135-143]
163. Ma, X.; Ying, F.; Li, Z.; Bai, L.; Wang, M.; Zhu, D.; Liu, D.; Wen, J.; Zhao, G.; Liu, R. New insights into the genetic loci related to egg weight and age at first egg traits in broiler breeder. Poult. Sci.; 2024; 103, 103613. [DOI: https://dx.doi.org/10.1016/j.psj.2024.103613]
164. Li, Y.D.; Liu, X.; Li, Z.W.; Wang, W.J.; Li, Y.M.; Cao, Z.P.; Luan, P.; Xiao, F.; Gao, H.H.; Guo, H.S. et al. A combination of genome-wide association study and selection signature analysis dissects the genetic architecture underlying bone traits in chickens. Animal; 2021; 15, 100322. [DOI: https://dx.doi.org/10.1016/j.animal.2021.100322] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34311193]
165. Qu, L.; Shen, M.; Guo, J.; Wang, X.; Dou, T.; Hu, Y.; Li, Y.; Ma, M.; Wang, K.; Liu, H. Identification of potential genomic regions and candidate genes for egg albumen quality by a genome-wide association study. Arch. Anim. Breed.; 2019; 62, pp. 113-123. [DOI: https://dx.doi.org/10.5194/aab-62-113-2019] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31807621]
166. 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, pp. 133-143. [DOI: https://dx.doi.org/10.1139/gen-2019-0022] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31794256]
167. Jang, H.M.; Erf, G.F.; Rowland, K.C.; Kong, B.W. Genome resequencing and bioinformatic analysis of SNP containing candidate genes in the autoimmune vitiligo Smyth line chicken model. BMC Genom.; 2014; 15, 707. [DOI: https://dx.doi.org/10.1186/1471-2164-15-707] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25151476]
168. Yang, L.; Li, S.; Mo, C.; Zhou, B.; Fan, S.; Shi, F.; Wei, X.; Zhao, Q.; Yang, G.; Li, S. et al. Transcriptome analysis and identification of age-associated fertility decreased genes in hen uterovaginal junction. Poult. Sci.; 2021; 100, 100892. [DOI: https://dx.doi.org/10.1016/j.psj.2020.12.005] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33516476]
169. van Dijk, F.S.; Semler, O.; Etich, J.; Köhler, A.; Jimenez-Estrada, J.A.; Bravenboer, N.; Claeys, L.; Riesebos, E.; Gegic, S.; Piersma, S.R. et al. Interaction between KDELR2 and HSP47 as a key determinant in osteogenesis imperfecta caused by bi-allelic variants in KDELR2. Am. J. Hum. Genet.; 2020; 107, pp. 989-999. [DOI: https://dx.doi.org/10.1016/j.ajhg.2020.09.009]
170. Chen, L.; Ni, Z.; Hua, J.; Ye, W.; Liu, K.; Yun, T.; Zhu, Y.; Zhang, C. Proteomic analysis of host cellular proteins co-immunoprecipitated with duck enteritis virus gC. J. Proteom.; 2021; 245, 104281. [DOI: https://dx.doi.org/10.1016/j.jprot.2021.104281]
171. Zhang, H.; Shen, L.; Li, Y.; Xu, Z.; Zhang, X.; Yu, J.; Cao, Z.; Luan, P. Genome-wide association study for plasma very low-density lipoprotein concentration in chicken. J. Anim. Breed. Genet.; 2019; 136, pp. 351-361. [DOI: https://dx.doi.org/10.1111/jbg.12397] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31037768]
172. Peng, M.; Li, S.; He, Q.; Zhao, J.; Li, L.; Ma, H. Proteomics reveals changes in hepatic proteins during chicken embryonic development: An alternative model to study human obesity. BMC Genom.; 2018; 19, 29. [DOI: https://dx.doi.org/10.1186/s12864-017-4427-6]
173. Jahejo, A.R.; Zhang, D.; Niu, S.; Mangi, R.A.; Khan, A.; Qadir, M.F.; Khan, A.; Chen, H.C.; Tian, W.X. Transcriptome-based screening of intracellular pathways and angiogenesis related genes at different stages of thiram induced tibial lesions in broiler chickens. BMC Genom.; 2020; 21, 50. [DOI: https://dx.doi.org/10.1186/s12864-020-6456-9]
174. Orgeur, M.; Martens, M.; Leonte, G.; Nassari, S.; Bonnin, M.A.; Börno, S.T.; Timmermann, B.; Hecht, J.; Duprez, D.; Stricker, S. Genome-wide strategies identify downstream target genes of chick connective tissue-associated transcription factors. Development; 2018; 145, dev161208. [DOI: https://dx.doi.org/10.1242/dev.161208]
175. Zhang, Z.W.; Rong, E.G.; Shi, M.X.; Wu, C.Y.; Sun, B.; Wang, Y.X.; Wang, N.; Li, H. Expression and functional analysis of Krüppel-like factor 2 in chicken adipose tissue. J. Anim. Sci.; 2014; 92, pp. 4797-4805. [DOI: https://dx.doi.org/10.2527/jas.2014-7997]
176. Zhang, C.; Lin, D.; Wang, Y.; Peng, D.; Li, H.; Fei, J.; Chen, K.; Yang, N.; Hu, X.; Zhao, Y. et al. Widespread introgression in Chinese indigenous chicken breeds from commercial broiler. Evol. Appl.; 2019; 12, pp. 610-621. [DOI: https://dx.doi.org/10.1111/eva.12742] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30828377]
177. Dunnington, E.A.; Siegel, P.B. Comparison of sex-linked dwarf genes in chickens from two sources when introgressed into unrelated genetic backgrounds. Br. Poult. Sci.; 1998; 39, pp. 216-220. [DOI: https://dx.doi.org/10.1080/00071669889141]
178. Baydevlyatova, O.N.; Ogurtsova, N.S.; Shomina, N.V.; Tereshchenko, A.V. Morphological indicators of egg quality in a new chicken subpopulation of the meat-egg type of productivity. Ptakhivnytstvo [Poultry Farm.]; 2009; 64, pp. 109-115. (In Russian with English summary)
179. Katerynych, O.O.; Pankova, S.M.; Bondarenko, Y.V. Adaptability of domestically bred poultry of different directions of productivity. Ptakhivnytstvo [Poultry Farm.]; 2012; 68, pp. 210-216. (In Ukrainian)
180. Bondarenko, Y.V.; Khvostik, V.P. Pokrashhennya produktyvnosti m’yaso-yayechnyh kurej vitchyznyanoyi selekciyi [Improving the productivity of meat and egg chickens of domestic selection]. Vìsn. Sumsʹkogo Nac. Agrar. Univ. Ser. Tvarynnytstvo [Bull. Sumy Natl. Agrar. Univ. Ser. Livest.]; 2020; 2, pp. 29-32. (In Ukrainian with English summary) [DOI: https://dx.doi.org/10.32845/bsnau.lvst.2020.2.5]
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Shakhin, Alexey V 2
; Abdelmanova, Alexandra S 2
; Volkova, Natalia A 2
; Efimov, Dmitry N 3
; Fisinin, Vladimir I 3
; Korshunova, Liudmila G 3
; Anshakov, Dmitry V 4
; Dotsev, Arsen V 2
; Griffin, Darren K 5
; Zinovieva, Natalia A 2
1 L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia;
2 L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia;
3 Federal State Budget Scientific Institution Federal Scientific Center “All-Russian Research and Technological Poultry Institute”, Sergiev Posad 141311, Moscow Oblast, Russia;
4 Breeding and Genetic Center “Zagorsk Experimental Breeding Farm”—Branch of the Federal Research Center “All-Russian Poultry Research and Technological Institute”, Russian Academy of Sciences, Sergiev Posad 141311, Moscow Oblast, Russia;
5 School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK;