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Sun Y, Li Y, Jiang X, Wu Q, Lin R, Chen H, Zhang M, Zeng T, Tian Y, Xu E, Zhang Y, Lu L. Genome-wide association study identified candidate genes for egg production traits in the Longyan Shan-ma duck. Poult Sci 2024; 103:104032. [PMID: 39003796 PMCID: PMC11298941 DOI: 10.1016/j.psj.2024.104032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 06/19/2024] [Accepted: 06/22/2024] [Indexed: 07/16/2024] Open
Abstract
Egg production is an important economic trait in layer ducks and understanding the genetics basis is important for their breeding. In this study, a genome-wide association study (GWAS) for egg production traits in 303 female Longyan Shan-ma ducks was performed based on a genotyping-by-sequencing strategy. Sixty-two single nucleotide polymorphisms (SNPs) associated with egg weight traits were identified (P < 9.48 × 10-5), including 8 SNPs at 5% linkage disequilibrium (LD)-based Bonferroni-corrected genome-wide significance level (P < 4.74 × 10-6). One hundred and nineteen SNPs were associated with egg number traits (P < 9.48 × 10-5), including 13 SNPs with 5% LD-based Bonferroni-corrected genome-wide significance (P < 4.74 × 10-6). These SNPs annotated 146 target genes which contained known candidate genes for egg production traits, such as prolactin and prolactin releasing hormone receptor. This study identified that these associated genes were significantly enriched in egg production-related pathways (P < 0.05), such as the oxytocin signaling, MAPK signaling, and calcium signaling pathways. It was notable that 18 genes were differentially expressed in ovarian tissues between higher and lower egg production in Shan-ma ducks. The identified potential candidate genes and pathways provide insight into the genetic basis underlying the egg production trait of layer ducks.
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Affiliation(s)
- Yanfa Sun
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P.R. China
| | - Yan Li
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P.R. China
| | - Xiaobing Jiang
- Fujian Provincial Animal Husbandry Headquarters, Fuzhou, Fujian 350003, P.R. China
| | - Qiong Wu
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P.R. China
| | - Rulong Lin
- Longyan Shan-ma Duck Original Breeding Farm, Agricultural Bureau of Xinluo District, Longyan, 364031, P.R. China
| | - Hongping Chen
- Longyan Shan-ma Duck Original Breeding Farm, Agricultural Bureau of Xinluo District, Longyan, 364031, P.R. China
| | - Min Zhang
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P.R. China
| | - Tao Zeng
- Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, P.R. China
| | - Yong Tian
- Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, P.R. China
| | - Enrong Xu
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P.R. China
| | - Yeqiong Zhang
- College of Life Science, Fujian Provincial Key Laboratory for the Prevention and Control of Animal Infectious Diseases and Biotechnology, Fujian Provincial Universities Key Laboratory of Preventive Veterinary Medicine and Biotechnology (Longyan University), Longyan University, Longyan, Fujian, 364012, P.R. China
| | - Lizhi Lu
- Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, P.R. China..
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Lei Q, Zhang S, Wang J, Qi C, Liu J, Cao D, Li F, Han H, Liu W, Li D, Tang C, Zhou Y. Genome-wide association studies of egg production traits by whole genome sequencing of Laiwu Black chicken. Poult Sci 2024; 103:103705. [PMID: 38598913 DOI: 10.1016/j.psj.2024.103705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/24/2024] [Accepted: 03/26/2024] [Indexed: 04/12/2024] Open
Abstract
Compared to high-yield commercial laying hens, Chinese indigenous chicken breeds have poor egg laying capacity due to the lack of intensive selection. However, as these breeds have not undergone systematic selection, it is possible that there is a greater abundance of genetic variations related to egg laying traits. In this study, we assessed 5 egg number (EN) traits at different stages of the egg-laying period: EN1 (from the first egg to 23 wk), EN2 (from 23 to 35 wk), EN3 (from 35 to 48 wk), EN4 (from the first egg to 35 wk), and EN5 (from the first egg to 48 wk). To investigate the molecular mechanisms underlying egg number traits in a Chinese local chicken breed, we conducted a genome-wide association study (GWAS) using data from whole-genome sequencing (WGS) of 399 Laiwu Black chickens. We obtained a total of 3.01 Tb of raw data with an average depth of 7.07 × per individual. A total of 86 genome-wide suggestive or significant single-nucleotide polymorphisms (SNP) contained within a set of 45 corresponding candidate genes were identified and found to be associated with stages EN1-EN5. The genes vitellogenin 2 (VTG2), lipase maturation factor 1 (LMF1), calcium voltage-gated channel auxiliary subunit alpha2delta 3 (CACNA2D3), poly(A) binding protein cytoplasmic 1 (PABPC1), programmed cell death 11 (PDCD11) and family with sequence similarity 213 member A (FAM213A) can be considered as the candidate genes associated with egg number traits, due to their reported association with animal reproduction traits. Noteworthy, results suggests that VTG2 and PDCD11 are not only involved in the regulation of EN3, but also in the regulation of EN5, implies that VTG2 and PDCD11 have a significant influence on egg production traits. Our study offers valuable genomic insights into the molecular genetic mechanisms that govern egg number traits in a Chinese indigenous egg-laying chicken breed. These findings have the potential to enhance the egg-laying performance of chickens.
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Affiliation(s)
- Qiuxia Lei
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Shuer Zhang
- Shandong Animal Husbandry General Station, 250023, Ji'nan, China
| | - Jie Wang
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Chao Qi
- Shandong Animal Husbandry General Station, 250023, Ji'nan, China
| | - Jie Liu
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Dingguo Cao
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Fuwei Li
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Haixia Han
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Wei Liu
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Dapeng Li
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China
| | - Cunwei Tang
- Fujian Sunnzer Biological Technology Development Co. Ltd., 354100, Guang'ze, China
| | - Yan Zhou
- Poultry Institute, Shandong Academy of Agricultural Sciences, 250100, Ji'nan, China.; Poultry Breeding Engineering Technology Center of Shandong Province, 250100, Ji'nan, China..
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Li X, Chen X, Wang Q, Yang N, Sun C. Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens. Genes (Basel) 2024; 15:690. [PMID: 38927626 PMCID: PMC11202573 DOI: 10.3390/genes15060690] [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: 04/09/2024] [Revised: 05/12/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increasingly intricate genetic regulatory patterns. Hence, novel approaches are necessary for future genomic prediction. In this study, we used an illumina 50K SNP chip to genotype 4190 egg-type female Rhode Island Red chickens. Machine learning (ML) and classical bioinformatics methods were integrated to fit genotypes with 10 economic traits in chickens. We evaluated the effectiveness of ML methods using Pearson correlation coefficients and the RMSE between predicted and actual phenotypic values and compared them with rrBLUP and BayesA. Our results indicated that ML algorithms exhibit significantly superior performance to rrBLUP and BayesA in predicting body weight and eggshell strength traits. Conversely, rrBLUP and BayesA demonstrated 2-58% higher predictive accuracy in predicting egg numbers. Additionally, the incorporation of suggestively significant SNPs obtained through the GWAS into the ML models resulted in an increase in the predictive accuracy of 0.1-27% across nearly all traits. These findings suggest the potential of combining classical bioinformatics methods with ML techniques to improve genomic prediction in the future.
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Affiliation(s)
| | | | | | | | - Congjiao Sun
- State Key Laboratory of Animal Biotech Breeding and Frontiers Science Center for Molecular Design Breeding (MOE), China Agricultural University, Beijing 100193, China; (X.L.); (X.C.); (Q.W.); (N.Y.)
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Ma X, Ying F, Li Z, Bai L, Wang M, Zhu D, Liu D, Wen J, Zhao G, Liu R. New insights into the genetic loci related to egg weight and age at first egg traits in broiler breeder. Poult Sci 2024; 103:103613. [PMID: 38492250 PMCID: PMC10959720 DOI: 10.1016/j.psj.2024.103613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/29/2024] [Accepted: 02/29/2024] [Indexed: 03/18/2024] Open
Abstract
Egg weight (EW) and age at first egg (AFE) are economically important traits in breeder chicken production. The genetic basis of these traits, however, is far from understood, especially for broiler breeders. In this study, genetic parameter estimation, genome-wide association analysis, meta-analysis, and selective sweep analysis were carried out to identify genetic loci associated with EW and AFE in 6,842 broiler breeders. The study found that the heritability of EW ranged from 0.42 to 0.44, while the heritability of AFE was estimated at 0.33 in the maternal line. Meta-analysis and selective sweep analysis identified two colocalized regions on GGA4 that significantly influenced EW at 32 wk (EW32W) and at 43 wk (EW43W) with both paternal and maternal lines. The genes AR, YIPF6, and STARD8 were located within the significant region (GGA4: 366.86-575.50 kb), potentially affecting EW through the regulation of follicle development, cell proliferation, and lipid transfer etc. The promising genes LCORL and NCAPG were positioned within the significant region (GGA4:75.35-75.42 Mb), potentially influencing EW through pleiotropic effects on growth and development. Additionally, 3 significant regions were associated with AFE on chromosomes GGA7, GGA19, and GGA27. All of these factors affected the AFE by influencing ovarian development. In our study, the genomic information from both paternal and maternal lines was used to identify genetic regions associated with EW and AFE. Two genomic regions and eight genes were identified as the most likely candidates affecting EW and AFE. These findings contribute to a better understanding of the genetic basis of egg production traits in broiler breeders and provide new insights into future technology development.
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Affiliation(s)
- Xiaochun Ma
- State Key Laboratory of Animal Biotech Breeding; State Key Laboratory of Animal Nutrition and Feeding; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Fan Ying
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan 528515, China
| | - Zhengda Li
- State Key Laboratory of Animal Biotech Breeding; State Key Laboratory of Animal Nutrition and Feeding; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Lu Bai
- State Key Laboratory of Animal Biotech Breeding; State Key Laboratory of Animal Nutrition and Feeding; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Mengjie Wang
- State Key Laboratory of Animal Biotech Breeding; State Key Laboratory of Animal Nutrition and Feeding; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Dan Zhu
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan 528515, China
| | - Dawei Liu
- Foshan Gaoming Xinguang Agricultural and Animal Industrials Corporation, Foshan 528515, China
| | - Jie Wen
- State Key Laboratory of Animal Biotech Breeding; State Key Laboratory of Animal Nutrition and Feeding; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Guiping Zhao
- State Key Laboratory of Animal Biotech Breeding; State Key Laboratory of Animal Nutrition and Feeding; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ranran Liu
- State Key Laboratory of Animal Biotech Breeding; State Key Laboratory of Animal Nutrition and Feeding; Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture; Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
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Ni A, Calus MPL, Bovenhuis H, Yuan J, Wang Y, Sun Y, Chen J. Genetic parameters, reciprocal cross differences, and age-related heterosis of egg-laying performance in chickens. Genet Sel Evol 2023; 55:87. [PMID: 38062365 PMCID: PMC10702067 DOI: 10.1186/s12711-023-00862-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Egg-laying performance is economically important in poultry breeding programs. Crossbreeding between indigenous and elite commercial lines to exploit heterosis has been an upward trend in traditional layer breeding for niche markets. The objective of this study was to analyse the genetic background and to estimate the heterosis of longitudinal egg-laying traits in reciprocal crosses between an indigenous Beijing-You and an elite commercial White Leghorn layer line. Egg weights were measured for the first three eggs, monthly from 28 to 76 weeks of age, and at 86 and 100 weeks of age. Egg quality traits were measured at 32, 54, 72, 86, and 100 weeks of age. Egg production traits were measured from the start of lay until 43, 72, and 100 weeks of age. Heritabilities and phenotypic and genetic correlations were estimated. Heterosis was estimated as the percentage difference of performance of a crossbred from that of the parental average. Reciprocal cross differences were estimated as the difference between the reciprocal crossbreds as a percentage of the parental average. RESULTS Estimates of heritability of egg weights ranged from 0.29 to 0.75. Estimates of genetic correlations between egg weights at different ages ranged from 0.72 to 1.00. Estimates of heritability for cumulative egg numbers until 43, 72, and 100 weeks of age were around 0.15. Estimates of heterosis for egg weight and cumulative egg number increased with age, ranging from 1.0 to 9.0% and from 1.4 to 11.6%, respectively. From 72 to 100 weeks of age, crossbreds produced more eggs per week than the superior parent White Leghorn (3.5 eggs for White Leghorn, 3.8 and 3.9 eggs for crossbreds). Heterosis for eggshell thickness ranged from 2.7 to 6.6% when using Beijing-You as the sire breed. No significant difference between reciprocal crosses was observed for the investigated traits, except for eggshell strength at 54 weeks of age. CONCLUSIONS The heterosis was substantial for egg weight and cumulative egg number, and increased with age, suggesting that non-additive genetic effects are important in crossbreds between the indigenous and elite breeds. Generally, the crossbreds performed similar to or even outperformed the commercial White Leghorns for egg production persistency.
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Affiliation(s)
- Aixin Ni
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Mario P L Calus
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Henk Bovenhuis
- Animal Breeding and Genomics, Wageningen University and Research, P.O. Box 338, 6700 AH, Wageningen, The Netherlands
| | - Jingwei Yuan
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yuanmei Wang
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yanyan Sun
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
| | - Jilan Chen
- State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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Haqani MI, Nakano M, Nagano AJ, Nakamura Y, Tsudzuki M. Association analysis of production traits of Japanese quail (Coturnix japonica) using restriction-site associated DNA sequencing. Sci Rep 2023; 13:21307. [PMID: 38042890 PMCID: PMC10693557 DOI: 10.1038/s41598-023-48293-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 10/10/2023] [Accepted: 11/24/2023] [Indexed: 12/04/2023] Open
Abstract
This study was designed to perform an association analysis and identify SNP markers associated with production traits of Japanese quail using restriction-site-associated DNA sequencing. Weekly body weight data from 805 quail were collected from hatching to 16 weeks of age. A total number of 3990 eggs obtained from 399 female quail were used to assess egg quality traits. Egg-related traits were measured at the beginning of egg production (first stage) and at 12 weeks of age (second stage). Five eggs were analyzed at each stage. Traits, such as egg weight, egg length and short axes, eggshell strength and weight, egg equator thickness, yolk weight, diameter, and colour, albumen weight, age of first egg, total number of laid eggs, and egg production rate, were assessed. A total of 383 SNPs and 1151 associations as well as 734 SNPs and 1442 associations were identified in relation to quail production traits using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. The GLM-identified SNPs were located on chromosomes 1-13, 15, 17-20, 24, 26-28, and Z, underlying phenotypic traits, except for egg and albumen weight at the first stage and yolk yellowness at the second stage. The MLM-identified SNPs were positioned on defined chromosomes associated with phenotypic traits except for the egg long axis at the second stage of egg production. Finally, 35 speculated genes were identified as candidate genes for the targeted traits based on their nearest positions. Our findings provide a deeper understanding and allow a more precise genetic improvement of production traits of Galliformes, particularly in Japanese quail.
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Affiliation(s)
- Mohammad Ibrahim Haqani
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
| | - Michiharu Nakano
- Faculty of Agriculture and Marine Sciences, Kochi University, Nankoku, Kochi, 783-8502, Japan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku University, Otsu, Shiga, 520-2194, Japan
- Institute for Advanced Biosciences, Keio University, Yamagata, 997-0017, Japan
| | - Yoshiaki Nakamura
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan
| | - Masaoki Tsudzuki
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
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Fu M, Wu Y, Shen J, Pan A, Zhang H, Sun J, Liang Z, Huang T, Du J, Pi J. Genome-Wide Association Study of Egg Production Traits in Shuanglian Chickens Using Whole Genome Sequencing. Genes (Basel) 2023; 14:2129. [PMID: 38136951 PMCID: PMC10742582 DOI: 10.3390/genes14122129] [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: 10/28/2023] [Revised: 11/23/2023] [Accepted: 11/24/2023] [Indexed: 12/24/2023] Open
Abstract
Egg production is the most important economic trait in laying hens. To identify molecular markers and candidate genes associated with egg production traits, such as age at first egg (AFE), weight at first egg (WFE), egg weight (EW), egg number (EN), and maximum consecutive egg laying days (MCD), a genome-wide analysis by whole genome sequencing was performed in Shuanglian chickens. Through whole genome sequencing and quality control, a total of 11,006,178 SNPs were obtained for further analysis. Heritability estimates ranged from moderate to high for EW (0.897) and MCD (0.632), and from low to moderate (0.193~0.379) for AFE, WFE, and EN. The GWAS results showed 11 genome-wide significant SNPs and 23 suggestive significant SNPs were identified to be associated with EN, MCD, WFE, and EW. Linkage disequilibrium analysis revealed twenty-seven SNPs associated with EN were located in a 0.57 Mb region on GGA10, and clustered into five blocks. Through functional annotation, three candidate genes NEO1, ADPGK, and CYP11A1, were identified to be associated with EN, while the S1PR4, LDB2, and GRM8 genes was linked to MCD, WFE, and EW, respectively. These findings may help us to better understand the molecular mechanisms underlying egg production traits in chickens and contribute to genetic improvement of these traits.
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Affiliation(s)
- Ming Fu
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Yan Wu
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Jie Shen
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Ailuan Pan
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Hao Zhang
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Jing Sun
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Zhenhua Liang
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Tao Huang
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Jinping Du
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
| | - Jinsong Pi
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan 430064, China; (M.F.); (J.S.); (A.P.); (H.Z.); (J.S.); (Z.L.); (T.H.); (J.D.); (J.P.)
- Hubei Key Laboratory of Animal Embryo and Molecular Breeding, Hubei Academy of Agricultural Science, Wuhan 430064, China
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Heinrich F, Lange TM, Kircher M, Ramzan F, Schmitt AO, Gültas M. Exploring the potential of incremental feature selection to improve genomic prediction accuracy. Genet Sel Evol 2023; 55:78. [PMID: 37946104 PMCID: PMC10634161 DOI: 10.1186/s12711-023-00853-8] [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: 05/03/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The ever-increasing availability of high-density genomic markers in the form of single nucleotide polymorphisms (SNPs) enables genomic prediction, i.e. the inference of phenotypes based solely on genomic data, in the field of animal and plant breeding, where it has become an important tool. However, given the limited number of individuals, the abundance of variables (SNPs) can reduce the accuracy of prediction models due to overfitting or irrelevant SNPs. Feature selection can help to reduce the number of irrelevant SNPs and increase the model performance. In this study, we investigated an incremental feature selection approach based on ranking the SNPs according to the results of a genome-wide association study that we combined with random forest as a prediction model, and we applied it on several animal and plant datasets. RESULTS Applying our approach to different datasets yielded a wide range of outcomes, i.e. from a substantial increase in prediction accuracy in a few cases to minor improvements when only a fraction of the available SNPs were used. Compared with models using all available SNPs, our approach was able to achieve comparable performances with a considerably reduced number of SNPs in several cases. Our approach showcased state-of-the-art efficiency and performance while having a faster computation time. CONCLUSIONS The results of our study suggest that our incremental feature selection approach has the potential to improve prediction accuracy substantially. However, this gain seems to depend on the genomic data used. Even for datasets where the number of markers is smaller than the number of individuals, feature selection may still increase the performance of the genomic prediction. Our approach is implemented in R and is available at https://github.com/FelixHeinrich/GP_with_IFS/ .
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Affiliation(s)
- Felix Heinrich
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany.
| | - Thomas Martin Lange
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany
| | - Magdalena Kircher
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Bünteweg 17p, 30559, Hannover, Germany
| | - Faisal Ramzan
- Institute of Animal and Dairy Sciences, University of Agriculture Faisalabad, Jail Road, 38000, Faisalabad, Pakistan
| | - Armin Otto Schmitt
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Albrecht-Thaer-Weg 3, 37075, Göttingen, Germany
| | - Mehmet Gültas
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Albrecht-Thaer-Weg 3, 37075, Göttingen, Germany.
- Faculty of Agriculture, South Westphalia University of Applied Sciences, 59494, Soest, Germany.
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Gao J, Xu W, Zeng T, Tian Y, Wu C, Liu S, Zhao Y, Zhou S, Lin X, Cao H, Lu L. Genome-Wide Association Study of Egg-Laying Traits and Egg Quality in LingKun Chickens. Front Vet Sci 2022; 9:877739. [PMID: 35795788 PMCID: PMC9251537 DOI: 10.3389/fvets.2022.877739] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022] Open
Abstract
Egg production is the most important trait of laying hens. To identify molecular markers and candidate genes associated with egg production and quality, such as body weight at first oviposition (BWF), the number of eggs produced in 500 days (EN500), egg weight (EW), egg shell thickness (EST), egg shell strength (ESS), and Haugh unit (HU), a genome-wide analysis was performed in 266 LingKun Chickens. The results showed that thirty-seven single nucleotide polymorphisms (SNPs) were associated with all traits (p < 9.47 × 10−8, Bonferroni correction). These SNPs were located in close proximity to or within the sequence of the thirteen candidate genes, such as Galanin And GMAP Prepropeptide (GAL), Centromere Protein (CENPF), Glypican 2 (GPC2), Phosphatidylethanolamine N-Methyltransferase (PEMT), Transcription Factor AP-2 Delta (TFAP2D), and Carboxypeptidase Q (CPQ) gene related to egg-laying and Solute Carrier Family 5 Member 7 (SLC5A7), Neurocalcin Delta (NCALD), Proteasome 20S Subunit Beta 2 (PSMB2), Slit Guidance Ligand 3 (SLIT3), and Tubulin Tyrosine Ligase Like 7 (TTLL7) genes related to egg quality. Interestingly, one of the genes involved in bone formation (SLIT3) was identified as a candidate gene for ESS. Our candidate genes and SNPs associated with egg-laying traits were significant for molecular breeding of egg-laying traits and egg quality in LingKun chickens.
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Affiliation(s)
- Jinfeng Gao
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Science, Hangzhou, China
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Wenwu Xu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Science, Hangzhou, China
| | - Tao Zeng
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Science, Hangzhou, China
| | - Yong Tian
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Science, Hangzhou, China
| | - Chunqin Wu
- Wenzhou Vocational College of Science and Technology, Wenzhou, China
| | - Suzhen Liu
- Wenzhou Vocational College of Science and Technology, Wenzhou, China
| | - Yan Zhao
- Wenzhou Vocational College of Science and Technology, Wenzhou, China
| | - Shuhe Zhou
- Wenzhou Golden Land Agricultural Development Co., Ltd., Wenzhou, China
| | - Xinqin Lin
- Wenzhou Golden Land Agricultural Development Co., Ltd., Wenzhou, China
| | - Hongguo Cao
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
- Hongguo Cao
| | - Lizhi Lu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Institute of Animal Science and Veterinary, Zhejiang Academy of Agricultural Science, Hangzhou, China
- *Correspondence: Lizhi Lu
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10
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Li J, Wang Z, Lubritz D, Arango J, Fulton J, Settar P, Rowland K, Cheng H, Wolc A. Genome-wide association studies for egg quality traits in White Leghorn layers using low-pass sequencing and SNP chip data. J Anim Breed Genet 2022; 139:380-397. [PMID: 35404478 DOI: 10.1111/jbg.12679] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/05/2022] [Accepted: 03/27/2022] [Indexed: 12/24/2022]
Abstract
Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.
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Affiliation(s)
- Jinghui Li
- Department of Animal Science, University of California, Davis, California, USA
| | - Zigui Wang
- Department of Animal Science, University of California, Davis, California, USA
| | | | | | | | | | | | - Hao Cheng
- Department of Animal Science, University of California, Davis, California, USA
| | - Anna Wolc
- Hy-Line International, Dallas Center, Iowa, USA.,Department of Animal Science, Iowa State University, Ames, Iowa, USA
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11
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MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes. BIOLOGY 2021; 10:biology10090921. [PMID: 34571798 PMCID: PMC8469369 DOI: 10.3390/biology10090921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/09/2021] [Accepted: 09/13/2021] [Indexed: 11/17/2022]
Abstract
Simple Summary The interactions between SNPs, which are known as epistasis, can strongly influence the phenotype. Their detection is still a challenge, which is made even more difficult through the existence of background associations that can hide correct epistatic interactions. To address the limitations of existing methods, we present in this study our novel method MIDESP for the detection of epistatic SNP pairs. It is the first mutual information-based method that can be applied to both qualitative and quantitative phenotypes and which explicitly accounts for background associations in the dataset. Abstract The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.
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12
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Tarsani E, Kranis A, Maniatis G, Hager-Theodorides AL, Kominakis A. Detection of loci exhibiting pleiotropic effects on body weight and egg number in female broilers. Sci Rep 2021; 11:7441. [PMID: 33811218 PMCID: PMC8018976 DOI: 10.1038/s41598-021-86817-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 03/16/2021] [Indexed: 12/14/2022] Open
Abstract
The objective of the present study was to discover the genetic variants, functional candidate genes, biological processes and molecular functions underlying the negative genetic correlation observed between body weight (BW) and egg number (EN) traits in female broilers. To this end, first a bivariate genome-wide association and second stepwise conditional-joint analyses were performed using 2586 female broilers and 240 k autosomal SNPs. The aforementioned analyses resulted in a total number of 49 independent cross-phenotype (CP) significant SNPs with 35 independent markers showing antagonistic action i.e., positive effects on one trait and negative effects on the other trait. A number of 33 independent CP SNPs were located within 26 and 14 protein coding and long non-coding RNA genes, respectively. Furthermore, 26 independent markers were situated within 44 reported QTLs, most of them related to growth traits. Investigation of the functional role of protein coding genes via pathway and gene ontology analyses highlighted four candidates (CPEB3, ACVR1, MAST2 and CACNA1H) as most plausible pleiotropic genes for the traits under study. Three candidates (CPEB3, MAST2 and CACNA1H) were associated with antagonistic pleiotropy, while ACVR1 with synergistic pleiotropic action. Current results provide a novel insight into the biological mechanism of the genetic trade-off between growth and reproduction, in broilers.
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Affiliation(s)
- Eirini Tarsani
- Department of Animal Science and Aquaculture, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece.
| | - Andreas Kranis
- Aviagen, Newbridge, EH28 8SZ, Midlothian, UK
- The Roslin Institute, University of Edinburgh, Midlothian, EH25 9RG, UK
| | | | - Ariadne L Hager-Theodorides
- Department of Animal Science and Aquaculture, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece
| | - Antonios Kominakis
- Department of Animal Science and Aquaculture, Agricultural University of Athens, Iera Odos 75, 11855, Athens, Greece
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13
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Gao G, Gao D, Zhao X, Xu S, Zhang K, Wu R, Yin C, Li J, Xie Y, Hu S, Wang Q. Genome-Wide Association Study-Based Identification of SNPs and Haplotypes Associated With Goose Reproductive Performance and Egg Quality. Front Genet 2021; 12:602583. [PMID: 33777090 PMCID: PMC7994508 DOI: 10.3389/fgene.2021.602583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 02/24/2021] [Indexed: 01/10/2023] Open
Abstract
Geese are one of the most economically important waterfowl. However, the low reproductive performance and egg quality of geese hinder the development of the goose industry. The identification and application of genetic markers may improve the accuracy of beneficial trait selection. To identify the genetic markers associated with goose reproductive performance and egg quality traits, we performed a genome-wide association study (GWAS) for body weight at birth (BBW), the number of eggs at 48 weeks of age (EN48), the number of eggs at 60 weeks of age (EN60) and egg yolk color (EYC). The GWAS acquired 2.896 Tb of raw sequencing data with an average depth of 12.44× and identified 9,279,339 SNPs. The results of GWAS showed that 26 SNPs were significantly associated with BBW, EN48, EN60, and EYC. Moreover, five of these SNPs significantly associated with EN48 and EN60 were in a haplotype block on chromosome 35 from 4,512,855 to 4,541,709 bp, oriented to TMEM161A and another five SNPs significantly correlated to EYC were constructed in haplotype block on chromosome 5 from 21,069,009 to 21,363,580, which annotated by TMEM161A, CALCR, TFPI2, and GLP1R. Those genes were enriched in epidermal growth factor-activated receptor activity, regulation of epidermal growth factor receptor signaling pathway. The SNPs, haplotype markers, and candidate genes identified in this study can be used to improve the accuracy of marker-assisted selection for the reproductive performance and egg quality traits of geese. In addition, the candidate genes significantly associated with these traits may provide a foundation for better understanding the mechanisms underlying reproduction and egg quality in geese.
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Affiliation(s)
- Guangliang Gao
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
| | - Dengfeng Gao
- State Key Laboratory of Agrobiotechnology, China Agricultural University, Beijing, China
| | - Xianzhi Zhao
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
| | | | - Keshan Zhang
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
| | - Rui Wu
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
| | - Chunhui Yin
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
| | - Jing Li
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
| | - Youhui Xie
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
| | - Silu Hu
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Qigui Wang
- Institute of Poultry Science, Chongqing Academy of Animal Science, Chongqing, China
- Chongqing Engineering Research Center of Goose Genetic Improvement, Chongqing, China
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14
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Liu Z, Sun C, Yan Y, Li G, Li XC, Wu G, Yang N. Design and evaluation of a custom 50K Infinium SNP array for egg-type chickens. Poult Sci 2021; 100:101044. [PMID: 33743497 PMCID: PMC8010521 DOI: 10.1016/j.psj.2021.101044] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 12/14/2020] [Accepted: 02/04/2021] [Indexed: 11/28/2022] Open
Abstract
With the development of molecular genetics and high-throughput sequencing technology, genotyping arrays consisting of large numbers of SNP have raised great interest in animal and plant research. However, the application of commercial chicken 600K SNP arrays has varied in different populations of egg-type chickens. Moreover, their genotyping cost is too high for large-scale population applications. Herein, we independently developed a custom Illumina 50K BeadChip, named PhenoixChip-I, for egg-type chickens based on SNP from 479 sequenced individuals in 7 lines. We filtered and selected SNP with stringent criteria, such as high polymorphism, genome coverage, design score, and priorities. Finally, a total of 43,681 effective SNP successfully genotyped were included on our custom array. Approximately 14K SNP were previously reported to be associated with important economic traits in egg-type chickens. Subsequently, we verified the applicability and efficiency of the PhenoixChip-I SNP array from many aspects, including evaluating its use scientific research (population structure analysis and genome-wide association study) and the poultry breeding industry (genomic selection). The findings in our study will play a crucial role in accelerating the genetic improvement of egg-type chickens.
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Affiliation(s)
- Zhuang Liu
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Congjiao Sun
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yiyuan Yan
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; Beijing Engineering Research Centre of Layer, Beijing 101206, China
| | - Guangqi Li
- Beijing Engineering Research Centre of Layer, Beijing 101206, China
| | - Xiao Chang Li
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Guiqin Wu
- Beijing Engineering Research Centre of Layer, Beijing 101206, China
| | - Ning Yang
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
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15
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Chen X, Li T, He K, Geng Z, Wan X. Dietary green tea powder supplementation enriched egg nutrients and physicochemical property in an indigenous chicken breed. Poult Sci 2020; 100:388-395. [PMID: 33357704 PMCID: PMC7772676 DOI: 10.1016/j.psj.2020.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 09/26/2020] [Accepted: 10/01/2020] [Indexed: 11/24/2022] Open
Abstract
Dietary supplementation of green tea changes the antioxidative capacity of chickens. However, the effect of green tea supplementation in the diet on egg quality and the consequent change in processing capacity is still not well known. The aim of this study was to determine whether green tea powder (GTP) supplementation could affect egg quality, egg antioxidant capacity, and sensory and egg processing characteristics. Huainan partridge chickens (1,080) at 20 wk old were divided into 2 groups, one group fed a basal diet (control) and one group fed a basal diet plus 10 g kgˆ-1 GTP for 12 wk. After the levels of yolk cholesterol had been determined, chickens from the control group were further divided into low- and high-cholesterol groups and were fed a basal diet or a diet with 10 g kgˆ-1 GTP by orthogonal design. After 4 wk, the egg processing characteristics were investigated. Egg specific gravity, shell strength, shell thickness, albumin height, Haugh unit (HU) and cholesterol content were significantly lower in the GTP group than in the control group (P < 0.05). Egg weight, albumin height, yolk color, and HU increased in a time-dependent manner in both the control and GTP groups (P < 0.01). The yolk C16:0, C20:0, C18:1, C18:2, and polyunsaturated fatty acid (PUFA) contents were higher in the GTP group than in the control group (P < 0.05). Egg whites from the GTP group showed increased radical scavenging activity (P < 0.05). Egg appearance and texture from the GTP group were more preferred than those of the control group (P < 0.05). Eggs from the GTP group had lower hardness, chewiness, and water retention capacity than those of eggs from the control group (P < 0.05). Eggs from the GTP group with high yolk cholesterol showed lower chewiness than those from the basal diet group (P < 0.05). The results suggested that GTP supplementation could enrich the PUFA content in egg yolks, improve the overall taste, and change processing characteristics.
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Affiliation(s)
- Xingyong Chen
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, PR China
| | - Tao Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, PR China
| | - Kaiqin He
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, PR China
| | - Zhaoyu Geng
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, PR China
| | - Xiaochun Wan
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, PR China.
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16
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Ramzan F, Gültas M, Bertram H, Cavero D, Schmitt AO. Combining Random Forests and a Signal Detection Method Leads to the Robust Detection of Genotype-Phenotype Associations. Genes (Basel) 2020; 11:E892. [PMID: 32764260 PMCID: PMC7465705 DOI: 10.3390/genes11080892] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 07/28/2020] [Accepted: 08/03/2020] [Indexed: 12/21/2022] Open
Abstract
Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype-phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.
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Affiliation(s)
- Faisal Ramzan
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
- Department of Animal Breeding and Genetics, University of Agriculture Faisalabad, 38000 Faisalabad, Pakistan
| | - Mehmet Gültas
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
- Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, Georg-August University, 37075 Göttingen, Germany
| | - Hendrik Bertram
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
| | | | - Armin Otto Schmitt
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075 Göttingen, Germany; (F.R.); (M.G.); (H.B.)
- Center for Integrated Breeding Research (CiBreed), Albrecht-Thaer-Weg 3, Georg-August University, 37075 Göttingen, Germany
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17
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Deciphering the mode of action and position of genetic variants impacting on egg number in broiler breeders. BMC Genomics 2020; 21:512. [PMID: 32709222 PMCID: PMC7379350 DOI: 10.1186/s12864-020-06915-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 07/15/2020] [Indexed: 12/14/2022] Open
Abstract
Background Aim of the present study was first to identify genetic variants associated with egg number (EN) in female broilers, second to describe the mode of their gene action (additive and/or dominant) and third to provide a list with implicated candidate genes for the trait. A number of 2586 female broilers genotyped with the high density (~ 600 k) SNP array and with records on EN (mean = 132.4 eggs, SD = 29.8 eggs) were used. Data were analyzed with application of additive and dominant multi-locus mixed models. Results A number of 7 additive, 4 dominant and 6 additive plus dominant marker-trait significant associations were detected. A total number of 57 positional candidate genes were detected within 50 kb downstream and upstream flanking regions of the 17 significant markers. Functional enrichment analysis pinpointed two genes (BHLHE40 and CRTC1) to be involved in the ‘entrainment of circadian clock by photoperiod’ biological process. Gene prioritization analysis of the positional candidate genes identified 10 top ranked genes (GDF15, BHLHE40, JUND, GDF3, COMP, ITPR1, ELF3, ELL, CRLF1 and IFI30). Seven prioritized genes (GDF15, BHLHE40, JUND, GDF3, COMP, ELF3, CRTC1) have documented functional relevance to reproduction, while two more prioritized genes (ITPR1 and ELL) are reported to be related to egg quality in chickens. Conclusions Present results have shown that detailed exploration of phenotype-marker associations can disclose the mode of action of genetic variants and help in identifying causative genes associated with reproductive traits in the species.
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Du Y, Liu L, He Y, Dou T, Jia J, Ge C. Endocrine and genetic factors affecting egg laying performance in chickens: a review. Br Poult Sci 2020; 61:538-549. [PMID: 32306752 DOI: 10.1080/00071668.2020.1758299] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
1. Egg-laying performance reflects the overall reproductive performance of breeding hens. The genetic traits for egg-laying performance have low or medium heritability, and, depending on the period involved, usually ranges from 0.16 to 0.64. Egg-laying in chickens is regulated by a combination of environmental, endocrine and genetic factors. 2. The main endocrine factors that regulate egg-laying are gonadotropin-releasing hormone (GnRH), prolactin (PRL), follicle-stimulating hormone (FSH) and luteinising hormone (LH). 3. In the last three decades, many studies have explored this aspect at a molecular genetic level. Recent studies identified 31 reproductive hormone-based candidate genes that were significantly associated with egg-laying performance. With the development of genome-sequencing technology, 64 new candidate genes and 108 single nucleotide polymorphisms (SNPs) related to egg-laying performance have been found using genome-wide association studies (GWAS), providing novel insights into the molecular genetic mechanisms governing egg production. At the same time, microRNAs that regulate genes responsible for egg-laying in chickens were reviewed. 4. Research on endocrinological and genetic factors affecting egg-laying performance will greatly improve the reproductive performance of chickens and promote the protection, development, and utilisation of poultry. This review summarises studies on the endocrine and genetic factors of egg-laying performance in chickens from 1972 to 2019.
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Affiliation(s)
- Y Du
- College of Animal Science and Technology, Yunnan Agricultural University , Kunming, Yunnan, The People's Republic of China
| | - L Liu
- School of Forensic Medicine, Kunming Medical University , Kunming, Yunnan, The People's Republic of China
| | - Y He
- College of Animal Science and Technology, Yunnan Agricultural University , Kunming, Yunnan, The People's Republic of China
| | - T Dou
- College of Animal Science and Technology, Yunnan Agricultural University , Kunming, Yunnan, The People's Republic of China
| | - J Jia
- College of Animal Science and Technology, Yunnan Agricultural University , Kunming, Yunnan, The People's Republic of China
| | - C Ge
- College of Animal Science and Technology, Yunnan Agricultural University , Kunming, Yunnan, The People's Republic of China
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Khaltabadi Farahani AH, Mohammadi H, Moradi MH, Ghasemi HA. Identification of potential genomic regions for egg weight by a haplotype-based genome-wide association study using Bayesian methods. Br Poult Sci 2020; 61:251-257. [PMID: 32008362 DOI: 10.1080/00071668.2020.1724879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they are usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in genome-wide association studies. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27% to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.
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Affiliation(s)
- A H Khaltabadi Farahani
- Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University , Arak, Iran
| | - H Mohammadi
- Department of Animal Science, Faculty of Agricultural Sciences, University of Tabriz , Tabriz, Iran
| | - M H Moradi
- Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University , Arak, Iran
| | - H A Ghasemi
- Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University , Arak, Iran
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Zhuang ZX, Chen SE, Chen CF, Lin EC, Huang SY. Genome-wide association study on the body temperature changes of a broiler-type strain Taiwan country chickens under acute heat stress. J Therm Biol 2019; 82:33-42. [DOI: 10.1016/j.jtherbio.2019.03.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 03/13/2019] [Accepted: 03/13/2019] [Indexed: 10/27/2022]
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