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

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)

Identification of polymorphisms in the chicken chromosome 2 region associated with muscle deposition

A produção brasileira de carne de frango tem uma grande importância econômica no mundo todo devido principalmente aos avanços do melhoramento genético. O surgimento de novas tecnologias de sequenciamento (sequenciamento de nova geração) tem se tornado uma ferramenta poderosa, pois por meio da identificação de SNPs (polimorfismo de nucleotídeo único) e INDELs (deleções/inserções) possibilita a adição de novas informações ao melhoramento genético. A deposição de músculo, em especial o músculo de peito, é uma das características que mais merecem destaque por causa da sua importância nutricional e econômica. Sendo assim o objetivo deste trabalho foi ressequenciar o genoma de 18 aves de duas linhagens distintas experimentais e identificar SNPs e INDELs em uma região de QTL no cromossomo 2 da galinha associado anteriormente com deposição de músculo do peito, além de caracterizar variantes potencialemente funcionais e propor mutações candidatas para estudos futuros. Para isso, dezoito galinhas de duas diferentes linhagens experimentais (corte e postura), ambas desenvolvidas pela Embrapa Suíno e Aves, foram sequenciadas pela plataforma de nova geração da Illumina. SNPs e INDELs foram identificados por meio de ferramentas de bioinformática em uma região de QTL no cromossomo 2 da galinha (105.848.755-112.648.761 pb) que foi previamente associada com deposição de músculo de peito. O sequenciamento dos 18 animais gerou em torno 2,7 bilhões de reads e após a filtragem por qualidade foram mantidas 77% das reads. Em seguida, as reads foram alinhadas ao genoma referência (Gallus_gallus-4.0, NCBI) pela ferramenta Bowtie2 e gerou em média 10,6X de cobertura de sequenciamento na região-alvo. , Foram identificados 722.832 SNPs e 63.727 INDELs para os 18 animais por meio do programa SAMtools, e após uma filtragem rigorosa, foram mantidos 77% dos SNPs (n=558.767) e 60% das INDELs (n=38.402). Com base nas variantes únicas para os 18 animais (85.765 SNPs e 7.824 INDELs) foi realizada a anotação funcional por meio da ferramenta ANNOVAR. Dentre os SNPs não sinônimos (n=153) e stopgain (n=3), 15 foram classificados como deletérios. Um dos SNPs deletérios que já foi depositado em banco de dados foi identificado no gene RB1CC1, que tem sua função relacionada ao desenvolvimento do músculo de peito. Utilizando a ferramenta DAVID foi possível analisar 37 genes relacionados aos SNPs não sinônimos, stopgain, INDELs frameshift e não frameshift. Dentre estes genes, três (DTNA, RB1CC1 e C-MOS) foram selecionados por terem suas funções relacionadas ao desenvolvimento muscular e suas mutações foram analisadas. Sendo assim, futuros estudos podem ser realizados nestes genes candidatos e nas mutações identificadas, por meio de análises de associação e validação em populações comerciais, permitindo assim uma melhor explicação o efeito do QTL estudado.The Brazilian chicken meat production has a great economic importance in worldwide mainly due to advances in breeding. The emergence of new techniques of sequencing (nextgeneration sequencing) becomes a powerful tool because through identification of SNPs (single nucleotide polymorphism) and INDELs (deletions/insertions) allows the addition of new information for genetic improvement. The muscle deposition, particularly the breast muscle, is one of the features that are most noteworthy because of its nutritional and economic importance. Therefore the aim of this study was to perform the genome resequencing of 18 chicken from two distinct experimental lines and identify SNPs and INDELs in a QTL region on chromosome 2 previously associated with breast muscle, and characterize the variants to identify potentially function ones and propose candidate mutations for future studies. To achieve these objectives, eighteen chickens of two different experimental lines (broiler and layer), both developed by Embrapa Swine and Poultry were sequenced by Illumina next-generation platform. SNPs and INDELs were identified by bioinformatic tools in a QTL region on chicken chromosome 2 (105,848,755-112,648,761 bp) which was previously associated with breast muscle deposition. Sequencing of the eighteen animals generated around 2.7 billion of reads, and 77% of the reads were retained after filtering. The reads were aligned against the chicken genome reference (Gallus_gallus-4.0, NCBI) by Bowtie2 tool resulting in a 10.6X coverage across the target region. Using SAMtools, 722,832 SNPs and 63,727 INDELs were identified in the all individuals, and after a stringent filtration, 77% of SNPs (n=558,767) and 60% of INDELs (n=38,402) were maintained. Based on unique variants for all the animal (85,765 SNPs and 7,828 INDELs) were performed the functional annotation by ANNOVAR tool. Among the non-synonymous SNPs (n=153) and stopgain (n=3), fifteen were predicted like a deleterious mutation. One of deleterious SNPs has already deposited in public database, and it was identified in RB1CC1 gene, which function is related to breast muscle development. Using the DAVID tool was possible to analyze the 37 genes related to the non-synonymous SNPs, stopgain, frameshift and non-frameshift INDELs. Among these genes, three (DTNA, RB1CC1 and C-MOS) were selected due their functions related to muscle development and their mutations were analyzed. Therefore, further association studies can be performed with these candidate genes and their mutations, and also validation in commercial populations, allowing a better explanation of QTL effects

Variação de número de cópias herdadas no genoma da galinha e associação com características de músculo de peito

Copy number variation (CNV) is an important polymorphism that is associated with a wide range of traits in human, wild and livestock species. In chicken, an important source of animal protein and a developmental model organism, CNV is associated with several phenotypes and evolutionary footprints. However, identification and characterization of CNV inheritance on chicken genome lacks further investigation. We screened CNVs in chicken using two distinct populations with known pedigree. In 826 broilers we identified 25,819 CNVs (4,299 deletions and 21,520 duplications) of which 21,077 were inherited, 201 showed no inheritance and 4,541 were classified as de novo CNVs. In 514 F2 animals (layer and broiler cross) we identified 21,796 CNVs (2,254 deletions and 19,543 duplications) of which 18,230 were inherited, 587 not inherited and 2,979 were classified as de novo CNVs. After a strict filtering step to remove potential false positives and negative CNVs, only 220 (4.84%) and 430 (14.43%) de novo CNVs remained in the broiler and F2 populations, respectively. A total of 33.11% (50 out of 151) of the inherited CNVs identified in ten animals were validated by sequencing data. From the validated CNVs, 64% had more than 80% of their size (bp) validated. A total of 59% and 48.8% were classified as novel CNVs regions (CNVRs) in the broiler and F2, respectively. Considering the Bonferroni-corrected p-values for multiple testing and statistically significant p-values ≤ 0.01, we found two CNV segments significantly associated with breast weight, one with breast weight yield, six with breast meat weight, 18 CNV segments with breast meat yield, four with breast filet weight and two with breast yield. These CNV segments that were significantly associated overlapped with 181 protein-coding genes. The CNVseg 300, that was associated with all traits and encompass six CNVRs, overlapped a total of 26 protein-coding genes. Among these genes, the gene MYL1 (Myosin Light Chain 1) is expressed in the fast skeletal muscle fibers, and the genes MLPH (Melanophilin), PRLH (Prolactin Releasing Hormone) and RAB17 (Member RAS Oncogene Family), that were associated with the lavender phenotype (feather blue-grey color) and regulation of homeothermy and the metabolism. The present study improves our knowledge about CNV in the chicken genome and provides insight in the distribution and of different classes of CNVs, i.e. inherited and de novo CNVs, in two experimental chicken populations. In addition, the genome-wide association analyses were the first performed on broiler population with breast muscle traits, that are important characteristics for poultry production. The GWAS results allow us to understand the probably relationship between some genes and CNVRs that are significantly associated with breast muscle traits.A variação de número de cópias (CNV) é um polimorfismo importante que está associado a uma ampla gama de características em seres humanos, espécies selvagens e domésticas. Em frango, que é uma importante fonte de proteína e considerado um modelo biológico, CNVs foram associados a vários fenótipos e passos evolutivos. No entanto, nenhum estudo foi realizado para a identificação e caracterização da herança da CNV no genoma da galinha. Identificamos as CNVs no genoma da galinha usando duas populações experimentais e com pedigree conhecido: uma população de frangos de corte e uma F2. Em 826 frangos de corte, identificamos 25.819 CNVs (4.299 deleções e 21.520 duplicações), dos quais 21.077 foram herdados, 201 não foram herdados e 4.541 foram CNVs denominados de novo. Em 514 animais F2, identificamos 21.796 CNVs (2.254 deleções e 19.543 duplicações) das quais 18.230 foram herdadas, 587 não foram herdadas e 2.979 foram de novo CNVs. Após a etapa de filtragem nos de novo CNVs, apenas 220 (4,84%) e 430 (14,43%) permaneceram nas populações de frango de corte e F2, respectivamente. Um total de 33,11% (50 de 151) das CNV identificadas por dados de genotipagem em dez animais foram validados por dados de sequenciamento. Dos validados, 64% tinham mais de 80% do tamanho (pb) validados. Um total de 59% e 48,8% foram classificados como novas regiões de CNVs (CNVRs) nas populações de frango de corte e F2, respectivamente. Considerando os p-values corrigidos por Bonferroni para testes múltiplos e estatisticamente significativos (≤ 0,01), encontramos dois segmentos de CNV significativamente associados ao peso do peito, um ao rendimento de peso de peito, seis ao peso de carne de peito, 18 ao rendimento de carne de peito, quatro ao peso de filé de peito e dois ao rendimento do filé de peito. Esses segmentos de CNV significativamente associados estão sobrepostos com 181 genes codificadores de proteínas. O CNVseg 300, que foi associado a todas as características e abrange seis CNVRs, foram sobrepostos a um total de 26 genes codificadores de proteínas. Entre estes genes, o gene MYL1 (Myosin Light Chain 1) é expresso nas fibras rápidas do músculo esquelético, e os genes MLPH (Melanophilin), PRLH (Prolactin Releasing Hormone) e RAB17 (Member RAS Oncogene Family), que foram anteiromente associados ao fenótipo de cor azul acinzentado de penas e à regulação da homeotermia e do metabolismo. O presente estudo melhora o conhecimento sobre CNVs no genoma de frango, especialmente sobre a distribuição de CNV herdadas, não herdadas e de novo, em duas populações experimentais de frango. Além disso, a associação genômica foi a primeira realizada na população de frangos de corte com características do músculo do peito, que são muito importantes para a avicultura. Os resultados do GWAS nos permitem compreender a provável relação entre alguns genes e CNVRs que foram significativamente associados às características do músculo do peito

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.This article is published as Moreira, Gabriel Costa Monteiro, Mayara Salvian, Clarissa Boschiero, Aline Silva Mello Cesar, James M. Reecy, Thaís Fernanda Godoy, Mônica Corrêa Ledur, Dorian Garrick, Gerson Barreto Mourão, and Luiz L. Coutinho. "Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens." BMC Genomics 20 (2019): 669. doi: 10.1186/s12864-019-6040-3.</p

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.This article is published as Moreira, Gabriel Costa Monteiro, Mirele Daiana Poleti, Fábio Pértille, Clarissa Boschiero, Aline Silva Mello Cesar, Thaís Fernanda Godoy, Mônica Corrêa Ledur, James M. Reecy, Dorian J. Garrick, and Luiz Lehmann Coutinho. "Unraveling genomic associations with feed efficiency and body weight traits in chickens through an integrative approach." BMC Genetics 20 (2019): 83. doi: 10.1186/s12863-019-0783-3.</p