Mapas de ligação dos cromossomos 6, 7, 8, 11 e 13 de uma população brasileira de galinha

A linkage map is essential not only for quantitative trait loci (QTL) mapping, but also for the organization and location of genes along the chromosomes. The present study is part of a project whose major objective is, besides from construction the linkage maps, the whole genome scan for mapping QTL for performance traits in the Brazilian experimental chicken population. Linkage maps of chicken chromosomes 6 to 8, 11 and 13 were constructed based on this population. The population was developed from two generations of crossbreeding between a broiler and a layer line. Fifty-one microsatellite markers were tested, from which 28 were informative: 4, 8, 7, 4 and 5 for chromosomes 6, 7, 8, 11 and 13, respectively. A SNP located in the leptin receptor gene was included for chromosome 8. Ten parental, 8 F1 and 459 F2 chickens from five full-sib families were genotyped with these markers. The number of total informative meioses per locus varied from 232 to 862, and the number of phase-known informative meioses from 0 to 764. Marker orders in the chromosomes coincided with those of the chicken consensus map, except for markers ADL0147 and MCW0213, on chromosome 13, which were inverted. The reduced number of phase-known informative meioses for ADL0147 (150) may be pointed out as a possible cause for this inversion, apart from the relative short distance between the two markers involved in the inversion (10.5 cM).O mapa de ligação além de ser fundamental no mapeamento de locos de características quantitativas (QTLs) é importante na organização e localização de genes distribuídos ao longo dos cromossomos. O presente estudo é parte de um trabalho cujo objetivo maior, é a análise de mapeamento de QTLs para características de desempenho no genoma de uma população experimental desenvolvida no Brasil. Com base nesta população foram construídos os mapas de ligação dos cromossomos 6 a 8, 11 e 13 da galinha. A população foi desenvolvida a partir de duas gerações de cruzamentos entre uma linhagem de corte e uma de postura. Foram testados 51 marcadores microssatélites, dos quais 28 foram informativos: 4, 8, 7, 4 e 5 dos cromossomos 6, 7, 8, 11 e 13, respectivamente. Um SNP localizado no gene do receptor da leptina foi incluído no cromossomo 8. Os 10 parentais, 8 F1 e um total de 459 aves F2 de cinco famílias de irmãos completos foram genotipados com estes marcadores. O número de meioses informativas totais por loco variou de 232 a 862 e o de meioses informativas de fase conhecida de 0 a 764. A ordem dos marcadores nos cromossomos coincidiu com a do mapa consenso da galinha, com exceção dos marcadores ADL0147 e MCW0213 do cromossomo 13 que tiveram sua ordem invertida. O número reduzido de meioses informativas de fase conhecida para o marcador ADL0147 (150) pode ser apontado como uma possível causa para a inversão, além da relativa proximidade entre os dois marcadores envolvidos na inversão (10,5 cM)

Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens

Background: Poultry breeding programs have been focused on improvement of growth and carcass traits, however, this has resulted in correlated changes in internal organ weights and increased incidence of metabolic disorders. These disorders can affect feed efficiency or even cause death. We used a high density SNP array (600 K, Affymetrix) to estimate genomic heritability, perform genome-wide association analysis, and identify genomic regions and positional candidate genes (PCGs) associated with internal organ traits in an F2 chicken population. We integrated knowledge of haplotype blocks, selection signature regions and sequencing data to refine the list of PCGs. Results: Estimated genomic heritability for internal organ traits in chickens ranged from low (LUNGWT, 0.06) to high (GIZZWT, 0.45). A total of 20 unique 1 Mb windows identified on GGA1, 2, 4, 7, 12, 15, 18, 19, 21, 27 and 28 were significantly associated with intestine length, and weights or percentages of liver, gizzard or lungs. Within these windows, 14 PCGs were identified based on their biological functions: TNFSF11, GTF2F2, SPERT, KCTD4, HTR2A, RB1, PCDH7, LCORL, LDB2, NR4A2, GPD2, PTPN11, ITGB4 and SLC6A4. From those genes, two were located within haplotype blocks and three overlapped with selection signature regions. A total of 13,748 annotated sequence SNPs were in the 14 PCGs, including 156 SNPs in coding regions (124 synonymous, 26 non-synonymous, and 6 splice variants). Seven deleterious SNPs were identified in TNFSF11, NR4A2 or ITGB4 genes. Conclusions: The results from this study provide novel insights to understand the genetic architecture of internal organ traits in chickens. The QTL detection performed using a high density SNP array covered the whole genome allowing the discovery of novel QTL associated with organ traits. We identified PCGs within the QTL involved in biological processes that may regulate internal organ growth and development. Potential functional genetic variations were identified generating crucial information that, after validation, might be used in poultry breeding programs to reduce the occurrence of metabolic disorders

Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach

Background: Feed efficiency and growth rate have been targets for selection to improve chicken production. The incorporation of genomic tools may help to accelerate selection. We genotyped 529 individuals using a high-density SNP chip (600 K, Affymetrix®) to estimate genomic heritability of performance traits and to identify genomic regions and their positional candidate genes associated with performance traits in a Brazilian F2 Chicken Resource population. Regions exhibiting selection signatures and a SNP dataset from resequencing were integrated with the genomic regions identified using the chip to refine the list of positional candidate genes and identify potential causative mutations. Results: Feed intake (FI), feed conversion ratio (FC), feed efficiency (FE) and weight gain (WG) exhibited low genomic heritability values (i.e. from 0.0002 to 0.13), while body weight at hatch (BW1), 35 days-of-age (BW35), and 41 days-of-age (BW41) exhibited high genomic heritability values (i.e. from 0.60 to 0.73) in this F2 population. Twenty unique 1-Mb genomic windows were associated with BW1, BW35 or BW41, located on GGA1–4, 6–7, 10, 14, 24, 27 and 28. Thirty-eight positional candidate genes were identified within these windows, and three of them overlapped with selection signature regions. Thirteen predicted deleterious and three high impact sequence SNPs in these QTL regions were annotated in 11 positional candidate genes related to osteogenesis, skeletal muscle development, growth, energy metabolism and lipid metabolism, which may be associated with body weight in chickens. Conclusions: The use of a high-density SNP array to identify QTL which were integrated with whole genome sequence signatures of selection allowed the identification of candidate genes and candidate causal variants. One novel QTL was detected providing additional information to understand the genetic architecture of body weight traits. We identified QTL for body weight traits, which were also associated with fatness in the same population. Our findings form a basis for further functional studies to elucidate the role of specific genes in regulating body weight and fat deposition in chickens, generating useful information for poultry breeding programs

Integration of genome wide association studies and whole genome sequencing provides novel insights into fat deposition in chicken

Excessive fat deposition is a negative factor for poultry production because it reduces feed efficiency, increases the cost of meat production and is a health concern for consumers. We genotyped 497 birds from a Brazilian F2 Chicken Resource Population, using a high-density SNP array (600 K), to estimate the genomic heritability of fat deposition related traits and to identify genomic regions and positional candidate genes (PCGs) associated with these traits. Selection signature regions, haplotype blocks and SNP data from a previous whole genome sequencing study in the founders of this chicken F2 population were used to refine the list of PCGs and to identify potential causative SNPs. We obtained high genomic heritabilities (0.43–0.56) and identified 22 unique QTLs for abdominal fat and carcass fat content traits. These QTLs harbored 26 PCGs involved in biological processes such as fat cell differentiation, insulin and triglyceride levels, and lipid biosynthetic process. Three of these 26 PCGs were located within haplotype blocks there were associated with fat traits, five overlapped with selection signature regions, and 12 contained predicted deleterious variants. The identified QTLs, PCGs and potentially causative SNPs provide new insights into the genetic control of fat deposition and can lead to improved accuracy of selection to reduce excessive fat deposition in chickens

Development of a high density 600K SNP genotyping array for chicken

Background: High density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.Results: We report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources (experimental, commercial broiler and layer lines) by pooling 10-15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses.Conclusions: This Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants

Development of a high density 600K SNP genotyping array for chicken

Background High density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species. Results We report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources (experimental, commercial broiler and layer lines) by pooling 10–15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses. Conclusions This Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants.Additional co-authors: Georg Haberer, Steffen Weigend, Rudolf Preisinger, Mahmood Gholami, Saber Qanbari, Henner Simianer, Kellie A Watson, John A Woolliams & David W Bur

[Avian cytogenetics goes functional] Third report on chicken genes and chromosomes 2015

High-density gridded libraries of large-insert clones using bacterial artificial chromosome (BAC) and other vectors are essential tools for genetic and genomic research in chicken and other avian species... Taken together, these studies demonstrate that applications of large-insert clones and BAC libraries derived from birds are, and will continue to be, effective tools to aid high-throughput and state-of-the-art genomic efforts and the important biological insight that arises from them

Genome-wide characterization of genetic variants and putative regions under selection in meat and egg-type chicken lines

Abstract\ud \ud Background\ud Meat and egg-type chickens have been selected for several generations for different traits. Artificial and natural selection for different phenotypes can change frequency of genetic variants, leaving particular genomic footprints throghtout the genome. Thus, the aims of this study were to sequence 28 chickens from two Brazilian lines (meat and white egg-type) and use this information to characterize genome-wide genetic variations, identify putative regions under selection using Fst method, and find putative pathways under selection.\ud \ud \ud Results\ud A total of 13.93 million SNPs and 1.36 million INDELs were identified, with more variants detected from the broiler (meat-type) line. Although most were located in non-coding regions, we identified 7255 intolerant non-synonymous SNPs, 512 stopgain/loss SNPs, 1381 frameshift and 1094 non-frameshift INDELs that may alter protein functions. Genes harboring intolerant non-synonymous SNPs affected metabolic pathways related mainly to reproduction and endocrine systems in the white-egg layer line, and lipid metabolism and metabolic diseases in the broiler line. Fst analysis in sliding windows, using SNPs and INDELs separately, identified over 300 putative regions of selection overlapping with more than 250 genes. For the first time in chicken, INDEL variants were considered for selection signature analysis, showing high level of correlation in results between SNP and INDEL data. The putative regions of selection signatures revealed interesting candidate genes and pathways related to important phenotypic traits in chicken, such as lipid metabolism, growth, reproduction, and cardiac development.\ud \ud \ud Conclusions\ud In this study, Fst method was applied to identify high confidence putative regions under selection, providing novel insights into selection footprints that can help elucidate the functional mechanisms underlying different phenotypic traits relevant to meat and egg-type chicken lines. In addition, we generated a large catalog of line-specific and common genetic variants from a Brazilian broiler and a white egg layer line that can be used for genomic studies involving association analysis with phenotypes of economic interest to the poultry industry.CB received a fellowship from the program Science Without Borders - National Council for Scientific and Technological Development (CNPq, grant 370620/2013–5). GCMM and TFG received fellowships from São Paulo Research Foundation (FAPESP, grants 14/21380–9 and 15/00616–7). LLC is recipient of productivity fellowship from CNPq. This project was funded by São Paulo Research Foundation (FAPESP) - thematic project (2014/08704–0)