Whole-genome scan for signatures of recent selection reveals loci associated with important traits in White Leghorn chickens

Gut microbiota and transcriptome analysis reveals a genetic component to dropping moisture in chickens

High dropping moisture (DM) in poultry production has deleterious effects on the environment, feeding cost, and public health of people and animals. To explore the contributing genetic components, we classified DM of 67-wk-old Rhode Island Red (RIR) hens at 4 different levels and evaluated the underlying genetic heritability. We found the heritability of DM to be 0.219, indicating a moderately heritable trait. We then selected chickens with the highest and lowest DM levels. Using transcriptome, we only detected 12 differentially expressed genes (DEGs) between these 2 groups from the spleen, and 1,507 DEGs from intestinal tissues (jejunum and cecum). The low number of DEGs observed in the spleen suggests that differing moisture levels are not attributed to pathogenic infection. Fourteen of the intestinal high expressed genes are associated with water-salt metabolism (WSM). We also investigated the gut microbial composition by 16S rRNA gene amplicon sequencing. Six different microbial operational taxonomic units (OTUs) (Cetobacterium, Sterolibacterium, Elusimicrobium, Roseburia, Faecalicoccus, and Megamonas) between the 2 groups from jejunum and cecum are potentially biomarkers related to DM levels. Our results identify a genetic component to chicken DM, and can guide breeding strategies.

The Genetic Architecture of the Chickens Dropping Moisture by Genetic Parameter Estimation and Genome-Wide Association

Dropping moisture (DM) refers to the water content of feces. High DM in chickens could be disadvantageous to pathogen control and fecal treatment in chicken farms. DM can be affected by environment, nutrition, disease, and genetics. In the present study, significant individual differences were presented in the DM of Rhode Island Red (RIR) chicken population, indicating that genetics could contribute to DM in the chickens. Subsequently, we estimated the genetic parameters of DM and conducted a genome-wide association study (GWAS) to find the potential genomic regions related to DM. The results showed that the heritability of DM ranged from 0.25 to 0.32. Furthermore, 11 significant loci on chromosome 7 were found to be associated with DM levels by the GWAS. The SNP rs15833816 within the COL6A3 gene was the most significant SNP related to DM. Hens carrying the G allele including GA and GG produced higher DM (P < 0.01) levels than those carrying the other genotype AA. Our results showed that DM is a medium-inheritable trait and that COL6A3 could be a potential candidate gene that regulates DM level in chickens.

Determination of Polymorphisms in Pituitary Genes of the Native Afghani Naked Neck Chicken

We investigated means to improve the production of the indigenous Naked Neck chicken in Afghanistan. Specifically, we analyzed single nucleotide polymorphisms (SNPs) in the prolactin (PRL) (24 bp indel), growth hormone (GH) (T185G), and pituitary specific transcript factor 1 (PIT-1) (intron 5) genes. Blood samples were collected from 52 birds and genomic DNA was extracted. Polymorphisms in the mentioned loci were analyzed by PCR, allele-specific PCR, and PCR-restriction fragment length polymorphism (RFLP) using TaqI and MspI endonucleases. Cloning followed by DNA sequencing was performed to ascertain the accuracy of the PCR-RFLP analysis for PIT-1.Two alleles were found for the PRL 24 bp indel, GH (T185G), and PIT-1/TaqI, with the following respective allelic frequencies: PRL-In 0.64 and PRL-Del 0.36, GH-T 0.91 and GH-G 0.09, and PIT-1-A 0.64 and PIT-1-B 0.36. Regarding the PIT-1/MspI polymorphism, three novel MspI recognition sites, as well as two reported MspI recognition sites, were detected in intron 5. Moreover, during sequence screening, two novel SNPs were found that generated restriction sites for MseI. Therefore, our results suggest that the PRL indel, GH T185G, and PIT-1/TaqI polymorphisms may be used as selection markers for Afghanistan Naked Neck chickens. Intron 5 of PIT-1 in the Afghani Naked Neck chicken was highly polymorphic compared to the reported Gallus gallus PIT-1 gene (GenBank accession no. NC_006088.4).

Identifying Genetic Differences Between Dongxiang Blue-Shelled and White Leghorn Chickens Using Sequencing Data

The Dongxiang Blue-shelled chicken is one of the most valuable Chinese indigenous poultry breeds. However, compared to the Italian native White Leghorn, although this Chinese breed possesses numerous favorable characteristics, it also exhibits lower growth performance and fertility. Here, we utilized genotyping sequencing data obtained via genome reduction on a sequencing platform to detect 100,114 single nucleotide polymorphisms and perform further biological analysis and functional annotation. We employed cross-population extended haplotype homozygosity, eigenvector decomposition combined with genome-wide association studies (EigenGWAS), and efficient mixed-model association expedited methods to detect areas of the genome that are potential selected regions (PSR) in both chicken breeds, and performed gene ontology (GO) enrichment and quantitative trait loci (QTL) analyses annotating using the Kyoto Encyclopedia of Genes and Genomes. The results of this study revealed a total of 2424 outlier loci (p-value <0.01), of which 2144 occur in the White Leghorn breed and 280 occur in the Dongxiang Blue-shelled chicken. These correspond to 327 and 94 PSRs containing 297 and 54 genes, respectively. The most significantly selected genes in Blue-shelled chicken are TMEM141 and CLIC3, while the SLCO1B3 gene, related to eggshell color, was identified via EigenGWAS. We show that the White Leghorn genes JARID2, RBMS3, GPC3, TRIB2, ROBO1, SAMSN1, OSBP2, and IGFALS are involved in immunity, reproduction, and growth, and thus might represent footprints of the selection process. In contrast, we identified six significantly enriched pathways in the Dongxiang Blue-shelled chicken that are related to amino acid and lipid metabolism as well as signal transduction. Our results also reveal the presence of a GO term associated with cell metabolism that occurs mainly in the White Leghorn breed, while the most significant QTL regions mapped to the Chicken QTL Database (GG_4.0) for the Dongxiang Blue-shelled breed are predominantly related to lesions, bone mineral content, and other related traits compared to tibia length and body weight (i.e., at 14, 28, 42, and 70 d) in the White Leghorn. The results of this study highlight differences in growth, immunity, and egg quality traits between the two breeds, and provide a foundation for the exploration of their genetic mechanisms.

Genome-Wide Detection of Selective Signatures in Chicken through High Density SNPs

Chicken is recognized as an excellent model for studies of genetic mechanism of phenotypic and genomic evolution, with large effective population size and strong human-driven selection. In the present study, we performed Extended Haplotype Homozygosity (EHH) tests to identify significant core regions employing 600K SNP Chicken chip in an F2 population of 1,534 hens, which was derived from reciprocal crosses between White Leghorn and Dongxiang chicken. Results indicated that a total of 49,151 core regions with an average length of 9.79 Kb were identified, which occupied approximately 52.15% of genome across all autosomes, and 806 significant core regions attracted us mostly. Genes in candidate regions may experience positive selection and were considered to have possible influence on beneficial economic traits. A panel of genes including AASDHPPT, GDPD5, PAR3, SOX6, GPC1 and a signal pathway of AKT1 were detected with the most extreme P-values. Further enrichment analyses indicated that these genes were associated with immune function, sensory organ development and neurogenesis, and may have experienced positive selection in chicken. Moreover, some of core regions exactly overlapped with genes excavated in our previous GWAS, suggesting that these genes have undergone positive selection may affect egg production. Findings in our study could draw a comparatively integrate genome-wide map of selection signature in the chicken genome, and would be worthy for explicating the genetic mechanisms of phenotypic diversity in poultry breeding.

Changes in selective pressures associated with human population expansion may explain metabolic and immune related pathways enriched for signatures of positive selection

Background

The study of local adaptation processes is a very important research topic in the field of population genomics. There is a particular interest in the study of human populations because they underwent a process of rapid spatial expansion and faced important environmental changes that translated into changes in selective pressures. New mutations may have been selected for in the new environment and previously existing genetic variants may have become detrimental. Immune related genes may have been released from the selective pressure exerted by pathogens in the ancestral environment and new variants may have been positively selected due to pathogens present in the newly colonized habitat. Also, variants that had a selective advantage in past environments may have become deleterious in the modern world due to external stimuli including climatic, dietary and behavioral changes, which could explain the high prevalence of some polygenic diseases such as diabetes and obesity.

Results

We performed an enrichment analysis to identify gene sets enriched for signals of positive selection in humans. We used two genome scan methods, XPCLR and iHS to detect selection using a dense coverage of SNP markers combined with two gene set enrichment approaches. We identified immune related gene sets that could be involved in the protection against pathogens especially in the African population. We also identified the glycolysis & gluconeogenesis gene set, related to metabolism, which supports the thrifty genotype hypothesis invoked to explain the current high prevalence of diseases such as diabetes and obesity. Extending our analysis to the gene level, we found signals for 23 candidate genes linked to metabolic syndrome, 13 of which are new candidates for positive selection.

Conclusions

Our study provides a list of genes and gene sets associated with immunity and metabolic syndrome that are enriched for signals of positive selection in three human populations (Europeans, Africans and Asians). Our results highlight differences in the relative importance of pathogens as drivers of local adaptation in different continents and provide new insights into the evolution and high incidence of metabolic syndrome in modern human populations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2783-2) contains supplementary material, which is available to authorized users.

A Scale-Corrected Comparison of Linkage Disequilibrium Levels between Genic and Non-Genic Regions

The understanding of non-random association between loci, termed linkage disequilibrium (LD), plays a central role in genomic research. Since causal mutations are generally not included in genomic marker data, LD between those and available markers is essential for capturing the effects of causal loci on localizing genes responsible for traits. Thus, the interpretation of association studies requires a detailed knowledge of LD patterns. It is well known that most LD measures depend on minor allele frequencies (MAF) of the considered loci and the magnitude of LD is influenced by the physical distances between loci. In the present study, a procedure to compare the LD structure between genomic regions comprising several markers each is suggested. The approach accounts for different scaling factors, namely the distribution of MAF, the distribution of pair-wise differences in MAF, and the physical extent of compared regions, reflected by the distribution of pair-wise physical distances. In the first step, genomic regions are matched based on similarity in these scaling factors. In the second step, chromosome- and genome-wide significance tests for differences in medians of LD measures in each pair are performed. The proposed framework was applied to test the hypothesis that the average LD is different in genic and non-genic regions. This was tested with a genome-wide approach with data sets for humans (Homo sapiens), a highly selected chicken line (Gallus gallus domesticus) and the model plant Arabidopsis thaliana. In all three data sets we found a significantly higher level of LD in genic regions compared to non-genic regions. About 31% more LD was detected genome-wide in genic compared to non-genic regions in Arabidopsis thaliana, followed by 13.6% in human and 6% chicken. Chromosome-wide comparison discovered significant differences on all 5 chromosomes in Arabidopsis thaliana and on one third of the human and of the chicken chromosomes.

Applications in the search for genomic selection signatures in fish

Selection signatures are genomic regions harboring DNA sequences functionally involved in the genetic variation of traits subject to selection. Selection signatures have been intensively studied in recent years because of their relevance to evolutionary biology and their potential association with genes that control phenotypes of interest in wild and domestic populations. Selection signature research in fish has been confined to a smaller scale, due in part to the relatively recent domestication of fish species and limited genomic resources such as molecular markers, genetic mapping, DNA sequences, and reference genomes. However, recent genomic technology advances are paving the way for more studies that may contribute to the knowledge of genomic regions underlying phenotypes of biological and productive interest in fish.

Genome-wide association study for egg production and quality in layer chickens

Discovery of genes with large effects on economically important traits has for many years been of interest to breeders. The development of SNP panels which cover the whole genome with high density and, more importantly, that can be genotyped on large numbers of individuals at relatively low cost, has opened new opportunities for genome-wide association studies (GWAS). The objective of this study was to find genomic regions associated with egg production and quality traits in layers using analysis methods developed for the purpose of whole genome prediction. Genotypes on over 4500 birds and phenotypes on over 13,000 hens from eight generations of a brown egg layer line were used. Birds were genotyped with a custom 42K Illumina SNP chip. Recorded traits included two egg production and 11 egg quality traits (puncture score, albumen height, yolk weight and shell colour) at early and late stages of production, as well as body weight and age at first egg. Egg weight was previously analysed by Wolc et al. (2012). The Bayesian whole genome prediction model--BayesB (Meuwissen et al. 2001) was used to locate 1 Mb regions that were most strongly associated with each trait. The posterior probability of a 1 Mb window contributing to genetic variation was used as the criterion for suggesting the presence of a quantitative trait locus (QTL) in that window. Depending upon the trait, from 1 to 7 significant (posterior probability >0.9) 1 Mb regions were found. The largest QTL, a region explaining 32% of genetic variance, was found on chr4 at 78 Mb for body weight but had pleiotropic effects on other traits. For the other traits, the largest effects were much smaller, explaining <7% of genetic variance, with regions on chromosomes 2, 12 and 17 explaining above 5% of genetic variance for albumen height, shell colour and egg production, respectively. In total, 45 of 1043 1 Mb windows were estimated to have a non-zero effect with posterior probability > 0.9 for one or more traits.