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Qiao J, Li K, Miao N, Xu F, Han P, Dai X, Abdelkarim OF, Zhu M, Zhao Y. Additive and Dominance Genome-Wide Association Studies Reveal the Genetic Basis of Heterosis Related to Growth Traits of Duhua Hybrid Pigs. Animals (Basel) 2024; 14:1944. [PMID: 38998055 PMCID: PMC11240614 DOI: 10.3390/ani14131944] [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: 06/03/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/14/2024] Open
Abstract
Heterosis has been extensively used for pig genetic breeding and production, but the genetic basis of heterosis remains largely elusive. Crossbreeding between commercial and native breeds provides a good model to parse the genetic basis of heterosis. This study uses Duhua hybrid pigs, a crossbreed of Duroc and Liangguang small spotted pigs, as materials to explore the genetic basis underlying heterosis related to growth traits at the genomic level. The mid-parent heterosis (MPH) analysis showed heterosis of this Duhua offspring on growth traits. In this study, we examined the impact of additive and dominance effects on 100 AGE (age adjusted to 100 kg) and 100 BF (backfat thickness adjusted to 100 kg) of Duhua hybrid pigs. Meanwhile, we successfully identified SNPs associated with growth traits through both additive and dominance GWASs (genome-wide association studies). These findings will facilitate the subsequent in-depth studies of heterosis in the growth traits of Duhua pigs.
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Affiliation(s)
- Jiakun Qiao
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Kebiao Li
- School of Life Science and Engineering, Foshan University, Foshan 528000, China
| | - Na Miao
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Fangjun Xu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Pingping Han
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiangyu Dai
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Omnia Fathy Abdelkarim
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
| | - Mengjin Zhu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
- The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan 430070, China
| | - Yunxiang Zhao
- College of Animal Science and Technology, Guangxi University, Nanning 530004, China
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Wang J, Liu J, Lei Q, Liu Z, Han H, Zhang S, Qi C, Liu W, Li D, Li F, Cao D, Zhou Y. Elucidation of the genetic determination of body weight and size in Chinese local chicken breeds by large-scale genomic analyses. BMC Genomics 2024; 25:296. [PMID: 38509464 PMCID: PMC10956266 DOI: 10.1186/s12864-024-10185-6] [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: 08/10/2023] [Accepted: 03/04/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Body weight and size are important economic traits in chickens. While many growth-related quantitative trait loci (QTLs) and candidate genes have been identified, further research is needed to confirm and characterize these findings. In this study, we investigate genetic and genomic markers associated with chicken body weight and size. This study provides new insights into potential markers for genomic selection and breeding strategies to improve meat production in chickens. METHODS We performed whole-genome resequencing of and Wenshang Barred (WB) chickens (n = 596) and three additional breeds with varying body sizes (Recessive White (RW), WB, and Luxi Mini (LM) chickens; (n = 50)). We then used selective sweeps of mutations coupled with genome-wide association study (GWAS) to identify genomic markers associated with body weight and size. RESULTS We identified over 9.4 million high-quality single nucleotide polymorphisms (SNPs) among three chicken breeds/lines. Among these breeds, 287 protein-coding genes exhibited positive selection in the RW and WB populations, while 241 protein-coding genes showed positive selection in the LM and WB populations. Genomic heritability estimates were calculated for 26 body weight and size traits, including body weight, chest breadth, chest depth, thoracic horn, body oblique length, keel length, pelvic width, shank length, and shank circumference in the WB breed. The estimates ranged from 0.04 to 0.67. Our analysis also identified a total of 2,522 genome-wide significant SNPs, with 2,474 SNPs clustered around two genomic regions. The first region, located on chromosome 4 (7.41-7.64 Mb), was linked to body weight after ten weeks and body size traits. LCORL, LDB2, and PPARGC1A were identified as candidate genes in this region. The other region, located on chromosome 1 (170.46-171.53 Mb), was associated with body weight from four to eighteen weeks and body size traits. This region contained CAB39L and WDFY2 as candidate genes. Notably, LCORL, LDB2, and PPARGC1A showed highly selective signatures among the three breeds of chicken with varying body sizes. CONCLUSION Overall this study provides a comprehensive map of genomic variants associated with body weight and size in chickens. We propose two genomic regions, one on chromosome 1 and the other on chromosome 4, that could helpful for developing genome selection breeding strategies to enhance meat yield in chickens.
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Affiliation(s)
- Jie Wang
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Jie Liu
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Qiuxia Lei
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Zhihe Liu
- Sichuan agricultural university college of animal science and technology, Chengdu, 611130, China
| | - Haixia Han
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Shuer Zhang
- Shandong Animal Husbandry General Station, Jinan, 250023, China
| | - Chao Qi
- Shandong Animal Husbandry General Station, Jinan, 250023, China
| | - Wei Liu
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Dapeng Li
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Fuwei Li
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Dingguo Cao
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China
| | - Yan Zhou
- Poultry Breeding Engineering Technology Center of Shandong Province, Poultry Institute, Shandong Academy of Agricultural Sciences, Jinan, Shandong, 250023, China.
- Jinan Key Laboratory of Poultry Germplasm Resources Innovation and Healthy Breeding, Jinan, Shandong, 250023, China.
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Blancon J, Buet C, Dubreuil P, Tixier MH, Baret F, Praud S. Maize green leaf area index dynamics: genetic basis of a new secondary trait for grain yield in optimal and drought conditions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:68. [PMID: 38441678 PMCID: PMC10914915 DOI: 10.1007/s00122-024-04572-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/03/2024] [Indexed: 03/07/2024]
Abstract
KEY MESSAGE Green Leaf Area Index dynamics is a promising secondary trait for grain yield and drought tolerance. Multivariate GWAS is particularly well suited to identify the genetic determinants of the green leaf area index dynamics. Improvement of maize grain yield is impeded by important genotype-environment interactions, especially under drought conditions. The use of secondary traits, that are correlated with yield, more heritable and less prone to genotype-environment interactions, can increase breeding efficiency. Here, we studied the genetic basis of a new secondary trait: the green leaf area index (GLAI) dynamics over the maize life cycle. For this, we used an unmanned aerial vehicle to characterize the GLAI dynamics of a diverse panel in well-watered and water-deficient trials in two years. From the dynamics, we derived 24 traits (slopes, durations, areas under the curve), and showed that six of them were heritable traits representative of the panel diversity. To identify the genetic determinants of GLAI, we compared two genome-wide association approaches: a univariate (single-trait) method and a multivariate (multi-trait) method combining GLAI traits, grain yield, and precocity. The explicit modeling of correlation structure between secondary traits and grain yield in the multivariate mixed model led to 2.5 times more associations detected. A total of 475 quantitative trait loci (QTLs) were detected. The genetic architecture of GLAI traits appears less complex than that of yield with stronger-effect QTLs that are more stable between environments. We also showed that a subset of GLAI QTLs explains nearly one fifth of yield variability across a larger environmental network of 11 water-deficient trials. GLAI dynamics is a promising grain yield secondary trait in optimal and drought conditions, and the detected QTLs could help to increase breeding efficiency through a marker-assisted approach.
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Affiliation(s)
- Justin Blancon
- UMR GDEC, INRAE, Université Clermont Auvergne, 63000, Clermont-Ferrand, France.
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France.
| | - Clément Buet
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | - Pierre Dubreuil
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
| | | | | | - Sébastien Praud
- Biogemma, Centre de Recherche de Chappes, 63720, Chappes, France
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Lázaro SF, Tonhati H, Oliveira HR, Silva AA, Scalez DCB, Nascimento AV, Santos DJA, Stefani G, Carvalho IS, Sandoval AF, Brito LF. Genetic parameters and genome-wide association studies for mozzarella and milk production traits, lactation length, and lactation persistency in Murrah buffaloes. J Dairy Sci 2024; 107:992-1021. [PMID: 37730179 DOI: 10.3168/jds.2023-23284] [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: 01/18/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023]
Abstract
Genetic and genomic analyses of longitudinal traits related to milk production efficiency are paramount for optimizing water buffaloes breeding schemes. Therefore, this study aimed to (1) compare single-trait random regression models under a single-step genomic BLUP setting based on alternative covariance functions (i.e., Wood, Wilmink, and Ali and Schaeffer) to describe milk (MY), fat (FY), protein (PY), and mozzarella (MZY) yields, fat-to-protein ratio (FPR), somatic cell score (SCS), lactation length (LL), and lactation persistency (LP) in Murrah dairy buffaloes (Bubalus bubalis); (2) combine the best functions for each trait under a multiple-trait framework; (3) estimate time-dependent SNP effects for all the studied longitudinal traits; and (4) identify the most likely candidate genes associated with the traits. A total of 323,140 test-day records from the first lactation of 4,588 Murrah buffaloes were made available for the study. The model included the average curve of the population nested within herd-year-season of calving, systematic effects of number of milkings per day, and age at first calving as linear and quadratic covariates, and additive genetic, permanent environment, and residual as random effects. The Wood model had the best goodness of fit based on the deviance information criterion and posterior model probabilities for all traits. Moderate heritabilities were estimated over time for most traits (0.30 ± 0.02 for MY; 0.26 ± 0.03 for FY; 0.45 ± 0.04 for PY; 0.28 ± 0.05 for MZY; 0.13 ± 0.02 for FPR; and 0.15 ± 0.03 for SCS). The heritability estimates for LP ranged from 0.38 ± 0.02 to 0.65 ± 0.03 depending on the trait definition used. Similarly, heritabilities estimated for LL ranged from 0.10 ± 0.01 to 0.14 ± 0.03. The genetic correlation estimates across days in milk (DIM) for all traits ranged from -0.06 (186-215 DIM for MY-SCS) to 0.78 (66-95 DIM for PY-MZY). The SNP effects calculated for the random regression model coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d). Numerous relevant genomic regions and candidate genes were identified for all traits, confirming their polygenic nature. The candidate genes identified contribute to a better understanding of the genetic background of milk-related traits in Murrah buffaloes and reinforce the value of incorporating genomic information in their breeding programs.
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Affiliation(s)
- Sirlene F Lázaro
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907; Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Humberto Tonhati
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Hinayah R Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - Alessandra A Silva
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Daiane C B Scalez
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - André V Nascimento
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | | | - Gabriela Stefani
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Isabella S Carvalho
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Amanda F Sandoval
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907.
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Atashi H, Chen Y, Wilmot H, Bastin C, Vanderick S, Hubin X, Gengler N. Single-step genome-wide association analyses for selected infrared-predicted cheese-making traits in Walloon Holstein cows. J Dairy Sci 2023; 106:7816-7831. [PMID: 37567464 DOI: 10.3168/jds.2022-23206] [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: 12/28/2022] [Accepted: 05/01/2023] [Indexed: 08/13/2023]
Abstract
This study aimed to perform genome-wide association study to identify genomic regions associated with milk production and cheese-making properties (CMP) in Walloon Holstein cows. The studied traits were milk yield, fat percentage, protein percentage, casein percentage (CNP), calcium content, somatic cell score (SCS), coagulation time, curd firmness after 30 min from rennet addition, and titratable acidity. The used data have been collected from 2014 to 2020 on 78,073 first-parity (485,218 test-day records), 48,766 second-parity (284,942 test-day records), and 21,948 third-parity (105,112 test-day records) Holstein cows distributed in 671 herds in the Walloon Region of Belgium. Data of 565,533 single nucleotide polymorphisms (SNP), located on 29 Bos taurus autosomes (BTA) of 6,617 animals (1,712 males), were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of ∼216 KB) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for positional candidate genes. Heritability estimates for the studied traits ranged from 0.10 (SCS) to 0.53 (CNP), 0.10 (SCS) to 0.50 (CNP), and 0.12 (SCS) to 0.49 (CNP) in the first, second, and third parity, respectively. Genome-wide association analyses identified 6 genomic regions (BTA1, BTA14 [4 regions], and BTA20) associated with the considered traits. Genes including the SLC37A1 (BTA1), SHARPIN, MROH1, DGAT1, FAM83H, TIGD5, MROH6, NAPRT, ADGRB1, GML, LYPD2, JRK (BTA14), and TRIO (BTA20) were identified as positional candidate genes for the studied CMP. The findings of this study help to unravel the genomic background of a cow's ability for cheese production and can be used for the future implementation and use of genomic evaluation to improve the cheese-making traits in Walloon Holstein cows.
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Affiliation(s)
- H Atashi
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; Department of Animal Science, Shiraz University, 71441-13131 Shiraz, Iran.
| | - Y Chen
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - H Wilmot
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; National Fund for Scientific Research (FRS-FNRS), 1000 Brussels, Belgium
| | - C Bastin
- National Fund for Scientific Research (FRS-FNRS), 1000 Brussels, Belgium
| | - S Vanderick
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - X Hubin
- Elevéo asbl Awé Group, 5590 Ciney, Belgium
| | - N Gengler
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
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Baba T, Morota G, Kawakami J, Gotoh Y, Oka T, Masuda Y, Brito LF, Cockrum RR, Kawahara T. Longitudinal genome-wide association analysis using a single-step random regression model for height in Japanese Holstein cattle. JDS COMMUNICATIONS 2023; 4:363-368. [PMID: 37727246 PMCID: PMC10505781 DOI: 10.3168/jdsc.2022-0347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/22/2023] [Indexed: 09/21/2023]
Abstract
Growth traits, such as body weight and height, are essential in the design of genetic improvement programs of dairy cattle due to their relationship with feeding efficiency, longevity, and health. We investigated genomic regions influencing height across growth stages in Japanese Holstein cattle using a single-step random regression model. We used 72,921 records from birth to 60 mo of age with 4,111 animals born between 2000 and 2016. The analysis included 1,410 genotyped animals with 35,319 single nucleotide polymorphisms, consisting of 883 females with records and 527 bulls, and 30,745 animals with pedigree information. A single genomic region at the 58.4 megabase pair on chromosome 18 was consistently identified across 6 age points of 10, 20, 30, 40, 50, and 60 mo after multiple testing corrections for the significance threshold. Twelve candidate genes, previously reported for longevity and gestation length, were found near the identified genomic region. Another location near the identified region was also previously associated with body conformation, fertility, and calving difficulty. Functional Gene Ontology enrichment analysis suggested that the candidate genes regulate dephosphorylation and phosphatase activity. Our findings show that further study of the identified candidate genes will contribute to a better understanding of the genetic basis of height in Japanese Holstein cattle.
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Affiliation(s)
- Toshimi Baba
- Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo, Hokkaido, Japan 001-8555
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
| | - Gota Morota
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
| | - Junpei Kawakami
- Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo, Hokkaido, Japan 001-8555
| | - Yusaku Gotoh
- Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo, Hokkaido, Japan 001-8555
| | - Taro Oka
- Holstein Cattle Association of Japan, Tokyo, Japan 164-0012
| | - Yutaka Masuda
- Department of Sustainable Agriculture, Rakuno Gakuen University, Ebetsu, Hokkaido, Japan 069-8501
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Rebbeca R. Cockrum
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
| | - Takayoshi Kawahara
- Holstein Cattle Association of Japan, Hokkaido Branch, Sapporo, Hokkaido, Japan 001-8555
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Xia H, Hao Z, Shen Y, Tu Z, Yang L, Zong Y, Li H. Genome-wide association study of multiyear dynamic growth traits in hybrid Liriodendron identifies robust genetic loci associated with growth trajectories. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 115:1544-1563. [PMID: 37272730 DOI: 10.1111/tpj.16337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/30/2023] [Accepted: 05/29/2023] [Indexed: 06/06/2023]
Abstract
The genetic factors underlying growth traits differ over time points or stages. However, most current studies of phenotypes at single time points do not capture all loci or explain the genetic differences underlying growth trajectories. Hybrid Liriodendron exhibits obvious heterosis and is widely cultivated, although its complex genetic mechanism underlying growth traits remains unknown. A genome-wide association study (GWAS) is an effective method for elucidating the genetic architecture by identifying genetic loci underlying complex quantitative traits. In the present study, using a GWAS, we identified robust loci associated with growth trajectories in hybrid Liriodendron populations. We selected 233 hybrid progenies derived from 25 crosses for resequencing, and measured their tree height (H) and diameter at breast height (DBH) for 11 consecutive years; 192 972 high-quality single nucleotide polymorphisms (SNPs) were obtained. The dynamics of the multiyear single-trait GWAS showed that year-specific SNPs predominated, and only five robust SNPs for DBH were identified in at least three different years. Multitrait GWAS analysis with model parameters as latent variables also revealed 62 SNPs for H and 52 for DBH associated with the growth trajectory, displaying different biomass accumulation patterns, among which four SNPs exerted pleiotropic effects. All identified SNPs also exhibited temporal variations in effect sizes and inheritance patterns potentially related to different growth and developmental stages. The haplotypes resulting from these significant SNPs might pyramid favorable loci, benefitting the selection of superior genotypes. The present study provides insights into the genetic architecture of dynamic growth traits and lays a basis for future molecular-assisted breeding.
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Affiliation(s)
- Hui Xia
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Ziyuan Hao
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Yufang Shen
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Zhonghua Tu
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Lichun Yang
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Yaxian Zong
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Huogen Li
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
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Teng J, Wang D, Zhao C, Zhang X, Chen Z, Liu J, Sun D, Tang H, Wang W, Li J, Mei C, Yang Z, Ning C, Zhang Q. Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data. J Dairy Sci 2023; 106:2535-2550. [PMID: 36797187 DOI: 10.3168/jds.2022-22277] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 10/20/2022] [Indexed: 02/16/2023]
Abstract
Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.
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Affiliation(s)
- Jun Teng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Dan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Changheng Zhao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Xinyi Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Zhi Chen
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Jianfeng Liu
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Dongxiao Sun
- College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Hui Tang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Wenwen Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China
| | - Jianbin Li
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan 250100, China
| | - Cheng Mei
- Dongying Shenzhou AustAsia Modern Dairy Farm Co. Ltd., Dongying 257200, China
| | - Zhangping Yang
- College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Chao Ning
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China.
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Tai'an 271018, China.
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Rajawat D, Panigrahi M, Kumar H, Nayak SS, Parida S, Bhushan B, Gaur GK, Dutt T, Mishra BP. Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds. Gene 2022; 816:146165. [PMID: 35026292 DOI: 10.1016/j.gene.2021.146165] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 12/25/2022]
Abstract
In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, we used eight different statistical tools (Tajima's D, Fu & Li's D*, CLR, ROH, iHS, FST, FLK, and hapFLK) on seven indigenous and five exotic cattle breeds. We further performed composite analysis by comparing their covariance matrix. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). The outcome of this investigation provides a basis for detecting selective sweeps that explain the genetic variation of traits. They may possess functional importance for multiple cattle breeds in different subcontinents. However, further studies are required to improve the findings using high-density arrays or whole-genome sequencing with higher resolution and greater sample sizes.
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Affiliation(s)
- Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Subhashree Parida
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - G K Gaur
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - B P Mishra
- Division of Animal Biotechnology, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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10
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Grid-based Gaussian process models for longitudinal genetic data. COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 2022. [DOI: 10.29220/csam.2022.29.1.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Grid-based Gaussian process models for longitudinal genetic data. COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 2022. [DOI: 10.29220/csam.2022.29.1.745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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12
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Pedrosa VB, Schenkel FS, Chen SY, Oliveira HR, Casey TM, Melka MG, Brito LF. Genomewide Association Analyses of Lactation Persistency and Milk Production Traits in Holstein Cattle Based on Imputed Whole-Genome Sequence Data. Genes (Basel) 2021; 12:genes12111830. [PMID: 34828436 PMCID: PMC8624223 DOI: 10.3390/genes12111830] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 11/13/2021] [Accepted: 11/17/2021] [Indexed: 12/22/2022] Open
Abstract
Lactation persistency and milk production are among the most economically important traits in the dairy industry. In this study, we explored the association of over 6.1 million imputed whole-genome sequence variants with lactation persistency (LP), milk yield (MILK), fat yield (FAT), fat percentage (FAT%), protein yield (PROT), and protein percentage (PROT%) in North American Holstein cattle. We identified 49, 3991, 2607, 4459, 805, and 5519 SNPs significantly associated with LP, MILK, FAT, FAT%, PROT, and PROT%, respectively. Various known associations were confirmed while several novel candidate genes were also revealed, including ARHGAP35, NPAS1, TMEM160, ZC3H4, SAE1, ZMIZ1, PPIF, LDB2, ABI3, SERPINB6, and SERPINB9 for LP; NIM1K, ZNF131, GABRG1, GABRA2, DCHS1, and SPIDR for MILK; NR6A1, OLFML2A, EXT2, POLD1, GOT1, and ETV6 for FAT; DPP6, LRRC26, and the KCN gene family for FAT%; CDC14A, RTCA, HSTN, and ODAM for PROT; and HERC3, HERC5, LALBA, CCL28, and NEURL1 for PROT%. Most of these genes are involved in relevant gene ontology (GO) terms such as fatty acid homeostasis, transporter regulator activity, response to progesterone and estradiol, response to steroid hormones, and lactation. The significant genomic regions found contribute to a better understanding of the molecular mechanisms related to LP and milk production in North American Holstein cattle.
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Affiliation(s)
- Victor B. Pedrosa
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
- Department of Animal Sciences, State University of Ponta Grossa, Ponta Grossa 84030-900, Brazil
| | - Flavio S. Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G2W1, Canada;
| | - Shi-Yi Chen
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, College of Animal Science & Technology, Sichuan Agricultural University, Chengdu 611130, China
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON N1G2W1, Canada;
| | - Theresa M. Casey
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
| | - Melkaye G. Melka
- Department of Animal and Food Science, University of Wisconsin River Falls, River Falls, WI 54022, USA;
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907, USA; (V.B.P.); (S.-Y.C.); (H.R.O.); (T.M.C.)
- Correspondence:
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13
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Freitas Moreira F, Rojas de Oliveira H, Lopez MA, Abughali BJ, Gomes G, Cherkauer KA, Brito LF, Rainey KM. High-Throughput Phenotyping and Random Regression Models Reveal Temporal Genetic Control of Soybean Biomass Production. FRONTIERS IN PLANT SCIENCE 2021; 12:715983. [PMID: 34539708 PMCID: PMC8446606 DOI: 10.3389/fpls.2021.715983] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
Understanding temporal accumulation of soybean above-ground biomass (AGB) has the potential to contribute to yield gains and the development of stress-resilient cultivars. Our main objectives were to develop a high-throughput phenotyping method to predict soybean AGB over time and to reveal its temporal quantitative genomic properties. A subset of the SoyNAM population (n = 383) was grown in multi-environment trials and destructive AGB measurements were collected along with multispectral and RGB imaging from 27 to 83 days after planting (DAP). We used machine-learning methods for phenotypic prediction of AGB, genomic prediction of breeding values, and genome-wide association studies (GWAS) based on random regression models (RRM). RRM enable the study of changes in genetic variability over time and further allow selection of individuals when aiming to alter the general response shapes over time. AGB phenotypic predictions were high (R 2 = 0.92-0.94). Narrow-sense heritabilities estimated over time ranged from low to moderate (from 0.02 at 44 DAP to 0.28 at 33 DAP). AGB from adjacent DAP had highest genetic correlations compared to those DAP further apart. We observed high accuracies and low biases of prediction indicating that genomic breeding values for AGB can be predicted over specific time intervals. Genomic regions associated with AGB varied with time, and no genetic markers were significant in all time points evaluated. Thus, RRM seem a powerful tool for modeling the temporal genetic architecture of soybean AGB and can provide useful information for crop improvement. This study provides a basis for future studies to combine phenotyping and genomic analyses to understand the genetic architecture of complex longitudinal traits in plants.
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Affiliation(s)
| | | | - Miguel Angel Lopez
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Bilal Jamal Abughali
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Guilherme Gomes
- Department of Statistics, Purdue University, West Lafayette, IN, United States
| | - Keith Aric Cherkauer
- Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, United States
| | - Luiz Fernando Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Katy Martin Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
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Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef Cattle. Animals (Basel) 2021; 11:ani11092524. [PMID: 34573489 PMCID: PMC8470172 DOI: 10.3390/ani11092524] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/24/2021] [Accepted: 08/25/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Genome-wide association study (GWAS) has become the main approach for detecting functional genes that affects complex traits. For growth traits, the conventional GWAS method can only deal with the single-record traits observed at specific time points, rather than the longitudinal traits measured at multiple time points. Previous studies have reported the random regression model (RRM) for longitudinal data could overcome the limitation of the traditional GWAS model. Here, we present an association analysis based on RRM (GWAS-RRM) for 808 Chinese Simmental beef cattle at four stages of age. Ultimately, 37 significant single-nucleotide polymorphisms (SNPs) and several important candidate genes were screened to be associated with the body weight. Enrichment analysis showed these genes were significantly enriched in the signaling transduction pathway and lipid metabolism. This study not only offers a further understanding of the genetic basis for growth traits in beef cattle, but also provides a robust analytics tool for longitudinal traits in various species. Abstract Body weight (BW) is an important longitudinal trait that directly described the growth gain of bovine in production. However, previous genome-wide association study (GWAS) mainly focused on the single-record traits, with less attention paid to longitudinal traits. Compared with traditional GWAS models, the association studies based on the random regression model (GWAS-RRM) have better performance in the control of the false positive rate through considering time-stage effects. In this study, the BW trait data were collected from 808 Chinese Simmental beef cattle aged 0, 6, 12, and 18 months, then we performed a GWAS-RRM to fit the time-varied SNP effect. The results showed a total of 37 significant SNPs were associated with BW. Gene functional annotation and enrichment analysis indicated FGF4, ANGPT4, PLA2G4A, and ITGA5 were promising candidate genes for BW. Moreover, these genes were significantly enriched in the signaling transduction pathway and lipid metabolism. These findings will provide prior molecular information for bovine gene-based selection, as well as facilitate the extensive application of GWAS-RRM in domestic animals.
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15
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Meta-analysis of genome-wide association studies and gene networks analysis for milk production traits in Holstein cows. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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16
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Zhang Y, Song Y, Gao J, Zhang H, Yang N, Yang R. Hierarchical mixed-model expedites genome-wide longitudinal association analysis. Brief Bioinform 2021; 22:6217728. [PMID: 33834187 DOI: 10.1093/bib/bbab096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 11/13/2022] Open
Abstract
A hierarchical random regression model (Hi-RRM) was extended into a genome-wide association analysis for longitudinal data, which significantly reduced the dimensionality of repeated measurements. The Hi-RRM first modeled the phenotypic trajectory of each individual using a RRM and then associated phenotypic regressions with genetic markers using a multivariate mixed model (mvLMM). By spectral decomposition of genomic relationship and regression covariance matrices, the mvLMM was transformed into a multiple linear regression, which improved computing efficiency while implementing mvLMM associations in efficient mixed-model association expedited (EMMAX). Compared with the existing RRM-based association analyses, the statistical utility of Hi-RRM was demonstrated by simulation experiments. The method proposed here was also applied to find the quantitative trait nucleotides controlling the growth pattern of egg weights in poultry data.
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Affiliation(s)
- Ying Zhang
- College of Animal Science and Veterinary Medicine, Heilongjiang Bayi Agricultural University, People's Republic of China
| | - Yuxin Song
- Wuxi Fisheries College, Nanjing Agricultural University, People's Republic of China
| | - Jin Gao
- Wuxi Fisheries College, Nanjing Agricultural University, People's Republic of China
| | - Hengyu Zhang
- Department of Information and Computing Science, Heilongjiang Bayi Agricultural University, People's Republic of China
| | - Ning Yang
- College of Animal Science and Technology, China Agricultural University, People's Republic of China
| | - Runqing Yang
- Research Centre for Aquatic biotechnology, Chinese Academy of Fishery Sciences, People's Republic of China
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17
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Lázaro SF, Tonhati H, Oliveira HR, Silva AA, Nascimento AV, Santos DJA, Stefani G, Brito LF. Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models. J Dairy Sci 2021; 104:5768-5793. [PMID: 33685677 DOI: 10.3168/jds.2020-19534] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 01/02/2021] [Indexed: 01/14/2023]
Abstract
Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from -0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies.
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Affiliation(s)
- Sirlene F Lázaro
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907; Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Humberto Tonhati
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Hinayah R Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907; Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, N1G 2W1, ON, Canada
| | - Alessandra A Silva
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - André V Nascimento
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Daniel J A Santos
- Department of Animal and Avian Science, University of Maryland, College Park 20742
| | - Gabriela Stefani
- Department of Animal Science, College of Agricultural and Veterinary Sciences, São Paulo State University (UNESP), Jaboticabal, 14884-900, SP, Brazil
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907.
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18
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Liang Y, Li B, Zhang Q, Zhang S, He X, Jiang L, Jin Y. Interaction analyses based on growth parameters of GWAS between Escherichia coli and Staphylococcus aureus. AMB Express 2021; 11:34. [PMID: 33646434 PMCID: PMC7921238 DOI: 10.1186/s13568-021-01192-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 02/09/2021] [Indexed: 01/02/2023] Open
Abstract
To accurately explore the interaction mechanism between Escherichia coli and Staphylococcus aureus, we designed an ecological experiment to monoculture and co-culture E. coli and S. aureus. We co-cultured 45 strains of E. coli and S. aureus, as well as each species individually to measure growth over 36 h. We implemented a genome wide association study (GWAS) based on growth parameters (λ, R, A and s) to identify significant single nucleotide polymorphisms (SNPs) of the bacteria. Three commonly used growth regression equations, Logistic, Gompertz, and Richards, were used to fit the bacteria growth data of each strain. Then each equation's Akaike's information criterion (AIC) value was calculated as a commonly used information criterion. We used the optimal growth equation to estimate the four parameters above for strains in co-culture. By plotting the estimates for each parameter across two strains, we can visualize how growth parameters respond ecologically to environment stimuli. We verified that different genotypes of bacteria had different growth trajectories, although they were the same species. We reported 85 and 52 significant SNPs that were associated with interaction in E. coli and S. aureus, respectively. Many significant genes might play key roles in interaction, such as yjjW, dnaK, aceE, tatD, ftsA, rclR, ftsK, fepA in E. coli, and scdA, trpD, sdrD, SAOUHSC_01219 in S. aureus. Our study illustrated that there were multiple genes working together to affect bacterial interaction, and laid a solid foundation for the later study of more complex inter-bacterial interaction mechanisms.
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Duan X, An B, Du L, Chang T, Liang M, Yang BG, Xu L, Zhang L, Li J, E G, Gao H. Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle. Animals (Basel) 2021; 11:ani11010192. [PMID: 33467455 PMCID: PMC7830728 DOI: 10.3390/ani11010192] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 01/11/2021] [Accepted: 01/11/2021] [Indexed: 12/17/2022] Open
Abstract
The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight-age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R2 = 0.954). The parameters' mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.
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Affiliation(s)
- Xinghai Duan
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
- College of Animal Science and Technology, Southwest University, Chongqing 400715, China;
| | - Bingxing An
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
| | - Lili Du
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
| | - Tianpeng Chang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
| | - Mang Liang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
| | - Bai-Gao Yang
- College of Animal Science and Technology, Southwest University, Chongqing 400715, China;
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
| | - Lupei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
| | - Junya Li
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
| | - Guangxin E
- College of Animal Science and Technology, Southwest University, Chongqing 400715, China;
- Correspondence: (G.E); (H.G.)
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China; (X.D.); (B.A.); (L.D.); (T.C.); (M.L.); (L.X.); (L.Z.); (J.L.)
- Correspondence: (G.E); (H.G.)
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20
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Liu L, Zhou J, Chen CJ, Zhang J, Wen W, Tian J, Zhang Z, Gu Y. GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle. Animals (Basel) 2020; 10:ani10112048. [PMID: 33167458 PMCID: PMC7694478 DOI: 10.3390/ani10112048] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/01/2020] [Accepted: 11/03/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Understanding the genetic architecture underlying milk production traits in cattle is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we performed a genome-wide association study for milk production and quality traits in Holstein cattle. In the total of ten significant single-nucleotide polymorphisms (SNPs) associated with milk fat and protein, six are located in previously reported quantitative traits locus (QTL) regions. The study not only identified the effect of DGAT1 gene on milk fat and protein but also found several novel candidate genes. In addition, some pleiotropic SNPs and QTLs were identified that associated with more than two traits, these results could provide some basis for molecular breeding in dairy cattle. Abstract High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10−7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.
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Affiliation(s)
- Liyuan Liu
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Jinghang Zhou
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Chunpeng James Chen
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Juan Zhang
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
| | - Wan Wen
- Animal Husbandry Workstation, Yinchuan 750001, Ningxia, China; (W.W.); (J.T.)
| | - Jia Tian
- Animal Husbandry Workstation, Yinchuan 750001, Ningxia, China; (W.W.); (J.T.)
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
- Correspondence: (Z.Z.); (Y.G.)
| | - Yaling Gu
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Correspondence: (Z.Z.); (Y.G.)
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Vanhatalo J, Li Z, Sillanpää MJ. A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data. Bioinformatics 2020; 35:3684-3692. [PMID: 30850830 PMCID: PMC6761969 DOI: 10.1093/bioinformatics/btz164] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 12/05/2018] [Accepted: 03/06/2019] [Indexed: 12/22/2022] Open
Abstract
Motivation Recent advances in high dimensional phenotyping bring time as an extra dimension into the phenotypes. This promotes the quantitative trait locus (QTL) studies of function-valued traits such as those related to growth and development. Existing approaches for analyzing functional traits utilize either parametric methods or semi-parametric approaches based on splines and wavelets. However, very limited choices of software tools are currently available for practical implementation of functional QTL mapping and variable selection. Results We propose a Bayesian Gaussian process (GP) approach for functional QTL mapping. We use GPs to model the continuously varying coefficients which describe how the effects of molecular markers on the quantitative trait are changing over time. We use an efficient gradient based algorithm to estimate the tuning parameters of GPs. Notably, the GP approach is directly applicable to the incomplete datasets having even larger than 50% missing data rate (among phenotypes). We further develop a stepwise algorithm to search through the model space in terms of genetic variants, and use a minimal increase of Bayesian posterior probability as a stopping rule to focus on only a small set of putative QTL. We also discuss the connection between GP and penalized B-splines and wavelets. On two simulated and three real datasets, our GP approach demonstrates great flexibility for modeling different types of phenotypic trajectories with low computational cost. The proposed model selection approach finds the most likely QTL reliably in tested datasets. Availability and implementation Software and simulated data are available as a MATLAB package ‘GPQTLmapping’, and they can be downloaded from GitHub (https://github.com/jpvanhat/GPQTLmapping). Real datasets used in case studies are publicly available at QTL Archive. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jarno Vanhatalo
- Department of Mathematics and Statistics and Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki, Finland
| | - Zitong Li
- CSIRO Agriculture & Food, GPO Box 1600, Canberra, ACT 2601, Australia
| | - Mikko J Sillanpää
- Department of Mathematical Sciences, Biocenter Oulu and Infotech Oulu University of Oulu, Oulu FI-90014, Finland
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22
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Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
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Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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23
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Atashi H, Salavati M, De Koster J, Ehrlich J, Crowe M, Opsomer G, Hostens M. Genome-wide association for milk production and lactation curve parameters in Holstein dairy cows. J Anim Breed Genet 2019; 137:292-304. [PMID: 31576624 PMCID: PMC7217222 DOI: 10.1111/jbg.12442] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/07/2019] [Accepted: 09/12/2019] [Indexed: 12/31/2022]
Abstract
The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.
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Affiliation(s)
- Hadi Atashi
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium.,Department of Animal Science, Shiraz University, Shiraz, Iran
| | - Mazdak Salavati
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, UK
| | - Jenne De Koster
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium
| | | | - Mark Crowe
- University College Dublin, Dublin, Ireland
| | - Geert Opsomer
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium
| | | | - Miel Hostens
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke, Belgium
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24
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Oliveira HR, Brito LF, Lourenco DAL, Silva FF, Jamrozik J, Schaeffer LR, Schenkel FS. Invited review: Advances and applications of random regression models: From quantitative genetics to genomics. J Dairy Sci 2019; 102:7664-7683. [PMID: 31255270 DOI: 10.3168/jds.2019-16265] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 05/02/2019] [Indexed: 12/23/2022]
Abstract
An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.
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Affiliation(s)
- H R Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil
| | - L F Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - D A L Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | - F F Silva
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil
| | - J Jamrozik
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Canadian Dairy Network, Guelph, ON, N1K 1E5, Canada
| | - L R Schaeffer
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada
| | - F S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada.
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Ning C, Wang D, Zhou L, Wei J, Liu Y, Kang H, Zhang S, Zhou X, Xu S, Liu JF. Efficient multivariate analysis algorithms for longitudinal genome-wide association studies. Bioinformatics 2019; 35:4879-4885. [DOI: 10.1093/bioinformatics/btz304] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/16/2019] [Accepted: 04/25/2019] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation
Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues.
Results
We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes.
Availability and implementation
A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chao Ning
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Dan Wang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Lei Zhou
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Julong Wei
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Yuanxin Liu
- School of English, Beijing International Studies University, Beijing, China
| | - Huimin Kang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shengli Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Shizhong Xu
- Department of Botany and Plant Science, University of California, Riverside, CA, USA
| | - Jian-Feng Liu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, China
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Genetic evaluation of growth in Barki sheep using random regression models. Trop Anim Health Prod 2019; 51:1893-1901. [PMID: 31011923 DOI: 10.1007/s11250-019-01885-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 04/01/2019] [Indexed: 10/27/2022]
Abstract
The objective of the current study was to estimate covariance components of growth at different ages from birth to yearling in Barki lambs. A total of 16,496 records for body weights at birth (W0), 3 (W3), 6 (W6), 9 (W9), and 12 (12) months of age for Barki lambs were available. Two statistical approaches were used; multi-trait (MT) and random regression (RR) animal models assuming two random effects only, additive genetic effect (σ2a) and permanent environmental effect (σ2pe) of the animal. Regarding the RR model, Legendre polynomials (LP) of different orders for the random parts were compared in order to evaluate the most appropriate model. Bayesian information and Akaike information criteria suggested that the optimal RR model included the third order for fixed effect of lamb age and σ2pe, and fourth order of LP for σ2a (LP343). Estimates of direct heritability (h2a) from LP343 showed an ascending pattern, as it was 0.06 ± 0.03 for birth weight and reached to the peak at 9 months (0.42 ± 0.02). Thereafter, it declined again at the end of trajectory (12 months of age; 0.27 ± 0.03). The MT model showed a fluctuated pattern and lower estimates of h2a (0.19 ± 0.03, 0.11 ± 0.02, 0.12 ± 0.02, 0.11 ± 0.03, and 0.16 ± 0.04 for W0, W3, W6, W9, and W12, respectively). Considerably, similar ascending patterns of the ratio of σ2pe to phenotypic variance were reported from both RR (from 3 to 50%) and MT models (from 5 to 20%). Of interest, the RR model showed higher predicting ability of the breeding values compared with the MT model, which is an indicator for the suitability of RR models for analyzing the consecutive growth traits in sheep. Results suggested that the Barki sheep has a potential for genetic selection based on weight at different ages with selection likely to be more efficient at 9 months of age.
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Morton MJL, Awlia M, Al‐Tamimi N, Saade S, Pailles Y, Negrão S, Tester M. Salt stress under the scalpel - dissecting the genetics of salt tolerance. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:148-163. [PMID: 30548719 PMCID: PMC6850516 DOI: 10.1111/tpj.14189] [Citation(s) in RCA: 123] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 11/28/2018] [Accepted: 11/30/2018] [Indexed: 05/08/2023]
Abstract
Salt stress limits the productivity of crops grown under saline conditions, leading to substantial losses of yield in saline soils and under brackish and saline irrigation. Salt tolerant crops could alleviate these losses while both increasing irrigation opportunities and reducing agricultural demands on dwindling freshwater resources. However, despite significant efforts, progress towards this goal has been limited, largely because of the genetic complexity of salt tolerance for agronomically important yield-related traits. Consequently, the focus is shifting to the study of traits that contribute to overall tolerance, thus breaking down salt tolerance into components that are more genetically tractable. Greater consideration of the plasticity of salt tolerance mechanisms throughout development and across environmental conditions furthers this dissection. The demand for more sophisticated and comprehensive methodologies is being met by parallel advances in high-throughput phenotyping and sequencing technologies that are enabling the multivariate characterisation of vast germplasm resources. Alongside steady improvements in statistical genetics models, forward genetics approaches for elucidating salt tolerance mechanisms are gaining momentum. Subsequent quantitative trait locus and gene validation has also become more accessible, most recently through advanced techniques in molecular biology and genomic analysis, facilitating the translation of findings to the field. Besides fuelling the improvement of established crop species, this progress also facilitates the domestication of naturally salt tolerant orphan crops. Taken together, these advances herald a promising era of discovery for research into the genetics of salt tolerance in plants.
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Affiliation(s)
- Mitchell J. L. Morton
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Mariam Awlia
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Nadia Al‐Tamimi
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Stephanie Saade
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Yveline Pailles
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Sónia Negrão
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
| | - Mark Tester
- Division of Biological and Environmental Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)Thuwal23955‐6900Kingdom of Saudi Arabia
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Wang Z, Wang N, Wu R, Wang Z. fGWAS: An R package for genome-wide association analysis with longitudinal phenotypes. J Genet Genomics 2018; 45:411-413. [PMID: 30049619 PMCID: PMC6179436 DOI: 10.1016/j.jgg.2018.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 06/18/2018] [Accepted: 06/27/2018] [Indexed: 10/28/2022]
Affiliation(s)
- Zhong Wang
- College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14850, USA.
| | - Nating Wang
- College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Center for Computational Biology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Department of Public Health Sciences, Pennsylvania State College of Medicine, Hershey, PA 17033, USA
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, USA.
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Ning C, Wang D, Zheng X, Zhang Q, Zhang S, Mrode R, Liu JF. Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein. Genet Sel Evol 2018; 50:12. [PMID: 29576014 PMCID: PMC5868076 DOI: 10.1186/s12711-018-0383-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2017] [Accepted: 03/01/2018] [Indexed: 11/16/2022] Open
Abstract
Background Pseudo-phenotypes, such as 305-day yields, estimated breeding values or deregressed proofs, are usually used as response variables for genome-wide association studies (GWAS) of milk production traits in dairy cattle. Computational inefficiency challenges the direct use of test-day records for longitudinal GWAS with large datasets. Results We propose a rapid longitudinal GWAS method that is based on a random regression model. Our method uses Eigen decomposition of the phenotypic covariance matrix to rotate the data, thereby transforming the complex mixed linear model into weighted least squares analysis. We performed a simulation study that showed that our method can control type I errors well and has higher power than a longitudinal GWAS method that does not include time-varied additive genetic effects. We also applied our method to the analysis of milk production traits in the first three parities of 6711 Chinese Holstein cows. The analysis for each trait was completed within 1 day with known variances. In total, we located 84 significant single nucleotide polymorphisms (SNPs) of which 65 were within previously reported quantitative trait loci (QTL) regions. Conclusions Our rapid method can control type I errors in the analysis of longitudinal data and can be applied to other longitudinal traits. We detected QTL that were for the most part similar to those reported in a previous study in Chinese Holstein. Moreover, six additional SNPs for fat percentage and 13 SNPs for protein percentage were identified by our method. These additional 19 SNPs could be new candidate quantitative trait nucleotides for milk production traits in Chinese Holstein. Electronic supplementary material The online version of this article (10.1186/s12711-018-0383-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chao Ning
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Dan Wang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Xianrui Zheng
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Qin Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Shengli Zhang
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Raphael Mrode
- Animal Biosciences, International Livestock Research Institute, Nairobi, 00100, Kenya
| | - Jian-Feng Liu
- National Engineering Laboratory for Animal Breeding, Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.
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