scholarly journals Evaluation of Genomic Prediction for Pasmo Resistance in Flax

2018 ◽  
Author(s):  
Liqiang He ◽  
Jin Xiao ◽  
Khalid Y. Rashid ◽  
Gaofeng Jia ◽  
PingChuan Li ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Field Experiments ◽  
Training Dataset ◽  
Nucleotide Polymorphisms ◽  
Training Population ◽  
Yield And Quality ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Breeding Efficiency

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality, and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134, and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

scholarly journals Evaluation of Genomic Prediction for Pasmo Resistance in Flax

2019 ◽  
Vol 20 (2) ◽  
pp. 359 ◽  
Author(s):  
Liqiang He ◽  
Jin Xiao ◽  
Khalid Rashid ◽  
Gaofeng Jia ◽  
Pingchuan Li ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Field Experiments ◽  
Training Dataset ◽  
Nucleotide Polymorphisms ◽  
Training Population ◽  
Yield And Quality ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Breeding Efficiency

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.

scholarly journals Genome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle)

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0241848
Author(s):  
Hyo Jun Lee ◽  
Yoon Ji Chung ◽  
Sungbong Jang ◽  
Dong Won Seo ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Text Mining ◽  
Genomic Prediction ◽  
Causal Variant ◽  
Longissimus Dorsi ◽  
Nucleotide Polymorphisms ◽  
Muscle Area ◽  
Eye Muscle ◽  
Korean Cattle ◽  
Genome Wide ◽  
Snp Panels

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner–Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

scholarly journals Genomic Prediction in Pea: Effect of Marker Density and Training Population Size and Composition on Prediction Accuracy

2015 ◽  
Vol 6 ◽  
Author(s):  
Nadim Tayeh ◽  
Anthony Klein ◽  
Marie-Christine Le Paslier ◽  
Françoise Jacquin ◽  
Hervé Houtin ◽  
...  
Keyword(s):  
Population Size ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Marker Density ◽  
Training Population ◽  
And Training

scholarly journals A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247824
Author(s):  
Morteza Shabannejad ◽  
Mohammad-Reza Bihamta ◽  
Eslam Majidi-Hervan ◽  
Hadi Alipour ◽  
Asa Ebrahimi
Keyword(s):  
Drought Stress ◽  
Genomic Selection ◽  
Bread Wheat ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Terminal Drought ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Terminal Drought Stress ◽  
Trait Associations

The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method (ridge regression-best linear unbiased prediction, genomic best-linear unbiased predictions, and Bayesian ridge regression), training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.

scholarly journals Improving the Efficiency of Genomic Selection in Chinese Simmental beef cattle

2015 ◽  
Author(s):  
Jiangwei Xia ◽  
Yang Wu ◽  
Huizhong Fang ◽  
Wengang Zhang ◽  
Yuxin Song ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Nucleotide Polymorphisms ◽  
Linear Unbiased Prediction ◽  
Single Nucleotide ◽  
Kinship Matrix ◽  
Genome Wide ◽  
Best Linear Unbiased ◽  
Selection Of

Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNPs).In this study, we investigate an approach to increase the efficiency of genomic prediction by using genome-wide markers. The approach is a feature selection based on genomic best linear unbiased prediction (GBLUP),which is a statistical method used to predict breeding values using SNPs for selection in animal and plant breeding. The objective of this study is the choice of kinship matrix for genomic best linear unbiased prediction (GBLUP).The G-matrix is using the information of genome-wide dense markers. We compare three kinds of kinships based on different combinations of centring and scaling of marker genotypes.And find a suitable kinship approach that adjusts for the resource population of Chinese Simmental beef cattle.Single nucleotide polymorphism (SNPs) can be used to estimate kinship matrix and individual inbreeding coefficients more accurately. So in our research a genomic relationship matrix was developed for 1059 Chinese Simmental beef cattle using 640000 single nucleotide polymorphisms and breeding values were estimated using phenotypes about Carcass weight and Sirloin weight. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population. Another aim of this study was to optimize the selection of markers and determine the required number of SNPs for estimation of kinship in the Chinese Simmental beef cattle. We find that the feature selection of GBLUP using Xu’s and the Astle and Balding’s kinships model performed similarly well, and were the best-performing methods in our study. Inbreeding and kinship matrix can be estimated with high accuracy using ≥12,000s in Chinese Simmental beef cattle.

scholarly journals Genome-Wide Diversity, Population Structure and Demographic History of Dromedaries in the Central Desert of Iran

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 599 ◽  
Author(s):  
Morteza Bitaraf Sani ◽  
Javad Zare Harofte ◽  
Ahmad Bitaraf ◽  
Saeid Esmaeilkhanian ◽  
Mohammad Hossein Banabazi ◽  
...  
Keyword(s):  
Population Structure ◽  
Population Size ◽  
Balancing Selection ◽  
Demographic History ◽  
Genomic Analysis ◽  
Genotyping By Sequencing ◽  
Genomic Diversity ◽  
Nucleotide Polymorphisms ◽  
Effective Population ◽  
Genome Wide

The development of camel husbandry for good production in a desert climate is very important, thus we need to understand the genetic basis of camels and give attention to genomic analysis. We assessed genome-wide diversity, linkage disequilibrium (LD), effective population size (Ne) and relatedness in 96 dromedaries originating from five different regions of the central desert of Iran using genotyping-by-sequencing (GBS). A total of 14,522 Single Nucleotide Polymorphisms (SNPs) with an average minor allele frequency (MAF) of 0.19 passed quality control and filtering steps. The average observed heterozygosity in the population was estimated at 0.25 ± 0.03. The mean of LD at distances shorter than 40 kb was low (r2 = 0.089 ± 0.234). The camels sampled from the central desert of Iran exhibited higher relatedness than Sudanese and lower than Arabian Peninsula dromedaries. Recent Ne of Iran’s camels was estimated to be 89. Predicted Tajima’s D (1.28) suggested a bottleneck or balancing selection in dromedary camels in the central desert of Iran. A general decrease in effective and census population size poses a threat for Iran’s dromedaries. This report is the first SNP calling report on nearly the chromosome level and a first step towards understanding genomic diversity, population structure and demography in Iranian dromedaries.

scholarly journals Genome-Wide Association Mapping and Genomic Prediction of Anther Extrusion in CIMMYT Hybrid Wheat Breeding Program via Modeling Pedigree, Genomic Relationship, and Interaction With the Environment

2020 ◽  
Vol 11 ◽  
Author(s):  
Anil Adhikari ◽  
Bhoja Raj Basnet ◽  
Jose Crossa ◽  
Susanne Dreisigacker ◽  
Fatima Camarillo ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Field Experiments ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Hybrid Wheat ◽  
Test Set ◽  
Genome Wide ◽  
Anther Extrusion

Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017–2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from −0.03 to 0.74 for AC and −0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle.

Accuracies of direct genomic breeding values for birth and weaning weights of registered Charolais cattle in Mexico

2020 ◽  
Vol 60 (6) ◽  
pp. 772
Author(s):  
Francisco J. Jahuey-Martínez ◽  
Gaspar M. Parra-Bracamonte ◽  
Dorian J. Garrick ◽  
Nicolás López-Villalobos ◽  
Juan C. Martínez-González ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Reference Population ◽  
Single Step ◽  
Bayesian Regression ◽  
Nucleotide Polymorphisms ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Charolais Cattle ◽  
Best Linear Unbiased

Context Genomic prediction is now routinely used in many livestock species to rank individuals based on genomic breeding values (GEBV). Aims This study reports the first assessment aimed to evaluate the accuracy of direct GEBV for birth (BW) and weaning (WW) weights of registered Charolais cattle in Mexico. Methods The population assessed included 823 animals genotyped with an array of 77000 single nucleotide polymorphisms. Genomic prediction used genomic best linear unbiased prediction (GBLUP), Bayes C (BC), and single-step Bayesian regression (SSBR) methods in comparison with a pedigree-based BLUP method. Key results Our results show that the genomic prediction methods provided low and similar accuracies to BLUP. The prediction accuracy of GBLUP and BC were identical at 0.31 for BW and 0.29 for WW, similar to BLUP. Prediction accuracies of SSBR for BW and WW were up to 4% higher than those by BLUP. Conclusions Genomic prediction is feasible under current conditions, and provides a slight improvement using SSBR. Implications Some limitations on reference population size and structure were identified and need to be addressed to obtain more accurate predictions in liveweight traits under the prevalent cattle breeding conditions of Mexico.

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