A global search reveals epistatic interaction between QTL for early growth in the chicken

MeRIP sequencing reveals the regulation of N6-methyladenosine in muscle development between hypertrophic and leaner broilers

Meat production performance is the most important economic trait in broilers, and skeletal muscle, as the largest organ in animals, is directly related to meat production during embryonic and postnatal growth and development. N6-Methyladenosine (m6A) is a chemical modification occurs on RNA adenosine that has been reported to participate in a variety of biological processes in all species. However, there are still few reports on the regulatory role of muscle growth and development in poultry after birth. This study aims to reveal the distribution of m6A modification sites in chicken pectoralis major muscle after birth and find out the regulatory relationship between m6A and muscle development. As representatives of leaner (Xinghua chicken [XH]) and hypertrophic (White Recessive Rock chicken [WRR]) broilers, there are significant differences in body weight, muscle fiber diameter, and muscle fiber cross-sectional area between XH and WRR chickens. RNA sequencing detected a total of 397 differentially expressed genes (DEG) in the pectoralis major muscle of XH and WRR chicken, and these DEGs were mainly enriched in catalytic activity and metabolic pathways. MeRIP sequencing results showed that among all 6,476 differentially modified m6A peaks, about 90% peaks (5,823) were differentially down regulated in XH chickens. The joint analysis of the mRNA and MeRIP sequencing data found 145 DEGs with differential m6A peak, ALKBH5 as a m6A demethylase, was also included. The highly expression of ALKBH5 in the muscle tissue of poultry and differential expression between XH and WRR chickens suggest that ALKBH5 may play a crucial role in regulating muscle development. Our results revealed that there were significant differences in growth rate, body weight, muscle fiber diameter, and fiber cross-section area between WRR and XH chicken, as well as significant differences in m6A methylation level and muscle metabolism level.

Complex genetic architecture of the chicken Growth1 QTL region

The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin's finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Genome-wide unraveling SNP pairwise epistatic effects associated with sheep body weight

Epistatic effects are an important part of the genetic effect of complex traits in livestock. In this study, we used 218 synthetic ewes from the Xinjiang Academy of Agricultural Reclamation in China to identify interacting paired with genome-wide single nucleotide polymorphisms (SNPs) associated with birth weight, weaning weight, and one-yearling weight. We detected 2 and 66 SNP-SNP interactions of sheep birth weight and weaning weight, respectively. No significant epistatic interaction of one-year-old body weight was detected. The genetic interaction of sheep body weight is dynamic and time-dependent. Most significant interactions of weaning body weight contributed 1% or higher. In the weaning weight trait, 66 significant SNP pairs consisted of 98 single SNPs covering 23 chromosomes, 5 of which were nonsynonymous SNPs (nsSNPs), resulting in single amino acid substitution. We found that genes that interact with transcription factors (TFs) are target genes for the corresponding TFs. Four epitron networks affecting weaning weight, including subnetworks of HIVEP3 and BACH2 transcription factors, constructed using significant SNP pairs, were also analyzed and annotated. These results suggest that transcription factors may play an important role in explaining epistatic effects. It provides a new idea to study the genetic mechanism of weight developing.

RNA-Seq-based discovery of genetic variants and allele-specific expression of two layer lines and broiler chicken

Recent advances in the selective breeding of broilers and layers have made poultry production one of the fastest-growing industries. In this study, a transcriptome variant calling approach from RNA-seq data was used to determine population diversity between broilers and layers. In total, 200 individuals were analyzed from three different chicken populations (Lohmann Brown (LB), n = 90), Lohmann Selected Leghorn (LSL, n = 89), and Broiler (BR, n = 21). The raw RNA-sequencing reads were pre-processed, quality control checked, mapped to the reference genome, and made compatible with Genome Analysis ToolKit for variant detection. Subsequently, pairwise fixation index (F ST) analysis was performed between broilers and layers. Numerous candidate genes were identified, that were associated with growth, development, metabolism, immunity, and other economically significant traits. Finally, allele-specific expression (ASE) analysis was performed in the gut mucosa of LB and LSL strains at 10, 16, 24, 30, and 60 weeks of age. At different ages, the two-layer strains showed significantly different allele-specific expressions in the gut mucosa, and changes in allelic imbalance were observed across the entire lifespan. Most ASE genes are involved in energy metabolism, including sirtuin signaling pathways, oxidative phosphorylation, and mitochondrial dysfunction. A high number of ASE genes were found during the peak of laying, which were particularly enriched in cholesterol biosynthesis. These findings indicate that genetic architecture as well as biological processes driving particular demands relate to metabolic and nutritional requirements during the laying period shape allelic heterogeneity. These processes are considerably affected by breeding and management, whereby elucidating allele-specific gene regulation is an essential step towards deciphering the genotype to phenotype map or functional diversity between the chicken populations. Additionally, we observed that several genes showing significant allelic imbalance also colocalized with the top 1% of genes identified by the FST approach, suggesting a fixation of genes in cis-regulatory elements.

Direct and maternal genetic effects for preinflection point growth traits and humoral immunity in quail

Early growth traits in quails are considered as the growth performances before the inflection point which are genetically different from body weights (BW) at later stages. Moreover, in addition to growth performance, humoral immunity is moderately heritable and is considered in some breeding programs. However, estimating the direct genetic, particularly the maternal genetic correlations between growth and immunity in quail, are not studied sufficiently, which were the aims of the present study. The quails' BW were recorded at hatch (BW0) to 25 d of age with a 5-d interval and body weight gains (BWG) were measured as average growth performance of the birds in a 5-d period. Antibody titer against Newcastle disease virus (IgN) was measured through the hemagglutination inhibition (HI) test. For titration of anti-SRBC antibodies (IgY and IgM), a hemagglutination microtiter assay was used. In general, growth records in 4,181 birds and humoral immune responses in 1,023 birds were assigned to the study. The genetic parameters were estimated by single-trait analysis via Gibb's sampling. After finding the best model for each trait, multi-trait analysis was done to estimate the direct and maternal genetic correlations. Direct heritabilities (h²) were estimated to be moderate for BW (0.481-0.551) and BWG (0.524-0.557), while h² for immune responses were low (0.035-0.079). Maternal environmental effect (c²) was only significant for BW0, BW5, and BWG0-5. Maternal heritabilities (m²) for BW and BWG were all lower than corresponding h², ranging from 0.072 (BW25) to 0.098 (BW0). The m² for IgN (0.098) was more than 2.5 times greater than h² (0.040) for this trait. Direct (r_a) and maternal (r_m) genetic correlations between IgN-BW, IgY-BW, and IgY-BWG were negative, while ra and rm for IgM-BW, IgN-BWG, and IgM-BWG were positive. The r_a between humoral immune responses were low to moderate and rm was significant only for IgY-IgM (0.339). Given positive genetic correlations in BWG-IgN and BWG-IgM as well as positive genetic correlations between both IgN and IgM with IgY, it is suggested that including the BWG in the breeding programs would directly result in the improvement of the birds' growth performance. It would also contribute indirectly to the improvement of the birds' humoral immune responses.

Liver fat metabolism of broilers regulated by Bacillus amyloliquefaciens TL via stimulating IGF-1 secretion and regulating the IGF signaling pathway

Bacillus amyloliquefaciens TL (B.A-TL) is well-known for its capability of promoting protein synthesis and lipid metabolism, in particular, the abdominal fat deposition in broilers. However, the underlying molecular mechanism remains unclear. In our study, the regulations of lipid metabolism of broilers by B.A-TL were explored both in vivo and in vitro. The metabolites of B.A-TL were used to simulate in vitro the effect of B.A-TL on liver metabolism based on the chicken hepatocellular carcinoma cell line (i.e., LMH cells). The effects of B.A-TL on lipid metabolism by regulating insulin/IGF signaling pathways were investigated by applying the signal pathway inhibitors in vitro. The results showed that the B.A-TL metabolites enhanced hepatic lipid synthesis and stimulated the secretion of IGF-1. The liver transcriptome analysis revealed the significantly upregulated expressions of four genes (SI, AMY2A, PCK1, and FASN) in the B.A-TL treatment group, mainly involved in carbohydrate digestion and absorption as well as biomacromolecule metabolism, with a particularly prominent effect on fatty acid synthase (FASN). Results of cellular assays showed that B.A-TL metabolites were involved in the insulin/IGF signaling pathway, regulating the expressions of lipid metabolism genes (e.g., FASN, ACCα, LPIN, and ACOX) and the FASN protein, ultimately regulating the lipid metabolism via the IGF/PI3K/FASN pathway in broilers.

Age-dependent genetic architecture across ontogeny of body size in sticklebacks

Heritable variation in traits under natural selection is a prerequisite for evolutionary response. While it is recognized that trait heritability may vary spatially and temporally depending on which environmental conditions traits are expressed under, less is known about the possibility that genetic variance contributing to the expected selection response in a given trait may vary at different stages of ontogeny. Specifically, whether different loci underlie the expression of a trait throughout development and thus providing an additional source of variation for selection to act on in the wild, is unclear. Here we show that body size, an important life-history trait, is heritable throughout ontogeny in the nine-spined stickleback (Pungitius pungitius). Nevertheless, both analyses of quantitative trait loci and genetic correlations across ages show that different chromosomes/loci contribute to this heritability in different ontogenic time-points. This suggests that body size can respond to selection at different stages of ontogeny but that this response is determined by different loci at different points of development. Hence, our study provides important results regarding our understanding of the genetics of ontogeny and opens an interesting avenue of research for studying age-specific genetic architecture as a source of non-parallel evolution.

New insights into genetics underlying of plumage color

Plumage color can be considered as a social signal in chickens and a breeding identification tool among breeders. The relationship between plumage color and trait groups of immunity, growth and fertility is still a controversial issue. This research aimed to determine the genome-wide additive and epistatic variants affecting plumage color variation in chickens using the chicken Illumina 60k high-density SNP array. Two scenarios of genome-wide additive association studies using all SNPs and independent SNPs were carried out. To perform epistatic association analysis, the LD pruning approach was used to reduce the complexity of the analysis. We detected seven novel significant loci using all of the SNPs in the model and 14 SNPs using the LD pruning approach associated with plumage color. Moreover, 89 significantly associated SNP-SNP interactions (P-value <10^-6 ) distributed in 25 chromosomes were identified, indicating that all of the signals together putatively influence the quantitative variation of plumage color. By annotating genes relevant to top SNPs, we have distinguished 18 potential candidate genes comprising HNF4beta, CKMT1B, TBC1D22A, RPL8, CACNA2D1, FZD4, SGMS1, IRF8, OPTN, LOC420362, TRABD, OvoDA1, DAD1, USP6, RBM12B, MIR1772, MIR1709 and MIR6696 and also 89 putative gene-gene combinations responsible for plumage color variation in chickens. Furthermore, several KEGG pathways including metabolic pathway, cytokine-cytokine receptor interaction, focal adhesion, melanogenesis, glycosaminoglycan biosynthesis-keratan sulfate and sphingolipid metabolism were enriched in the gene-set analysis. The results indicated that plumage color is a highly polygenic trait which, in turn, can be affected by multiple coding genes, regulatory genes and gene-gene epistasis interactions. In addition to genes with additive effects, epistatic genes with tiny individual effect sizes but significant effects in a pair have the potential to control plumage coloration in chickens.

A genome-wide association analysis for body weight at 35 days measured on 137,343 broiler chickens

BACKGROUND

Body weight (BW) is an economically important trait in the broiler (meat-type chickens) industry. Under the assumption of polygenicity, a "large" number of genes with "small" effects is expected to control BW. To detect such effects, a large sample size is required in genome-wide association studies (GWAS). Our objective was to conduct a GWAS for BW measured at 35 days of age with a large sample size.

METHODS

The GWAS included 137,343 broilers spanning 15 pedigree generations and 392,295 imputed single nucleotide polymorphisms (SNPs). A false discovery rate of 1% was adopted to account for multiple testing when declaring significant SNPs. A Bayesian ridge regression model was implemented, using AlphaBayes, to estimate the contribution to the total genetic variance of each region harbouring significant SNPs (1 Mb up/downstream) and the combined regions harbouring non-significant SNPs.

RESULTS

GWAS revealed 25 genomic regions harbouring 96 significant SNPs on 13 Gallus gallus autosomes (GGA1 to 4, 8, 10 to 15, 19 and 27), with the strongest associations on GGA4 at 65.67-66.31 Mb (Galgal4 assembly). The association of these regions points to several strong candidate genes including: (i) growth factors (GGA1, 4, 8, 13 and 14); (ii) leptin receptor overlapping transcript (LEPROT)/leptin receptor (LEPR) locus (GGA8), and the STAT3/STAT5B locus (GGA27), in connection with the JAK/STAT signalling pathway; (iii) T-box gene (TBX3/TBX5) on GGA15 and CHST11 (GGA1), which are both related to heart/skeleton development); and (iv) PLAG1 (GGA2). Combined together, these 25 genomic regions explained ~ 30% of the total genetic variance. The region harbouring significant SNPs that explained the largest portion of the total genetic variance (4.37%) was on GGA4 (~ 65.67-66.31 Mb).

CONCLUSIONS

To the best of our knowledge, this is the largest GWAS that has been conducted for BW in chicken to date. In spite of the identified regions, which showed a strong association with BW, the high proportion of genetic variance attributed to regions harbouring non-significant SNPs supports the hypothesis that the genetic architecture of BW35 is polygenic and complex. Our results also suggest that a large sample size will be required for future GWAS of BW35.

Quantitative trait loci for growth-related traits in Japanese quail (Coturnix japonica) using restriction-site associated DNA sequencing

This study aimed to identify quantitative trait loci (QTLs) for growth-related traits by constructing a genetic linkage map based on single nucleotide polymorphism (SNP) markers in Japanese quail. A QTL mapping population of 277 F₂ birds was obtained from an intercross between a male of a large-sized strain and three females of a normal-sized strain. Body weight (BW) was measured weekly from hatching to 16 weeks of age. Non-linear regression growth models of Weibull, Logistic, Gompertz, Richards, and Brody were analyzed, and growth curve parameters of Richards was selected as the best model to describe the quail growth curve of the F₂ birds. Restriction-site associated DNA sequencing developed 125 SNP markers that were informative between their parental strains. The SNP markers were distributed on 16 linkage groups that spanned 795.9 centiMorgan (cM) with an average marker interval of 7.3 cM. QTL analysis of phenotypic traits revealed four main-effect QTLs. Detected QTLs were located on chromosomes 1 and 3 and were associated with BW from 4 to 16 weeks of age and asymptotic weight of Richards model at genome-wide significant at 1% or 5% level. No QTL was detected for BW from 0 to 3 weeks of age. This is the first report identified QTLs for asymptotic weight of the Richards parameter in Japanese quail. These results highlight that the combination of QTL studies and the RAD-seq method will aid future breeding programs identify genes underlying the QTL and the application of marker-assisted selection in the poultry industry, particularly the Japanese quail.

Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep

Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep.

Epistatic regulation of growth in Atlantic salmon revealed: a QTL study performed on the domesticated-wild interface

Background

Quantitative traits are typically considered to be under additive genetic control. Although there are indications that non-additive factors have the potential to contribute to trait variation, experimental demonstration remains scarce. Here, we investigated the genetic basis of growth in Atlantic salmon by exploiting the high level of genetic diversity and trait expression among domesticated, hybrid and wild populations.

Results

After rearing fish in common-garden experiments under aquaculture conditions, we performed a variance component analysis in four mapping populations totaling ~ 7000 individuals from six wild, two domesticated and three F1 wild/domesticated hybrid strains. Across the four independent datasets, genome-wide significant quantitative trait loci (QTLs) associated with weight and length were detected on a total of 18 chromosomes, reflecting the polygenic nature of growth. Significant QTLs correlated with both length and weight were detected on chromosomes 2, 6 and 9 in multiple datasets. Significantly, epistatic QTLs were detected in all datasets.

Discussion

The observed interactions demonstrated that the phenotypic effect of inheriting an allele deviated between half-sib families. Gene-by-gene interactions were also suggested, where the combined effect of two loci resulted in a genetic effect upon phenotypic variance, while no genetic effect was detected when the two loci were considered separately. To our knowledge, this is the first documentation of epistasis in a quantitative trait in Atlantic salmon. These novel results are of relevance for breeding programs, and for predicting the evolutionary consequences of domestication-introgression in wild populations.

The Impact of Non-additive Effects on the Genetic Correlation Between Populations

Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations ([Formula: see text]). This [Formula: see text] can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of [Formula: see text] remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of [Formula: see text] In addition, we found that with epistasis, [Formula: see text] decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, [Formula: see text] had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true [Formula: see text] values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, [Formula: see text] dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations.

Genotype frequency distributions of 28 SNP markers in two commercial lines and five Chinese native chicken populations

Background

Modern breeding in the poultry industry mainly aims to produce high-performance poultry lines and breeds in two main directions of productivity, meat and eggs. To understand more about the productive potential of lowly selected Chinese native chicken populations, we selected 14 representative SNP markers strongly associated with growth traits or carcass traits and 14 SNP markers strongly associated with egg laying traits through previous reports. By using the MassArray technology, we detected the genotype frequency distributions of these 28 SNP markers in seven populations including four lowly selected as well as one moderately selected Sichuan native chicken populations, one commercial broiler line and one commercial layer line.

Results

Based on the genotype frequency distributions of these 28 SNP markers in 5 native chicken populations and 2 commercial lines, the results suggested that these Chinese indigenous chicken populations have a relatively close relationship with the commercial broiler line but a marked distinction from the commercial layer line. Two native chicken breeds, Shimian Caoke Chicken and Daheng Broilers, share similar genetic structure with the broiler line.

Conclusions

Our observations may help us to better select and breed superior domestic chickens and provide new clues for further study of breeding programs in local chicken populations.

A time-dependent genome-wide SNP-SNP interaction analysis of chicken body weight

Background

The important property of the quantitative traits of model organisms is time-dependent. However, the methodology for investigating the genetic interaction network over time is still lacking. Our study aims to provide insights into the mechanistic basis of epistatic interactions affecting the phenotypes of model organisms.

Results

We performed an exhaustive genome-wide search for significant SNP-SNP interactions associated with male birds’ body weight (BW) (n = 475) at multiple time points (day of hatch (BW0) and 1, 3, 5, and 7 weeks (BW1, BW3, BW5, and BW7)). Statistical analysis detected 67, four, and two significant SNP pairs associated with BW0, BW1, and BW3, respectively, with a significance threshold at 8.67 × 10^− 12 (Bonferroni-adjusted: 1%). Meanwhile, no significant SNP pairs associated with BW5 and BW7 were found. The SNP-SNP interaction networks of BW0, BW1, and BW3 were built and annotated.

Conclusions

With strong annotated information and a strict significant threshold, SNP-SNP interactions underpinned the gene-gene interactions that might occur between chromosomes or within the same chromosome. Comparing and combing the networks, the results indicated that the genetic network for chicken body weight was dynamic and time-dependent.

Genetic analysis of production, physiological, and egg quality traits in heat-challenged commercial white egg-laying hens using 600k SNP array data

Background

Heat stress negatively affects the welfare and production of chickens. High ambient temperature is considered one of the most ubiquitous abiotic environmental challenges to laying hens around the world. In this study, we recorded several production traits, feed intake, body weight, digestibility, and egg quality of 400 commercial white egg-laying hens before and during a 4-week heat treatment. For the phenotypes that had estimated heritabilities (using 600k SNP chip data) higher than 0, SNP associations were tested using the same 600k genotype data.

Results

Seventeen phenotypes had heritability estimates higher than 0, including measurements at various time points for feed intake, feed efficiency, body weight, albumen weight, egg quality expressed in Haugh units, egg mass, and also for change in egg mass from prior to heat exposure to various time points during the 4-week heat treatment. Quantitative trait loci (QTL) were identified for 10 of these 17 phenotypes. Some of the phenotypes shared QTL including Haugh units before heat exposure and after 4 weeks of heat treatment.

Conclusions

Estimated heritabilities differed from 0 for 17 traits, which indicates that they are under genetic control and that there is potential for improving these traits through selective breeding. The association of different QTL with the same phenotypes before heat exposure and during heat treatment indicates that genomic control of traits under heat stress is distinct from that under thermoneutral conditions. This study contributes to the knowledge on the genomic control of response to heat stress in laying hens.

Electronic supplementary material

The online version of this article (10.1186/s12711-019-0474-6) contains supplementary material, which is available to authorized users.

Quantitative trait loci mapping for the shear force value in breast muscle of F2 chickens

The shear force value is one of the major traits that determine meat quality. In the present study, we performed QTL analysis for chicken breast muscle shear force value at 7 wk of age using 545 single nucleotide polymorphism (SNP) markers developed via restriction-site associated DNA sequencing (RAD-seq). An F2 resource family was generated by mating Oh-Shamo, a native Japanese chicken breed, and the White Plymouth Rock chicken breed. A total of 215 F2 birds were produced. Simple interval mapping revealed one significant main-effect QTL between 6.28 and 8.10 Mb SNPs on the chromosome Z with a logarithm of odds score of 5.53 at the genome-wide 5% level. At this QTL, the confidence interval, phenotypic variance explained, and additive effect were 26 cM, 12.24%, and -0.31 in males and -0.34 in females, respectively. No QTL with epistatic interaction effects were detected. To our knowledge, this is the first report on a QTL affecting the shear force value in the chicken breast muscle, using SNP markers derived from RAD-seq.

Genome-wide association study of the level of blood components in Pekin ducks

Blood components are considered to reflect nutrient metabolism and immune activity in both humans and animals. In this study, we measured 12 blood components in Pekin ducks and performed genome-wide association analysis to identify the QTLs (quantitative trait locus) using a genotyping-by-sequencing strategy. A total of 54 QTLs were identified for blood components. One genome-wide significant QTL for alkaline phosphatase was identified within the intron-region of the OTOG gene (P = 1.31E-07). Moreover, 21 genome-wide significant SNPs for the level of serum cholinesterase were identified on six different scaffolds. In addition, for serum calcium, one genome-wide significant QTL was identified in the upstream region of gene RAB11B. These results provide new markers for functional studies in Pekin ducks, and several candidate genes were identified, which may provide additional insights into specific mechanisms for blood metabolism in ducks and their potential application for duck breeding programs.

Mapping of Quantitative Trait Loci for Growth and Carcass-Related Traits in Chickens Using a Restriction-Site Associated DNA Sequencing Method

In the present study, quantitative trait loci (QTLs) analysis was performed to identify the chromosomal positions of growth and carcass-related trait QTLs using 319 F₂ chickens obtained from intercrosses of an Oh-Shamo male and four White Plymouth Rock females. Body weight was measured weekly until the birds were 7 weeks old. Carcass-related traits were also measured at this timepoint. A genetic linkage map was constructed using 545 single nucleotide polymorphism (SNP) markers that were developed using a restriction-site associated DNA sequencing method. The linkage map included the 23 autosomes and the Z chromosome. Using simple interval QTL mapping, we were able to identify 10 significant and suggestive main-effect QTLs for growth and carcass-related traits present on chromosomes 1, 2, 3, 5, 8, 19, 24, and Z. These loci explained 5.60–16.52% of the phenotypic variances. The chromosomal positions of the 10 QTLs overlapped with those of previously reported QTLs, whereas the targeted traits varied. Our QTLs will aid future breeding programs in improving growth and meat yield of chickens (e.g., via marker-assisted selection), particularly in the Japanese brand chicken industry.

Quantitative trait loci for resistance to Flavobacterium psychrophilum in rainbow trout: effect of the mode of infection and evidence of epistatic interactions

Background

Bacterial cold-water disease, which is caused by Flavobacterium psychrophilum, is one of the major diseases that affect rainbow trout (Oncorhynchus mykiss) and a primary concern for trout farming. Better knowledge of the genetic basis of resistance to F. psychrophilum would help to implement this trait in selection schemes and to investigate the immune mechanisms associated with resistance. Various studies have revealed that skin and mucus may contribute to response to infection. However, previous quantitative trait loci (QTL) studies were conducted by using injection as the route of infection. Immersion challenge, which is assumed to mimic natural infection by F. psychrophilum more closely, may reveal different defence mechanisms.

Results

Two isogenic lines of rainbow trout with contrasting susceptibilities to F. psychrophilum were crossed to produce doubled haploid F2 progeny. Fish were infected with F. psychrophilum either by intramuscular injection (115 individuals) or by immersion (195 individuals), and genotyped for 9654 markers using RAD-sequencing. Fifteen QTL associated with resistance traits were detected and only three QTL were common between the injection and immersion. Using a model that accounted for epistatic interactions between QTL, two main types of interactions were revealed. A “compensation-like” effect was detected between several pairs of QTL for the two modes of infection. An “enhancing-like” interaction effect was detected between four pairs of QTL. Integration of the QTL results with results of a previous transcriptomic analysis of response to F. psychrophilum infection resulted in a list of potential candidate immune genes that belong to four relevant functional categories (bacterial sensors, effectors of antibacterial immunity, inflammatory factors and interferon-stimulated genes).

Conclusions

These results provide new insights into the genetic determinism of rainbow trout resistance to F. psychrophilum and confirm that some QTL with large effects are involved in this trait. For the first time, the role of epistatic interactions between resistance-associated QTL was evidenced. We found that the infection protocol used had an effect on the modulation of defence mechanisms and also identified relevant immune functional candidate genes.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0431-9) contains supplementary material, which is available to authorized users.

Identification of quantitative trait loci affecting production and biochemical traits in a unique Japanese quail resource population

The objective of the current study was to identify QTL associated with body weight, growth rate, egg quality traits, concentration of selected blood plasma, and yolk lipids as well as concentration of selected macro- and microelements, color, pH, basic chemical composition, and drip loss of breast muscle of Japanese quail (Coturnix japonica). Twenty-two meat-type males (line F33) were crossed with twenty-two laying-type females (line S22) to produce a generation of F1 hybrids. The F2 generation was created by mating 44 randomly chosen F1 hybrids, which were full siblings. The birds were individually weighed from the first to eighth week of age. At the age of 19 wk, 2 to 4 eggs were individually collected from each female and an analysis of the egg quality traits was performed. At slaughter, blood and breast muscles were collected from 324 individuals of the resource population. The basic chemical composition, concentration of chosen macro- and microelements, color, pH, and drip loss were determined in the muscle samples. The concentration of chosen lipids was determined in egg yolk and blood plasma. In total, 30 microsatellite markers located on chromosome 1 and 2 were genotyped. QTL mapping including additive and dominance genetic effects revealed 6 loci on chromosome 1 of the Japanese quail affecting the egg number, egg production rate, egg weight, specific gravity, egg shell weight, concentration of Na in breast muscle. In turn, there were 9 loci on chromosome 2 affecting the body weight in the first, fourth, and sixth week of age, growth rate in the second and seventh week of age, specific gravity, concentration of K and Cu in breast muscle, and the levels of triacylglycerols in blood plasma. In this study, QTL with a potential effect on the Na, K, and Cu content in breast muscles in poultry and on specific gravity in the Japanese quail were mapped for the first time.

Genomic Insights Into the Multiple Factors Controlling Abdominal Fat Deposition in a Chicken Model

Genetic selection for an increased growth rate in meat-type chickens has been accompanied by excessive fat accumulation particularly in abdominal cavity. These progressed to indirect and often unhealthy effects on meat quality properties and increased feed cost. Advances in genomics technology over recent years have led to the surprising discoveries that the genome is more complex than previously thought. Studies have identified multiple-genetic factors associated with abdominal fat deposition. Meanwhile, the obesity epidemic has focused attention on adipose tissue and the development of adipocytes. The aim of this review is to summarize the current understanding of genetic/epigenetic factors associated with abdominal fat deposition, or as it relates to the proliferation and differentiation of preadipocytes in chicken. The results discussed here have been identified by different genomic approaches, such as QTL-based studies, the candidate gene approach, epistatic interaction, copy number variation, single-nucleotide polymorphism screening, selection signature analysis, genome-wide association studies, RNA sequencing, and bisulfite sequencing. The studies mentioned in this review have described multiple-genetic factors involved in an abdominal fat deposition. Therefore, it is inevitable to further study the multiple-genetic factors in-depth to develop novel molecular markers or potential targets, which will provide promising applications for reducing abdominal fat deposition in meat-type chicken.

Multi-environment fitness landscapes of a tRNA gene

A fitness landscape (FL) describes the genotype-fitness relationship in a given environment. To explain and predict evolution, it is imperative to measure the FL in multiple environments because the natural environment changes frequently. Using a high-throughput method that combines precise gene replacement with next-generation sequencing, we determine the in vivo FL of a yeast tRNA gene comprising over 23,000 genotypes in four environments. Although genotype-by-environment interaction (G×E) is abundantly detected, its pattern is so simple that we can transform an existing FL to that in a new environment with fitness measures of only a few genotypes in the new environment. Under each environment, we observe prevalent, negatively biased epistasis between mutations (G×G). Epistasis-by-environment interaction (G×G×E) is also prevalent, but trends in epistasis difference between environments are predictable. Our study thus reveals simple rules underlying seemingly complex FLs, opening the door to understanding and predicting FLs in general.

Genetical genomics of growth in a chicken model

BACKGROUND

The genetics underlying body mass and growth are key to understanding a wide range of topics in biology, both evolutionary and developmental. Body mass and growth traits are affected by many genetic variants of small effect. This complicates genetic mapping of growth and body mass. Experimental intercrosses between individuals from divergent populations allows us to map naturally occurring genetic variants for selected traits, such as body mass by linkage mapping. By simultaneously measuring traits and intermediary molecular phenotypes, such as gene expression, one can use integrative genomics to search for potential causative genes.

RESULTS

In this study, we use linkage mapping approach to map growth traits (N = 471) and liver gene expression (N = 130) in an advanced intercross of wild Red Junglefowl and domestic White Leghorn layer chickens. We find 16 loci for growth traits, and 1463 loci for liver gene expression, as measured by microarrays. Of these, the genes TRAK1, OSBPL8, YEATS4, CEP55, and PIP4K2B are identified as strong candidates for growth loci in the chicken. We also show a high degree of sex-specific gene-regulation, with almost every gene expression locus exhibiting sex-interactions. Finally, several trans-regulatory hotspots were found, one of which coincides with a major growth locus.

CONCLUSIONS

These findings not only serve to identify several strong candidates affecting growth, but also show how sex-specificity and local gene-regulation affect growth regulation in the chicken.

Use and optimization of different sources of information for genomic prediction

Background

Molecular data is now commonly used to predict breeding values (BV). Various methods to calculate genomic relationship matrices (GRM) have been developed, with some studies proposing regression of coefficients back to the reference matrix of pedigree-based relationship coefficients (A). The objective was to compare the utility of two GRM: a matrix based on linkage analysis (LA) and anchored to the pedigree, i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{{{\mathbf{LA}}}} ,$$\end{document}GLA, and a matrix based on linkage disequilibrium (LD), i.e. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{{{\mathbf{LD}}}}$$\end{document}GLD, using genomic and phenotypic data collected on 5416 broiler chickens. Furthermore, the effects of regressing the coefficients of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{{{\mathbf{LD}}}}$$\end{document}GLD back to A (LDA) and to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{{{\mathbf{LA}}}}$$\end{document}GLA (LDLA) were evaluated, using a range of weighting factors. The performance of the matrices and their composite products was assessed by the fit of the models to the data, and the empirical accuracy and bias of the BV that they predicted. The sensitivity to marker choice was examined by using two chips of equal density but including different single nucleotide polymorphisms (SNPs).

Results

The likelihood of models using GRM and composite matrices exceeded the likelihood of models based on pedigree alone and was highest with intermediate weighting factors for both the LDA and LDLA approaches. For these data, empirical accuracies were not strongly affected by the weighting factors, although they were highest when different sources of information were combined. The optimum weighting factors depended on the type of matrices used, as well as on the choice of SNPs from which the GRM were constructed. Prediction bias was strongly affected by the chip used and less by the form of the GRM.

Conclusions

Our findings provide an empirical comparison of the efficacy of pedigree and genomic predictions in broiler chickens and examine the effects of fitting GRM with coefficients regressed back to a reference anchored to the pedigree, either A or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{{{\mathbf{LA}}}}$$\end{document}GLA. For the analysed dataset, the best results were obtained when \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{{{\mathbf{LD}}}}$$\end{document}GLD was combined with relationships in A or \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{G}}_{{{\mathbf{LA}}}}$$\end{document}GLA, with optimum weighting factors that depended on the choice of SNPs used. The optimum weighting factor for broiler body weight differed from weighting factors that were based on the density of SNPs and theoretically derived using generalised assumptions.

On the relationship between epistasis and genetic variance heterogeneity

Epistasis and genetic variance heterogeneity are two non-additive genetic inheritance patterns that are often, but not always, related. Here we use theoretical examples and empirical results from earlier analyses of experimental data to illustrate the connection between the two. This includes an introduction to the relationship between epistatic gene action, statistical epistasis, and genetic variance heterogeneity, and a brief discussion about how genetic processes other than epistasis can also give rise to genetic variance heterogeneity.

Identification of genome-wide SNP-SNP interactions associated with important traits in chicken

BACKGROUND

In addition to additive genetic effects, epistatic interactions can play key roles in the control of phenotypic variation of traits of interest. In the current study, 475 male birds from lean and fat chicken lines were utilized as a resource population to detect significant epistatic effects associated with growth and carcass traits.

RESULTS

A total of 421 significant epistatic effects were associated with testis weight (TeW), from which 11 sub-networks (Sub-network1 to Sub-network11) were constructed. In Sub-network1, which was the biggest network, there was an interaction between GGA21 and GGAZ. Three genes on GGA21 (SDHB, PARK7 and VAMP3) and nine genes (AGTPBP1, CAMK4, CDC14B, FANCC, FBP1, GNAQ, PTCH1, ROR2 and STARD4) on GGAZ that might be potentially important candidate genes for testis growth and development were detected based on the annotated gene function. In Sub-network2, there was a SNP on GGA19 that interacted with 8 SNPs located on GGA10. The SNP (Gga_rs15834332) on GGA19 was located between C-C motif chemokine ligand 5 (CCL5) and MIR142. There were 32 Refgenes on GGA10, including TCF12 which is predicted to be a target gene of miR-142-5p. We hypothesize that miR-142-5p and TCF12 may interact with one another to regulate testis growth and development. Two genes (CDH12 and WNT8A) in the same cadherin signaling pathway were implicated as potentially important genes in the control of metatarsus circumference (MeC). There were no significant epistatic effects identified for the other carcass and growth traits, e.g. heart weight (HW), liver weight (LW), spleen weight (SW), muscular and glandular stomach weight (MGSW), carcass weight (CW), body weight (BW1, BW3, BW5, BW7), chest width (ChWi), metatarsus length (MeL).

CONCLUSIONS

The results of the current study are helpful to better understand the genetic basis of carcass and growth traits, especially for testis growth and development in broilers.

Consequences of Epistasis on Growth in an Erhualian × White Duroc Pig Cross

Epistasis describes an interaction between the effects of loci. We included epistasis in quantitative trait locus (QTL) mapping of growth at a series of ages in a cross of a Chinese pig breed, Erhualian, with a commercial line, White Duroc. Erhualian pigs have much lower growth rates than White Duroc. We improved a method for genomewide testing of epistasis and present a clear analysis workflow. We also suggest a new approach for interpreting epistasis results where significant additive and dominance effects of a locus in specific backgrounds are determined. In total, seventeen QTL were found and eleven showed epistasis. Loci on chromosomes 2, 3, 4 and 7 were highlighted as affecting growth at more than one age or forming an interaction network. Epistasis resulted in both the QTL on chromosomes 3 and 7 having effects in opposite directions. We believe it is the first time for the chromosome 7 locus that an allele from a Chinese breed has been found to decrease growth. The consequences of epistasis were diverse. Results were impacted by using growth rather than body weight as the phenotype and by correcting for an effect of mother. Epistasis made a considerable contribution to growth in this population and modelling epistasis was important for accurately determining QTL effects.

Genetics of Interactive Behavior in Silver Foxes (Vulpes vulpes)

Individuals involved in a social interaction exhibit different behavioral traits that, in combination, form the individual's behavioral responses. Selectively bred strains of silver foxes (Vulpes vulpes) demonstrate markedly different behaviors in their response to humans. To identify the genetic basis of these behavioral differences we constructed a large F₂ population including 537 individuals by cross-breeding tame and aggressive fox strains. 98 fox behavioral traits were recorded during social interaction with a human experimenter in a standard four-step test. Patterns of fox behaviors during the test were evaluated using principal component (PC) analysis. Genetic mapping identified eight unique significant and suggestive QTL. Mapping results for the PC phenotypes from different test steps showed little overlap suggesting that different QTL are involved in regulation of behaviors exhibited in different behavioral contexts. Many individual behavioral traits mapped to the same genomic regions as PC phenotypes. This provides additional information about specific behaviors regulated by these loci. Further, three pairs of epistatic loci were also identified for PC phenotypes suggesting more complex genetic architecture of the behavioral differences between the two strains than what has previously been observed.

Comparative population genomics reveals genetic basis underlying body size of domestic chickens

Body size is the most important economic trait for animal production and breeding. Several hundreds of loci have been reported to be associated with growth trait and body weight in chickens. The loci are mapped to large genomic regions due to the low density and limited number of genetic markers in previous studies. Herein, we employed comparative population genomics to identify genetic basis underlying the small body size of Yuanbao chicken (a famous ornamental chicken) based on 89 whole genomes. The most significant signal was mapped to the BMP10 gene, whose expression was upregulated in the Yuanbao chicken. Overexpression of BMP10 induced a significant decrease in body length by inhibiting angiogenic vessel development in zebrafish. In addition, three other loci on chromosomes 1, 2, and 24 were also identified to be potentially involved in the development of body size. Our results provide a paradigm shift in identification of novel loci controlling body size variation, availing a fast and efficient strategy. These loci, particularly BMP10, add insights into ongoing research of the evolution of body size under artificial selection and have important implications for future chicken breeding.

Genetic Determinism of Fearfulness, General Activity and Feeding Behavior in Chickens and Its Relationship with Digestive Efficiency

The genetic relationships between behavior and digestive efficiency were studied in 860 chickens from a cross between two lines divergently selected on digestive efficiency. At 2 weeks of age each chick was video-recorded in the home pen to characterize general activity and feeding behavior. Tonic immobility and open-field tests were also carried out individually to evaluate emotional reactivity (i.e. the propensity to express fear responses). Digestive efficiency was measured at 3 weeks. Genetic parameters of behavior traits were estimated. Birds were genotyped on 3379 SNP markers to detect QTLs. Heritabilities of behavioral traits were low, apart from tonic immobility (0.17-0.18) and maximum meal length (0.14). The genetic correlations indicated that the most efficient birds fed more frequently and were less fearful. We detected 14 QTL (9 for feeding behavior, 3 for tonic immobility, 2 for frequency of lying). Nine of them co-localized with QTL for efficiency, anatomy of the digestive tract, feed intake or microbiota composition. Four genes involved in fear reactions were identified in the QTL for tonic immobility on GGA1.

A molecular genome scan to identify DNA segments associated with live weight in Japanese quail

Japanese quail is an animal model in biological studies and also a commercial bird for eggs and meat production. This study was conducted to map quantitative trait loci (QTL) affecting live weight in Japanese quail. An F2 mapping population was developed by crossing two diverse lines (meat type and egg layer) of Japanese quail. A total number of 34 F1 and 422 F2 progeny were produced by reciprocal crossing of eight pairs of parental birds. All the birds from three generations were genotyped for SSR markers that were spread across all the autosomal linkage groups. The studied traits were hatching weight and live weights at 1-5 weeks of age. QTL analysis was conducted by the regression interval mapping. Significant QTL were detected on chromosomes 1, 2, 3 (chromosome-wide significant) and 5 (genome-wide significant, P < 0.05) for body weight. Although the additive effect of the detected QTL on chromosome 5 was significant, the dominance and imprinting effects were not significant. This finding is the first report of a genome-wide significant QTL associated with live weight in Japanese quail. Our results point out to candidate DNA regions affecting live weight, a trait of great economic relevance to the Japanese quail breeding. Although these results enhance our current knowledge about the genetic control of live weight in the Japanese quail, it should be noted that the initial QTL results from the experimental designs such as backcross or F2 cannot be applied directly to the breeding programs and require further validation within the commercial lines.

Genetic interactions contribute less than additive effects to quantitative trait variation in yeast

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL–QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL–QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies.

This study uses a large number of crosses between a common lab strain and vineyard-isolated strain of yeast, and estimates the phenotypic variance for various quantitative traits. Using this data set, the authors show additive quantitative trait loci (QTL) and QTL–QTL interactions to be on average 43% and 9%, respectively.

A crossbred reference population can improve the response to genomic selection for crossbred performance

Background

Breeding goals in a crossbreeding system should be defined at the commercial crossbred level. However, selection is often performed to improve purebred performance. A genomic selection (GS) model that includes dominance effects can be used to select purebreds for crossbred performance. Optimization of the GS model raises the question of whether marker effects should be estimated from data on the pure lines or crossbreds. Therefore, the first objective of this study was to compare response to selection of crossbreds by simulating a two-way crossbreeding program with either a purebred or a crossbred training population. We assumed a trait of interest that was controlled by loci with additive and dominance effects. Animals were selected on estimated breeding values for crossbred performance. There was no genotype by environment interaction. Linkage phase and strength of linkage disequilibrium between quantitative trait loci (QTL) and single nucleotide polymorphisms (SNPs) can differ between breeds, which causes apparent effects of SNPs to be line-dependent. Thus, our second objective was to compare response to GS based on crossbred phenotypes when the line origin of alleles was taken into account or not in the estimation of breeding values.

Results

Training on crossbred animals yielded a larger response to selection in crossbred offspring compared to training on both pure lines separately or on both pure lines combined into a single reference population. Response to selection in crossbreds was larger if both phenotypes and genotypes were collected on crossbreds than if phenotypes were only recorded on crossbreds and genotypes on their parents. If both parental lines were distantly related, tracing the line origin of alleles improved genomic prediction, whereas if both parental lines were closely related and the reference population was small, it was better to ignore the line origin of alleles.

Conclusions

Response to selection in crossbreeding programs can be increased by training on crossbred genotypes and phenotypes. Moreover, if the reference population is sufficiently large and both pure lines are not very closely related, tracing the line origin of alleles in crossbreds improves genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0155-z) contains supplementary material, which is available to authorized users.

Detection of QTL controlling feed efficiency and excretion in chickens fed a wheat-based diet

Background

Improving feed efficiency is a major goal in poultry production in order to reduce production costs, increase the possibility of using alternative feedstuffs and decrease the volume of manure. However, in spite of their economic and environmental impact, very few quantitative trait loci (QTL) have been reported on these traits. Thus, we undertook the detection of QTL on 820 meat-type chickens from a F2 cross between D− and D+ lines that were divergently selected on low or high digestive efficiency at 3 weeks of age. Birds were measured for growth between 0 and 23 days, feed intake and feed conversion ratio between 9 and 23 days, breast and abdominal fat yields at 23 days, and the anatomy of their digestive tract (density, relative weight and length of the duodenum, jejunum, ileum, and ratio of proventriculus to gizzard weight) was examined. To evaluate excretion traits, fresh and dry weight, water content, pH, nitrogen to phosphorus ratio from 0 to 23 days, and pH of gizzard and jejunum contents at 23 days were measured. A set of 3379 single nucleotide polymorphisms distributed on 28 Gallus gallus (GGA) autosomes, the Z chromosome and one unassigned linkage group was used for QTL detection.

Results

Using the QTLMap software developed for linkage analyses by interval mapping, we detected 16 QTL for feed intake, 13 for feed efficiency, 49 for anatomy-related traits, seven for growth, six for body composition and ten for excretion. Nine of these QTL were genome-wide significant (four for feed intake on GGA1, one for feed efficiency on GGA2, and four for anatomy on GGA1, 2, 3 and 4). GGA16, 19, and 26 carried many QTL for different types of traits that co-localize at the same position.

Conclusions

This study identified several QTL regions that are involved in the control of digestive efficiency in chicken. Further studies are needed to identify the genes that underlie these effects, and to validate these in other commercial populations and for different breeding environments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0156-y) contains supplementary material, which is available to authorized users.

Deep Sequencing Analysis of miRNA Expression in Breast Muscle of Fast-Growing and Slow-Growing Broilers

Growth performance is an important economic trait in chicken. MicroRNAs (miRNAs) have been shown to play important roles in various biological processes, but their functions in chicken growth are not yet clear. To investigate the function of miRNAs in chicken growth, breast muscle tissues of the two-tail samples (highest and lowest body weight) from Recessive White Rock (WRR) and Xinghua Chickens (XH) were performed on high throughput small RNA deep sequencing. In this study, a total of 921 miRNAs were identified, including 733 known mature miRNAs and 188 novel miRNAs. There were 200, 279, 257 and 297 differentially expressed miRNAs in the comparisons of WRRh vs. WRRl, WRRh vs. XHh, WRRl vs. XHl, and XHh vs. XHl group, respectively. A total of 22 highly differentially expressed miRNAs (fold change > 2 or < 0.5; p-value < 0.05; q-value < 0.01), which also have abundant expression (read counts > 1000) were found in our comparisons. As far as two analyses (WRRh vs. WRRl, and XHh vs. XHl) are concerned, we found 80 common differentially expressed miRNAs, while 110 miRNAs were found in WRRh vs. XHh and WRRl vs. XHl. Furthermore, 26 common miRNAs were identified among all four comparisons. Four differentially expressed miRNAs (miR-223, miR-16, miR-205a and miR-222b-5p) were validated by quantitative real-time RT-PCR (qRT-PCR). Regulatory networks of interactions among miRNAs and their targets were constructed using integrative miRNA target-prediction and network-analysis. Growth hormone receptor (GHR) was confirmed as a target of miR-146b-3p by dual-luciferase assay and qPCR, indicating that miR-34c, miR-223, miR-146b-3p, miR-21 and miR-205a are key growth-related target genes in the network. These miRNAs are proposed as candidate miRNAs for future studies concerning miRNA-target function on regulation of chicken growth.

Epistasis Is a Major Determinant of the Additive Genetic Variance in Mimulus guttatus

The influence of genetic interactions (epistasis) on the genetic variance of quantitative traits is a major unresolved problem relevant to medical, agricultural, and evolutionary genetics. The additive genetic component is typically a high proportion of the total genetic variance in quantitative traits, despite that underlying genes must interact to determine phenotype. This study estimates direct and interaction effects for 11 pairs of Quantitative Trait Loci (QTLs) affecting floral traits within a single population of Mimulus guttatus. With estimates of all 9 genotypes for each QTL pair, we are able to map from QTL effects to variance components as a function of population allele frequencies, and thus predict changes in variance components as allele frequencies change. This mapping requires an analytical framework that properly accounts for bias introduced by estimation errors. We find that even with abundant interactions between QTLs, most of the genetic variance is likely to be additive. However, the strong dependency of allelic average effects on genetic background implies that epistasis is a major determinant of the additive genetic variance, and thus, the population’s ability to respond to selection.

Complex traits are influenced not only by the effects of individual genes but also by the myriad ways that these genes interact with one another, commonly referred to as epistasis. Theory suggests that epistasis could have important population-level implications in terms of the genetic variance components that govern evolution in response to natural or artificial selection. Unfortunately, empirical examples extending from observed interactions between genes to genetic variances are scant, particularly for natural populations. Here, we characterize epistasis between naturally segregating polymorphisms in M. guttatus and determine the cumulative effect of epistasis on population genetic variance components. To do this, we first elaborate the necessary statistical theory to accommodate estimation error in genetic effects, as failing to do so will upwardly bias variance predictions. We find that gene interactions have a net positive effect on both the total and additive genetic variance for most traits; however, the contribution of individual loci to the additive variance depends heavily on the genotype frequencies at other loci. Therefore, the effect of epistasis extends beyond the individual’s phenotype to influence how both populations and their component alleles respond to selection.

Quantitative trait loci segregating in crosses between New Hampshire and White Leghorn chicken lines: IV. Growth performance

Reciprocal crosses between the inbred lines New Hampshire (NHI) and White Leghorn (WL77) comprising 579 F2 individuals were used to map QTL for body weight and composition. Here, we examine the growth performance until 20 weeks of age. Linkage analysis provided evidence for highly significant QTL on GGA1, 2, 4, 10 and 27 which had specific effects on early or late growth. The highest QTL effects, accounting for 4.6-25.6% of the phenotypic F2 variance, were found on the distal region of GGA4 between 142 and 170 cM (F ≥ 13.68). The NHI QTL allele increased body mass by 141.86 g at 20 weeks. Using body weight as a covariate in the analysis of body composition traits provided evidence for genes in the GGA4 QTL region affecting fat mass independently of body mass. The QTL effect size differed between sexes and depended on the direction of cross. TBC1D1, CCKAR and PPARGC1A are functional candidate genes in the QTL peak region. Our study confirmed the importance of the distal GGA4 region for chicken growth performance. The strong effect of the GGA4 QTL makes fine mapping and gene discovery feasible.

Candidate genes in quantitative trait loci associated with absolute and relative kidney weight in rats with Inherited Stress Induced Arterial Hypertension

BACKGROUND

The kidney mass is significantly increased in hypertensive ISIAH rats with Inherited Stress Induced Arterial Hypertension as compared with normotensive WAG rats. The QTL/microarray approach was carried out to determine the positional candidate genes in the QTL for absolute and relative kidney weight.

RESULTS

Several known and predicted genes differentially expressed in ISIAH and WAG kidney were mapped to genetic loci associated with the absolute and relative kidney weight in 6-month old F2 hybrid (ISIAHxWAG) males. The knowledge-driven filtering of the list of candidates helped to suggest several positional candidate genes, which may be related to the structural and mass changes in hypertensive ISIAH kidney.

CONCLUSIONS

The further experimental validation of causative genes and detection of polymorphisms will provide opportunities to advance our understanding of the underlying nature of structural and mass changes in hypertensive ISIAH kidney.