S. E. Aggrey

Poultry genetics, breeding and biotechnology

In this comprehensive research book issues associated with poultry breeding are addressed, by examining quantitative and molecular genetics and the uses of transgenic technology. The important area of disease resistance and transmission is also covered in a special section looking at the genetics of disease resistance. This book represents the first complete integration of our current knowledge of biotechnology and quantitative and molecular genetics as applied to poultry breeding.

Genetic properties of feed efficiency parameters in meat-type chickens

BackgroundFeed cost constitutes about 70% of the cost of raising broilers, but the efficiency of feed utilization has not kept up the growth potential of todays broilers. Improvement in feed efficiency would reduce the amount of feed required for growth, the production cost and the amount of nitrogenous waste. We studied residual feed intake (RFI) and feed conversion ratio (FCR) over two age periods to delineate their genetic inter-relationships.MethodsWe used an animal model combined with Gibb sampling to estimate genetic parameters in a pedigreed random mating broiler control population.ResultsHeritability of RFI and FCR was 0.42-0.45. Thus selection on RFI was expected to improve feed efficiency and subsequently reduce feed intake (FI). Whereas the genetic correlation between RFI and body weight gain (BWG) at days 28-35 was moderately positive, it was negligible at days 35-42. Therefore, the timing of selection for RFI will influence the expected response. Selection for improved RFI at days 28-35 will reduce FI, but also increase growth rate. However, selection for improved RFI at days 35-42 will reduce FI without any significant change in growth rate. The nature of the pleiotropic relationship between RFI and FCR may be dependent on age, and consequently the molecular factors that govern RFI and FCR may also depend on stage of development, or on the nature of resource allocation of FI above maintenance directed towards protein accretion and fat deposition. The insignificant genetic correlation between RFI and BWG at days 35-42 demonstrates the independence of RFI on the level of production, thereby making it possible to study the molecular, physiological and nutrient digestibility mechanisms underlying RFI without the confounding effects of growth. The heritability estimate of FCR was 0.49 and 0.41 for days 28-35 and days 35-42, respectively.ConclusionSelection for FCR will improve efficiency of feed utilization but because of the genetic dependence of FCR and its components, selection based on FCR will reduce FI and increase growth rate. However, the correlated responses in both FI and BWG cannot be predicted accurately because of the inherent problem of FCR being a ratio trait.

Functional genomics in chickens: development of integrated-systems microarrays for transcriptional profiling and discovery of regulatory pathways.

The genetic networks that govern the differentiation and growth of major tissues of economic importance in the chicken are largely unknown. Under a functional genomics project, our consortium has generated 30 609 expressed sequence tags (ESTs) and developed several chicken DNA microarrays, which represent the Chicken Metabolic/Somatic (10 K) and Neuroendocrine/Reproductive (8 K) Systems (http://udgenome.ags.udel.edu/cogburn/). One of the major challenges facing functional genomics is the development of mathematical models to reconstruct functional gene networks and regulatory pathways from vast volumes of microarray data. In initial studies with liver-specific microarrays (3.1 K), we have examined gene expression profiles in liver during the peri-hatch transition and during a strong metabolic perturbation—fasting and re-feeding—in divergently selected broiler chickens (fast vs. slow-growth lines). The expression of many genes controlling metabolic pathways is dramatically altered by these perturbations. Our analysis has revealed a large number of clusters of functionally related genes (mainly metabolic enzymes and transcription factors) that control major metabolic pathways. Currently, we are conducting transcriptional profiling studies of multiple tissues during development of two sets of divergently selected broiler chickens (fast vs. slow growing and fat vs. lean lines). Transcriptional profiling across multiple tissues should permit construction of a detailed genetic blueprint that illustrates the developmental events and hierarchy of genes that govern growth and development of chickens. This review will briefly describe the recent acquisition of chicken genomic resources (ESTs and microarrays) and our consortiums efforts to help launch the new era of functional genomics in the chicken.

Identification of QTL controlling meat quality traits in an F2 cross between two chicken lines selected for either low or high growth rate

BackgroundMeat technological traits (i.e. meat pH, water retention and color) are important considerations for improving further processing of chicken meat. These quality traits were originally characterized in experimental lines selected for high (HG) and low (LG) growth. Presently, quantitative trait loci (QTL) for these traits were analyzed in an F2 population issued from the HG × LG cross. A total of 698 animals in 50 full-sib families were genotyped for 108 microsatellite markers covering 21 linkage groups.ResultsThe HG and LG birds exhibit large differences in body weight and abdominal fat content. Several meat quality traits [pH at 15 min post-slaughter (pH15) and ultimate pH (pHu), breast color-redness (BCo-R) and breast color-yellowness (BCo-Y)] were lower in HG chickens. In contrast, meat color-lightness (BCo-L) was higher in HG chickens, whereas meat drip loss (DL) was similar in both lines. HG birds were more active on the shackle line. Association analyses were performed using maximum-likelihood interval mapping in QTLMAP. Five genome-wide significant QTLs were revealed: two for pH15 on GGA1 and GGA2, one for DL on GGA1, one for BCo-R and one for BCo-Y both on GGA11. In addition, four suggestive QTLs were identified by QTLMAP for BCo-Y, pHu, pH15 and DL on GGA1, GGA4, GGA12 and GGA14, respectively. The QTL effects, averaged on heterozygous families, ranged from 12 to 31% of the phenotypic variance. Further analyses with QTLExpress confirmed the two genome-wide QTLs for meat color on GGA11, failed to identify the genome-wide QTL for pH15 on GGA2, and revealed only suggestive QTLs for pH15 and DL on GGA1. However, QTLExpress qualified the QTL for pHu on GGA4 as genome-wide.ConclusionThe present study identified genome-wide significant QTLs for all meat technological traits presently assessed in these chickens, except for meat lightness. This study highlights the effects of divergent selection for growth rate on some behavioral traits, muscle biochemistry and ultimately meat quality traits. Several QTL regions were identified that are worthy of further characterization. Some QTLs may in fact co-localize, suggesting pleiotropic effects for some chromosomal regions.

The effects of growth rate on leg morphology and tibia breaking strength, mineral density, mineral content, and bone ash in broilers

Fast-growing broilers are especially susceptible to bone abnormalities, causing major problems for broiler producers. The cortical bones of fast-growing broilers are highly porous, which may lead to leg deformities. Leg problems were investigated in 6-wk-old Arkansas randombred broilers. Body weight was measured at hatch and at 6 wk. There were 8 different settings of approximately 450 eggs each. Two subpopulations, slow-growing (SG; bottom quarter, n=511) and fast-growing (FG; top quarter, n=545), were created from a randombred population based on their growth rate from hatch until 6 wk of age. At 6 wk of age, the broilers were processed and chilled at 4°C overnight before deboning. Shank (78.27±8.06 g), drum stick (190.92±16.91 g), and thigh weights (233.88±22.66 g) of FG broilers were higher than those of SG broilers (54.39±6.86, 135.39±15.45, and 168.50±21.13 g, respectivly; P<0.001). Tibia weights (15.36±2.28 g) of FG broilers were also greater than those of SG broilers (11.23±1.81 g; P<0.001). Shank length (81.50±4.71 g) and tibia length (104.34±4.45 mm) of FG broilers were longer than those of SG broilers (71.88±4.66 and 95.98±4.85 mm, respectively; P<0.001). Shank diameter (11.59±1.60 mm) and tibia diameter (8.20±0.62 mm) of FG broilers were wider than those of SG broilers (9.45±1.74, 6.82±0.58 mm, respectively; P<0.001). Tibia breaking strength (28.42±6.37 kg) of FG broilers was higher than those of SG broiler tibia (21.81±5.89 kg; P<0.001). Tibia density and bone mineral content (0.13±0.01 g/cm2 and 1.29±0.23 g, respectively) of FG broilers were higher than those of SG broiler tibia (0.11±0.01 g/cm2 and 0.79±0.1 g; P<0.001). Tibia percentage of ash content (39.76±2.81) of FG broilers was lower than that of SG broilers (39.99±2.67; P=0.173). Fast-growing broiler bones were longer, wider, heavier, stronger, more dense, and contained more ash than SG ones. After all parameters were calculated per unit of final BW at 6 wk, tibia density and bone ash percentage of FG broilers were lower than those of SG broilers.

Mapping main, epistatic and sex-specific QTL for body composition in a chicken population divergently selected for low or high growth rate

BackgroundDelineating the genetic basis of body composition is important to agriculture and medicine. In addition, the incorporation of gene-gene interactions in the statistical model provides further insight into the genetic factors that underlie body composition traits. We used Bayesian model selection to comprehensively map main, epistatic and sex-specific QTL in an F2 reciprocal intercross between two chicken lines divergently selected for high or low growth rate.ResultsWe identified 17 QTL with main effects across 13 chromosomes and several sex-specific and sex-antagonistic QTL for breast meat yield, thigh + drumstick yield and abdominal fatness. Different sets of QTL were found for both breast muscles [Pectoralis (P) major and P. minor], which suggests that they could be controlled by different regulatory mechanisms. Significant interactions of QTL by sex allowed detection of sex-specific and sex-antagonistic QTL for body composition and abdominal fat. We found several female-specific P. major QTL and sex-antagonistic P. minor and abdominal fatness QTL. Also, several QTL on different chromosomes interact with each other to affect body composition and abdominal fatness.ConclusionsThe detection of main effects, epistasis and sex-dimorphic QTL suggest complex genetic regulation of somatic growth. An understanding of such regulatory mechanisms is key to mapping specific genes that underlie QTL controlling somatic growth in an avian model.

Transcriptional profiling of hypothalamus during development of adiposity in genetically selected fat and lean chickens

The hypothalamus integrates peripheral signals to regulate food intake, energy metabolism, and ultimately growth rate and body composition in vertebrates. Deviations in hypothalamic regulatory controls can lead to accumulation of excess body fat. Many regulatory genes involved in this process remain unidentified, and comparative studies may be helpful to unravel evolutionarily conserved mechanisms controlling body weight and food intake. In the present study, divergently selected fat (FL) and lean (LL) lines of chickens were used to characterize differences in hypothalamic gene expression in these unique genetic lines that develop differences in adiposity without differences in food intake or body weight. Hypothalamic transcriptional profiles were defined with cDNA microarrays before and during divergence of adiposity between the two lines. Six differentially expressed genes identified in chickens are related to genes associated with control of body fat in transgenic or knockout mice, supporting the importance of these genes across species. We identified differences in expression of nine genes involved in glucose metabolism, suggesting that alterations in hypothalamic glycolysis might contribute to differences in levels of body fat between genotypes. Expression of the sweet taste receptor (TAS1R1), which in mammals is involved in glucose sensing and energy uptake, was also higher in FL chickens, suggesting that early differences in glucose sensing might alter the set point for subsequent body composition. Differences in expression of genes associated with tumor necrosis factor (TNF) signaling were also noted. In summary, we identified alterations in transcriptional and metabolic processes within the hypothalamus that could contribute to excessive accumulation of body fat in FL chickens in the absence of differences in food intake, thereby contributing to the genetic basis for obesity in this avian model.

Logistic nonlinear mixed effects model for estimating growth parameters

This study was undertaken to apply the logistic model with nonlinear mixed effects to model growth in Japanese quail. Nonlinear mixed models (NLMM) allow for the inclusion of random factors in a linear manner, which accounts for the between-individual variability and heterogeneous variance. A fixed effects model (M1) was compared with NLMM containing either 1 (M2) or 2 (M3) random effects using the residual error variance, -2 log-likelihood, Akaike information criterion, and Bayesian information criterion as the criteria for evaluating these alternative models. In M2, the between-bird variability was modeled by varying the asymptotic BW, which led to a 57% reduction in the residual variance compared with M1 in males. In M3, the between-bird variation was partitioned into variances due to varying asymptotic BW and the age at the inflection point. The residual variance in M3 was reduced by about 72 and 38% compared with M1 and M2, respectively, in males. The correlation coefficient between the actual and predicted BW for M1, M2, and M3 were 0.9887, 0.9955, and 0.9975, respectively. Similar results were found in females. The model evaluation criteria indicated that the mixed effect models fitted the data better than the fixed effect model because they account for between-bird variation. The use of NLMM is recommended for modeling growth data in poultry because the predicted BW at different ages is more accurate than using the mean prediction function of the fixed effect model.

Mapping QTL for growth and shank traits in chickens divergently selected for high or low body weight

An F(2) population (695 individuals) was established from broiler chickens divergently selected for either high (HG) or low (LG) growth, and used to localize QTL for developmental changes in body weight (BW), shank length (SL9) and shank diameter (SD9) at 9 weeks. QTL mapping revealed three genome-wide QTL on chromosomes (GGA) 2, 4 and 26 and three suggestive QTL on GGA 1, 3 and 5. Most of the BW QTL individually explained 2-5% of the phenotypic variance. The BW QTL on GGA2 explained about 7% of BW from 3 to 7 weeks of age, while that on GGA4 explained 15% of BW from 5 to 9 weeks. The BW QTL on GGA2 and GGA4 could be associated with early and late growth respectively. The GGA4 QTL also had the largest effect on SL9 and SD9 and explained 7% and 10% of their phenotypic variances respectively. However, when SL9 and SD9 were corrected with BW9, a shank length percent QTL was identified on GGA2. We identified novel QTL and also confirmed previously identified loci in other chicken populations. As the foundation population was established from commercial broiler strains, it is possible that QTL identified in this study could still be segregating in commercial strains.

Experiences with a single-step genome evaluation1

Genomic selection can be implemented based on the genomic relationship matrix (GBLUP) and can be combined with phenotypes from nongenotyped animals through the use of best linear unbiased prediction (BLUP). A common method to combine both sources of information involves multiple steps, but is difficult to use with complicated models and is nonoptimal. A simpler method, termed single-step GBLUP, or ssGBLUP, integrates the genomically derived relationships (G) with population-based pedigree relationships (A) into a combined relationship matrix (H) and allows for genomic selection in a single step. The ssGBLUP method is easy to implement and uses standard BLUP-based programs. Experiences with field data in chickens, pigs, and dairy indicate that ssGBLUP is more accurate yet much simpler than multi-step methods. The current limits of ssGBLUP are approximately 100,000 genotypes and 18 traits. Models involving 10 million animals have been run successfully. The inverse of H can also be used in existing programs for parameter estimationm, but a properly scaled G is needed for unbiased estimation. Also, as genomic predictions can be converted to SNP effects, ssGBLUP is useful for genomic-wide association studies. The single-step method for genomic selection translates the use of genomic information into standard BLUP, and variance-component estimation programs become a routine.