In Tibet, the two most important breeds are Tibetan chicken and Lhasa white chicken, and the duo exhibit specific adaptations to the high altitude thereby supplying proteins for humans living in the ...plateau. These breeds are partly included in the conservation plans because they represent important chicken genetic resources. However, the genetic diversity of these chickens is rarely investigated. Based on whole-genome sequencing data of 113 chickens from 4 populations of Tibetan chicken including Shigatse (SH), Nyemo (NM), Dagze (DZ) and Nyingchi (LZ), as well as Lhasa white (LW) chicken breed, we investigated the genetic diversity of these chicken breeds by genetic differentiation, run of homozygosity (ROH), genomic inbreeding and selection signature analyses.
Our results revealed high genetic diversity across the five chicken populations. The linkage disequilibrium decay was highest in LZ, while subtle genetic differentiation was found between LZ and other populations (Fst ranging from 0.05 to 0.10). Furthermore, the highest ROH-based inbreeding estimate (F
) of 0.11 was observed in LZ. In other populations, the F
ranged from 0.04 to 0.06. In total, 74, 111, 62, 42 and 54 ROH islands containing SNPs ranked top 1% for concurrency were identified in SH, NM, DZ, LZ and LW, respectively. Genes common to the ROH islands in the five populations included BDNF, CCDC34, LGR4, LIN7C, GLS, LOC101747789, MYO1B, STAT1 and STAT4. This suggested their essential roles in adaptation of the chickens. We also identified a common candidate genomic region harboring AMY2A, NTNG1 and VAV3 genes in all populations. These genes had been implicated in digestion, neurite growth and high-altitude adaptation.
High genetic diversity is observed in Tibetan native chickens. Inbreeding is more intense in the Nyingchi population which is also genetically distant from other chicken populations. Candidate genes in ROH islands are likely to be the drivers of adaptation to high altitude exhibited by the five Tibetan native chicken populations. Our findings contribute to the understanding of genetic diversity offer valuable insights for the genetic mechanism of adaptation, and provide veritable tools that can help in the design and implementation of breeding and conservation strategies for Tibetan native chickens.
Chicken plumage color is an important economical trait in poultry breeding, as triple-yellow indigenous broilers are preferred over western commercial broilers in the Chinese market. However, the ...studies on the pigmentation of plumage coloration are relatively rare at present. Here, we performed a genome-wide mapping study on an F2 intercross, whose 2 founders were one hybrid commercial line “High Quality chicken Line A” that originated from the Anak red chicken and one indigenous line “Huiyang Beard” chicken that is a classical “triple-yellow” Chinese indigenous breed. Moreover, we used an automatic colorimeter that can quantitatively assess the colorations in L∗, a∗, and b∗ values. One major quantitative trait locus (QTL) on chromosome 2 was thus identified by both genome-wide association and linkage analyses, which could explain 10 to 20% of the total phenotypic variance of the b∗ measurements of the back plumage color. Using linkage analysis, 2 additional QTL on chromosome 1 and 20 were also found to be significantly associated with the plumage coloration in this cross. With additional samples from Anak red and Huiyang Beard chickens as well as pooled resequencing data from the 2 founders of this cross, we then further narrowed down the QTL regions and identified several candidate genes, such as CABLES1, CHST11, BCL2L1, and CHD22. As the effects of QTL found in this study were substantial, quantitatively measuring the coloration rather than the descriptive measurements provides stronger statistical power for the analyses. In addition, this major QTL on chromosome 2 that was associated with feather pigmentation at the genome-wide level will facilitate the future chicken breeding for yellow plumage color. In conclusions, we mapped 3 associated QTL on chromosome 1, 2, and 20. The candidate genes identified in this study shed light in the genetic basis of yellow plumage color in chicken.
As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public ...data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress.
Tibetan chicken is one of the most common and widely distributed highland breeds, and is often used as a model organism for understanding genetic adaptation to extreme environments in Tibet. Despite ...its apparent geographical diversity and large variations in plumage patterns, the genetic differences within breed were not accounted for in most studies and have not been systematically investigated. In order to reveal and genetically differentiate the current existing TBC sub-populations that might have major implications for genomic research in TBCs, we systematically evaluated the population structure and demography of current TBC populations. Based on 344 whole-genome sequenced birds including 115 Tibetan chickens that were mostly sampled from family-farms across Tibet, we revealed a clear separation of Tibetan chickens into 4 sub-populations that broadly aligns with their geographical distribution. Moreover, population structure, population size dynamics, and the extent of admixture jointly suggest complex demographic histories of these sub-populations, including possible multiple origins, inbreeding, and introgressions. While most of the candidate selected regions found between the TBC sub-populations and Red Jungle fowls were nonoverlapping, 2 genes RYR2 and CAMK2D were revealed as strong selection candidates in all 4 sub-populations. These 2 previously identified high altitude associated genes indicated that the sub-populations responded to similar selection pressures in an independent but functionally similar fashion. Our results demonstrate robust population structure in Tibetan chickens that will help inform future genetic analyses on chickens and other domestic animals alike in Tibet, recommending thoughtful experimental design.
Muffs and beard (Mb) is a phenotype in chickens where groups of elongated feathers gather from both sides of the face (muffs) and below the beak (beard). It is an autosomal, incomplete dominant ...phenotype encoded by the Muffs and beard (Mb) locus. Here we use genome-wide association (GWA) analysis, linkage analysis, Identity-by-Descent (IBD) mapping, array-CGH, genome re-sequencing and expression analysis to show that the Mb allele causing the Mb phenotype is a derived allele where a complex structural variation (SV) on GGA27 leads to an altered expression of the gene HOXB8. This Mb allele was shown to be completely associated with the Mb phenotype in nine other independent Mb chicken breeds. The Mb allele differs from the wild-type mb allele by three duplications, one in tandem and two that are translocated to that of the tandem repeat around 1.70 Mb on GGA27. The duplications contain total seven annotated genes and their expression was tested during distinct stages of Mb morphogenesis. A continuous high ectopic expression of HOXB8 was found in the facial skin of Mb chickens, strongly suggesting that HOXB8 directs this regional feather-development. In conclusion, our results provide an interesting example of how genomic structural rearrangements alter the regulation of genes leading to novel phenotypes. Further, it again illustrates the value of utilizing derived phenotypes in domestic animals to dissect the genetic basis of developmental traits, herein providing novel insights into the likely role of HOXB8 in feather development and differentiation.
Body weight (BW) is one of the most important economic traits for animal production and breeding, and it has been studied extensively for its phenotype-genotype associations. While mapping studies ...have mostly aimed at finding as many loci as possible that contributed to the variation in BW, the role of other factors in its genetic architecture, including their frequencies in the population and their interactions, have been largely overlooked. To comprehensively characterized the genetic architecture of BW, we performed a genome-wide association study (GWAS) both at the single-marker and haplotype level on birds from four indigenous Chinese chicken breeds (Chahua, Silkie, Langshan, and Beard), rather than studying crosses between two founder lines. Additionally, samples from two more breeds (Red Junglefowl and Recessive White) were included to better reflect variable genetic characteristics across populations. Six loci were mapped in this study, revealing the polygenic basis underlying BW. Moreover, by further examining the frequencies of the significantly associated haplotypes in each subpopulation and their effect sizes, most of the loci were found to affect BW in the Beard chicken breed alone. Two loci in GGA9 and GGA27, however, had a common effect on BW across subpopulations, showing that different underlying genetic mechanisms contribute to the phenotypic variability. These findings, particularly the variable genetic architectures found in different loci, improve our understanding of the overall genetic contributions to the large variability in BW among Chinese indigenous chicken breeds. These findings thus will have important implications for future chicken breeding.
In China, consumers often prefer indigenous broiler chickens over commercial breeds, as they have characteristic meat qualities requested within traditional culinary customs. However, the growth-rate ...of these indigenous breeds is slower than that of the commercial broilers, which means they have not yet reached their full economic value. Therefore, combining the valuable meat quality of the native chickens with the efficiency of the commercial broilers is of interest. In this study, we generated an F2 intercross between the slow growing native broiler breed, Huiyang Beard chicken, and the fast growing commercial broiler breed, High Quality chicken Line A, and used it to map loci explaining the difference in growth rate between these breeds.
A genome scan to identify main-effect loci affecting 24 growth-related traits revealed nine distinct QTL on six chromosomes. Many QTL were pleiotropic and conformed to the correlation patterns observed between phenotypes. Most of the mapped QTL were found in locations where growth QTL have been reported in other populations, although the effects were greater in this population. A genome scan for pairs of interacting loci identified a number of additional QTL in 10 other genomic regions. The epistatic pairs explained 6-8% of the residual phenotypic variance. Seven of the 10 epistatic QTL mapped in regions containing candidate genes in the ubiquitin mediated proteolysis pathway, suggesting the importance of this pathway in the regulation of growth in this chicken population.
The main-effect QTL detected using a standard one-dimensional genome scan accounted for a significant fraction of the observed phenotypic variance in this population. Furthermore, genes in known pathways present interesting candidates for further exploration. This study has thus located several QTL regions as promising candidates for further study, which will increase our understanding of the genetic mechanisms underlying growth-related traits in chickens.
In depth studies of quantitative trait loci (QTL) can provide insights to the genetic architectures of complex traits. A major effect QTL at the distal end of chicken chromosome 1 has been associated ...with growth traits in multiple populations. This locus was fine-mapped in a fifteen-generation chicken advanced intercross population including 1119 birds and explored in further detail using 222 sequenced genomes from 10 high/low body weight chicken stocks. We detected this QTL that, in total, contributed 14.4% of the genetic variance for growth. Further, nine mosaic precise intervals (Kb level) which contain ancestral regulatory variants were fine-mapped and we chose one of them to demonstrate the key regulatory role in the duodenum. This is the first study to break down the detail genetic architectures for the well-known QTL in chicken and provides a good example of the fine-mapping of various of quantitative traits in any species.
Automatically identifying abnormal behaviors of caged laying hens in a thermal environment improves manual management efficiency. It also provides reference indicators for breeding heat-tolerant ...hens. In this study, we propose a deep learning-based method for automatic recognition and evaluation of typical heat stress behaviors in hens. We developed a lightweight object detection algorithm, YOLO-HGP, based on the YOLOv8n as the baseline model. YOLO-HGP achieves Precision (P), Recall (R), and mean average precision (mAP) of 95.952%, 94.127%, and 97.667%, respectively, effectively detecting typical heat stress behaviors in hens. Compared to the original YOLO v8n, YOLO-HGP improves R, and mAP by 6.257%, and 1.963%, respectively. The FLOPs (floating point operations) and parameter count of YOLO-HGP are 4.3G and 1.729M, reducing by 47.56% and 42.58% compared to the original model. Additionally, we introduce the “ORC-ratio” (The ratio of the combined frequency of open-beak breathing and retching behaviors to the frequency of closed-beak behaviors.) as an evaluation indicator for the frequency of typical heat stress behaviors in hens and combine it with the Hybrid-SORT multiobject tracking algorithm to achieve tracking detection of individual hens. The study demonstrates that the proposed model effectively identifies and quantitatively evaluates typical behaviors of hens in a thermal environment, providing an effective approach for the automated recognition of heat stress behaviors in hens.
Given the escalating global warming and the intense nature of modern poultry production, layers are becoming increasingly susceptible to heat stress. This stress disrupts the physiological processes ...of layers, which leads to reduced productivity and welfare. To address this issue, it is crucial to first evaluate the stress response systematically. However, such evaluations are still lacking in this field. The objective of this study was to accurately monitor the impact of thermal stress and identify common and key indicators that would support decision-making to maintain layer welfare and productivity under stress. We constructed two heat stress models to reflect moderate (32 °C) to severe (36 °C) stress effects and obtained a comprehensive profile of blood physiological parameters associated with the layers’ responses to heat stress. We found that genetic differences had limited influence on their physiological responses to heat stress after 32 °C heat challenges. Using 8 selected and significantly changed parameters, layers’ physiological status under heat stress could be accurately determined (judgmental accuracy of 98%). As ambient temperature increased to 36 °C, birds suffered more severe challenges that parameters changed in larger percentages. Additionally, breed variations of the physiological responses became apparent, a Fisher discriminant function based on 5 selected parameters could distinguish heat stress effects at 32 °C or 36 °C with 80% accuracy. The results obtained from this study provide two discriminant models for assessing heat stress and shed lights on developing effective and widely applicable heat stress mitigation strategies targeting these indicators.
•11 key physiological indicators for evaluating layer heat stress were determined.•Genetics had limited influence on layers’ physiological responses to heat stress.•The status and levels of layers’ heat stress can be distinguished and classified.
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