Genetic analysis of an F(2) intercross between two chicken lines divergently selected for body-weight

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.

Research Note: Phenotypic trends for the multigenerational advanced intercross of the Virginia body weight lines of chickens

Random samples from generation S41 of the Virginia high and low 8-week body weight lines formed the base population for producing a multigenerational reciprocal intercross population. Although genetic mapping from this intercross has been reported, lacking are phenotypic trends across multiple generations. Here, we provide phenotypic information for the parental base population, the F1 reciprocal cross, and subsequent segregating recombinant generations F2 to F17. Heterosis for the selected trait in the F1 was negative for both reciprocal crosses. Phenotypic correlations for the selected trait in the recombinant generations were essentially nil for both males and females as was percent sexual dimorphism and coefficients of variation.

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.

Low-coverage sequencing in a deep intercross of the Virginia body weight lines provides insight to the polygenic genetic architecture of growth: novel loci revealed by increased power and improved genome-coverage

Genetic dissection of highly polygenic traits is a challenge, in part due to the power necessary to confidently identify loci with minor effects. Experimental crosses are valuable resources for mapping such traits. Traditionally, genome-wide analyses of experimental crosses have targeted major loci using data from a single generation (often the F₂) with individuals from later generations being generated for replication and fine-mapping. Here, we aim to confidently identify minor-effect loci contributing to the highly polygenic basis of the long-term, bi-directional selection responses for 56-d body weight in the Virginia body weight chicken lines. To achieve this, a strategy was developed to make use of data from all generations (F₂-F₁₈) of the advanced intercross line, developed by crossing the low and high selected lines after 40 generations of selection. A cost-efficient low-coverage sequencing based approach was used to obtain high-confidence genotypes in 1Mb bins across 99.3% of the chicken genome for >3,300 intercross individuals. In total, 12 genome-wide significant, and 30 additional suggestive QTL reaching a 10% FDR threshold, were mapped for 56-d body weight. Only 2 of these QTL reached genome-wide significance in earlier analyses of the F₂ generation. The minor-effect QTL mapped here were generally due to an overall increase in power by integrating data across generations, with contributions from increased genome-coverage and improved marker information content. The 12 significant QTL explain >37% of the difference between the parental lines, three times more than 2 previously reported significant QTL. The 42 significant and suggestive QTL together explain >80%. Making integrated use of all available samples from multiple generations in experimental crosses are economically feasible using the low-cost, sequencing-based genotyping strategies outlined here. Our empirical results illustrate the value of this strategy for mapping novel minor-effect loci contributing to complex traits to provide a more confident, comprehensive view of the individual loci that form the genetic basis of the highly polygenic, long-term selection responses for 56-d body weight in the Virginia body weight chicken lines.

Dynamics of Small Non-coding RNA Profiles and the Intestinal Microbiome of High and Low Weight Chickens

The host and its symbiotic bacteria form a biological entity, holobiont, in which they share a dynamic connection characterized by symbiosis, co-metabolism, and coevolution. However, how these collaborative relationships were maintained over evolutionary time remains unclear. In this research, the small non-coding RNA (sncRNA) profiles of cecum and their bacteria contents were measured from lines of chickens that have undergone long-term selection for high (HWS) or low (LWS) 56-day body weight. The results from these lines that originated from a common founder population and maintained under the same husbandry showed an association between host intestinal sncRNA expression profile (miRNA, lncRNA fragment, mRNA fragment, snoRNA, and snRNA) and intestinal microbiota. Correlation analyses suggested that some central miRNAs and mRNA fragments had interactions with the abundance of intestinal microbial species and microbiota functions. miR-6622-3p, a significantly differentially expressed (DE) miRNA was correlated with a body weight gain related bacterium, Alistipes putredinis. Our results showed that host sncRNAs may be mediators of interaction between the host and its intestinal microbiome. This provides additional clue for holobiont concepts.

Heterogeneity of a dwarf phenotype in Dutch traditional chicken breeds revealed by genomic analyses

The growth of animals is a complex trait, in chicken resulting in a diverse variety of forms, caused by a heterogeneous genetic basis. Bantam chicken, known as an exquisite form of dwarfism, has been used for crossbreeding to create corresponding dwarf counterparts for native fowls in the Dutch populations. Here, we demonstrate the heterogeneity of the bantam trait in Dutch chickens and reveal the underlying genetic causes, using whole-genome sequence data from matching pairs of bantam and normal-sized breeds. During the bantam-oriented crossbreeding, various bantam origins were used to introduce the bantam phenotype, and three major bantam sources were identified and clustered. The genome-wide association studies revealed multiple genetic variants and genes associated with bantam phenotype, including HMGA2 and PRDM16, genes involved in body growth and stature. The comparison of associated variants among studies illustrated differences related to divergent bantam origins, suggesting a clear heterogeneity among bantam breeds. We show that in neo-bantam breeds, the bantam-related regions underwent a strong haplotype introgression from the bantam source, outcompeting haplotypes from the normal-sized counterpart. The bantam heterogeneity is further confirmed by the presence of multiple haplotypes comprising associated alleles, which suggests the selection of the bantam phenotype is likely subject to a convergent direction across populations. Our study demonstrates that the diverse history of human-mediated crossbreeding has contributed to the complexity and heterogeneity of the bantam phenotype.

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.

Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders: a cost-efficient approach

BACKGROUND

Experimental intercrosses between outbred founder populations are powerful resources for mapping loci that contribute to complex traits i.e. quantitative trait loci (QTL). Here, we present an approach and its accompanying software for high-resolution reconstruction of founder mosaic genotypes in the intercross offspring from such populations using whole-genome high-coverage sequence data on founder individuals (~ 30×) and very low-coverage sequence data on intercross individuals (< 0.5×). Sets of founder-line informative markers were selected for each full-sib family and used to infer the founder mosaic genotypes of the intercross individuals. The application of this approach and the quality of the estimated genome-wide genotypes are illustrated in a large F₂ pedigree between two divergently selected lines of chickens.

RESULTS

We describe how we obtained whole-genome genotype data for hundreds of individuals in a cost- and time-efficient manner by using a Tn5-based library preparation protocol and an imputation algorithm that was optimized for this application. In total, 7.6 million markers segregated in this pedigree and, within each full-sib family, between 10.0 and 13.7% of these were fully informative, i.e. fixed for alternative alleles in the founders from the divergent lines, and were used for reconstruction of the offspring mosaic genotypes. The genotypes that were estimated based on the low-coverage sequence data were highly consistent (> 95% agreement) with those obtained using individual single nucleotide polymorphism (SNP) genotyping. The estimated resolution of the inferred recombination breakpoints was relatively high, with 50% of them being defined on regions shorter than 10 kb.

CONCLUSIONS

A method and software for inferring founder mosaic genotypes in intercross offspring from low-coverage whole-genome sequencing in pedigrees from heterozygous founders are described. They provide high-quality, high-resolution genotypes in a time- and cost-efficient manner. The software is freely available at https://github.com/CarlborgGenomics/Stripes .

A Polygenic Genetic Architecture of Flowering Time in the Worldwide Arabidopsis thaliana Population

Here, we report an empirical study of the polygenic basis underlying the evolution of complex traits. Flowering time variation measured at 10 and 16°C in the 1,001-genomes Arabidopsis thaliana collection of natural accessions were used as a model. The polygenic architecture of flowering time was defined as the 48 loci that were significantly associated with flowering time-at 10 and/or 16°C and/or their difference-in this population. Contributions from alleles at flowering time associated loci to global and local adaptation were explored by evaluating their distribution across genetically and geographically defined subpopulations across the native range of the species. The dynamics in the genetic architecture of flowering time in response to temperature was evaluated by estimating how the effects of these loci on flowering changed with growth temperature. Overall, the genetic basis of flowering time was stable-about 2/3 of the flowering time loci had similar effects at 10°C and 16°C-but many loci were involved in gene by temperature interactions. Globally present alleles, mostly of moderate effect, contributed to the differences in flowering times between the subpopulations via subtle changes in allele frequencies. More extreme local adaptations were, on several occasions, due to regional alleles with relatively large effects, and their linkage disequilibrium-patterns suggest coevolution of functionally connected alleles within local populations. Overall, these findings provide a significant contribution to our understanding about the possible modes of global and local evolution of a complex adaptive trait in A. thaliana.

An integrative genomic analysis of the Longshanks selection experiment for longer limbs in mice

Evolutionary studies are often limited by missing data that are critical to understanding the history of selection. Selection experiments, which reproduce rapid evolution under controlled conditions, are excellent tools to study how genomes evolve under selection. Here we present a genomic dissection of the Longshanks selection experiment, in which mice were selectively bred over 20 generations for longer tibiae relative to body mass, resulting in 13% longer tibiae in two replicates. We synthesized evolutionary theory, genome sequences and molecular genetics to understand the selection response and found that it involved both polygenic adaptation and discrete loci of major effect, with the strongest loci tending to be selected in parallel between replicates. We show that selection may favor de-repression of bone growth through inactivating two limb enhancers of an inhibitor, Nkx3-2. Our integrative genomic analyses thus show that it is possible to connect individual base-pair changes to the overall selection response.

Humans have been making use of artificial selection for thousands of years. Much of what we eat, for example, from beef to poultry to cereals, comes from a collection of organisms with genomes that have been completely reshaped by the actions of generations of farmers and breeders. Yet, despite decades of research in evolutionary biology, it remains difficult to predict what will happen to an organism’s genes when selective pressure is applied.

Traits that at first seem simple often arise from layers upon layers of complexity. It can take hundreds if not thousands of tiny changes to many genes, plus just the right alterations to a few key ones, to have a desired effect on a single trait. Also, if you consider that often the genomes of the starting population are unknown and that many traits are under simultaneous selection in wild populations, it becomes clear why many questions remain unanswered.

Castro, Yancoskie, et al. have analyzed an on-going laboratory experiment dubbed “the Longshanks experiment” to explore how an animal’s genome changes under strong selection. Over five years, two independent populations of mice were selectively bred to have longer legs. In each generation, the mice were measured and those with the longest tibia – a bone in the shin – relative to their body mass were allowed to breed. Genetic data were also recorded. Now, Castro, Yancoskie, et al. have analyzed the genetic data up to the first 17 generations in the Longshanks experiment to find out what kind of genes may be relevant to the 13% increase in leg length seen in the mice so far.

This analysis uncovered many genes, possibly thousands, all acting in concert to increase tibia length. But the gene with the largest effect by far was a key developmental gene called Nkx3-2. Mutations in this gene cause a disease called spondylo-megaepiphyseal-metaphyseal dysplasia in people, which can lead to long limbs and a short trunk. Although inactivating this gene completely in mice is lethal, among the founding group of mice in the Longshanks experiment was a rare copy of Nkx3-2. This copy of the gene worked perfectly in all tissues with the exception of the legs, where a genetic switch that controls it was left in the “off” state. Mice inheriting this short stretch of DNA ended up having longer tibia. In effect, these mice held the winning ticket in the genetic lottery that was the Longshanks experiment.

Even in highly controlled experiments that record a great deal of information about the organisms involved, predicting how the genome will change and which genes will be involved is not a straightforward question. Finding out how the genome may change in response to selection is important because scientists can build better models to help with breeding farm animals or crops, or with predicting the consequences of climate change. As a result, experiments such as these may have broad applications in conservation, genomic medicine and agriculture.

Bidirectional Selection for Body Weight on Standing Genetic Variation in a Chicken Model

Experimental populations of model organisms provide valuable opportunities to unravel the genomic impact of selection in a controlled system. The Virginia body weight chicken lines represent a unique resource to investigate signatures of selection in a system where long-term, single-trait, bidirectional selection has been carried out for more than 60 generations. At 55 generations of divergent selection, earlier analyses of pooled genome resequencing data from these lines revealed that 14.2% of the genome showed extreme differentiation between the selected lines, contained within 395 genomic regions. Here, we report more detailed analyses of these data exploring the regions displaying within- and between-line genomic signatures of the bidirectional selection applied in these lines. Despite the strict selection regime for opposite extremes in body weight, this did not result in opposite genomic signatures between the lines. The lines often displayed a duality of the sweep signatures, where an extended region of homozygosity in one line, in contrast to mosaic pattern of heterozygosity in the other line. These haplotype mosaics consisted of short, distinct haploblocks of variable between-line divergence, likely the results of a complex demographic history involving bottlenecks, introgressions and moderate inbreeding. We demonstrate this using the example of complex haplotype mosaicism in the growth1 QTL. These mosaics represent the standing genetic variation available at the onset of selection in the founder population. Selection on standing genetic variation can thus result in different signatures depending on the intensity and direction of selection.

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.

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.

Integrating Selection Mapping With Genetic Mapping and Functional Genomics

Genomic scans for signatures of selection allow us to, in principle, detect variants and genes that underlie recent adaptations. By combining selection mapping with genetic mapping of traits known to be relevant to adaptation, we can simultaneously investigate whether genes and variants show signals of recent selection and whether they impact traits that have likely been selected. There are three ways to integrate selection mapping with genetic mapping or functional genomics: (1) To use genetic mapping data from other populations as a form of genome annotation. (2) To perform experimental evolution or artificial selection to be able to study selected variants when they segregate, either by performing genetic mapping before selection or by crossing the selected individuals to some reference population. (3) To perform a comparative study of related populations facing different selection regimes. This short review discusses these different ways of integrating selection mapping with genetic mapping and functional genomics, with examples of how each has been done.

Genomic signatures of 60 years of bidirectional selection for 8-week body weight in chickens

Sixty years, constituting 60 generations, have passed since the founding of the Virginia body weight lines, an experimental population of White Plymouth Rock chickens. Using a stringent breeding scheme for divergent 8-week body weight, the lines, which originated from a common founder population, have responded to bidirectional selection with an approximate 15-fold difference in the selected trait. They provide a model system to study the genetics of complex traits in general and the influences of artificial selection on quantitative genetic architectures in particular. As we reflect on the 60th anniversary of the initiation of the Virginia body weight lines, there is opportunity to discuss the findings obtained using different analytical and experimental genetic and genomic strategies and integrate them with a recent pooled genome resequencing dataset. Hundreds of regions across the genome show differentiation between the 2 lines, reinforcing previous findings that response to selection relied on standing variation across many genes and giving insights into the haplotype complexity underlying regions associated with body weight.

A multilocus association analysis method integrating phenotype and expression data reveals multiple novel associations to flowering time variation in wild-collected Arabidopsis thaliana

The adaptation to a new habitat often results in a confounding between genomewide genotype and beneficial alleles. When the confounding is strong, or the allelic effects is weak, it is a major statistical challenge to detect the adaptive polymorphisms. We describe a novel approach to dissect polygenic traits in natural populations. First, candidate adaptive loci are identified by screening for loci directly associated with the adaptive trait or the expression of genes known to affect it. Then, a multilocus genetic architecture is inferred using a backward elimination association analysis across all candidate loci with an adaptive false discovery rate-based threshold. Effects of population stratification are controlled by accounting for genomic kinship in both steps of the analysis and also by simultaneously testing all candidate loci in the multilocus model. We illustrate the method by exploring the polygenic basis of an important adaptive trait, flowering time in Arabidopsis thaliana, using public data from the 1,001 genomes project. We revealed associations between 33 (29) loci and flowering time at 10 (16)°C in this collection of natural accessions, where standard genomewide association analysis methods detected five (3) loci. The 33 (29) loci explained approximately 55.1 (48.7)% of the total phenotypic variance of the respective traits. Our work illustrates how the genetic basis of highly polygenic adaptive traits in natural populations can be explored in much greater detail using new multilocus mapping approaches taking advantage of prior biological information, genome and transcriptome data.

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.

Imputation-Based Fine-Mapping Suggests That Most QTL in an Outbred Chicken Advanced Intercross Body Weight Line Are Due to Multiple, Linked Loci

The Virginia chicken lines have been divergently selected for juvenile body weight for more than 50 generations. Today, the high- and low-weight lines show a >12-fold difference for the selected trait, 56-d body weight. These lines provide unique opportunities to study the genetic architecture of long-term, single-trait selection. Previously, several quantitative trait loci (QTL) contributing to weight differences between the lines were mapped in an F₂-cross between them, and these were later replicated and fine-mapped in a nine-generation advanced intercross of them. Here, we explore the possibility to further increase the fine-mapping resolution of these QTL via a pedigree-based imputation strategy that aims to better capture the genetic diversity in the divergently selected, but outbred, founder lines. The founders of the intercross were high-density genotyped, and then pedigree-based imputation was used to assign genotypes throughout the pedigree. Imputation increased the marker density 20-fold in the selected QTL, providing 6911 markers for the subsequent analysis. Both single-marker association and multi-marker backward-elimination analyses were used to explore regions associated with 56-d body weight. The approach revealed several statistically and population structure independent associations and increased the mapping resolution. Further, most QTL were also found to contain multiple independent associations to markers that were not fixed in the founder populations, implying a complex underlying architecture due to the combined effects of multiple, linked loci perhaps located on independent haplotypes that still segregate in the selected lines.

Variance Component Quantitative Trait Locus Analysis for Body Weight Traits in Purebred Korean Native Chicken

Quantitative trait locus (QTL) is a particular region of the genome containing one or more genes associated with economically important quantitative traits. This study was conducted to identify QTL regions for body weight and growth traits in purebred Korean native chicken (KNC). F₁ samples (n = 595) were genotyped using 127 microsatellite markers and 8 single nucleotide polymorphisms that covered 2,616.1 centi Morgan (cM) of map length for 26 autosomal linkage groups. Body weight traits were measured every 2 weeks from hatch to 20 weeks of age. Weight of half carcass was also collected together with growth rate. A multipoint variance component linkage approach was used to identify QTLs for the body weight traits. Two significant QTLs for growth were identified on chicken chromosome 3 (GGA3) for growth 16 to18 weeks (logarithm of the odds [LOD] = 3.24, Nominal p value = 0.0001) and GGA4 for growth 6 to 8 weeks (LOD = 2.88, Nominal p value = 0.0003). Additionally, one significant QTL and three suggestive QTLs were detected for body weight traits in KNC; significant QTL for body weight at 4 weeks (LOD = 2.52, nominal p value = 0.0007) and suggestive QTL for 8 weeks (LOD = 1.96, Nominal p value = 0.0027) were detected on GGA4; QTLs were also detected for two different body weight traits: body weight at 16 weeks on GGA3 and body weight at 18 weeks on GGA19. Additionally, two suggestive QTLs for carcass weight were detected at 0 and 70 cM on GGA19. In conclusion, the current study identified several significant and suggestive QTLs that affect growth related traits in a unique resource pedigree in purebred KNC. This information will contribute to improving the body weight traits in native chicken breeds, especially for the Asian native chicken breeds.

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.

Polymorphism of avian leukosis virus subgroup E loci showing selective footprints in chicken

Avian leukosis virus subgroup E (ALVE) is a family of endogenous retroviruses in the chicken genome. To investigate the genetic consequences of chicken domestication, we analyzed 18 ALVE loci in red jungle fowls, layers, broilers, and Chinese indigenous chickens. None of the ALVE loci tested were found in red jungle fowls, but 12 were present in domestic chickens. ALVE1 and ALVE16 are found in regions of the genome that harbor quantitative trait loci (QTL) affecting egg production traits. ALVE1 was fixed and ALVE16 was detected only in layers. By contrast, ALVE-b1, ALVE-b5, ALVE-b6, and ALVE-b8 integrated into regions of the genome that harbor QTL affecting meat production traits. Carrier frequencies of these four ALVE loci were high in broilers and low in Chinese local chickens; the loci were not found in the layers. This study demonstrated that insertionally polymorphic ALVE loci can illustrate the selective footprints in the chicken genome.

Identification of QTL for live weight and growth rate using DNA markers on chromosome 3 in an F2 population of Japanese quail

The Japanese quail (Coturnix japonica) is an important agricultural species and is an animal model for genetic researches. This study was conducted to identify quantitative trait loci (QTL) affecting live weight and growth rate on chromosome 3 in quail. Two strains of Japanese quail including wild and white were crossed reciprocally and F1 generation was created. The birds from F2 generation were measured for growth traits and all of 472 birds (8 pairs from the parental strains, 34 F1 birds and 422 F2 birds) were genotyped for microsatellite markers on chromosome 3. The results indicated chromosome wide significant QTL for hatching weight (P < 0.01) and weight at 1, 2, 3 and 4 weeks of age, average daily gain from hatch to 1, 1-2 and 3-4 weeks of age and Kleiber ratio (P < 0.05), an indirect criterion of feed efficiency. The highest QTL additive and imprinting effects (2.72 and 0.79 % of the trait variation in the F2 population, respectively) were related to hatching weight. The identified QTL for this trait (at 7 cM relative to the centromeric region of the chromosome) had significant interaction with sex and hatch (P < 0.01). The dominance effect of QTL was significant (P < 0.05) for bodyweight at one week of age accounting for 1.69 % of the trait variation in the F2 population.

Dynamics of adaptive alleles in divergently selected body weight lines of chickens

By studying genomic changes over time in populations subjected to strong artificial directional selection, we can gain insights to the dynamics of beneficial alleles originating from the founder population or emerging as novel mutations undergoing ongoing selection. The Virginia lines are a chicken resource population generated by long-term bi-directional, single-trait selection for juvenile body weight. We studied genome-wide allele frequency changes from generation 40 to 53 using genome-wide genotypes from directional and relaxed selection lines. Overall, there were small changes in allele frequencies at individual loci over the studied time period; but, on average, the changes were greater in lines with larger phenotypic changes. This is consistent with previous findings that much of the response to selection over the first 40 years of selection was attributable to utilization of standing genetic variation at many loci in the genome, indicating a mostly polygenic architecture for body weight. Over the course of the selection experiment, the largest phenotypic response to selection was observed in the high-weight selected line, and in this line we detected a single locus where the allele frequency changed rapidly during a late stage of the experiment. This locus likely contains a novel, beneficial mutation that appeared between generations 40 and 45 and was driven to fixation in 5 to 10 generations. This result illustrates the dependence of continued long-term selection response on standing genetic variation at many loci as well as strong, novel, beneficial mutations.

Genome-wide interval mapping using SNPs identifies new QTL for growth, body composition and several physiological variables in an F2 intercross between fat and lean chicken lines

BACKGROUND

For decades, genetic improvement based on measuring growth and body composition traits has been successfully applied in the production of meat-type chickens. However, this conventional approach is hindered by antagonistic genetic correlations between some traits and the high cost of measuring body composition traits. Marker-assisted selection should overcome these problems by selecting loci that have effects on either one trait only or on more than one trait but with a favorable genetic correlation. In the present study, identification of such loci was done by genotyping an F2 intercross between fat and lean lines divergently selected for abdominal fatness genotyped with a medium-density genetic map (120 microsatellites and 1302 single nucleotide polymorphisms). Genome scan linkage analyses were performed for growth (body weight at 1, 3, 5, and 7 weeks, and shank length and diameter at 9 weeks), body composition at 9 weeks (abdominal fat weight and percentage, breast muscle weight and percentage, and thigh weight and percentage), and for several physiological measurements at 7 weeks in the fasting state, i.e. body temperature and plasma levels of IGF-I, NEFA and glucose. Interval mapping analyses were performed with the QTLMap software, including single-trait analyses with single and multiple QTL on the same chromosome.

RESULTS

Sixty-seven QTL were detected, most of which had never been described before. Of these 67 QTL, 47 were detected by single-QTL analyses and 20 by multiple-QTL analyses, which underlines the importance of using different statistical models. Close analysis of the genes located in the defined intervals identified several relevant functional candidates, such as ACACA for abdominal fatness, GHSR and GAS1 for breast muscle weight, DCRX and ASPSCR1 for plasma glucose content, and ChEBP for shank diameter.

CONCLUSIONS

The medium-density genetic map enabled us to genotype new regions of the chicken genome (including micro-chromosomes) that influenced the traits investigated. With this marker density, confidence intervals were sufficiently small (14 cM on average) to search for candidate genes. Altogether, this new information provides a valuable starting point for the identification of causative genes responsible for important QTL controlling growth, body composition and metabolic traits in the broiler chicken.

[Genome-wide selective sweep analysis on Large White and Tongcheng pigs]

The production performance of pigs has been significantly improved due to long-term artificial selection, and the specific variation characterizations (selection signatures) emerged from the selected genome regions. Different types of breeds are subjected to different selection intensities and had different selection signatures. Selective sweep analysis is one of major methods to detect the selection signatures. In this study, based on the 60K BeadChip genotyping data of both commercial Large White (n=45) and local Tongcheng pigs (n=45), genetic differentiation coefficient Fst was applied to detect the selection signatures. Using gPLINK software to set quality control standards, a total of 34 304 SNPs were selected for statistical analysis. Fst values between two breeds were estimated with Genepop package and the average Fst value was 0.3209. Setting Fst>0.7036 (1% of total number of Fst values) as selection threshold, 344 SNPs were obtained and SNP location annotation indicated that there were 79 candidate genes (Sus scrofa Build 9). Furthermore, network analysis was performed using Ingenuity Pathway Analysis and the preliminary results suggested that most genes were involved in growth, reproduction, and immune response, such as NCOA6, ERBB4, RUNX2, and APOB genes. The findings from this study will contribute to further identification of candidate genes and causal mutations implying for meat production and disease resistance in pig.

Expression of carnitine palmitoyl-CoA transferase-1B is influenced by a cis-acting eQTL in two chicken lines selected for high and low body weight

Carnitine palmitoyl-CoA transferase-1B is a mitochondrial enzyme in the fatty acid oxidation pathway. In a previous study, CPT1B was identified as differentially expressed in the hypothalamus of two lines of chickens established by long-term selection for high (HWS) or low (LWS) body weight. Mammals have three paralogs (CPT1a, b and c) while nonmammalian vertebrates only have two (CPT1A, B). CPT1A is expressed in liver and CPT1B in muscle. CPT1c is expressed in hypothalamus, where it regulates feeding and energy expenditure. We identified an intronic length polymorphism, fixed for different alleles in the two populations, and mapped the hitherto missing CPT1B locus in the chicken genome assembly, to the distal tip of chromosome 1p. Based on molecular phylogeny and gene synteny we suggest that chicken CPT1B is pro-orthologous of the mammalian CPT1c. Chicken CPT1B was differentially expressed in both muscle and hypothalamus but in opposite directions: higher levels in hypothalamus but lower levels in muscle in the HWS than in the LWS line. Using an advanced intercross population of the lines, we found CPT1B expression to be influenced by a cis-acting expression quantitative trait locus in muscle. The increased expression in hypothalamus and reduced expression in muscle is consistent with an increased food intake in the HWS line and at the same time reduced fatty acid oxidation in muscle yielding a net accumulation of energy intake and storage. The altered expression of CPT1B in hypothalamus and peripheral tissue is likely to be a mechanism contributing to the remarkable difference between lines.

Genetic dissection of growth traits in a Chinese indigenous × commercial broiler chicken cross

Background

In China, consumers often prefer indigenous broiler chickens over commercial breeds, as they have characteristic meat qualities requested within traditional culinary customs. However, the growth-rate of these indigenous breeds is slower than that of the commercial broilers, which means they have not yet reached their full economic value. Therefore, combining the valuable meat quality of the native chickens with the efficiency of the commercial broilers is of interest. In this study, we generated an F₂ intercross between the slow growing native broiler breed, Huiyang Beard chicken, and the fast growing commercial broiler breed, High Quality chicken Line A, and used it to map loci explaining the difference in growth rate between these breeds.

Results

A genome scan to identify main-effect loci affecting 24 growth-related traits revealed nine distinct QTL on six chromosomes. Many QTL were pleiotropic and conformed to the correlation patterns observed between phenotypes. Most of the mapped QTL were found in locations where growth QTL have been reported in other populations, although the effects were greater in this population. A genome scan for pairs of interacting loci identified a number of additional QTL in 10 other genomic regions. The epistatic pairs explained 6–8% of the residual phenotypic variance. Seven of the 10 epistatic QTL mapped in regions containing candidate genes in the ubiquitin mediated proteolysis pathway, suggesting the importance of this pathway in the regulation of growth in this chicken population.

Conclusions

The main-effect QTL detected using a standard one-dimensional genome scan accounted for a significant fraction of the observed phenotypic variance in this population. Furthermore, genes in known pathways present interesting candidates for further exploration. This study has thus located several QTL regions as promising candidates for further study, which will increase our understanding of the genetic mechanisms underlying growth-related traits in chickens.

Detection of QTL controlling metabolism, meat quality, and liver quality traits of the overfed interspecific hybrid mule duck

The mule duck, an interspecific hybrid obtained by crossing common duck (Anas platyrhynchos) females with Muscovy (Cairina moschata) drakes, is widely used for fatty liver production. The purpose of the present study was to detect and map single and pleiotropic QTL that segregate in the common duck species, and influence the expression of traits in their overfed mule duck offspring. To this end, we generated a common duck backcross (BC) population by crossing Kaiya and heavy Pekin experimental lines, which differ notably in regard to the BW and overfeeding ability of their mule progeny. The BC females were mated to Muscovy drakes and, on average, 4 male mule ducks hatched per BC female (1600 in total) and were measured for growth, metabolism during growth and the overfeeding period, overfeeding ability, and the quality of their breast meat and fatty liver. The phenotypic value of BC females was estimated for each trait by assigning to each female the mean value of the phenotypes of her offspring. Estimations allowed for variance, which depended on the number of male offspring per BC and the heritability of the trait considered. The genetic map used for QTL detection consisted of 91 microsatellite markers aggregated into 16 linkage groups (LG) covering a total of 778 cM. Twenty-two QTL were found to be significant at the 1% chromosome-wide threshold level using the single-trait detection option of the QTLMap software. Most of the QTL detected were related to the quality of breast meat and fatty liver: QTL for meat pH 20 min post mortem were mapped to LG4 (at the 1% genome-wide significance level), and QTL for meat lipid content and cooking losses were mapped to LG2a. The QTL related to fatty liver weight and liver protein and lipid content were for the most part detected on LG2c and LG9. Multitrait analysis highlighted the pleiotropic effects of QTL in these chromosome regions. Apart from the strong QTL for plasma triglyceride content at the end of the overfeeding period mapped to chromosome Z using single-trait analysis, all metabolic trait QTL were detected with the multitrait approach: the QTL mapped to LG14 and LG21 affected the plasma cholesterol and triglyceride contents, whereas the QTL mapped to LG2a seemed to impact glycemia and the basal plasma corticosterone content. A greater density genetic map will be needed to further fine map the QTL.

Quantitative trait loci associated with body weight and abdominal fat traits on chicken chromosomes 3, 5 and 7

Body weight and abdominal fat traits in meat-type chickens are complex and economically important factors. Our objective was to identify quantitative trait loci (QTL) responsible for body weight and abdominal fat traits in broiler chickens. The Northeast Agricultural University Resource Population (NEAURP) is a cross between broiler sires and Baier layer dams. We measured body weight and abdominal fat traits in the F(2) population. A total of 362 F(2) individuals derived from four F(1) families and their parents and F(0) birds were genotyped using 29 fluorescent microsatellite markers located on chromosomes 3, 5 and 7. Linkage maps for the three chromosomes were constructed and interval mapping was performed to identify putative QTLs. Nine QTL for body weight were identified at the 5% genome-wide level, while 15 QTL were identified at the 5% chromosome-wide level. Phenotypic variance explained by these QTL varied from 2.95 to 6.03%. In particular, a QTL region spanning 31 cM, associated with body weight at 1 to 12 weeks of age and carcass weight at 12 weeks of age, was first identified on chromosome 5. Three QTLs for the abdominal fat traits were identified at the 5% chromosome-wide level. These QTLs explained 3.42 to 3.59% of the phenotypic variance. This information will help direct prospective fine mapping studies and can facilitate the identification of underlying genes and causal mutations for body weight and abdominal fat traits.

Generation of a multi-locus chicken introgression line to study the effects of genetic interactions on metabolic phenotypes in chickens

Most biological traits are regulated by a complex interplay between genetic and environmental factors. By intercrossing divergent lines, it is possible to identify individual and interacting QTL involved in the genetic architecture of these traits. When the loci have been mapped, alternative strategies are needed for fine-mapping and studying the individual and interactive effects of the QTL in detail. We have previously identified, replicated, and fine mapped a four-locus QTL network that determines nearly half of the eightfold difference in body weight at 56 days of age between two divergently selected chicken lines. Here, we describe, to our knowledge, the first generation of a three-locus QTL introgression line in chickens. Recurrent marker-assisted backcrossing was used to simultaneously transfer QTL alleles from the low-weight selected line into the high-weight selected line. Three generations of backcrossing and one generation of intercrossing resulted in an introgression line where all three introgressed QTL and several unlinked and linked control-loci were segregating at nearly expected allele frequencies. We show how intensive selection can be applied using artificial insemination to rapidly generate a multi-locus introgression line and provide recommendations for future breeding of introgression lines. This confirmed introgression line will facilitate later detailed studies of the effects of genetic interactions on complex traits in this population, including growth, and body-composition traits.

Genome-wide association study identified a narrow chromosome 1 region associated with chicken growth traits

Chicken growth traits are important economic traits in broilers. A large number of studies are available on finding genetic factors affecting chicken growth. However, most of these studies identified chromosome regions containing putative quantitative trait loci and finding causal mutations is still a challenge. In this genome-wide association study (GWAS), we identified a narrow 1.5 Mb region (173.5-175 Mb) of chicken (Gallus gallus) chromosome (GGA) 1 to be strongly associated with chicken growth using 47,678 SNPs and 489 F2 chickens. The growth traits included aggregate body weight (BW) at 0-90 d of age measured weekly, biweekly average daily gains (ADG) derived from weekly body weight, and breast muscle weight (BMW), leg muscle weight (LMW) and wing weight (WW) at 90 d of age. Five SNPs in the 1.5 Mb KPNA3-FOXO1A region at GGA1 had the highest significant effects for all growth traits in this study, including a SNP at 8.9 Kb upstream of FOXO1A for BW at 22-48 d and 70 d, a SNP at 1.9 Kb downstream of FOXO1A for WW, a SNP at 20.9 Kb downstream of ENSGALG00000022732 for ADG at 29-42 d, a SNP in INTS6 for BW at 90 d, and a SNP in KPNA3 for BMW and LMW. The 1.5 Mb KPNA3-FOXO1A region contained two microRNA genes that could bind to messenger ribonucleic acid (mRNA) of IGF1, FOXO1A and KPNA3. It was further indicated that the 1.5 Mb GGA1 region had the strongest effects on chicken growth during 22-42 d.

Genetic mapping of quantitative trait loci affecting bodyweight on chromosome 1 in a commercial strain of Japanese quail

This study was conducted to identify quantitative trait loci (QTL) affecting growth on chromosome 1 in quail. Liveweight data were recorded on 300 progeny from three half-sib families created from a commercial strain of Japanese quail. Three half-sib families were genotyped for nine microsatellite loci on chromosome 1 and QTL analysis was conducted applying the least-squares interval mapping approach. Significant QTL affecting bodyweight at 3, 4, 5 and 6 weeks of age, average daily gain, and Kleiber ratio, an indirect criterion for feed efficiency, were mapped at 0–23 cM on chromosome 1. The detected QTL segregated in two of the three half-sib families and the size of the QTL effect ranged from 0.6 to 1.1 in unit of the trait standard deviation. This is the first report of liveweight QTL segregating in a commercial strain of Japanese quail.

Replication and explorations of high-order epistasis using a large advanced intercross line pedigree

Dissection of the genetic architecture of complex traits persists as a major challenge in biology; despite considerable efforts, much remains unclear including the role and importance of genetic interactions. This study provides empirical evidence for a strong and persistent contribution of both second- and third-order epistatic interactions to long-term selection response for body weight in two divergently selected chicken lines. We earlier reported a network of interacting loci with large effects on body weight in an F(2) intercross between these high- and low-body weight lines. Here, most pair-wise interactions in the network are replicated in an independent eight-generation advanced intercross line (AIL). The original report showed an important contribution of capacitating epistasis to growth, meaning that the genotype at a hub in the network releases the effects of one or several peripheral loci. After fine-mapping of the loci in the AIL, we show that these interactions were persistent over time. The replication of five of six originally reported epistatic loci, as well as the capacitating epistasis, provides strong empirical evidence that the originally observed epistasis is of biological importance and is a contributor in the genetic architecture of this population. The stability of genetic interaction mechanisms over time indicates a non-transient role of epistasis on phenotypic change. Third-order epistasis was for the first time examined in this study and was shown to make an important contribution to growth, which suggests that the genetic architecture of growth is more complex than can be explained by two-locus interactions only. Our results illustrate the importance of designing studies that facilitate exploration of epistasis in populations for obtaining a comprehensive understanding of the genetics underlying a complex trait.

Genome-wide association study of body weight in chicken F2 resource population

Chicken body weight is an economically important trait and great genetic progress has been accomplished in genetic selective for body weight. To identify genes and chromosome regions associated with body weight, we performed a genome-wide association study using the chicken 60 k SNP panel in a chicken F2 resource population derived from the cross between Silky Fowl and White Plymouth Rock. A total of 26 SNP effects involving 9 different SNP markers reached 5% Bonferroni genome-wide significance. A chicken chromosome 4 (GGA4) region approximately 8.6 Mb in length (71.6-80.2 Mb) had a large number of significant SNP effects for late growth during weeks 7-12. The LIM domain-binding factor 2 (LDB2) gene in this region had the strongest association with body weight for weeks 7-12 and with average daily gain for weeks 6-12. This GGA4 region was previously reported to contain body weight QTL. GGA1 and GGA18 had three SNP effects on body weight with genome-wide significance. Some of the SNP effects with the significance of "suggestive linkage" overlapped with previously reported results.

Differentially expressed genes in hypothalamus in relation to genomic regions under selection in two chicken lines resulting from divergent selection for high or low body weight

Long-term divergent selection for low or high body weight from the same founder population has generated two extremely divergent lines of chickens, the high- (HWS) and low-weight (LWS) selected lines. At selection age (56 days), the lines differ by more than nine times in body weight. The HWS line chickens are compulsive feeders, whereas in the LWS line, some individuals are anorexic and others have very low appetite. Previous studies have implicated the central nervous system and particularly the hypothalamus in these behavioural differences. Here, we compared the mRNA expression in hypothalamus tissue from chickens on day 4 post-hatch using oligonucleotide arrays and found that the divergent selection had resulted in minor but multiple expression differences. Differentially expressed genes were enriched in processes 'DNA metabolism, repair, induction of apoptosis and metabolism'. Several differentially expressed genes participate in the regulation of neuronal plasticity and development, including apoptosis, or are neurotransmittor receptor subtypes. Less change was seen when comparing hypothalamic neuropeptide mediators of appetite such as the melanocortin receptors. The genomic locations of these differentially expressed genes were then compared to the locations of growth QTLs and to a genome-wide map of chromosomal regions that have been under divergent selection between the lines. The results indicate which differentially expressed hypothalamic genes have responded to the divergent selection and that the results predict that it is more likely to find causative genes among these most differentially expressed genes. Because of such differential gene expression in hypothalamus, the lines may adapt behaviourally different particularly to the post-hatch situation when independent feeding to obtain energy is established.

Whole genome QTL mapping for growth, meat quality and breast meat yield traits in turkey

Background

The turkey (Meleagris gallopavo) is an important agricultural species and is the second largest contributor to the world's poultry meat production. Demand of turkey meat is increasing very rapidly. Genetic markers linked to genes affecting quantitative traits can increase the selection response of animal breeding programs. The use of these molecular markers for the identification of quantitative trait loci, and subsequently fine-mapping of quantitative trait loci regions, allows for pinpointing of genes that underlie such economically important traits.

Results

The quantitative trait loci analyses of the growth curve, body weight, breast yield and the meat quality traits showed putative quantitative trait loci on 21 of the 27 turkey chromosomes covered by the linkage map. Forty-five quantitative trait loci were detected across all traits and these were found in 29 different regions on 21 chromosomes. Out of the 45 quantitative trait loci, twelve showed significant (p < 0.01) evidence of linkage while the remaining 33 showed suggestive evidence (p < 0.05) of linkage with different growth, growth curve, meat quality and breast yield traits.

Conclusion

A large number of quantitative trait loci were detected across the turkey genome, which affected growth, breast yield and meat quality traits. Pleiotropic effects or close linkages between quantitative trait loci were suggested for several of the chromosomal regions. The comparative analysis regarding the location of quantitative trait loci on different turkey, and on the syntenic chicken chromosomes, along with their phenotypic associations, revealed signs of functional conservation between these species.

The development and characterization of a 60K SNP chip for chicken

BACKGROUND

In livestock species like the chicken, high throughput single nucleotide polymorphism (SNP) genotyping assays are increasingly being used for whole genome association studies and as a tool in breeding (referred to as genomic selection). To be of value in a wide variety of breeds and populations, the success rate of the SNP genotyping assay, the distribution of the SNP across the genome and the minor allele frequencies (MAF) of the SNPs used are extremely important.

RESULTS

We describe the design of a moderate density (60k) Illumina SNP BeadChip in chicken consisting of SNPs known to be segregating at high to medium minor allele frequencies (MAF) in the two major types of commercial chicken (broilers and layers). This was achieved by the identification of 352,303 SNPs with moderate to high MAF in 2 broilers and 2 layer lines using Illumina sequencing on reduced representation libraries. To further increase the utility of the chip, we also identified SNPs on sequences currently not covered by the chicken genome assembly (Gallus_gallus-2.1). This was achieved by 454 sequencing of the chicken genome at a depth of 12x and the identification of SNPs on 454-derived contigs not covered by the current chicken genome assembly. In total we added 790 SNPs that mapped to 454-derived contigs as well as 421 SNPs with a position on Chr_random of the current assembly. The SNP chip contains 57,636 SNPs of which 54,293 could be genotyped and were shown to be segregating in chicken populations. Our SNP identification procedure appeared to be highly reliable and the overall validation rate of the SNPs on the chip was 94%. We were able to map 328 SNPs derived from the 454 sequence contigs on the chicken genome. The majority of these SNPs map to chromosomes that are already represented in genome build Gallus_gallus-2.1.0. Twenty-eight SNPs were used to construct two new linkage groups most likely representing two micro-chromosomes not covered by the current genome assembly.

CONCLUSIONS

The high success rate of the SNPs on the Illumina chicken 60K Beadchip emphasizes the power of Next generation sequence (NGS) technology for the SNP identification and selection step. The identification of SNPs from sequence contigs derived from NGS sequencing resulted in improved coverage of the chicken genome and the construction of two new linkage groups most likely representing two chicken micro-chromosomes.

Mapping quantitative trait loci for egg production traits in an F2 intercross of Oh-Shamo and White Leghorn chickens

We performed quantitative trait locus (QTL) analyses for egg production traits, including age at first egg (AFE) and egg production rates (EPR) measured every 4 weeks from 22 to 62 weeks of hen age, in a population of 421 F(2) hens derived from an intercross between the Oh-Shamo (Japanese Large Game) and White Leghorn breeds of chickens. Simple interval mapping revealed a main-effect QTL for AFE on chromosome 1 and four main-effect QTL for EPR on chromosomes 1 and 11 (three on chromosome 1 and one on chromosome 11) at the genome-wide 5% levels. Among the three EPR QTL on chromosome 1, two were identified at the early stage of egg laying (26-34 weeks of hen age) and the remaining one was discovered at the late stage (54-58 weeks). The alleles at the two EPR QTL derived from the Oh-Shamo breed unexpectedly increased the trait values, irrespective of the Oh-Shamo being inferior to the White Leghorn in the trait. This suggests that the Oh-Shamo, one of the indigenous Japanese breeds, is an untapped resource that is important for further improvement of current elite commercial laying chickens. In addition, six epistatic QTL were identified on chromosomes 2, 4, 7, 8, 17 and 19, where none of the above main-effect QTL were located. This is the first example of detection of epistatic QTL affecting egg production traits. The main and epistatic QTL identified accounted for 4-8% of the phenotypic variance. The total contribution of all QTL detected for each trait to the phenotypic and genetic variances ranged from 4.1% to 16.9% and from 11.5% to 58.5%, respectively.

Fine mapping and replication of QTL in outbred chicken advanced intercross lines

Background

Linkage mapping is used to identify genomic regions affecting the expression of complex traits. However, when experimental crosses such as F₂ populations or backcrosses are used to map regions containing a Quantitative Trait Locus (QTL), the size of the regions identified remains quite large, i.e. 10 or more Mb. Thus, other experimental strategies are needed to refine the QTL locations. Advanced Intercross Lines (AIL) are produced by repeated intercrossing of F₂ animals and successive generations, which decrease linkage disequilibrium in a controlled manner. Although this approach is seen as promising, both to replicate QTL analyses and fine-map QTL, only a few AIL datasets, all originating from inbred founders, have been reported in the literature.

Methods

We have produced a nine-generation AIL pedigree (n = 1529) from two outbred chicken lines divergently selected for body weight at eight weeks of age. All animals were weighed at eight weeks of age and genotyped for SNP located in nine genomic regions where significant or suggestive QTL had previously been detected in the F₂ population. In parallel, we have developed a novel strategy to analyse the data that uses both genotype and pedigree information of all AIL individuals to replicate the detection of and fine-map QTL affecting juvenile body weight.

Results

Five of the nine QTL detected with the original F₂ population were confirmed and fine-mapped with the AIL, while for the remaining four, only suggestive evidence of their existence was obtained. All original QTL were confirmed as a single locus, except for one, which split into two linked QTL.

Conclusions

Our results indicate that many of the QTL, which are genome-wide significant or suggestive in the analyses of large intercross populations, are true effects that can be replicated and fine-mapped using AIL. Key factors for success are the use of large populations and powerful statistical tools. Moreover, we believe that the statistical methods we have developed to efficiently study outbred AIL populations will increase the number of organisms for which in-depth complex traits can be analyzed.

Genome-wide effects of long-term divergent selection

To understand the genetic mechanisms leading to phenotypic differentiation, it is important to identify genomic regions under selection. We scanned the genome of two chicken lines from a single trait selection experiment, where 50 generations of selection have resulted in a 9-fold difference in body weight. Analyses of nearly 60,000 SNP markers showed that the effects of selection on the genome are dramatic. The lines were fixed for alternative alleles in more than 50 regions as a result of selection. Another 10 regions displayed strong evidence for ongoing differentiation during the last 10 generations. Many more regions across the genome showed large differences in allele frequency between the lines, indicating that the phenotypic evolution in the lines in 50 generations is the result of an exploitation of standing genetic variation at 100s of loci across the genome.

Genetic analysis of metabolic traits in an intercross between body weight-selected chicken lines

A network of four interacting loci has been reported previously to influence growth in two lines of chickens divergently selected for body weight at 56 days of age. Located on chromosomes 3 (Growth4), 4 (Growth6), 7 (Growth9), and 20 (Growth12), they explained nearly half of the difference in body weight at selection age between the two lines. The original study reported effects on body weight and fat deposition, but no attempts were made to explore the effects of the network on other phenotypes measured in the F(2) population. In this study we conducted further analyses to evaluate the specific effects of the four-locus network on other metabolic traits as well as refining results from the original study by including a larger number of genetic markers in the quantitative trait locus (QTL) regions. We confirm the previously described effect of the epistatic network on body weight and show that the network increases the total amount of muscle and fat as well as the weight of the internal organs. The network as a whole did not change the relative content of any studied organs or tissues in the body. There was, however, a significant interaction between the loci on chromosomes 3 and 7 that changed the relative proportion of abdominal fat and breast muscle in the chicken by increasing abdominal fat weight without a corresponding increase in muscle mass.