Identification of genomic regions and candidate genes for chicken meat ultimate pH by combined detection of selection signatures and QTL

Application potential of chicken DNA chip in domestic pigeon species - Preliminary results

Introducing the SNP technology to pigeon breeding will enhance the competitiveness of a sector that produces one of the healthiest and best quality meats. The present study aimed to test the applicability of the Illumina Chicken_50K_CobbCons array on 24 domestic pigeon individuals from the Mirthys hybrids and Racing pigeon breeds. A total of 53,313 SNPs were genotyped. Principal component analysis shows a significant overlap between the two groups. The chip performed poorly in this data set, with a call rate per sample of 0.474 (49%). The low call rate was likely due to an increase in the evolutionary distance. A total of 356 SNPs were retained after a relatively strict quality control. We have demonstrated that it is technically feasible to use a chicken microarray chip on pigeon samples. Presumably, with a larger sample size and by assigning phenotypic data, efficiency would be improved, allowing more thorough analyses, such as genome-wide association studies.

Identification of Key Candidate Genes in Runs of Homozygosity of the Genome of Two Chicken Breeds, Associated with Cold Adaptation

Simple Summary

The search for genomic regions related to adaptive abilities preserved in the chicken gene pool of two breeds, which have not been under intensive selection pressure, is of great importance for breeding in the future. This study aimed to identify key candidate genes associated with the adaptation of chickens to cold environments (using the example of the Russian White breed) by using molecular genetic methods. A total of 12 key genes on breed-specific ROH (runs of homozygosity) islands were identified, which may be potential candidate genes associated with the high level of adaptability of chickens to cold environments in the early postnatal period. These genes were associated with lipid metabolism, maintaining body temperature in cold environments, non-shivering thermogenesis and muscle development and are perspectives for further research.

Abstract

It is well known that the chicken gene pools have high adaptive abilities, including adaptation to cold environments. This research aimed to study the genomic distribution of runs of homozygosity (ROH) in a population of Russian White (RW) chickens as a result of selection for adaptation to cold environments in the early postnatal period, to perform a structural annotation of the discovered breed-specific regions of the genome (compared to chickens of the Amroks breed) and to suggest key candidate genes associated with the adaptation of RW chickens to cold environments. Genotyping of individual samples was performed using Illumina Chicken 60K SNP BeadChip^® chips. The search for homozygous regions by individual chromosomes was carried out using the PLINK 1.9 program and the detectRuns R package. Twelve key genes on breed-specific ROH islands were identified. They may be considered as potential candidate genes associated with the high adaptive ability of chickens in cold environments in the early postnatal period. Genes associated with lipid metabolism (SOCS3, NDUFA4, TXNRD2, IGFBP 1, IGFBP 3), maintaining body temperature in cold environments (ADIPOQ, GCGR, TRPM2), non-shivering thermogenesis (RYR2, CAMK2G, STK25) and muscle development (METTL21C) are perspectives for further research. This study contributes to our understanding of the mechanisms of adaptation to cold environments in chickens and provides a molecular basis for selection work.

Chronological Expression of PITX2 and SIX1 Genes and the Association between Their Polymorphisms and Chicken Meat Quality Traits

Meat quality is closely related to the development of skeletal muscle, in which PITX2 and SIX1 genes play important regulatory roles. The present study firstly provided the data of chronological expression files of PITX2 and SIX1 genes in the post-hatching pectoral muscle and analyzed the association of their polymorphisms with the meat quality traits of Wuliang Mountain Black-bone (WLMB) chickens. The results showed that both PITX2 and SIX1 genes were weakly expressed in the second and third weeks, and then increased significantly from the third week to the fourth week. Furthermore, there was a significant positive correlation between the expression levels of the two genes. Twelve and one SNPs were detected in the chicken PITX2 and SIX1 genes, respectively, of which four SNPs (g.9830C > T, g.10073C > T, g.13335G > A, g.13726A > G) of the PITX2 gene and one SNP (g.564G > A) of the SIX1 gene were significantly associated with chicken meat quality traits. For the PITX2 gene, chickens with the CT genotype of g.9830C > T showed the highest meat color L*, shear force (SF), pH, and the lowest electrical conductivity (EC), and drip loss (DL) (p < 0.05 or p < 0.01); chickens with the CC genotype of g.10073C > T had the lowest L*, pH, and the highest DL (p < 0.01). For the SIX1 gene, chickens with the GG genotype of g.564G > A had the highest (p < 0.05) SF and pH. Furthermore, pH had a significant correlation with all the other meat quality traits. The current study could contribute to the research of regulatory mechanisms of meat quality and lay the foundation for improving meat quality based on marker-assisted selection in chickens.

Estimation of Natural Selection and Allele Age from Time Series Allele Frequency Data Using a Novel Likelihood-Based Approach

Temporally spaced genetic data allow for more accurate inference of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel likelihood-based method for jointly estimating selection coefficient and allele age from time series data of allele frequencies. Our approach is based on a hidden Markov model where the underlying process is a Wright-Fisher diffusion conditioned to survive until the time of the most recent sample. This formulation circumvents the assumption required in existing methods that the allele is created by mutation at a certain low frequency. We calculate the likelihood by numerically solving the resulting Kolmogorov backward equation backward in time while reweighting the solution with the emission probabilities of the observation at each sampling time point. This procedure reduces the two-dimensional numerical search for the maximum of the likelihood surface, for both the selection coefficient and the allele age, to a one-dimensional search over the selection coefficient only. We illustrate through extensive simulations that our method can produce accurate estimates of the selection coefficient and the allele age under both constant and nonconstant demographic histories. We apply our approach to reanalyze ancient DNA data associated with horse base coat colors. We find that ignoring demographic histories or grouping raw samples can significantly bias the inference results.

Molecular Phenotyping of White Striping and Wooden Breast Myopathies in Chicken

The White Striping (WS) and Wooden Breast (WB) defects are two myopathic syndromes whose occurrence has recently increased in modern fast-growing broilers. The impact of these defects on the quality of breast meat is very important, as they greatly affect its visual aspect, nutritional value, and processing yields. The research conducted to date has improved our knowledge of the biological processes involved in their occurrence, but no solution has been identified so far to significantly reduce their incidence without affecting growing performance of broilers. This study aims to follow the evolution of molecular phenotypes in relation to both fast-growing rate and the occurrence of defects in order to identify potential biomarkers for diagnostic purposes, but also to improve our understanding of physiological dysregulation involved in the occurrence of WS and WB. This has been achieved through enzymatic, histological, and transcriptional approaches by considering breast muscles from a slow- and a fast-growing line, affected or not by WS and WB. Fast-growing muscles produced more reactive oxygen species (ROS) than slow-growing ones, independently of WS and WB occurrence. Within fast-growing muscles, despite higher mitochondria density, muscles affected by WS or WB defects did not show higher cytochrome oxidase activity (COX) activity, suggesting altered mitochondrial function. Among the markers related to muscle remodeling and regeneration, immunohistochemical staining of FN1, NCAM, and MYH15 was higher in fast- compared to slow-growing muscles, and their amount also increased linearly with the presence and severity of WS and WB defects, making them potential biomarkers to assess accurately their presence and severity. Thanks to an innovative histological technique based on fluorescence intensity measurement, they can be rapidly quantified to estimate the injuries induced in case of WS and WB. The muscular expression of several other genes correlates also positively to the presence and severity of the defects like TGFB1 and CTGF, both involved in the development of connective tissue, or Twist1, known as an inhibitor of myogenesis. Finally, our results suggested that a balance between TGFB1 and PPARG would be essential for fibrosis or adiposis induction and therefore for determining WS and WB phenotypes.

Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model

Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markov chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and their estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta with spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluated this model for population sizes not accessible to the Wright-Fisher model and illustrated its performance on a dataset of two divergently selected chicken populations.

Differential expression and co-expression gene network analyses reveal molecular mechanisms and candidate biomarkers involved in breast muscle myopathies in chicken

The broiler industry is facing an increasing prevalence of breast myopathies, such as white striping (WS) and wooden breast (WB), and the precise aetiology of these occurrences remains poorly understood. To progress our understanding of the structural changes and molecular pathways involved in these myopathies, a transcriptomic analysis was performed using an 8 × 60 K Agilent chicken microarray and histological study. The study used pectoralis major muscles from three groups: slow-growing animals (n = 8), fast-growing animals visually free from defects (n = 8), or severely affected by both WS and WB (n = 8). In addition, a weighted correlation network analysis was performed to investigate the relationship between modules of co-expressed genes and histological traits. Functional analysis suggested that selection for fast growing and breast meat yield has progressively led to conditions favouring metabolic shifts towards alternative catabolic pathways to produce energy, leading to an adaptive response to oxidative stress and the first signs of inflammatory, regeneration and fibrosis processes. All these processes are intensified in muscles affected by severe myopathies, in which new mechanisms related to cellular defences and remodelling seem also activated. Furthermore, our study opens new perspectives for myopathy diagnosis by highlighting fine histological phenotypes and genes whose expression was strongly correlated with defects.

Predicting the Quality of Meat: Myth or Reality?

This review is aimed at providing an overview of recent advances made in the field of meat quality prediction, particularly in Europe. The different methods used in research labs or by the production sectors for the development of equations and tools based on different types of biological (genomic or phenotypic) or physical (spectroscopy) markers are discussed. Through the various examples, it appears that although biological markers have been identified, quality parameters go through a complex determinism process. This makes the development of generic molecular tests even more difficult. However, in recent years, progress in the development of predictive tools has benefited from technological breakthroughs in genomics, proteomics, and metabolomics. Concerning spectroscopy, the most significant progress was achieved using near-infrared spectroscopy (NIRS) to predict the composition and nutritional value of meats. However, predicting the functional properties of meats using this method-mainly, the sensorial quality-is more difficult. Finally, the example of the MSA (Meat Standards Australia) phenotypic model, which predicts the eating quality of beef based on a combination of upstream and downstream data, is described. Its benefit for the beef industry has been extensively demonstrated in Australia, and its generic performance has already been proven in several countries.

Mapping of QTL for chicken body weight, carcass composition, and meat quality traits in a slow-growing line

Slow-growing chicken lines are valuable genetic resources for the development of well-perceived alternative free-range production. While there is no constraint on increasing growth rate, breeding programs have to evolve in order to include new traits improving the positioning of such lines in the growing market for parts and processed products. In this study, we used dense genotyping to fine map QTL for chicken growth, body composition, and meat quality traits in view of developing new tools for selection of a slow-growing line. The dataset included a total of 836 birds (10 sires, 87 dams, 739 descendants) and 40,203 SNP. QTL for the 15 traits analyzed were detected by 3 different methods, i.e., linkage and linkage disequilibrium haplotype-based analysis (LDLA), family-based single marker association (FASTA), and Bayesian multi-marker regression (Bayes Cπ). After filtering for QTL redundancy, we found 16, 16, and 9 QTL when using the FASTA, LDLA, and Bayes Cπ methods, respectively, with a threshold of 2.49 × 10-5 for FASTA and LDLA, and a Bayes factor of 150 for the Bayes Cπ analysis. They comprised 17 QTL for body weight, 9 QTL for body composition, and 15 QTL for breast meat quality or behavior at slaughter. The 3 methods agreed in the detection of highly significant QTL such as that detected on GGA24 for body weight at 3, 6, and 9 wk, and the 2 QTL detected on GGA17 and GGA18 for breast meat yield. Several significant QTL were also detected for the different components of breast meat quality. This study provided new locations for investigation in order to improve our understanding of the genetic architecture of growth, carcass composition, and meat quality in the chicken and to develop molecular tools for the selection of these traits in a slow-growing line.

Mapping QTL for white striping in relation to breast muscle yield and meat quality traits in broiler chickens

Background

White striping (WS) is an emerging muscular defect occurring on breast and thigh muscles of broiler chickens. It is characterized by the presence of white striations parallel to the muscle fibers and has significant consequences for meat quality. The etiology of WS remains poorly understood, even if previous studies demonstrated that the defect prevalence is related to broiler growth and muscle development. Moreover, recent studies showed moderate to high heritability values of WS, which emphasized the role of genetics in the expression of the muscle defect. The aim of this study was to identify the first quantitative trait loci (QTLs) for WS as well as breast muscle yield (BMY) and meat quality traits using a genome-wide association study (GWAS). We took advantage of two divergent lines of chickens selected for meat quality through Pectoralis major ultimate pH (pHu) and which exhibit the muscular defect. An expression QTL (eQTL) detection was further performed for some candidate genes, either suggested by GWAS analysis or based on their biological function.

Results

Forty-two single nucleotide polymorphisms (SNPs) associated with WS and other meat quality traits were identified. They defined 18 QTL regions located on 13 chromosomes. These results supported a polygenic inheritance of the studied traits and highlighted a few pleiotropic regions. A set of 16 positional and/or functional candidate genes was designed for further eQTL detection. A total of 132 SNPs were associated with molecular phenotypes and defined 21 eQTL regions located on 16 chromosomes. Interestingly, several co-localizations between QTL and eQTL regions were observed which could suggest causative genes and gene networks involved in the variability of meat quality traits and BMY.

Conclusions

The QTL mapping carried out in the current study for WS did not support the existence of a major gene, but rather suggested a polygenic inheritance of the defect and of other studied meat quality traits. We identified several candidate genes involved in muscle metabolism and structure and in muscular dystrophies. The eQTL analyses showed that they were part of molecular networks associated with WS and meat quality phenotypes and suggested a few putative causative genes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4598-9) contains supplementary material, which is available to authorized users.