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Song H, Zhang Q, Hu H. polyGBLUP: a modified genomic best linear unbiased prediction improved the genomic prediction efficiency for autopolyploid species. Brief Bioinform 2024; 25:bbae106. [PMID: 38517695 PMCID: PMC10959164 DOI: 10.1093/bib/bbae106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 12/22/2023] [Accepted: 02/26/2024] [Indexed: 03/24/2024] Open
Abstract
Given the universality of autopolyploid species in nature, it is crucial to develop genomic selection methods that consider different allele dosages for autopolyploid breeding. However, no method has been developed to deal with autopolyploid data regardless of the ploidy level. In this study, we developed a modified genomic best linear unbiased prediction (GBLUP) model (polyGBLUP) through constructing additive and dominant genomic relationship matrices based on different allele dosages. polyGBLUP could carry out genomic prediction for autopolyploid species regardless of the ploidy level. Through comprehensive simulations and analysis of real data of autotetraploid blueberry and guinea grass and autohexaploid sweet potato, the results showed that polyGBLUP achieved higher prediction accuracy than GBLUP and its superiority was more obvious when the ploidy level of autopolyploids is high. Furthermore, when the dominant effect was added to polyGBLUP (polyGDBLUP), the greater the dominance degree, the more obvious the advantages of polyGDBLUP over the diploid models in terms of prediction accuracy, bias, mean squared error and mean absolute error. For real data, the superiority of polyGBLUP over GBLUP appeared in blueberry and sweet potato populations and a part of the traits in guinea grass population due to the high correlation coefficients between diploid and polyploidy genomic relationship matrices. In addition, polyGDBLUP did not produce higher prediction accuracy than polyGBLUP for most traits of real data as dominant genetic variance was not captured for these traits. Our study will be a significant promising method for genomic prediction of autopolyploid species.
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Affiliation(s)
- Hailiang Song
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China
- Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou, 311799, China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, Shandong Agricultural University, Taian 271001, China
| | - Hongxia Hu
- Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China
- Key Laboratory of Sturgeon Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hangzhou, 311799, China
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Azizinia S, Mullan D, Rattey A, Godoy J, Robinson H, Moody D, Forrest K, Keeble-Gagnere G, Hayden MJ, Tibbits JFG, Daetwyler HD. Improved multi-trait prediction of wheat end-product quality traits by integrating NIR-predicted phenotypes. Front Plant Sci 2023; 14:1167221. [PMID: 37275257 PMCID: PMC10233148 DOI: 10.3389/fpls.2023.1167221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/14/2023] [Indexed: 06/07/2023]
Abstract
Historically, end-product quality testing has been costly and required large flour samples; therefore, it was generally implemented in the late phases of variety development, imposing a huge cost on the breeding effort and effectiveness. High genetic correlations of end-product quality traits with higher throughput and nondestructive testing technologies, such as near-infrared (NIR), could enable early-stage testing and effective selection of these highly valuable traits in a multi-trait genomic prediction model. We studied the impact on prediction accuracy in genomic best linear unbiased prediction (GBLUP) of adding NIR-predicted secondary traits for six end-product quality traits (crumb yellowness, water absorption, texture hardness, flour yield, grain protein, flour swelling volume). Bread wheat lines (1,400-1,900) were measured across 8 years (2012-2019) for six end-product quality traits with standard laboratory assays and with NIR, which were combined to generate predicted data for approximately 27,000 lines. All lines were genotyped with the Infinium™ Wheat Barley 40K BeadChip and imputed using exome sequence data. End-product and NIR phenotypes were genetically correlated (0.5-0.83, except for flour swelling volume 0.19). Prediction accuracies of end-product traits ranged between 0.28 and 0.64 and increased by 30% through the inclusion of NIR-predicted data compared to single-trait analysis. There was a high correlation between the multi-trait prediction accuracy and genetic correlations between end-product and NIR-predicted data (0.69-0.77). Our forward prediction validation revealed a gradual increase in prediction accuracy when adding more years to the multi-trait model. Overall, we achieved genomic prediction accuracy at a level that enables selection for end-product quality traits early in the breeding cycle.
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Affiliation(s)
- Shiva Azizinia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | | | | | | | | | - Kerrie Forrest
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | - Matthew J. Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | - Josquin FG. Tibbits
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Hans D. Daetwyler
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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Wijesena HR, Nonneman DJ, Snelling WM, Rohrer GA, Keel BN, Lents CA. gBLUP-GWAS identifies candidate genes, signaling pathways, and putative functional polymorphisms for age at puberty in gilts. J Anim Sci 2023; 101:skad063. [PMID: 36848325 PMCID: PMC10016198 DOI: 10.1093/jas/skad063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/27/2023] [Indexed: 03/01/2023] Open
Abstract
Successful development of replacement gilts determines their reproductive longevity and lifetime productivity. Selection for reproductive longevity is challenging due to low heritability and expression late in life. In pigs, age at puberty is the earliest known indicator for reproductive longevity and gilts that reach puberty earlier have a greater probability of producing more lifetime litters. Failure of gilts to reach puberty and display a pubertal estrus is a major reason for early removal of replacement gilts. To identify genomic sources of variation in age at puberty for improving genetic selection for early age at puberty and related traits, gilts (n = 4,986) from a multigeneration population representing commercially available maternal genetic lines were used for a genomic best linear unbiased prediction-based genome-wide association. Twenty-one genome-wide significant single nucleotide polymorphisms (SNP) located on Sus scrofa chromosomes (SSC) 1, 2, 9, and 14 were identified with additive effects ranging from -1.61 to 1.92 d (P < 0.0001 to 0.0671). Novel candidate genes and signaling pathways were identified for age at puberty. The locus on SSC9 (83.7 to 86.7 Mb) was characterized by long range linkage disequilibrium and harbors the AHR transcription factor gene. A second candidate gene on SSC2 (82.7 Mb), ANKRA2, is a corepressor for AHR, suggesting a possible involvement of AHR signaling in regulating pubertal onset in pigs. Putative functional SNP associated with age at puberty in the AHR and ANKRA2 genes were identified. Combined analysis of these SNP showed that an increase in the number of favorable alleles reduced pubertal age by 5.84 ± 1.65 d (P < 0.001). Candidate genes for age at puberty showed pleiotropic effects with other fertility functions such as gonadotropin secretion (FOXD1), follicular development (BMP4), pregnancy (LIF), and litter size (MEF2C). Several candidate genes and signaling pathways identified in this study play a physiological role in the hypothalamic-pituitary-gonadal axis and mechanisms permitting puberty onset. Variants located in or near these genes require further characterization to identify their impact on pubertal onset in gilts. Because age at puberty is an indicator of future reproductive success, these SNP are expected to improve genomic predictions for component traits of sow fertility and lifetime productivity expressed later in life.
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Affiliation(s)
| | - Dan J Nonneman
- Genetics and Animal Breeding Research Unit, USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Warren M Snelling
- Genetics and Animal Breeding Research Unit, USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Gary A Rohrer
- Genetics and Animal Breeding Research Unit, USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Brittney N Keel
- Genetics and Animal Breeding Research Unit, USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Clay A Lents
- LivestockBio-systems Research Unit, Clay Center, NE, USA
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Lozada D, Godoy JV, Murray TD, Ward BP, Carter AH. Genetic Dissection of Snow Mold Tolerance in US Pacific Northwest Winter Wheat Through Genome-Wide Association Study and Genomic Selection. Front Plant Sci 2019; 10:1337. [PMID: 31736994 PMCID: PMC6830427 DOI: 10.3389/fpls.2019.01337] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Accepted: 09/25/2019] [Indexed: 05/23/2023]
Abstract
Snow mold is a yield-limiting disease of wheat in the Pacific Northwest (PNW) region of the US, where there is prolonged snow cover. The objectives of this study were to identify genomic regions associated with snow mold tolerance in a diverse panel of PNW winter wheat lines in a genome-wide association study (GWAS) and to evaluate the usefulness of genomic selection (GS) for snow mold tolerance. An association mapping panel (AMP; N = 458 lines) was planted in Mansfield and Waterville, WA in 2017 and 2018 and genotyped using the Illumina® 90K single nucleotide polymorphism (SNP) array. GWAS identified 100 significant markers across 17 chromosomes, where SNPs on chromosomes 5A and 5B coincided with major freezing tolerance and vernalization loci. Increased number of favorable alleles was related to improved snow mold tolerance. Independent predictions using the AMP as a training population (TP) to predict snow mold tolerance of breeding lines evaluated between 2015 and 2018 resulted in a mean accuracy of 0.36 across models and marker sets. Modeling nonadditive effects improved accuracy even in the absence of a close genetic relatedness between the TP and selection candidates. Selecting lines based on genomic estimated breeding values and tolerance scores resulted in a 24% increase in tolerance. The identified genomic regions associated with snow mold tolerance demonstrated the genetic complexity of this trait and the difficulty in selecting tolerant lines using markers. GS was validated and showed potential for use in PNW winter wheat for selecting on complex traits such tolerance to snow mold.
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Affiliation(s)
- Dennis Lozada
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Jayfred V. Godoy
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Timothy D. Murray
- Department of Plant Pathology, Washington State University, Pullman, WA, United States
| | - Brian P. Ward
- USDA-ARS Plant Science Research Unit, Raleigh, NC, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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Belamkar V, Guttieri MJ, Hussain W, Jarquín D, El-Basyoni I, Poland J, Lorenz AJ, Baenziger PS. Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program. G3 (Bethesda) 2018; 8:2735-2747. [PMID: 29945967 PMCID: PMC6071594 DOI: 10.1534/g3.118.200415] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 06/19/2018] [Indexed: 01/07/2023]
Abstract
Genomic prediction (GP) is now routinely performed in crop plants to predict unobserved phenotypes. The use of predicted phenotypes to make selections is an active area of research. Here, we evaluate GP for predicting grain yield and compare genomic and phenotypic selection by tracking lines advanced. We examined four independent nurseries of F3:6 and F3:7 lines trialed at 6 to 10 locations each year. Yield was analyzed using mixed models that accounted for experimental design and spatial variations. Genotype-by-sequencing provided nearly 27,000 high-quality SNPs. Average genomic predictive ability, estimated for each year by randomly masking lines as missing in steps of 10% from 10 to 90%, and using the remaining lines from the same year as well as lines from other years in a training set, ranged from 0.23 to 0.55. The predictive ability estimated for a new year using the other years ranged from 0.17 to 0.28. Further, we tracked lines advanced based on phenotype from each of the four F3:6 nurseries. Lines with both above average genomic estimated breeding value (GEBV) and phenotypic value (BLUP) were retained for more years compared to lines with either above average GEBV or BLUP alone. The number of lines selected for advancement was substantially greater when predictions were made with 50% of the lines from the testing year added to the training set. Hence, evaluation of only 50% of the lines yearly seems possible. This study provides insights to assess and integrate genomic selection in breeding programs of autogamous crops.
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Affiliation(s)
- Vikas Belamkar
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
| | - Mary J Guttieri
- USDA, Agricultural Research Service, Center for Grain and Animal Health Research, Hard Winter Wheat Genetics Research Unit, Manhattan, KS 66502
| | - Waseem Hussain
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
| | - Diego Jarquín
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
| | - Ibrahim El-Basyoni
- Crop Science Department, Faculty of Agriculture, Damanhour University, Egypt
| | - Jesse Poland
- Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
| | - P Stephen Baenziger
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583
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Edwards SM, Woolliams JA, Hickey JM, Blott SC, Clements DN, Sánchez-Molano E, Todhunter RJ, Wiener P. Joint Genomic Prediction of Canine Hip Dysplasia in UK and US Labrador Retrievers. Front Genet 2018; 9:101. [PMID: 29643866 PMCID: PMC5883867 DOI: 10.3389/fgene.2018.00101] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 03/13/2018] [Indexed: 01/11/2023] Open
Abstract
Canine hip dysplasia, a debilitating orthopedic disorder that leads to osteoarthritis and cartilage degeneration, is common in several large-sized dog breeds and shows moderate heritability suggesting that selection can reduce prevalence. Estimating genomic breeding values require large reference populations, which are expensive to genotype for development of genomic prediction tools. Combining datasets from different countries could be an option to help build larger reference datasets without incurring extra genotyping costs. Our objective was to evaluate genomic prediction based on a combination of UK and US datasets of genotyped dogs with records of Norberg angle scores, related to canine hip dysplasia. Prediction accuracies using a single population were 0.179 and 0.290 for 1,179 and 242 UK and US Labrador Retrievers, respectively. Prediction accuracies changed to 0.189 and 0.260, with an increased bias of genomic breeding values when using a joint training set (biased upwards for the US population and downwards for the UK population). Our results show that in this study of canine hip dysplasia, little or no benefit was gained from using a joint training set as compared to using a single population as training set. We attribute this to differences in the genetic background of the two populations as well as the small sample size of the US dataset.
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Affiliation(s)
- Stefan M Edwards
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, United Kingdom
| | - John A Woolliams
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, United Kingdom
| | - John M Hickey
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, United Kingdom
| | - Sarah C Blott
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, United Kingdom
| | - Dylan N Clements
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, United Kingdom
| | - Enrique Sánchez-Molano
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, United Kingdom
| | - Rory J Todhunter
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, United States
| | - Pamela Wiener
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, United Kingdom
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Lukić B, Pong-Wong R, Rowe SJ, de Koning DJ, Velander I, Haley CS, Archibald AL, Woolliams JA. Efficiency of genomic prediction for boar taint reduction in Danish Landrace pigs. Anim Genet 2015; 46:607-16. [PMID: 26449733 PMCID: PMC4949655 DOI: 10.1111/age.12369] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2015] [Indexed: 02/01/2023]
Abstract
Genetic selection against boar taint, which is caused by high skatole and androstenone concentrations in fat, is a more acceptable alternative than is the current practice of castration. Genomic predictors offer an opportunity to overcome the limitations of such selection caused by the phenotype being expressed only in males at slaughter, and this study evaluated different approaches to obtain such predictors. Samples from 1000 pigs were included in a design which was dominated by 421 sib pairs, each pair having one animal with high and one with low skatole concentration (≥0.3 μg/g). All samples were measured for both skatole and androstenone and genotyped using the Illumina SNP60 porcine BeadChip for 62 153 single nucleotide polymorphisms. The accuracy of predicting phenotypes was assessed by cross‐validation using six different genomic evaluation methods: genomic best linear unbiased prediction (GBLUP) and five Bayesian regression methods. In addition, this was compared to the accuracy of predictions using only QTL that showed genome‐wide significance. The range of accuracies obtained by different prediction methods was narrow for androstenone, between 0.29 (Bayes Lasso) and 0.31 (Bayes B), and wider for skatole, between 0.21 (GBLUP) and 0.26 (Bayes SSVS). Relative accuracies, corrected for h2, were 0.54–0.56 and 0.75–0.94 for androstenone and skatole respectively. The whole‐genome evaluation methods gave greater accuracy than using only the QTL detected in the data. The results demonstrate that GBLUP for androstenone is the simplest genomic technology to implement and was also close to the most accurate method. More specialised models may be preferable for skatole.
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Affiliation(s)
- B Lukić
- Faculty of Agriculture in Osijek, J.J. Strossmayer University of Osijek, Kralja Petra Svačića 1d, 31000, Osijek, Croatia.,The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - R Pong-Wong
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - S J Rowe
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - D J de Koning
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.,Swedish University of Agricultural Sciences, SE-750 07, Uppsala, Sweden
| | - I Velander
- Pig Research Centre, Danish Agriculture & Food Council, Axeltorv 3, København, V 1609, Denmark
| | - C S Haley
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.,MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK
| | - A L Archibald
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - J A Woolliams
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
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Abdollahi-Arpanahi R, Nejati-Javaremi A, Pakdel A, Moradi-Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJM, Gianola D. Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens. J Anim Breed Genet 2014; 131:123-33. [PMID: 24397350 DOI: 10.1111/jbg.12075] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 11/29/2013] [Indexed: 01/09/2023]
Abstract
The objective was to assess goodness of fit and predictive ability of subsets of single nucleotide polymorphism (SNP) markers constructed based on minor allele frequency (MAF), effect sizes and varying marker density. Target traits were body weight (BW), ultrasound measurement of breast muscle (BM) and hen house egg production (HHP) in broiler chickens. We used a 600 K Affymetrix platform with 1352 birds genotyped. The prediction method was genomic best linear unbiased prediction (GBLUP) with 354 564 single nucleotide polymorphisms (SNPs) used to derive a genomic relationship matrix (G). Predictive ability was assessed as the correlation between predicted genomic values and corrected phenotypes from a threefold cross-validation. Predictive ability was 0.27 ± 0.002 for BW, 0.33 ± 0.001 for BM and 0.20 ± 0.002 for HHP. For the three traits studied, predictive ability decreased when SNPs with a higher MAF were used to construct G. Selection of the 20% SNPs with the largest absolute effect sizes induced a predictive ability equal to that from fitting all markers together. When density of markers increased from 5 K to 20 K, predictive ability enhanced slightly. These results provide evidence that designing a low-density chip using low-frequency markers with large effect sizes may be useful for commercial usage.
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Affiliation(s)
- R Abdollahi-Arpanahi
- Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
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