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Ou JH, Rönneburg T, Carlborg Ö, Honaker CF, Siegel PB, Rubin CJ. Complex genetic architecture of the chicken Growth1 QTL region. PLoS One 2024; 19:e0295109. [PMID: 38739572 PMCID: PMC11090294 DOI: 10.1371/journal.pone.0295109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 04/05/2024] [Indexed: 05/16/2024] Open
Abstract
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.
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Affiliation(s)
- Jen-Hsiang Ou
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Tilman Rönneburg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Örjan Carlborg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Christa Ferst Honaker
- Virginia Polytechnic Institute and State University, School of Animal Sciences, Blacksburg, Virginia, United States of America
| | - Paul B. Siegel
- Virginia Polytechnic Institute and State University, School of Animal Sciences, Blacksburg, Virginia, United States of America
| | - Carl-Johan Rubin
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- Institute of Marine Research, Bergen, Norway
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Romanov MN, Shakhin AV, Abdelmanova AS, Volkova NA, Efimov DN, Fisinin VI, Korshunova LG, Anshakov DV, Dotsev AV, Griffin DK, Zinovieva NA. Dissecting Selective Signatures and Candidate Genes in Grandparent Lines Subject to High Selection Pressure for Broiler Production and in a Local Russian Chicken Breed of Ushanka. Genes (Basel) 2024; 15:524. [PMID: 38674458 PMCID: PMC11050503 DOI: 10.3390/genes15040524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/16/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024] Open
Abstract
Breeding improvements and quantitative trait genetics are essential to the advancement of broiler production. The impact of artificial selection on genomic architecture and the genetic markers sought remains a key area of research. Here, we used whole-genome resequencing data to analyze the genomic architecture, diversity, and selective sweeps in Cornish White (CRW) and Plymouth Rock White (PRW) transboundary breeds selected for meat production and, comparatively, in an aboriginal Russian breed of Ushanka (USH). Reads were aligned to the reference genome bGalGal1.mat.broiler.GRCg7b and filtered to remove PCR duplicates and low-quality reads using BWA-MEM2 and bcftools software; 12,563,892 SNPs were produced for subsequent analyses. Compared to CRW and PRW, USH had a lower diversity and a higher genetic distinctiveness. Selective sweep regions and corresponding candidate genes were examined based on ZFST, hapFLK, and ROH assessment procedures. Twenty-seven prioritized chicken genes and the functional projection from human homologs suggest their importance for selection signals in the studied breeds. These genes have a functional relationship with such trait categories as body weight, muscles, fat metabolism and deposition, reproduction, etc., mainly aligned with the QTLs in the sweep regions. This information is pivotal for further executing genomic selection to enhance phenotypic traits.
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Affiliation(s)
- Michael N. Romanov
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK;
| | - Alexey V. Shakhin
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | - Alexandra S. Abdelmanova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | - Natalia A. Volkova
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | - Dmitry N. Efimov
- Federal State Budget Scientific Institution Federal Scientific Center “All-Russian Research and Technological Poultry Institute”, Sergiev Posad 141311, Moscow Oblast, Russia; (D.N.E.); (V.I.F.); (L.G.K.)
| | - Vladimir I. Fisinin
- Federal State Budget Scientific Institution Federal Scientific Center “All-Russian Research and Technological Poultry Institute”, Sergiev Posad 141311, Moscow Oblast, Russia; (D.N.E.); (V.I.F.); (L.G.K.)
| | - Liudmila G. Korshunova
- Federal State Budget Scientific Institution Federal Scientific Center “All-Russian Research and Technological Poultry Institute”, Sergiev Posad 141311, Moscow Oblast, Russia; (D.N.E.); (V.I.F.); (L.G.K.)
| | - Dmitry V. Anshakov
- Breeding and Genetic Center “Zagorsk Experimental Breeding Farm”—Branch of the Federal Research Center “All-Russian Poultry Research and Technological Institute”, Russian Academy of Sciences, Sergiev Posad 141311, Moscow Oblast, Russia;
| | - Arsen V. Dotsev
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
| | | | - Natalia A. Zinovieva
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy, Podolsk 142132, Moscow Oblast, Russia; (A.V.S.); (A.S.A.); (N.A.V.); (A.V.D.)
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Desbiez-Piat A, Ressayre A, Marchadier E, Noly A, Remoué C, Vitte C, Belcram H, Bourgais A, Galic N, Le Guilloux M, Tenaillon MI, Dillmann C. Pervasive G × E interactions shape adaptive trajectories and the exploration of the phenotypic space in artificial selection experiments. Genetics 2023; 225:iyad186. [PMID: 37824828 DOI: 10.1093/genetics/iyad186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 07/27/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
Quantitative genetics models have shown that long-term selection responses depend on initial variance and mutational influx. Understanding limits of selection requires quantifying the role of mutational variance. However, correlative responses to selection on nonfocal traits can perturb the selection response on the focal trait; and generations are often confounded with selection environments so that genotype by environment (G×E) interactions are ignored. The Saclay divergent selection experiments (DSEs) on maize flowering time were used to track the fate of individual mutations combining genotyping data and phenotyping data from yearly measurements (DSEYM) and common garden experiments (DSECG) with four objectives: (1) to quantify the relative contribution of standing and mutational variance to the selection response, (2) to estimate genotypic mutation effects, (3) to study the impact of G×E interactions in the selection response, and (4) to analyze how trait correlations modulate the exploration of the phenotypic space. We validated experimentally the expected enrichment of fixed beneficial mutations with an average effect of +0.278 and +0.299 days to flowering, depending on the genetic background. Fixation of unfavorable mutations reached up to 25% of incoming mutations, a genetic load possibly due to antagonistic pleiotropy, whereby mutations fixed in the selection environment (DSEYM) turned to be unfavorable in the evaluation environment (DSECG). Global patterns of trait correlations were conserved across genetic backgrounds but exhibited temporal patterns. Traits weakly or uncorrelated with flowering time triggered stochastic exploration of the phenotypic space, owing to microenvironment-specific fixation of standing variants and pleiotropic mutational input.
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Affiliation(s)
- Arnaud Desbiez-Piat
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
- Université Montpellier, INRAE, Institut Agro Montpellier, LEPSE, Montpellier 34000, France
| | - Adrienne Ressayre
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Elodie Marchadier
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Alicia Noly
- Université Paris-Saclay, CNRS, INRAE, Université Evry, Institut of Plants Sciences Paris-Saclay, Gif-sur-Yvette 91190, France
| | - Carine Remoué
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Clémentine Vitte
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Harry Belcram
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Aurélie Bourgais
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Nathalie Galic
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Martine Le Guilloux
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Maud I Tenaillon
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
| | - Christine Dillmann
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
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Sosa-Madrid BS, Maniatis G, Ibáñez-Escriche N, Avendaño S, Kranis A. Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits. Animals (Basel) 2023; 13:3306. [PMID: 37958060 PMCID: PMC10649193 DOI: 10.3390/ani13213306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/12/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023] Open
Abstract
Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a "double trait covariances" analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999-2001 to 2020-2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.
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Affiliation(s)
- Bolívar Samuel Sosa-Madrid
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK
- Institute for Animal Science and Technology, Universitat Politècnica de València, P.O. Box 2201, 46071 Valencia, Spain;
| | | | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, P.O. Box 2201, 46071 Valencia, Spain;
| | | | - Andreas Kranis
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK
- Aviagen Ltd., Newbridge, Edinburgh EH28 8SZ, UK; (G.M.); (S.A.)
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5
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Yu S, Liu Z, Li M, Zhou D, Hua P, Cheng H, Fan W, Xu Y, Liu D, Liang S, Zhang Y, Xie M, Tang J, Jiang Y, Hou S, Zhou Z. Resequencing of a Pekin duck breeding population provides insights into the genomic response to short-term artificial selection. Gigascience 2023; 12:giad016. [PMID: 36971291 PMCID: PMC10041536 DOI: 10.1093/gigascience/giad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 02/04/2023] [Accepted: 02/27/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Short-term, intense artificial selection drives fast phenotypic changes in domestic animals and leaves imprints on their genomes. However, the genetic basis of this selection response is poorly understood. To better address this, we employed the Pekin duck Z2 pure line, in which the breast muscle weight was increased nearly 3-fold after 10 generations of breeding. We denovo assembled a high-quality reference genome of a female Pekin duck of this line (GCA_003850225.1) and identified 8.60 million genetic variants in 119 individuals among 10 generations of the breeding population. RESULTS We identified 53 selected regions between the first and tenth generations, and 93.8% of the identified variations were enriched in regulatory and noncoding regions. Integrating the selection signatures and genome-wide association approach, we found that 2 regions covering 0.36 Mb containing UTP25 and FBRSL1 were most likely to contribute to breast muscle weight improvement. The major allele frequencies of these 2 loci increased gradually with each generation following the same trend. Additionally, we found that a copy number variation region containing the entire EXOC4 gene could explain 1.9% of the variance in breast muscle weight, indicating that the nervous system may play a role in economic trait improvement. CONCLUSIONS Our study not only provides insights into genomic dynamics under intense artificial selection but also provides resources for genomics-enabled improvements in duck breeding.
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Affiliation(s)
- Simeng Yu
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Zihua Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ming Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Dongke Zhou
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ping Hua
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Hong Cheng
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Wenlei Fan
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yaxi Xu
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Dapeng Liu
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Suyun Liang
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yunsheng Zhang
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ming Xie
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jing Tang
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Shuisheng Hou
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Zhengkui Zhou
- State Key Laboratory of Animal Nutrition; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Genome-Wide Genetic Structure of Henan Indigenous Chicken Breeds. Animals (Basel) 2023; 13:ani13040753. [PMID: 36830540 PMCID: PMC9952073 DOI: 10.3390/ani13040753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/14/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
There are five indigenous chicken breeds in Henan Province, China. These breeds have their own unique phenotypic characteristics in terms of morphology, behavior, skin and feather color, and productive performance, but their genetic basis is not well understood. Therefore, we analyzed the genetic structure, genomic diversity, and migration history of Henan indigenous chicken populations and the selection signals and genes responsible for Henan gamecock unique phenotypes using whole genome resequencing. The results indicate that Henan native chickens clustered most closely with the chicken populations in neighboring provinces. Compared to other breeds, Henan gamecock's inbreeding and selection intensity were more stringent. TreeMix analysis revealed the gene flow from southern chicken breeds into the Zhengyang sanhuang chicken and from the Xichuan black-bone chicken into the Gushi chicken. Selective sweep analysis identified several genes and biological processes/pathways that were related to body size, head control, muscle development, reproduction, and aggression control. Additionally, we confirmed the association between genotypes of SNPs in the strong selective gene LCORL and body size and muscle development in the Gushi-Anka F2 resource population. These findings made it easier to understand the traits of the germplasm and the potential for using the Henan indigenous chicken.
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Population dynamics of a long-term selection experiment in White Plymouth Rock chickens selected for low or high body weight. Poult Sci 2023; 102:102575. [PMID: 36907125 PMCID: PMC10024231 DOI: 10.1016/j.psj.2023.102575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023] Open
Abstract
The population dynamics of 2 lines of chickens from a long-term (59 generations) selection experiment were assessed based on pedigree data. These lines were propagated from phenotypic selection for low and high 8-wk BW in White Plymouth Rock chickens. Our objective was to determine whether the 2 lines maintained similar population structures over the selection horizon to allow meaningful comparisons of their performance data. A complete pedigree of 31,909 individuals, consisting of 102 founders, 1,064 from the parental generation, and 16,245 low weight (LWS) and 14,498 high weight (HWS) select chickens, was available. Inbreeding (F) and average relatedness (AR) coefficients were computed. Average F per generation and AR coefficients were 1.3 (SD 0.8) % and 0.53 (SD 0.001) for LWS, and 1.5 (SD 1.1) % and 0.66 (SD 0.001) for HWS. Mean F for the entire pedigree was 0.26 (0.16) and 0.33 (0.19), and maximum F was 0.64 and 0.63, in LWS and HWS, respectively. Based on Wright's fixation index, at generation 59, substantial genetic differences were established between lines. The effective population size was 39 in LWS and 33 in HWS. The effective number of founders was 17 and 15, effective number of ancestors were 12 and 8, and genome equivalents were 2.5 and 1.9 in LWS and HWS, respectively. About 30 founders explained the marginal contribution to both lines. By generation 59, only 7 male and 6 female founders contributed to both lines. Moderately high levels of inbreeding and low effective population sizes were inevitable, as this was a closed population. However, effects on the fitness of the population were expected to be less substantial because founders were a combination of 7 lines. The effective numbers of founders and ancestors were relatively low compared to the actual number of founders, as few ancestors contributed to descendants. Based on these evaluations, it can be inferred that LWS and HWS had similar population structures. Comparisons of selection responses in the 2 lines therefore should be reliable.
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Wang D, Li X, Zhang P, Cao Y, Zhang K, Qin P, Guo Y, Li Z, Tian Y, Kang X, Liu X, Li H. ELOVL gene family plays a virtual role in response to breeding selection and lipid deposition in different tissues in chicken (Gallus gallus). BMC Genomics 2022; 23:705. [PMID: 36253734 PMCID: PMC9575239 DOI: 10.1186/s12864-022-08932-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 10/10/2022] [Indexed: 11/11/2022] Open
Abstract
Background Elongases of very long chain fatty acids (ELOVLs), a family of first rate-limiting enzymes in the synthesis of long-chain fatty acids, play an essential role in the biosynthesis of complex lipids. Disrupting any of ELOVLs affects normal growth and development in mammals. Genetic variations in ELOVLs are associated with backfat or intramuscular fatty acid composition in livestock. However, the effects of ELOVL gene family on breeding selection and lipid deposition in different tissues are still unknown in chickens. Results Genetic variation patterns and genetic associations analysis showed that the genetic variations of ELOVL genes were contributed to breeding selection of commercial varieties in chicken, and 14 SNPs in ELOVL2-6 were associated with body weight, carcass or fat deposition traits. Especially, one SNP rs17631638T > C in the promoter of ELOVL3 was associated with intramuscular fat content (IMF), and its allele frequency was significantly higher in native and layer breeds compared to that in commercial broiler breeds. Quantitative real-time PCR (qRT-PCR) determined that the ELOVL3 expressions in pectoralis were affected by the genotypes of rs17631638T > C. In addition, the transcription levels of ELOVL genes except ELOVL5 were regulated by estrogen in chicken liver and hypothalamus with different regulatory pathways. The expression levels of ELOVL1-6 in hypothalamus, liver, abdominal fat and pectoralis were correlated with abdominal fat weight, abdominal fat percentage, liver lipid content and IMF. Noteworthily, expression of ELOVL3 in pectoralis was highly positively correlated with IMF and glycerophospholipid molecules, including phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl glycerol and phospholipids inositol, rich in ω-3 and ω-6 long-chain unsaturated fatty acids, suggesting ELOVL3 could contribute to intramuscular fat deposition by increasing the proportion of long-chain unsaturated glycerophospholipid molecules in pectoralis. Conclusions In summary, we demonstrated the genetic contribution of ELOVL gene family to breeding selection for specialized varieties, and revealed the expression regulation of ELOVL genes and their potential roles in regulating lipid deposition in different tissues. This study provides new insights into understanding the functions of ELOVL family on avian growth and lipid deposition in different tissues and the genetic variation in ELOVL3 may aid the marker-assisted selection of meat quality in chicken. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08932-8.
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Affiliation(s)
- Dandan Wang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China
| | - Xinyan Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China
| | - Panpan Zhang
- Henan Institute of Veterinary Drug and Feed Control, Zhengzhou, 450002, China
| | - Yuzhu Cao
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China
| | - Ke Zhang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China
| | - Panpan Qin
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China
| | - Yulong Guo
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China
| | - Zhuanjian Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China.,Henan Key Laboratory for Innovation and Utilization of Chicken Germplasm Resources, Zhengzhou, 450046, China.,International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450046, China
| | - Yadong Tian
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China.,Henan Key Laboratory for Innovation and Utilization of Chicken Germplasm Resources, Zhengzhou, 450046, China.,International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450046, China
| | - Xiangtao Kang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China.,Henan Key Laboratory for Innovation and Utilization of Chicken Germplasm Resources, Zhengzhou, 450046, China.,International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450046, China
| | - Xiaojun Liu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China. .,Henan Key Laboratory for Innovation and Utilization of Chicken Germplasm Resources, Zhengzhou, 450046, China. .,International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450046, China.
| | - Hong Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou, 450046, China. .,Henan Key Laboratory for Innovation and Utilization of Chicken Germplasm Resources, Zhengzhou, 450046, China. .,International Joint Research Laboratory for Poultry Breeding of Henan, Zhengzhou, 450046, China.
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9
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Rönneburg T, Zan Y, Honaker CF, Siegel PB, Carlborg Ö. 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. Poult Sci 2022; 102:102203. [PMID: 36907123 PMCID: PMC10024170 DOI: 10.1016/j.psj.2022.102203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 07/05/2022] [Accepted: 08/24/2022] [Indexed: 11/29/2022] Open
Abstract
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.
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Affiliation(s)
- T Rönneburg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Y Zan
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - C F Honaker
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg VA, USA
| | - P B Siegel
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg VA, USA
| | - Ö Carlborg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
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Selection signatures in melanocortin-1 receptor gene of turkeys (Meleagris gallopavo) raised in hot humid tropics. Trop Anim Health Prod 2022; 54:183. [PMID: 35525911 DOI: 10.1007/s11250-022-03185-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 04/28/2022] [Indexed: 10/18/2022]
Abstract
Feather colours are used by avian species for defense, adaptation and signaling. Melanocortin-1 receptor (MC1R) gene is one of the genes responsible for feather colour. This study identified selection signatures in MC1R gene of Nigerian indigenous turkeys (NIT) using British United turkeys (BUT) as control breed to investigate the evolutionary processes that have shaped NIT with various feather colours. Complete MC1R gene of 146 NIT (76 males and 70 females) and 32 BUT (18 males and 14 females) were sequenced. Transition/transversion and codon usage biases were predicted using MEGA v6 software. The selective force acting on the gene was predicted using HyPhy software. The FST values were estimated using Arlequin v3.5. The highest transition/transversion bias was predicted for white BUT (1.00) while the lowest was predicted for black NIT (0.50). Negative dN-dS values, indicative of purifying selection, were observed in MC1R gene of all the turkeys. The highest pairwise FST was observed between the MC1R gene of white BUT and black NIT while the least was observed between lavender NIT and white NIT. No recombination event was observed in black NIT and white BUT. The relative synonymous codon usage was the same among different colours for some codons. Presence of purifying selection in MC1R gene of all the turkeys with different feather colours confirms that the gene plays role in many biological processes such as feather colouration, behaviour, pain perception, immunity, growth and adaptation. The results also suggested that the genetic mechanisms generating different feather colours in turkeys are conserved.
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Abstract
Multiomic analyses reported here involved two lines of chickens, from a common founder population, that had undergone long-term selection for high (HWS) or low (LWS) 56-day body weight. In these lines that differ by around 15-fold in body weight, we observed different compositions of intestinal microbiota in the holobionts and variation in DNA methylation, mRNA expression, and microRNA profiles in the ceca. Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was the most upregulated gene in HWS ceca with its expression likely affected by the upregulation of expression of gga-miR-2128 and a methylated region near its transcription start site (388 bp). Correlation analysis showed that IGF2BP1 expression was associated with an abundance of microbes, such as Lactobacillus and Methanocorpusculum. These findings suggest that IGF2BP1 was regulated in the hologenome in adapting to long-term artificial selection for body weight. Our study provides evidence that adaptation of the holobiont can occur in the microbiome as well as in the epigenetic profile of the host. IMPORTANCE The hologenome concept has broadened our perspectives for studying host-microbe coevolution. The multiomic analyses reported here involved two lines of chickens, from a common founder population, that had undergone long-term selection for high (HWS) or low (LWS) 56-day body weight. In these lines that differ by around 15-fold in body weight, we observed different compositions of intestinal microbiota in the holobionts, and variation in DNA methylation, mRNA expression, and microRNA profiles in ceca. The insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) was the most upregulated gene in HWS ceca with its expression likely affected by a methylated region near its transcription start site and the upregulation of expression of gga-miR-2128. Correlation analysis also showed that IGF2BP1 expression was associated with the abundance of microbes, such as Lactobacillus and Methanocorpusculum. These findings suggest that IGF2BP1 was regulated in the hologenome in response to long-term artificial selection for body weight. Our study shows that the holobiont may adapt in both the microbiome and the host's epigenetic profile.
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12
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Desbiez-Piat A, Le Rouzic A, Tenaillon MI, Dillmann C. Interplay between extreme drift and selection intensities favors the fixation of beneficial mutations in selfing maize populations. Genetics 2021; 219:6339583. [PMID: 34849881 DOI: 10.1093/genetics/iyab123] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 07/21/2021] [Indexed: 11/13/2022] Open
Abstract
Population and quantitative genetic models provide useful approximations to predict long-term selection responses sustaining phenotypic shifts, and underlying multilocus adaptive dynamics. Valid across a broad range of parameters, their use for understanding the adaptive dynamics of small selfing populations undergoing strong selection intensity (thereafter High Drift-High selection regime, HDHS) remains to be explored. Saclay Divergent Selection Experiments (DSEs) on maize flowering time provide an interesting example of populations evolving under HDHS, with significant selection responses over 20 generations in two directions. We combined experimental data from Saclay DSEs, forward individual-based simulations, and theoretical predictions to dissect the evolutionary mechanisms at play in the observed selection responses. We asked two main questions: How do mutations arise, spread, and reach fixation in populations evolving under HDHS? How does the interplay between drift and selection influence observed phenotypic shifts? We showed that the long-lasting response to selection in small populations is due to the rapid fixation of mutations occurring during the generations of selection. Among fixed mutations, we also found a clear signal of enrichment for beneficial mutations revealing a limited cost of selection. Both environmental stochasticity and variation in selection coefficients likely contributed to exacerbate mutational effects, thereby facilitating selection grasp and fixation of small-effect mutations. Together our results highlight that despite a small number of polymorphic loci expected under HDHS, adaptive variation is continuously fueled by a vast mutational target. We discuss our results in the context of breeding and long-term survival of small selfing populations.
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Affiliation(s)
- Arnaud Desbiez-Piat
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Arnaud Le Rouzic
- Université Paris-Saclay, CNRS, IRD, UMR Évolution, Génomes, Comportement et Écologie, 91120 Gif-sur-Yvette, France
| | - Maud I Tenaillon
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Christine Dillmann
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190 Gif-sur-Yvette, France
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Hillis DA, Yadgary L, Weinstock GM, Pardo-Manuel de Villena F, Pomp D, Fowler AS, Xu S, Chan F, Garland T. Genetic Basis of Aerobically Supported Voluntary Exercise: Results from a Selection Experiment with House Mice. Genetics 2020; 216:781-804. [PMID: 32978270 PMCID: PMC7648575 DOI: 10.1534/genetics.120.303668] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 09/18/2020] [Indexed: 12/14/2022] Open
Abstract
The biological basis of exercise behavior is increasingly relevant for maintaining healthy lifestyles. Various quantitative genetic studies and selection experiments have conclusively demonstrated substantial heritability for exercise behavior in both humans and laboratory rodents. In the "High Runner" selection experiment, four replicate lines of Mus domesticus were bred for high voluntary wheel running (HR), along with four nonselected control (C) lines. After 61 generations, the genomes of 79 mice (9-10 from each line) were fully sequenced and single nucleotide polymorphisms (SNPs) were identified. We used nested ANOVA with MIVQUE estimation and other approaches to compare allele frequencies between the HR and C lines for both SNPs and haplotypes. Approximately 61 genomic regions, across all somatic chromosomes, showed evidence of differentiation; 12 of these regions were differentiated by all methods of analysis. Gene function was inferred largely using Panther gene ontology terms and KO phenotypes associated with genes of interest. Some of the differentiated genes are known to be associated with behavior/motivational systems and/or athletic ability, including Sorl1, Dach1, and Cdh10 Sorl1 is a sorting protein associated with cholinergic neuron morphology, vascular wound healing, and metabolism. Dach1 is associated with limb bud development and neural differentiation. Cdh10 is a calcium ion binding protein associated with phrenic neurons. Overall, these results indicate that selective breeding for high voluntary exercise has resulted in changes in allele frequencies for multiple genes associated with both motivation and ability for endurance exercise, providing candidate genes that may explain phenotypic changes observed in previous studies.
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Affiliation(s)
- David A Hillis
- Genetics, Genomics, and Bioinformatics Graduate Program, University of California, Riverside, California 92521
| | - Liran Yadgary
- Department of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599
| | - George M Weinstock
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032
| | | | - Daniel Pomp
- Department of Genetics, University of North Carolina at Chapel Hill, North Carolina 27599
| | - Alexandra S Fowler
- Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, California 92521
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, California 92521
| | - Frank Chan
- Friedrich Miescher Laboratory of the Max Planck Society, 72076 Tübingen, Germany
| | - Theodore Garland
- Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, California 92521
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