QTL analysis of body weight and conformation score in commercial broiler chickens using variance component and half-sib analyses

Genome-wide association studies of immune, disease and production traits in indigenous chicken ecotypes

Background

The majority of chickens in sub-Saharan Africa are indigenous ecotypes, well adapted to the local environment and raised in scavenging production systems. Although they are generally resilient to disease challenge, routine vaccination and biosecurity measures are rarely applied and infectious diseases remain a major cause of mortality and reduced productivity. Management and genetic improvement programmes are hampered by lack of routine data recording. Selective breeding based on genomic technologies may provide the means to enhance sustainability. In this study, we investigated the genetic architecture of antibody response to four major infectious diseases [infectious bursal disease (IBDV), Marek’s disease (MDV), fowl typhoid (SG), fowl cholera (PM)] and resistance to Eimeria and cestode parasitism, along with two production traits [body weight and body condition score (BCS)] in two distinct indigenous Ethiopian chicken ecotypes. We conducted variance component analyses, genome-wide association studies, and pathway and selective sweep analyses.

Results

The large majority of birds was found to have antibody titres for all pathogens and were infected with both parasites, suggesting almost universal exposure. We derived significant moderate to high heritabilities for IBDV, MDV and PM antibody titres, cestodes infestation, body weight and BCS. We identified single nucleotide polymorphisms (SNPs) with genome-wide significance for each trait. Based on these associations, we identified for each trait, pathways, networks and functional gene clusters that include plausible candidate genes. Selective sweep analyses revealed a locus on chromosome 18 associated with viral antibody titres and resistance to Eimeria parasitism that is within a positive selection signal. We found no significant genetic correlations between production, immune and disease traits, implying that selection for altered antibody response and/or disease resistance will not affect production.

Conclusions

We confirmed the presence of genetic variability and identified SNPs significantly associated with immune, disease and production traits in indigenous village chickens. Results underpin the feasibility of concomitant genetic improvement for enhanced antibody response, resistance to parasitism and productivity within and across indigenous chicken ecotypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0252-7) contains supplementary material, which is available to authorized users.

Identification of Quantitative Trait Loci Associated with the Skeletal Deformity LSK complex in Gilthead Seabream (Sparus aurata L.)

Morphological abnormalities, especially skeletal deformities, are some of the most important problems affecting gilthead seabream (Sparus aurata L.) aquaculture industry. In this study, a QTL analysis for LSK complex deformity in gilthead seabream is reported. LSK complex is a severe deformity consisting of a consecutive repetition of three vertebral deformities: lordosis, scoliosis, and kyphosis. Seventy-eight offspring from six breeders from a mass-spawning were analyzed: five full-sibling families, three maternal, and two paternal half-sibling families. They had shown a significant association with the LSK complex prevalence in a previous segregation analysis. Fish were genotyped using a set of multiplex PCRs (ReMsa1-13), which includes 106 microsatellite markers. Two methods were used to perform the QTL analysis: a linear regression with the GridQTL software and a linear mixed model with the Qxpak software. A total of 18 QTL were identified. Four of them (QTLSK3, 6, 12, and 14), located in LG5, 8, 17, and 20, respectively, were the most solid ones. These QTL were significant at genome level and showed an extremely large effect (>35%) with both methods. Markers close to the identified QTL showed a strong association with phenotype. Two of these molecular markers (DId-03-T and Bt-14-F) were considered as potential linked-to-this-deformity markers. The detection of these QTL supposes a critical step in the implementation of marker-assisted selection in this species, which could decrease the incidence of this deformity and other related deformities. The identification of these QTL also represents a major step towards the study of the etiology of skeletal deformities in this species.

Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens

The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.

Detection of QTL controlling digestive efficiency and anatomy of the digestive tract in chicken fed a wheat-based diet

Background

Improving digestive efficiency is a major goal in poultry production, to reduce production costs, make possible the use of alternative feedstuffs and decrease the volume of manure produced. Since measuring digestive efficiency is difficult, identifying molecular markers associated with genes controlling this trait would be a valuable tool for selection. Detection of QTL (quantitative trait loci) was undertaken on 820 meat-type chickens in a F2 cross between D- and D+ lines divergently selected on low or high AMEn (apparent metabolizable energy value of diet corrected to 0 nitrogen balance) measured at three weeks in animals fed a low-quality diet. Birds were measured for 13 traits characterizing digestive efficiency (AMEn, coefficients of digestive utilization of starch, lipids, proteins and dry matter (CDUS, CDUL, CDUP, CDUDM)), anatomy of the digestive tract (relative weights of the proventriculus, gizzard and intestine and proventriculus plus gizzard (RPW, RGW, RIW, RPGW), relative length and density of the intestine (RIL, ID), ratio of proventriculus and gizzard to intestine weight (PG/I); and body weight at 23 days of age. Animals were genotyped for 6000 SNPs (single nucleotide polymorphisms) distributed on 28 autosomes, the Z chromosome and one unassigned linkage group.

Results

Nine QTL for digestive efficiency traits, 11 QTL for anatomy-related traits and two QTL for body weight at 23 days of age were detected. On chromosome 20, two significant QTL at the genome level co-localized for CDUS and CDUDM, i.e. two traits that are highly correlated genetically. Moreover, on chromosome 16, chromosome-wide QTL for AMEn, CDUS, CDUDM and CDUP, on chromosomes 23 and 26, chromosome-wide QTL for CDUS, on chromosomes 16 and 26, co-localized QTL for digestive efficiency and the ratio of intestine length to body weight and on chromosome 27 a chromosome-wide QTL for CDUDM were identified.

Conclusions

This study identified several regions of the chicken genome involved in the control of digestive efficiency. Further studies are necessary to identify the underlying genes and to validate these in commercial populations and breeding environments.

Joint analysis of quantitative trait loci and major-effect causative mutations affecting meat quality and carcass composition traits in pigs

Background

Detection of quantitative trait loci (QTLs) affecting meat quality traits in pigs is crucial for the design of efficient marker-assisted selection programs and to initiate efforts toward the identification of underlying polymorphisms. The RYR1 and PRKAG3 causative mutations, originally identified from major effects on meat characteristics, can be used both as controls for an overall QTL detection strategy for diversely affected traits and as a scale for detected QTL effects. We report on a microsatellite-based QTL detection scan including all autosomes for pig meat quality and carcass composition traits in an F2 population of 1,000 females and barrows resulting from an intercross between a Pietrain and a Large White-Hampshire-Duroc synthetic sire line. Our QTL detection design allowed side-by-side comparison of the RYR1 and PRKAG3 mutation effects seen as QTLs when segregating at low frequencies (0.03-0.08), with independent QTL effects detected from most of the same population, excluding any carrier of these mutations.

Results

Large QTL effects were detected in the absence of the RYR1 and PRKGA3 mutations, accounting for 12.7% of phenotypic variation in loin colour redness CIE-a* on SSC6 and 15% of phenotypic variation in glycolytic potential on SSC1. We detected 8 significant QTLs with effects on meat quality traits and 20 significant QTLs for carcass composition and growth traits under these conditions. In control analyses including mutation carriers, RYR1 and PRKAG3 mutations were detected as QTLs, from highly significant to suggestive, and explained 53% to 5% of the phenotypic variance according to the trait.

Conclusions

Our results suggest that part of muscle development and backfat thickness effects commonly attributed to the RYR1 mutation may be a consequence of linkage with independent QTLs affecting those traits. The proportion of variation explained by the most significant QTLs detected in this work is close to the influence of major-effect mutations on the least affected traits, but is one order of magnitude lower than effect on variance of traits primarily affected by these causative mutations. This suggests that uncovering physiological traits directly affected by genetic polymorphisms would be an appropriate approach for further characterization of QTLs.

Detecting parent of origin and dominant QTL in a two-generation commercial poultry pedigree using variance component methodology

INTRODUCTION

Variance component QTL methodology was used to analyse three candidate regions on chicken chromosomes 1, 4 and 5 for dominant and parent-of-origin QTL effects. Data were available for bodyweight and conformation score measured at 40 days from a two-generation commercial broiler dam line. One hundred dams were nested in 46 sires with phenotypes and genotypes on 2708 offspring. Linear models were constructed to simultaneously estimate fixed, polygenic and QTL effects. Different genetic models were compared using likelihood ratio test statistics derived from the comparison of full with reduced or null models. Empirical thresholds were derived by permutation analysis.

RESULTS

Dominant QTL were found for bodyweight on chicken chromosome 4 and for bodyweight and conformation score on chicken chromosome 5. Suggestive evidence for a maternally expressed QTL for bodyweight and conformation score was found on chromosome 1 in a region corresponding to orthologous imprinted regions in the human and mouse.

CONCLUSION

Initial results suggest that variance component analysis can be applied within commercial populations for the direct detection of segregating dominant and parent of origin effects.

Detecting dominant QTL with variance component analysis in simulated pedigrees

Dominance is an important source of variation in complex traits. Here, we have carried out the first thorough investigation of quantitative trait locus (QTL) detection using variance component (VC) models extended to incorporate both additive and dominant QTL effects. Simulation results showed that the empirical distribution of the test statistic when testing for dominant QTL effects did not behave in accordance with existing theoretical expectations and varied with pedigree structure. Extensive simulations were carried out to assess accuracy of estimates, type 1 error and statistical power in two-generation human-, poultry- and pig-type pedigrees each with 1900 progeny in small-, medium- and large-sized families, respectively. The distribution of the likelihood-ratio test statistic was heavily dependent on family structure, with empirical thresholds lower for human pedigrees. Power to detect QTL was high (0.84-1.0) in pig and poultry scenarios for dominance effects accounting for >7% of phenotypic variance but much lower (0.42) in human-type pedigrees. Maternal or common environment effects can be partially confounded with dominance and must be fitted in the QTL model. Including dominance in the QTL model did not affect power to detect additive QTL effects. Also, detection of spurious dominance QTL effects only occurred when maternal effects were not included in the QTL model. When dominance effects were present in the data but not in the analysis model, this resulted in spurious detection of additive QTL or inflated estimates of additive QTL effects. The study demonstrates that dominance can be included routinely in QTL analysis of general pedigrees; however, optimal power is dependent on selection of the appropriate thresholds for pedigree structure.