scholarly journals Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Md. Abdullah Al Bari ◽  
Ping Zheng ◽  
Indalecio Viera ◽  
Hannah Worral ◽  
Stephen Szwiec ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Seed Yield ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Models ◽  
Germplasm Collection ◽  
Predictive Ability ◽  
Snp Markers ◽  
Breeding Values ◽  
Germplasm Collections

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder’s toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction’s potential to a set of 482 pea (Pisum sativum L.) accessions—genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components—for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

scholarly journals Harnessing genetic diversity in the USDA pea (Pisum sativum L.) germplasm collection through genomic prediction

2021 ◽  
Author(s):  
Md Abdullah Al Bari ◽  
Ping Zheng ◽  
Indalecio Viera ◽  
Hannah Worral ◽  
Stephen Szwiec ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Seed Yield ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Germplasm Collection ◽  
Predictive Ability ◽  
Germplasm Collections ◽  
Pisum Sativum L ◽  
Generation Sequencing ◽  
Germplasm Accessions

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic selection's potential to a set of 482 pea accessions, genotyped with 30,600 SNP markers and phenotyped for seed yield and yield-related components, for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. An increasing trend in predictive ability was also observed with an increasing number of SNPs. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the genomic prediction model from this study, we then examined the distribution of nonphenotyped accessions, and the reliability of genomic estimated breeding values (GEBV) of the USDA Pea accessions genotyped but not phenotyped. The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information into the genomic prediction framework could enhance prediction accuracy. Keywords: genomic selection, genomic prediction, reliability criteria, germplasm accessions, pea (Pisum sativum L.), next-generation sequencing

scholarly journals Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice

2018 ◽  
Author(s):  
Aditi Bhandari ◽  
Jérôme Bartholomé ◽  
Tuong-Vi Cao ◽  
Nilima Kumari ◽  
Julien frouin ◽  
...  
Keyword(s):  
Drought Stress ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Models ◽  
Predictive Ability ◽  
Reference Population ◽  
Snp Markers ◽  
Selection Strategy ◽  
Specific Marker ◽  
Marker Selection

AbstractDeveloping high yielding rice varieties that are tolerant to drought stress is crucial for the sustainable livelihood of rice farmers in rainfed rice cropping ecosystems. Genomic selection (GS) promises to be an effective breeding option for these complex traits. We evaluated the effectiveness of two rather new options in the implementation of GS: trait and environment-specific marker selection and the use of multi-environment prediction models. A reference population of 280 rainfed lowland accessions endowed with 215k SNP markers data was phenotyped under a favorable and two managed drought environments. Trait-specific SNP subsets (28k) were selected for each trait under each environment, using results of GWAS performed with the complete genotype dataset. Performances of single-environment and multi-environment genomic prediction models were compared using kernel regression based methods (GBLUP and RKHS) under two cross validation scenario: availability (CV2) or not (CV1) of phenotypic data for the validation set, in one of the environments. The most realistic trait-specific marker selection strategy achieved predictive ability (PA) of genomic prediction was up to 22% higher than markers selected on the bases of neutral linkage disequilibrium (LD). Tolerance to drought stress was up to 32% better predicted by multi-environment models (especially RKHS based models) under CV2 strategy. Under the less favorable CV1 strategy, the multi-environment models achieved similar PA than the single-environment predictions. We also showed that reasonable PA could be obtained with as few as 3,000 SNP markers, even in a population of low LD extent, provided marker selection is based on pairwise LD. The implications of these findings for breeding for drought tolerance are discussed. The most resource sparing option would be accurate phenotyping of the reference population in a favorable environment and under a managed drought, while the candidate population would be phenotyped only under one of those environments.

scholarly journals Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)

2020 ◽  
Vol 10 (3) ◽  
pp. 1113-1124 ◽  
Author(s):  
Madhav Bhatta ◽  
Lucia Gutierrez ◽  
Lorena Cammarota ◽  
Fernanda Cardozo ◽  
Silvia Germán ◽  
...  
Keyword(s):  
Prediction Model ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Models ◽  
Agronomic Traits ◽  
Predictive Ability ◽  
Malting Quality ◽  
Quality Traits ◽  
Multiple Traits ◽  
Correlated Traits

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

scholarly journals Prospects of genomic prediction in the USDA Soybean Germplasm Collection: Historical data creates robust models for enhancing selection of accessions

2016 ◽  
Author(s):  
Diego Jarquin ◽  
James Specht ◽  
Aaron Lorenz
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Germplasm Collection ◽  
Target Population ◽  
Appreciable Amount ◽  
Training Set ◽  
Phenotypic Data ◽  
Soybean Germplasm ◽  
Germplasm Collections ◽  
Snp Data

AbstractThe identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K SNP data collected on 18,480 domesticated soybean (G. max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Secondly, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7,608 non-phenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for non-phenotyped accessions, with no non-phenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions.

scholarly journals Genetic diversity and population structure of ridge gourd (Luffa acutangula) accessions in a Thailand collection using SNP markers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Grimar Abdiel Perez ◽  
Pumipat Tongyoo ◽  
Julapark Chunwongse ◽  
Hans de Jong ◽  
Anucha Wongpraneekul ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Population Structure ◽  
Germplasm Collection ◽  
Snp Markers ◽  
Fixation Index ◽  
Breeding Programs ◽  
Total Fixation ◽  
Unrooted Phylogenetic Tree ◽  
Luffa Acutangula ◽  
Selection Of

AbstractThis study explored a germplasm collection consisting of 112 Luffa acutangula (ridge gourd) accessions, mainly from Thailand. A total of 2834 SNPs were used to establish population structure and underlying genetic diversity while exploring the fruit characteristics together with genetic information which would help in the selection of parental lines for a breeding program. The study found that the average polymorphism information content value of 0.288 which indicates a moderate genetic diversity for this L. acutangula germplasm. STRUCTURE analysis (ΔK at K = 6) allowed us to group the accessions into six subpopulations that corresponded well with the unrooted phylogenetic tree and principal coordinate analyses. When plotted, the STRUCTURE bars to the area of collection, we observed an admixed genotype from surrounding accessions and a geneflow confirmed by the value of FST = 0.137. AMOVA based on STRUCTURE clustering showed a low 12.83% variation between subpopulations that correspond well with the negative inbreeding coefficient value (FIS =  − 0.092) and low total fixation index (FIT = 0.057). There were distinguishing fruit shapes and length characteristics in specific accessions for each subpopulation. The genetic diversity and different fruit shapes in the L. acutangula germplasm could benefit the ridge gourd breeding programs to meet the demands and needs of consumers, farmers, and vegetable exporters such as increasing the yield of fruit by the fruit width but not by the fruit length to solve the problem of fruit breakage during exportation.

scholarly journals GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2050
Author(s):  
Beatriz Castro Dias Cuyabano ◽  
Gabriel Rovere ◽  
Dajeong Lim ◽  
Tae Hun Kim ◽  
Hak Kyo Lee ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Phenotypic Expression ◽  
Genetic Evaluation ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Korean Cattle ◽  
Evaluation Programs ◽  
The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

scholarly journals The distribution of SNP marker effects for faecal worm egg count in sheep, and the feasibility of using these markers to predict genetic merit for resistance to worm infections

2011 ◽  
Vol 93 (3) ◽  
pp. 203-219 ◽  
Author(s):  
KATHRYN E. KEMPER ◽  
DAVID L. EMERY ◽  
STEPHEN C. BISHOP ◽  
HUTTON ODDY ◽  
BENJAMIN J. HAYES ◽  
...  
Keyword(s):  
Complex Traits ◽  
Sampling Error ◽  
Snp Markers ◽  
Snp Marker ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Genetic Merit ◽  
Independent Population

SummaryGenetic resistance to gastrointestinal worms is a complex trait of great importance in both livestock and humans. In order to gain insights into the genetic architecture of this trait, a mixed breed population of sheep was artificially infected with Trichostrongylus colubriformis (n=3326) and then Haemonchus contortus (n=2669) to measure faecal worm egg count (WEC). The population was genotyped with the Illumina OvineSNP50 BeadChip and 48 640 single nucleotide polymorphism (SNP) markers passed the quality controls. An independent population of 316 sires of mixed breeds with accurate estimated breeding values for WEC were genotyped for the same SNP to assess the results obtained from the first population. We used principal components from the genomic relationship matrix among genotyped individuals to account for population stratification, and a novel approach to directly account for the sampling error associated with each SNP marker regression. The largest marker effects were estimated to explain an average of 0·48% (T. colubriformis) or 0·08% (H. contortus) of the phenotypic variance in WEC. These effects are small but consistent with results from other complex traits. We also demonstrated that methods which use all markers simultaneously can successfully predict genetic merit for resistance to worms, despite the small effects of individual markers. Correlations of genomic predictions with breeding values of the industry sires reached a maximum of 0·32. We estimate that effective across-breed predictions of genetic merit with multi-breed populations will require an average marker spacing of approximately 10 kbp.

scholarly journals Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 752 ◽  
Author(s):  
Jungjae Lee ◽  
Yongmin Kim ◽  
Eunseok Cho ◽  
Kyuho Cho ◽  
Soojin Sa ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genome Wide Association Study ◽  
Genomic Analysis ◽  
Snp Markers ◽  
Snp Genotyping ◽  
Muscle Area ◽  
Bayesian Approaches ◽  
Breeding Values ◽  
Estimated Breeding Values ◽  
Response Variables

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding.

scholarly journals Dissection of the impact of prioritized QTL-linked and -unlinked SNP markers on the accuracy of genomic selection1

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ashley S. Ling ◽  
El Hamidi Hay ◽  
Samuel E. Aggrey ◽  
Romdhane Rekaya
Keyword(s):  
Genomic Prediction ◽  
Complex Traits ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Genetic Selection ◽  
Snp Markers ◽  
False Positives ◽  
Population Statistic ◽  
The Impact ◽  
Genomic Predictions

Abstract Background Use of genomic information has resulted in an undeniable improvement in prediction accuracies and an increase in genetic gain in animal and plant genetic selection programs in spite of oversimplified assumptions about the true biological processes. Even for complex traits, a large portion of markers do not segregate with or effectively track genomic regions contributing to trait variation; yet it is not clear how genomic prediction accuracies are impacted by such potentially nonrelevant markers. In this study, a simulation was carried out to evaluate genomic predictions in the presence of markers unlinked with trait-relevant QTL. Further, we compared the ability of the population statistic FST and absolute estimated marker effect as preselection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy. Results We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Furthermore, it was found that prediction accuracies are severely impacted by unlinked markers with large spurious associations. FST-preselected marker sets of 10 k and larger yielded accuracies 8.97 to 17.91% higher than those achieved using preselection by absolute estimated marker effects, despite selecting 5.1 to 37.7% more unlinked markers and explaining 2.4 to 5.0% less of the genetic variance. This was attributed to false positives selected by absolute estimated marker effects having a larger spurious association with the trait of interest and more negative impact on predictions. The Pearson correlation between FST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of FST scores to detect truly linked markers is comparable to absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak. Conclusion Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic FST presents an efficient and effective tool for preselection of trait-relevant markers.

scholarly journals Identification and Validation of a Core Single-Nucleotide Polymorphism Marker Set for Genetic Diversity Assessment, Fingerprinting Identification, and Core Collection Development in Bottle Gourd

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Wang ◽  
Xiaohua Wu ◽  
Yanwei Li ◽  
Zishan Feng ◽  
Zihan Mu ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Single Nucleotide Polymorphism ◽  
Snp Markers ◽  
Bottle Gourd ◽  
Snp Marker ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
The Core ◽  
Germplasm Collections ◽  
Diversity Assessment

Germplasm collections are indispensable resources for the mining of important genes and variety improvement. To preserve and utilize germplasm collections in bottle gourd, we identified and validated a highly informative core single-nucleotide polymorphism (SNP) marker set from 1,100 SNPs. This marker set consisted of 22 uniformly distributed core SNPs with abundant polymorphisms, which were established to have strong representativeness and discriminatory power based on analyses of 206 bottle gourd germplasm collections and a multiparent advanced generation inter-cross (MAGIC) population. The core SNP markers were used to assess genetic diversity and population structure, and to fingerprint important accessions, which could provide an optimized procedure for seed authentication. Furthermore, using the core SNP marker set, we developed an accessible core population of 150 accessions that represents 100% of the genetic variation in bottle gourds. This core population will make an important contribution to the preservation and utilization of bottle gourd germplasm collections, cultivar identification, and marker-assisted breeding.

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